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 .
Status of Claims
This Office action is in response to the application filed on November 06, 2024. Claims 1-20 are currently pending.
Priority
Request for priority to Provisional Application No. 63/596,961, filed on November 07, 2023, 63/599,975, filed on November 16, 2023, and 63/605,407, filed on December 01, 2023, are acknowledged. The Examiner notes that the current claims do not appear to be fully supported by the provisional applications, and further notes that the Applicant may be requested to perfect one or more of the claims in the situation where applied prior art has priority falling between the filing date of the non-provisional application date November 06, 2024, and the provisional applications dated November 07, 2023, November 16, 2023, and December 01, 2023. No action on the part of the Applicant is requested at this time.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: reference number 678 for a road sign in Figure 6 is not in the drawings.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite determining roadway environment data, detecting driving behavior of the vehicle, and providing an alert to a user, which can be done mentally. A person can observe the environment around the vehicle, visually determine driving behavior of other vehicles, and make a note to slow down, or avoid the vehicle with the erratic behavior.
101 Analysis – Step 1
Claim 1 is directed to an a method (i.e. a process). Claim 20 is directed to an apparatus (i.e., a machine). Therefore, Claims 1 and 20 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of a) an abstract idea, b) a law of nature, or c) a natural phenomenon.
In the present case, the additional limitations beyond the noted abstract ideas are as follows (where the bolded portions represent an “abstract idea”; and where the underlined portions are the “additional limitations”):
Claim 1 recites the following:
A method comprising:
obtaining, from one or more sensors included in a plurality of sensors associated with a roadway environment, monitoring information associated with one or more vehicles;
determining real-time mapping information indicative of one or more of movement information or trajectory information of the one or more vehicles, wherein the real-time mapping information is determined based at least in part on correlating each vehicle of the one or more vehicles to respective portions of the monitoring data;
detecting an unsafe driving behavior for a particular vehicle based on analyzing the real-time mapping information and one or more of historical mapping information obtained for the roadway environment or for the particular vehicle; and
transmitting, to a driver mobile application associated with a driver of the particular vehicle, a remediation message automatically generated in response to detection of the unsafe driving behavior.
The Examiner submits that the foregoing bolded limitations constitute “a mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitations in the human mind. The limitations of “monitoring …”, “determining …”, and “detecting …”, amount to an abstract idea. These limitations involve monitoring the environment and observing the behavior of other vehicles. A driver can visually observe the status of another vehicle to determine the need to change their own vehicle behavior. Such steps can be performed mentally or be observed visually.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Examiner further submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application. Regarding the additional limitations of “obtaining …”, and “transmitting …”, the Examiner submits that these limitations consists of insignificant extra-solution activity, which amounts to mere data gathering. A person can record observe the environmental information, and mentally issue a warning or note to themselves pay more attention to the driving behavior of another vehicle.
The limitation of “sensors associated with a roadway environment …” amounts to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
For the following reason(s), the Examiner submits that the limitation of “a processor …” in independent Claim 20, does not integrate the above-noted abstract idea into a practical application. The Examiner submits that the functions of the processor are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the execution using a generic computer component. A driver can visually determine the status of other vehicles, and communicate with another affected driver to share vehicle data.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitations do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Analysis of Step 2B is performed to determine if the claim as a while amounts to significantly more than the exception itself, and further analysis is required for all functions that are identified as well-understood, routine, and conventional.
Obtaining and transmitting sensor data amounts to insignificant extra-solution activity. The Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere receiving or transmitting data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here). The specification also demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a).
Even when viewed as a combination, nothing in the claims amounts to significantly more, and as such Claims 1 and 20 are not patent eligible under 35 USC §101.
Dependent Claims 2-19 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Claims 2-5 merely involve observing other vehicles within a threshold distance of the host vehicle. Claims 6 and 12 recite the use of accelerometers and cameras (i.e., specific sensors) which is a further characterization of the field of use of the sensors. Claim 7 recites another field of use characterization with the use of a Controller Area Network. Claims 14-16 further recite beacon devices (i.e., specific sensors) for transmitting data, and constitute insignificant extra-solution activity (see analysis regarding insignificant extra-solution activity with respect to independent claims above).
Therefore, dependent Claims 2-19 are not patent eligible under the same rationale as provided for in the rejection of Claim 1.
