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 .
Response to Arguments
Applicant’s arguments, see the section titled “Claim Rejections – 35 U.S.C. 102 & 103” starting on page 7 of the reply filed 07/08/2026, with respect to the rejections of claims 1-11 and 19 under 35 U.S.C. 102 & 103 have been fully considered and are persuasive. The rejections of claims 1-11 and 19 under 35 U.S.C. 102 & 103 have been withdrawn.
Applicant’s arguments with respect to claims 12-18 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Examiner notes that in the second to last paragraph beginning on page 8 of the reply filed 07/08/2026 Applicant argues that independent claim 12 includes “similar features to those discussed above with reference to claim 1”; however, the Examiner disagrees. Claim 12 and corresponding dependent claims are rejected in view of new prior art of record Vassilovski (US 2022/0400403 A1). See the rejections 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.
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 12-14 and 17-18 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 Vassilovski (US 2022/0400403 A1).
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).
Strohbeck does not explicitly disclose wherein the item of information characterizing the predicted behavior comprises a prediction sending reason indicating a criterion used by the origination ITS-S to decide whether to share the predicted data; and
wherein analyzing the received predicted behavior comprises comparing the received predicted behavior with a locally determined prediction generated by the receiving ITS-S for the same object.
However, Vassilovski teaches wherein the item of information characterizing the predicted behavior comprises a prediction sending reason indicating a criterion used by the origination ITS-S to decide whether to share the predicted data (In paragraph [0020], Vassilovski teaches that a device, such as a UE, vehicle, RSU, or VRU compares received detections of an object with its own detection of the object and determine whether its information will improve knowledge about the object for other V2X entities; in paragraphs [0066-0067], Vassilovski teaches that a UE 104 may determine whether the information it has detected about the object would be of use to other UEs or wireless communication devices including comparing one or more details about the detected object with the reported object to determine whether they are the same object); and
wherein analyzing the received predicted behavior comprises comparing the received predicted behavior with a locally determined prediction generated by the receiving ITS-S for the same object (In paragraph [0020], Vassilovski teaches that a device, such as a UE, vehicle, RSU, or VRU compares received detections of an object with its own detection of the object and determine whether its information will improve knowledge about the object for other V2X entities; in paragraphs [0066-0067], Vassilovski teaches that a UE 104 may determine whether the information it has detected about the object would be of use to other UEs or wireless communication devices including comparing one or more details about the detected object with the reported object to determine whether they are the same object).
Vassilovski is considered to be analogous to the claimed invention in that they both pertain to sending object detection information based on a criterion and comparing received object information with locally determined information. 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 Vassilovski with the method as disclosed by Strohbeck in that “determine whether its information will improve knowledge about the object for other V2X entities” as suggested by Vassilovski in paragraph [0020] may advantageously improve the efficiency of communication of information, for example, by ensuring that only valuable data transmissions utilize the communications resources.
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).
Claims 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) and Vassilovski (US 2022/0400403 A1), in view of ETSI (Intelligent Transport Systems (ITS); Vulnerable Road Users (VRU) awareness; Part 1: Use Cases definition; Release 2, 2021-04).
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, the combination of Strohbeck and Vassilovski 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 the combination of Strohbeck and Vassilovski 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, the combination of Strohbeck and Vassilovski 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 the combination of Strohbeck and Vassilovski, 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.
Allowable Subject Matter
Claims 1-11 and 19-20 are allowed.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 1, the closest prior art of record Strohbeck (An Extension Proposal for the Collective Perception Service to Avoid Transformation Errors and Include Object Predictions, 2021 IEEE Vehicular Networking Conference) 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).
However, the prior art of record, alone or in combination, does not explicitly disclose
determining a predicted behavior of an object perceived by the originating ITS-S within the monitored area;
determining a prediction inclusion score for the predicted behavior;
dynamically adjusting a threshold value as a function of a channel load of a communication channel; and
conditional upon the prediction inclusion score being greater than the dynamically adjusted threshold value, transmitting the CPM.
Similar reasoning is applied to independent claim 19 which contains corresponding subject matter to that of claim 1. Therefore, independent claims 1 and 19 and corresponding dependent claims 2-11 and 20.
Claim 21 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/HARRISON HEFLIN/ Examiner, Art Unit 3665
/HUNTER B LONSBERRY/ Supervisory Patent Examiner, Art Unit 3665
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