DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 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.
Information Disclosure Statements
There are no Information Disclosure Statements (IDS) of record.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 5/6/2023.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Objection to the Drawings
The drawings (Figures 1 and 3) are objected to as they have been submitted in color (Gray Scaled). Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via EFS-Web or three sets of color drawings or color photographs, as appropriate, if not submitted via EFS-Web, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
Title Objections
The title of the invention is not descriptive and appears to attempt to capture 3 distinct inventions when these are now just one inventive concept. A new title is required that is clearly indicative of the invention to which the Claims are directed.
Restriction/Election of Species
Applicant's election without traverse of Species I, (Claims 1,14 and 19-20) in the reply filed on 11/11/2025 is acknowledged and is made FINAL.
Office Note: Pursuant to applicants amendments to Claims 15-18 (to bring the claimed subject matter of Species II and Species III under the same Species as Species I) and remarks, Claims 15-18, directed to dependent claims, previously withdrawn from consideration as a result of a restriction requirement are hereby rejoined and fully examined. Because all claims previously withdrawn from consideration have been rejoined, the restriction requirement as set forth in the Office action mailed on 9/11/2025 is hereby withdrawn.
Status of Application
Claims 1-20 are pending.
Claims 15-18 have been amended (thus removing the requirement for “Election of Species”, please see above).
Claims 1, 19, and 20 are independent claims. (Claims 19 and 20 are independent for fee calculation, but for examination are dependent).
This Non-Final Office Action is in response to the “Election of Species” received on 11/11/2025.
Non-Final Office Action
CLAIM INTERPRETATION
During examination, claims are given the broadest reasonable interpretation consistent with the specification and limitations in the specification are not read into the claims. See MPEP §2111, MPEP §2111.01 and In re Yamamoto et al., 222 USPQ 934 10 (Fed. Cir. 1984). Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. See MPEP 2111.01 (I). It is further noted it is improper to import claim limitations from the specification, i.e., a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment. See 15 MPEP 2111.01 (II).
A first exception to the prohibition of reading limitations from the specification into the claims is when the Applicant for patent has provided a lexicographic definition for the term. See MPEP §2111.01 (IV). Following a review of the claims in view of the specification herein, the Office has found that Applicant has not provided any lexicographic definitions, either expressly or implicitly, for any claim terms or phrases with any reasonable clarity, deliberateness and precision. Accordingly, the Office concludes that Applicant has not acted as his/her own lexicographer.
A second exception to the prohibition of reading limitations from the specification into the claims is when the claimed feature is written as a means-plus-function. See 35 U.S.C. §112(f) and MPEP §2181-2183. As noted in MPEP §2181, a three prong test is used to determine the scope of a means-plus-function limitation in a claim:
the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function
the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"
the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
The Office has found herein that claims do not contain limitations of means or means type language that must be analyzed under 35 U.S.C. §112 (f).
Claim Rejections - 35 USC § 102
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.
Claim 1-14 and 19-20 are rejected under 35 U.S.C. 102 (a) (2) as being anticipated by Quirynen et al. (United States Patent Publication 2024/0331535).