The Examiner recommends incorporating language from Paragraphs [0060], [0137], to include features of causing the vehicle to perform automatic braking, steering, lane change functions, etc., or controlling or optimizing the signaling for traffic lights based on predicted vehicle behavior for the area. Such language would appear to overcome the current 35 U.S.C. 101 rejection.
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, 8-9, 11-12, and 14-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Publication No. 2023/0076648 A1, to Cardona, et al (hereinafter referred to as Cardona).
As per Claim 1, Cardona discloses the features of a method comprising:
obtaining, from one or more sensors included in a plurality of sensors associated with a roadway environment (e.g. Paragraph [0042], [0065]-[0066]; Figure 3; where an infrastructure device may represent environment-related data pertaining to a road segment that is collected by a sensor system external to vehicles at the road segment),
monitoring information associated with one or more vehicles (e.g. Paragraphs [0033], [0049]; where the vehicle sensors may actively or passively scan the vehicle environment for obstacles (e.g., other vehicles, buildings, pedestrians, etc.); and where the infrastructure device may capture data relating to an infrastructure component or the general environment of road segment, such as detected speed or motion or one or more vehicles);
determining real-time mapping information indicative of one or more of movement information or trajectory information of the one or more vehicles (e.g. Paragraph [0038]; where driver behavior may be analyzed in real-time or near real-time), wherein
the real-time mapping information is determined based at least in part on correlating each vehicle of the one or more vehicles to respective portions of the monitoring data (e.g. Paragraphs [0103], [0128], [0173]; where parameters relating to a vehicle event may each correspond to a vehicle driving through the intersection; and where the system may analyze a number of vehicles approaching an intersection before/during/ after a traffic signal turns yellow (each approach may be categorized as a “vehicle event”), and the system may assign a risk index to each approach for each vehicle);
detecting an unsafe driving behavior for a particular vehicle (e.g. Paragraphs [0086]-[0087], [0130], [0181]; where the system may determine a level of risk the driver assumes when engaging in a particular behavior, and builds a model for a plurality of types of vehicles based on predictive risk values assumed for a particular driver, vehicle, vehicle category, etc.; and the system executes a risk index notification routine to provide a driver an indication of the calculated risk index associated with a currently active vehicle event, such as if the driver exhibits signs of distracted driving) based on
analyzing the real-time mapping information and one or more of historical mapping information obtained for the roadway environment or for the particular vehicle (e.g. Paragraphs [0123], [0126]; where the risk assessment is based on patterns and correlations observed from historical road segment data (e.g., vehicle telematics data, infrastructure data, image data, etc.); and
transmitting, to a driver mobile application associated with a driver of the particular vehicle, a remediation message automatically generated in response to detection of the unsafe driving behavior (e.g. Paragraphs [0038], [0050]; where the on-board computer or mobile device may each be configured to execute one or more algorithms, programs, or applications, and may provide notifications when the driver or the vehicle are engaging in risky behavior).
As per Claim 2, Cardona discloses the features of Claim 1, and Cardona further discloses the features of wherein detecting the unsafe driving behavior includes:
determining, based on the real-time mapping information, one or more driving characteristics corresponding to the particular vehicle (e.g. Paragraphs [0086]-[0087], [0130], [0181]; where the system generates a driver profile for each vehicle detected at a given intersection; where the system may determine a level of risk the driver assumes when engaging in a particular behavior, and builds a model for a plurality of types of vehicles based on predictive risk values assumed for a particular driver, vehicle, vehicle category, etc.; and the system executes a risk index notification routine to provide a driver an indication of the calculated risk index associated with a currently active vehicle event, such as if the driver exhibits signs of distracted driving);
identifying neighboring vehicles within the roadway environment (e.g. Paragraphs [0033], [0049], [0142]; where the system may label data according to a risk level (e.g., depending on the nature and severity of swerving, braking, observed driver distraction, proximity to other vehicles, etc.); and where the infrastructure device may monitor environment conditions at a given road segment, such as weather conditions, conditions and/or statuses of vehicles near the road segment, etc.; and where the vehicle sensors may actively or passively scan the vehicle environment for obstacles (e.g., other vehicles, buildings, pedestrians, etc.)), wherein
the neighboring vehicles are included in the one or more vehicles and are located nearby to the particular vehicle (e.g. Paragraphs [0033], [0049], [0142]; where the system may label data according to a risk level (e.g., depending on the nature and severity of swerving, braking, observed driver distraction, proximity to other vehicles, etc.); and where the infrastructure device may monitor environment conditions at a given road segment, such as weather conditions, conditions and/or statuses of vehicles near the road segment, etc.; and where the vehicle sensors may actively or passively scan the vehicle environment for obstacles (e.g., other vehicles, buildings, pedestrians, etc.)); and
determining, based on the real-time mapping information, one or more baseline driving characteristics corresponding to the identified neighboring vehicles (e.g. Paragraphs [0037], [0142], [0152]-[0153], [0169]; Figure 10; where the system implements a “teaching process” by comparing predictions to known answers (labeled data) and makes corrections in the model, and comparing predicted results with actual results; and where a generic profile of a “typical” driver in a region is determined).
As per Claim 3, Cardona discloses the features of Claim 2, and Cardona further discloses the features of further comprising: detecting the unsafe driving behavior based on one or more deviations between the driving characteristics corresponding to the particular vehicle and the baseline driving characteristics corresponding to the identified neighboring vehicles (e.g. Paragraphs [0130], [0169]; Figure 6; where a model driver profile is generated based on region-specific tendencies of drivers, based upon patterns and correlations observed from historical road segment data (e.g., vehicle telematics data, infrastructure data, image data, etc.); and the system may calculate the risk index associated with a currently active vehicle event, based on identifying driver behavior, such as distracted driving, aggressively turning, etc., and providing feedback to the driver regarding their risky behavior).
As per Claim 6, Cardona discloses the features of Claim 1, and Cardona further discloses the features of wherein detecting the unsafe driving behavior is further based on
analyzing sensor data obtained from one or more sensors associated with the particular vehicle (e.g. Paragraphs [0033], [0041]-[0042], [0050]; where the vehicle telematics system may capture vehicle sensor data, and the on-board computer or mobile device may be configured to generate, collect, or analyze various types of vehicle parameters from the one or more vehicle sensors), wherein
the one or more sensors includes at least an accelerometer (e.g. Paragraph [0049]; where the vehicle sensors can include an accelerometer).
As per Claim 8, Cardona discloses the features of Claim 1, and Cardona further discloses the features of wherein the remediation message comprises automatically generated driver assistance information configured to remediate erratic driving characteristics associated with the unsafe driving behavior (e.g. Paragraphs [0037], [0050], [0170]; where various parameters for an autonomous or semi-autonomous vehicle may be set differently, such as maintaining more distance between itself and other vehicles; and where the autonomous control may be carried out in response to internal settings (e.g., indicating aggressiveness for certain behaviors relating to cornering, acceleration, braking, etc.)).
As per Claim 9, Cardona discloses the features of Claim 1, and Cardona further discloses the features of wherein the remediation message comprises a warning notification or a request for the driver to stop the unsafe driving behavior (e.g. Paragraphs [0038], [0050]; where the on-board computer or mobile device may each be configured to execute one or more algorithms, programs, or applications, and may provide notifications when the driver or the vehicle are engaging in risky behavior).
As per Claim 11, Cardona discloses the features of Claim 1, and Cardona further discloses the features of wherein the remediation message includes one or more of control commands or configuration information generated for an Advanced Driver Assistance System (ADAS) module of the particular vehicle (e.g. Paragraphs [0015], [0037], [0050]; where the autonomous control profile for a vehicle may be updated and maintain more distance between itself and other vehicles).
As per Claim 12, Cardona discloses the features of Claim 1, and Cardona further discloses the features of wherein:
the one or more sensors comprises a plurality of cameras deployed to roadside locations or overhead locations within the roadway environment (e.g. Paragraphs [0043], [0107]; where image data may represent still images or video of captured by an image sensor (e.g., a camera) disposed at any suitable location (e.g., mounted to walls, roads, stoplights, stop signs, etc.); and
the monitoring information corresponds to respective image data obtained from the plurality of cameras and depicting the one or more vehicles (e.g. Paragraphs [0033], [0042]-[0043], [0059]; where a camera may capture images or video of a road segment during a given time period, and an image sensor may capture images of vehicles, pedestrians, roads, sidewalks, and may be disposed on, in, or near the infrastructure component).