With respect to Claim 1: Quirynen discloses “A method for controlling a traffic light” [Quirynen, ¶ 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})];
“applied to a traffic light control end communicating with an unmanned vehicle navigation end” [Quirynen, ¶ 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})]];
“the method comprising: generating a reinforced traffic light state parameter according to vehicle state representation information of an unmanned vehicle currently contained in a preset area of a target traffic light and a current traffic light state parameter of the target traffic light” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (in some embodiments, one or both of CTC and ITC control policies may be approximated by a deep neural network architecture. In an example, but not limited to, reinforcement learning may be used to directly maximize a reward function for reducing congestion, travel time, emissions and energy consumption in the transportation network)];
“and generating, according to the reinforced traffic light state parameter, a traffic light control action matching the reinforced traffic light state parameter” [Quirynen, ¶ 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})];
“wherein the reinforced traffic light state parameter is used to cause the unmanned vehicle navigation end to: generate a reinforced vehicle state parameter according to the reinforced traffic light state parameter and a current vehicle state parameter of a target unmanned vehicle” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (in some embodiments, one or both of CTC and ITC control policies may be approximated by a deep neural network architecture. In an example, but not limited to, reinforcement learning may be used to directly maximize a reward function for reducing congestion, travel time, emissions and energy consumption in the transportation network)];
“and generate an unmanned vehicle navigation action matching the reinforced vehicle state parameter according to the reinforced vehicle state parameter” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
With respect to Claim 2: Quirynen discloses “The method according to claim 1, wherein the vehicle state representation information is generated by the unmanned vehicle navigation end according to a current vehicle state parameter of the unmanned vehicle contained in the preset area” [Quirynen, ¶ 0027, 0084, 0110, and 0161 (digital representation of states of each of the CAVs)];
“and historical vehicle state representation information at a plurality of previous moments” [Quirynen, ¶ 0084, 0110, 0159, 0161, and 0181 (historical data is collected from the database 108 and used to estimate predict vehicle density values 1020 and traffic flow values 1040 to improve the performance of the hierarchical traffic control system)].
With respect to Claim 3: Quirynen discloses “The method according to claim 1, wherein the generating a reinforced traffic light state parameter according to vehicle state representation information of an unmanned vehicle currently contained in a preset area of a target traffic light and a current traffic light state parameter of the target traffic light comprises: stitching the vehicle state representation information and the current traffic light state parameter into hybrid environment information” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
“and inputting the hybrid environment information into a first encoder” [Quirynen, ¶ 0027, 0084, 0110, 0119, and 0161 (including one or multiple layers of algorithms and technologies for decision making, motion planning, vehicle control or estimation];
“to obtain the reinforced traffic light state parameter” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
With respect to Claim 4: Quirynen discloses “The method according to claim 1, wherein the generating, according to the reinforced traffic light state parameter, a traffic light control action matching the reinforced traffic light state parameter comprises: acquiring associated traffic light state aggregation information of associated traffic lights associated with the target traffic light” [Quirynen, ¶ 0026, 0083, 0100, 0118, 0131 and 0215-0216 with Figure 12b (optimized for all intersections using the convex optimization)];
“and generating the traffic light control action matching the reinforced traffic light state parameter, according to the reinforced traffic light state parameter and the associated traffic light state aggregation information” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})]. intersections
With respect to Claim 5: Quirynen discloses “The method according to claim 4, wherein the associated traffic light state aggregation information is generated by: generating an associated traffic light state matrix according to current traffic light state parameters of the associated traffic lights” [Quirynen, ¶ 0015, 0147 and 0215-0216 with Figure 13(optimization problem that computes high-level targets for the traffic flow in the multi-intersection network, based on a simplified macroscopic traffic model for the network of connected intersections)];
“and generating the associated traffic light state aggregation information, according to the associated traffic light state matrix” [Quirynen, ¶ 0015, 0147 and 0215-0216 with Figure 13 (the cost function of the constrained optimization problem that is solved by the ITC 408 is performed. The cost function adaptation method 900 is based on a direct policy mapping from the CTC traffic flow values calculated at step 905 and from the real-time information of the infrastructure sensing 406 to one or multiple parameter values in the cost function of the ITC 408). In an example, the one or multiple parameter values may include one or multiple weight values that quantify the penalization of higher traffic flow values in one or multiple crossing directions for each of the traffic intersections of the transportation network. In some embodiments, the direct policy mapping can be implemented using a deep neural network architecture, for example, using reinforcement learning (RL)];
“a connectivity parameter of the target traffic light, and a weight matrix of the target traffic light” [Quirynen, ¶ 0015, 0147 and 0215-0216 with Figure 13 (optimized for all intersections using the convex optimization)].