As per Claim 14, Cardona discloses the features of Claim 1, and Cardona further discloses the features of wherein:
the one or more sensors comprises a plurality of beacon devices configured to transmit beacon signals (e.g. Paragraphs [0046], [0065]-[0066]; where the infrastructure devices may transmit collected information to the server via the network and to the vehicle via the communication component), and
a plurality of receiver devices configured to receive transmitted beacon signals (e.g. Paragraphs [0056], [0065]-[0066]; where the infrastructure devices may transmit collected information to the server via the network and to the vehicle via the communication component, and the on-board computer or mobile device may receive via the network, road segment data); and
the monitoring information corresponds to relative position information of one or more receiver devices included in the plurality of receiver devices (e.g. Paragraph [0182]; where the system may determine a distance between vehicles, by determining a position for each vehicle),
the relative position information determined based on measurements of transmitted beacon signals from the plurality of beacon devices (e.g. Paragraphs [0098], [0182]; where the infrastructure device may detect a status of a traffic signal, may timestamp a corresponding parameter, and synchronize the first and second sets of parameters so that a given event reflected in both sets of parameters will have timestamps consistent with each other; and where the system may calculate the distance between vehicles based on data gathered from the vehicle and the infrastructure devices).
As per Claim 15, Cardona discloses the features of Claim 14, and Cardona further discloses the features of wherein:
the plurality of beacon devices includes one or more stationary beacons each associated with a respective location within the roadway environment signals (e.g. Paragraphs [0042]-[0043], [0046]; where the infrastructure may be mounted to roads, stoplights, stop signs, etc. at the road segment and may include sensors such as image sensors, motion sensors, status devices, cameras, and a communication component for transmitting data); and
the one or more receiver devices are user computing devices each located within a respective vehicle of the one or more vehicles (e.g. Paragraphs [0049], [0182]; where the position data may be determined from vehicle sensors on-board the vehicles, such as via a GPS system of the relevant vehicle or via a mobile device in the vehicle).
As per Claim 16, Cardona discloses the features of Claim 14, and Cardona further discloses the features of further comprising determining one or more of: vehicle position information, vehicle movement information, or vehicle trajectory information for a particular vehicle, the determination based on the relative position information of a corresponding receiver device located within the particular vehicle (e.g. Paragraphs [0033], [0041], [0049], [0182]; where the system may determine a distance between vehicles, by determining a position for each vehicle; and where the system may determine road data for a given time period, and may capture data relating to the general environment at the road segment; and where the position data may be determined from vehicle sensors on-board the vehicles, such as via a GPS system of the relevant vehicle or via a mobile device in the vehicle, and the vehicle sensor data may be include mobile device GPS location, collected before, during, and after a collision).
As per Claim 17, Cardona discloses the features of Claim 16, and Cardona further discloses the features of wherein
the corresponding receiver device is included in the plurality of receiver devices (e.g. Paragraphs [0054]-[0055], [0066], [0077]; where the communication component may transmit and receive information from external sources, including other vehicles, and may receive information that another vehicle ahead is reducing speed, allowing adjustments for the operation of the vehicle (108); and the infrastructure device may monitor the vehicle (108) and directly or indirectly communicate information to other vehicles (i.e., a plurality of vehicles have a receiver devices); and where the system can include a plurality of servers, and hundreds of mobile devices or on-board computers which may be interconnected via the network) and
comprises a smartphone associated with a driver or a passenger of the particular vehicle (e.g. Paragraphs [0048], [0075]; where an on-board computer or mobile device (e.g., a smartphone, cellular phone, etc.) may include and transmit user credentials, which associated with a user to uniquely identify each customer).
As per Claim 18, Cardona discloses the features of Claim 14, and Cardona further discloses the features of wherein:
the plurality of beacon devices includes one or more user computing devices each located within a respective vehicle of the one or more vehicles (e.g. Paragraphs [0077], [0182]; where the system can include a plurality of servers, and hundreds of mobile devices or on-board computers which may be interconnected via the network; and where the position data may be determined from vehicle sensors on-board the vehicles, such as via a GPS system of the relevant vehicle or via a mobile device in the vehicle) and configured to
transmit a beacon signal including an identifier of the respective vehicle (e.g. Paragraphs [0048], [0075]; where an on-board computer or mobile device (e.g., a smartphone, cellular phone, etc.) may include and transmit user credentials to the server for verification, the credentials are associated with a user to uniquely identify each customer); and
the one or more receiver devices are stationary receivers each associated with a configured location within the roadway environment (e.g. Paragraphs [0043], [0046], [0065]-[0066], [0107]; where the infrastructure devices may transmit collected information to the server via the network and to the vehicle via the communication component; and where image data associated with an image sensor on the infrastructure device may represent still images or video of captured by an image sensor (e.g., a camera) disposed at any suitable location (e.g., mounted to walls, roads, stoplights, stop signs, etc.).
As per Claim 19, Cardona discloses the features of Claim 14, and Cardona further discloses the features of wherein the relative position information is further determined based on a configured location determined for a particular beacon device associated with each one of the transmitted beacon signals (e.g. Paragraphs [0033], [0178]; Figure 9; where the road segment data or parameters are captured by one or more infrastructure devices at a particular moment in time, at a particular road segment or intersection (i.e. location of the infrastructure device at an intersection determines the vehicle event data)).
As per Claim 20, Cardona discloses the features of an apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory (e.g. Paragraphs [0083], [0085]; where the controller may include a program memory, one or more processors, to executing one or more routines), the at least one processor configured to:
obtain, from one or more sensors included in a plurality of sensors associated with a roadway environment (e.g. Paragraph [0042], [0065]-[0066]; Figure 3; where an infrastructure device may represent environment-related data pertaining to a road segment that is collected by a sensor system external to vehicles at the road segment),
monitoring information associated with one or more vehicles (e.g. Paragraphs [0033], [0049]; where the sensors may actively or passively scan the vehicle environment for obstacles (e.g., other vehicles, buildings, pedestrians, etc.); and where the infrastructure device may capture data relating to an infrastructure component or the general environment of road segment, such as detected speed or motion or one or more vehicles);
determine real-time mapping information indicative of one or more of movement information or trajectory information of the one or more vehicles e.g. Paragraph [0038]; where driver behavior may be analyzed in real-time or near real-time), wherein
the real-time mapping information is determined based at least in part on correlating each vehicle of the one or more vehicles to respective portions of the monitoring data (e.g. Paragraphs [0103], [0128], [0173]; where parameters relating to a vehicle event may each correspond to a vehicle driving through the intersection; and where the system may analyze a number of vehicles approaching an intersection before/during/ after a traffic signal turns yellow (each approach may be categorized as a “vehicle event”), and the system may assign a risk index to each approach for each vehicle);
detect an unsafe driving behavior for a particular vehicle (e.g. Paragraphs [0086]-[0087], [0130], [0181]; where the system may determine a level of risk the driver assumes when engaging in a particular behavior, and builds a model for a plurality of types of vehicles based on predictive risk values assumed for a particular driver, vehicle, vehicle category, etc.; and the system executes a risk index notification routine to provide a driver an indication of the calculated risk index associated with a currently active vehicle event, such as if the driver exhibits signs of distracted driving) based on
analyzing the real-time mapping information and one or more of historical mapping information obtained for the roadway environment or for the particular vehicle (e.g. Paragraphs [0123], [0126]; where the risk assessment is based on patterns and correlations observed from historical road segment data (e.g., vehicle telematics data, infrastructure data, image data, etc.); and
transmit, to a driver mobile application associated with a driver of the particular vehicle, a remediation message automatically generated in response to detection of the unsafe driving behavior (e.g. Paragraphs [0038], [0050]; where the on-board computer or mobile device may each be configured to execute one or more algorithms, programs, or applications, and may provide notifications when the driver or the vehicle are engaging in risky behavior).
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 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0076648 A1, to Cardona, et al (hereinafter referred to as Cardona), in view of U.S. Patent Publication No. 2018/0319402 A1, to Mills, et al (hereinafter referred to as Mills).
As per Claim 4, Cardona discloses the features of Claim 1, but Cardona fails to disclose every feature of wherein the neighboring vehicles are located within a configured threshold distance from the particular vehicle.
However, Mills, in a similar field of endeavor, teaches a method for automatic activation of a driver assistance feature, where the vehicle sensors can detect other vehicles adjacent to or near the vehicle, within a path of the vehicle, and/or moving towards the vehicle, and the driver assistance system may alert the driver to other vehicles within a predetermined vicinity (e.g. Paragraphs [0002], [0031]-[0032], [0068], [0068]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for collecting and displaying road information in the system of Cardona, with the feature of determining vehicles that are located within a threshold distance of the host vehicle in the system of Mills, in order to improve driver awareness and alerting (see at least Paragraphs [0007]-[0008] of Mills).
As per Claim 5, Cardona discloses the features of Claim 2, but Cardona fails to disclose every feature of wherein the neighboring vehicles are located in an adjacent lane position relative to a current lane position of the particular vehicle.