With respect to Claim 6: Quirynen discloses “The method according to claim 5, wherein the generating the associated traffic light state aggregation information, according to the associated traffic light state matrix, a connectivity parameter of the target traffic light, and a weight matrix of the target traffic light comprises: generating, through a first graph neural network the associated traffic light state aggregation information according to the associated traffic light state matrix, the connectivity parameter of the target traffic light, and the weight matrix of the target traffic light” [Quirynen, ¶ 0015, 0147 and 0215-0216 with Figure 13 (the cost function of the constrained optimization problem that is solved by the ITC 408 is performed. The cost function adaptation method 900 is based on a direct policy mapping from the CTC traffic flow values calculated at step 905 and from the real-time information of the infrastructure sensing 406 to one or multiple parameter values in the cost function of the ITC 408). In an example, the one or multiple parameter values may include one or multiple weight values that quantify the penalization of higher traffic flow values in one or multiple crossing directions for each of the traffic intersections of the transportation network. In some embodiments, the direct policy mapping can be implemented using a deep neural network architecture, for example, using reinforcement learning (RL)].
With respect to Claim 7: Quirynen discloses “The method according to claim 1, wherein the generating a traffic light control action matching the reinforced traffic light state parameter according to the reinforced traffic light state parameter comprises: inputting the reinforced traffic light state parameter into a first reinforcement learning model, to obtain the traffic light control action matching the reinforced traffic light state parameter” [Quirynen, ¶ 0026, 0083, 0100, 0118, 0147, and 0215-0216 with Figure 12b (in some embodiments, one or both of CTC and ITC control policies may be approximated by a deep neural network architecture. In an example, but not limited to, reinforcement learning may be used to directly maximize a reward function for reducing congestion, travel time, emissions and energy consumption in the transportation network)].
With respect to Claim 8: Quirynen discloses “The method according to claim 1, wherein, after the generating a reinforced traffic light state parameter according to vehicle state representation information of an unmanned vehicle currently contained in a preset area of a target traffic light and a current traffic light state parameter of the target traffic light, the method further comprises: inputting the reinforced traffic light state parameter into a pre-trained goal network to obtain a goal vector” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (in some embodiments, one or both of CTC and ITC control policies may be approximated by a deep neural network architecture. In an example, but not limited to, reinforcement learning may be used to directly maximize a reward function for reducing congestion, travel time, emissions and energy consumption in the transportation network)];
“wherein the unmanned vehicle navigation end generates, through a second reinforcement learning model, the unmanned vehicle navigation action matching the reinforced vehicle state parameter according to the reinforced vehicle state parameter” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0218 with Figure 12b (one or multiple motion models)];
“and the goal vector is used to cause the unmanned vehicle navigation end to adjust the second reinforcement learning model according to the goal vector” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
With respect to Claim 9: Quirynen discloses “The method according to claim 1, wherein the method further comprises navigating the unmanned vehicle” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b];
“applied to an unmanned vehicle navigation end communicating with a traffic light control end” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b];
“the method comprising: generating a reinforced vehicle state parameter according to a current reinforced traffic light state parameter of a target traffic light that is acquired from the traffic light control end and a current vehicle state parameter of a target unmanned vehicle” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})];
“and generating, according to the reinforced vehicle state parameter, an unmanned vehicle navigation action matching the reinforced vehicle state parameter, wherein the traffic light control end generates the reinforced traffic light state parameter according to the method of claim 1” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
With respect to Claim 10: Quirynen discloses “The method according to claim 9, wherein, before the generating a reinforced vehicle state parameter according to a current reinforced traffic light state parameter of a target traffic light that is acquired from the traffic light control end and a current vehicle state parameter of a target unmanned vehicle, the method further comprises: generating vehicle state aggregation information according to a vehicle state parameter of an unmanned vehicle currently contained in a preset area of the target traffic light” [Quirynen, ¶ 0084, 0110, 0159, 0161, and 0181 (historical data is collected from the database 108 and used to estimate predict vehicle density values 1020 and traffic flow values 1040 to improve the performance of the hierarchical traffic control system)];
“and generating the current vehicle state representation information according to the vehicle state aggregation information and historical vehicle state representation information at a plurality of previous moments” [Quirynen, ¶ 0084, 0110, 0159, 0161, and 0181 (historical data is collected from the database 108 and used to estimate predict vehicle density values 1020 and traffic flow values 1040 to improve the performance of the hierarchical traffic control system)];
“wherein the vehicle state representation information is used to cause the traffic light control end to generate the reinforced traffic light state parameter according to the vehicle state representation information of the unmanned vehicle currently contained in the preset area of the target traffic light and a current traffic light state parameter of the target traffic light” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
With respect to Claim 11: Quirynen discloses “The method according to claim 10, wherein the generating vehicle state aggregation information according to a vehicle state parameter of an unmanned vehicle currently contained in a preset area of the target traffic light comprises: generating, through a second graph neural network, the vehicle state aggregation information according to the vehicle state parameter of the unmanned vehicle currently contained in the preset area of the target traffic light” [Quirynen, ¶ 0084, 0110, 0159, 0161, and 0181 (the construction of the CP in the CTC 404 includes a macro-level traffic network structure using multiple network parameters, for example, including road segment length, number of lanes, road segment connections and traffic intersection connectivity. For each road segment r, the macro-level traffic network structure defines possible future traffic flow maneuvers w∈{straight, left, right}, i.e., a vehicle can drive straight, turn left or turn right in road segment r when arriving at a traffic intersection. In some embodiments of the disclosure, the CTC 404 computes vehicle density probability values P.sub.r.sup.w for each road segment-maneuver pair (r, w), e.g., using historical data that is collected for the same or a similar transportation network during a similar time period in the past. Computing probability values P.sub.r.sup.w for each road segment-maneuver pair (r, w) using historical data involves collecting the usage of each road and maneuver over a large enough time window in order to make accurate predictions for a future time period)].
With respect to Claim 12: Quirynen discloses “The method according to claim 10, wherein the generating the current vehicle state representation information according to the vehicle state aggregation information and historical vehicle state representation information at a plurality of previous moments comprises: inputting the vehicle state aggregation information and the historical vehicle state representation information at the plurality of previous moments into a recurrent neural network to obtain the current vehicle state representation information” [Quirynen, ¶ 0084, 0110, 0159, 0161, and 0181 (the construction of the CP in the CTC 404 includes a macro-level traffic network structure using multiple network parameters, for example, including road segment length, number of lanes, road segment connections and traffic intersection connectivity. For each road segment r, the macro-level traffic network structure defines possible future traffic flow maneuvers w∈{straight, left, right}, i.e., a vehicle can drive straight, turn left or turn right in road segment r when arriving at a traffic intersection. In some embodiments of the disclosure, the CTC 404 computes vehicle density probability values P.sub.r.sup.w for each road segment-maneuver pair (r, w), e.g., using historical data that is collected for the same or a similar transportation network during a similar time period in the past. Computing probability values P.sub.r.sup.w for each road segment-maneuver pair (r, w) using historical data involves collecting the usage of each road and maneuver over a large enough time window in order to make accurate predictions for a future time period)].
“or constructing a linear function according to the historical vehicle state representation information at the plurality of previous moments, and obtaining, through the linear function, the current vehicle state representation information according to the vehicle state aggregation information” [Quirynen, ¶ 0084, 0110, 0159, 0161, 0181 and 0215-0216 (an MIP problem based on a microscopic traffic model for mixed traffic in a local area that is controlled by an ITC around one or multiple traffic intersections within the transportation network, according to some embodiments of the present disclosure. FIG. 12B shows a schematic diagram 1200B of a formulation of an MIP problem 1210 based on a microscopic traffic model for mixed traffic in the local area that is controlled by an ITC around one or multiple traffic intersections within the transportation network. In some embodiments, the MIP problem 1210 is formulated based on multiple constraints that include, for example, a motion model for one or multiple CAVs 1211, a motion model for one or multiple HDVs 1212, a model for one or multiple TLCs 1213, one or multiple physical and/or safety constraints for CAVs 1214, traffic rules in multi-lane road segments and timing constraints 1215, collision avoidance constraints 1216, dynamic traffic rules and timing constraints 1217, and traffic rules for crossing one or multiple traffic intersections 1218. The MIP problem 1210 is formulated based on a cost function 1205 that is minimized, or a reward function that is maximized, e.g., for reducing congestion, travel time, emissions and energy consumption in the local area that is controlled by the ITC around one or multiple traffic intersections within the transportation network. An optimal solution to the MIP problem 1225 defines optimal values of control commands 1222 that are sent to the one or multiple CAVs and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs)].