However, Mills, in a similar field of endeavor, teaches a method for automatic activation of a driver assistance feature, where the vehicle sensors can detect other vehicles adjacent to or near the vehicle, within a path of the vehicle, and/or moving towards the vehicle, and the driver assistance system may alert the driver to other vehicles within a predetermined vicinity (e.g. Paragraphs [0002], [0031]-[0032], [0068], [0068]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for collecting and displaying road information in the system of Cardona, with the feature of determining vehicles that are located adjacent to the host vehicle in the system of Mills, in order to improve driver awareness and alerting (see at least Paragraphs [0007]-[0008] of Mills).
As per Claim 7, Cardona discloses the features of Claim 6, but Cardona fails to disclose every feature of wherein at least a portion of the sensor data is obtained from a Controller Area Network (CAN) bus associated with the particular vehicle, or is obtained from a CAN bus associated with additional vehicles included in the one or more vehicles.
However, Mills, in a similar field of endeavor, teaches a method for automatic activation of a driver assistance feature, where the wireless communication module may facilitate communications with nearby vehicles, and where the ECUs of the vehicle computing system (VCS, 100) are interconnected by the vehicle data bus, such as a controller area network (CAN) bus (e.g. Paragraphs [0029], [0041]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for collecting and displaying road information in the system of Cardona, with the feature of using a controller area network in the system of Mills, in order to adapt or enhance select vehicle systems (see at least Paragraph [0049] of Mills).
Claims 10 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2023/0076648 A1, to Cardona, et al (hereinafter referred to as Cardona), in view of U.S. Patent Publication No. 2017/0131111 A1, to Rothschild (hereinafter referred to as Rothschild).
As per Claim 10, Cardona discloses the features of Claim 1, but Cardona fails to disclose every feature of wherein the remediation message comprises an automatically generated ticket or infraction instance for the driver, the ticket or infraction instance generated based on license plate information determined for the particular vehicle based on the monitoring information.
However, Rothschild, in a similar field of endeavor, teaches a method for traffic citation delivery, where the speed of the vehicle and the amount of the traffic fine may be transmitted to an on-board navigation system of the vehicle, and the processor may automatically issue a paper citation to an owner of the vehicle, who may be identified by identifying a license tag number on the vehicle, correlating the tag number with the owner as recorded in a motor vehicle department database (e.g. Paragraphs [0018], [0035], [0040]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for collecting and displaying road information in the system of Cardona, with the feature of generating ticket information in the system of Rothschild, in order to decrease the danger posed to law enforcement officials when pulling a vehicle over (see at least Paragraph [0008] of Rothschild).
As per Claim 13, Cardona discloses the features of Claim 12, but Cardona fails to disclose every feature of wherein the monitoring information comprises a unique identifier or registration information associated with a vehicle, determined based on detecting license plate information within the respective image data obtained from the plurality of cameras.
However, Rothschild, in a similar field of endeavor, teaches a method for traffic citation delivery, where the speed of the vehicle and the amount of the traffic fine may be transmitted to an on-board navigation system of the vehicle, and the processor may automatically issue a paper citation to an owner of the vehicle, who may be identified by identifying a license tag number on the vehicle, correlating the tag number with the owner as recorded in a motor vehicle department database (e.g. Paragraphs [0018], [0034]-[0035], [0040]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the Applicant’s invention, with a reasonable expectation for success, to modify the system for collecting and displaying road information in the system of Cardona, with the feature of determining ticket information in the system of Rothschild, in order to determine who to send the ticket to (see at least Paragraph [0026] of Rothschild).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Ansari (U.S. 2016/0357187 A1), which teaches a method for determining the locations and trajectories of neighboring vehicles to operate the host vehicle.
Arndt, et al (U.S. 2020/0342753 A1), which teaches a method for detecting poor driving behavior of a vehicle, and alerting a user of another vehicle of the poor driving.
Mukundan, et al (U.S. 2023/0249693 A1), which teaches a method for alerting a driver of detected erratic driving behavior.
Pasch, et al (U.S. 2024/0233526 A1), which teaches a method for managing an infrastructure device, and changing the status of the device based on the intent of a user.
Wilkes, et al (U.S. 2011/0077028 A1), which teaches a method for identifying risky driving situations in real time, and implementing measures to mitigate the impact of the risky event.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MERRITT LEVY whose telephone number is (571)270-5595. The examiner can normally be reached Mon-Fri 0630-1600.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached at (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MERRITT LEVY/Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663