With respect to Claim 13: Quirynen discloses “The method according to claim 9, wherein the generating, according to the reinforced vehicle state parameter, an unmanned vehicle navigation action matching the reinforced vehicle state parameter comprises: inputting the reinforced vehicle state parameter into a second reinforcement learning model, to obtain the unmanned vehicle navigation action matching the reinforced vehicle state parameter” [Quirynen, ¶ 0084, 0110, 0159, 0161, 0181, and 0215-0216 (the construction of the CP in the CTC 404 includes a macro-level traffic network structure using multiple network parameters, for example, including road segment length, number of lanes, road segment connections and traffic intersection connectivity. For each road segment r, the macro-level traffic network structure defines possible future traffic flow maneuvers w∈{straight, left, right}, i.e., a vehicle can drive straight, turn left or turn right in road segment r when arriving at a traffic intersection. In some embodiments of the disclosure, the CTC 404 computes vehicle density probability values P.sub.r.sup.w for each road segment-maneuver pair (r, w), e.g., using historical data that is collected for the same or a similar transportation network during a similar time period in the past. Computing probability values P.sub.r.sup.w for each road segment-maneuver pair (r, w) using historical data involves collecting the usage of each road and maneuver over a large enough time window in order to make accurate predictions for a future time period)].
With respect to Claim 14: Quirynen discloses “The method according to claim 13, further comprising: adjusting the second reinforcement learning model according to a goal vector, wherein the goal vector is generated by the traffic light control end by inputting the reinforced traffic light state parameter into a pre-trained goal network” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (in some embodiments, one or both of CTC and ITC control policies may be approximated by a deep neural network architecture. In an example, but not limited to, reinforcement learning may be used to directly maximize a reward function for reducing congestion, travel time, emissions and energy consumption in the transportation network)].
With respect to Claim 19: Quirynen discloses “An apparatus for controlling a traffic light according to claim 1, comprising” [Quirynen, ¶ 0026-0027, 0083, 0100, 0118, and 0215-0216 with Figure 12b];
“at least one processor” [Quirynen, ¶ 0027];
“and a memory” [Quirynen, ¶ 0027];
“communicating with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, to enable the at least one processor to perform the method according to claim 1” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
With respect to Claim 20: Quirynen discloses “An apparatus for navigating an unmanned vehicle according to claim 9, comprising” [Quirynen, ¶ 0026-0027, 0083, 0100, 0118, and 0215-0216 with Figure 12b];
“at least one processor; and” [Quirynen, ¶ 0027];
a memory” [Quirynen, ¶ 0027];
communicating with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, to enable the at least one processor to perform the method according to claim 9” [Quirynen, ¶ 0026, 0083, 0100, 0118, and 0215-0216 with Figure 12b (optimal values of control commands 1222 that are sent to the one or multiple CAVs {Connected and Automated Vehicles} and optimal values of control commands 1223 for states that are sent to the one or multiple TLCs {Traffic Light Controllers})].
Claim Objections
Claims 15-18 are 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.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Prior Art (Not relied upon)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached form 892.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESS G WHITTINGTON whose telephone number is (571)272-7937. The examiner can normally be reached on 7-5.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Browne can be reached on (571)-270-0151. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JESS WHITTINGTON/Primary Examiner, Art Unit 3666c