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 .
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because it repeats information in the title and uses implied phrases. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 a mental process of observation, evaluation and judgement. This judicial exception is not integrated into a practical application and does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional limitations of the claims are mere insignificant extra solution activity in combination of generic computer functions that are implemented to perform the abstract idea. See the analysis before for further details.
Claims 1, 14 and 20
Step 1: The claim recites a method, non-transitory computer-readable medium, and an apparatus therefore, they fall into the statutory categories.
Identifying an event associated with the vehicle based at least in part on the set of inputs; (This is a mental process of observation, evaluation and judgement, wherein a user identifies an event based on data.)
Generating an event report associated with the event, (This is a mental process of observation, evaluation and judgment wherein a user creates a report about a event, can be done with the aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
a memory system of a vehicle; one or more sensors of the vehicle; one or more processing units of the vehicle; a deep learning device directly coupled with the memory system of the vehicle, an output device associated with the vehicle; a non-transitory computer-readable medium storing code, the code comprising instructions executable by a processor - (Claim 14); a controller associated with a memory device, wherein the controller is configured – (Claim 20); (These limitations amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
receiving a set of inputs from one or more sensors of the vehicle; transmitting the event report to; and (This amount to receiving data, which is data collection, and as such extra-solution activity, see MPEP 2106.05(g).)
storing the set of inputs in a volatile memory device of the memory system based at least in part on receiving the set of inputs; storing the event report in a non-volatile memory device of the memory system; (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
the deep learning device for performing one or more operations using a machine learning model and the set of inputs; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of using a machine learning model with previously determined input data);
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are:
a memory system of a vehicle; one or more sensors of the vehicle; one or more processing units of the vehicle; a deep learning device directly coupled with the memory system of the vehicle, an output device associated with the vehicle; a non-transitory computer-readable medium storing code, the code comprising instructions executable by a processor - (Claim 14); a controller associated with a memory device, wherein the controller is configured – (Claim 20); (These limitations amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
the deep learning device for performing one or more operations using a machine learning model and the set of inputs; (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of using a machine learning model with previously determined input data);
receiving a set of inputs from one or more sensors of the vehicle; transmitting the event report to; and (These limitations are extra-solution activity and are also well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”)
storing the set of inputs in a volatile memory device of the memory system based at least in part on receiving the set of inputs; storing the event report in a non-volatile memory device of the memory system; (These limitations are extra-solution activity and are also well-understood, routine and conventional. See MPEP 2106.05(d)(II)(iv) that indicates that merely “storing and retrieving information in memory” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer).
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above.
Claims 2 and 15
Step 2A Prong 1: The claim recites, inter alia:
Generating a model associated with an environment of the vehicle using the set of inputs, wherein identifying the event is further based at least in part on the model. (This amounts to mental process wherein a user generates a model and identifying events from data.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites: one or more processing units (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are: one or more processing units (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 3 and 16
Step 2A Prong 1: The claim recites, inter alia:
Generating an indication of a first action associated with the event; and (This amounts to a mental process wherein a user identifying and indicating a first action associated with an event.)
determining an evaluation of a second action executed by the vehicle based at least in part on the indication of the first action, wherein the event report comprises the evaluation. (This amounts to a mental process of observation, evaluation and judgement, wherein a user evaluates an action taken by a vehicle and then includes it in a report, can be done with the aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
the deep learning device (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are: the deep learning device (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 4 and 17
Step 2A Prong 1: The claim recites, inter alia:
generating an indication of a recommended action associated with the event; and
(This amounts to a mental process wherein a user indicating/recommending an action associated with an event.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim recites:
the machine learning model (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
transmitting the indication of the recommended action to a display component of the vehicle. (This is transmitting data, which is extra-solution activity, see MPEP 2106.05(g), and the display component of the vehicle is generic hardware used to implement the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional limitations are:
the machine learning model (This amount to using generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
transmitting the indication of the recommended action to a display component of the vehicle. (This is transmitting data, which is extra-solution activity and is also well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well-understood, routine and conventional functions in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claims 5 and 18
Step 2A Prong 1: The claim recites, inter alia:
identifying one or more second vehicles included in a video stream, the video stream included in the set of inputs; and identifying respective speeds of the one or more second vehicles using one or more parameters included in the set of inputs. (This amounts to a mental process of observation, evaluation and judgment wherein a user identifying a vehicle and speed in a video.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim does not recite additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the claim does not recite additional limitations.
Claims 6 and 19
Step 2A Prong 1: The claim recites, inter alia:
identifying metadata of a model associated with an environment of the vehicle based at least in part on identifying the event; and (This amounts to a mental process of observation, evaluation and judgment wherein a user identifying metadata associated with an environment, such as weather condition in a vides. This is also supported in para. [0062] of instant specification that cites “At 430, the memory system may identify and extract metadata associated with the event. For example, the memory system may identify: a speed of the vehicle; speeds of other nearby vehicles; forces experienced by the vehicle (e.g., using and IMU); environmental conditions (e.g., weather conditions, road conditions); or a combination thereof.”.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the additional limitations are:
transmitting the metadata to a remote server associated with the vehicle. (This amount to receiving/sending data, which is data collection, and as such extra-solution activity, see MPEP 2106.05(g). The remote server is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the additional limitations is:
transmitting the metadata to a remote server associated with the vehicle. (The remote server is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The receiving/sending data element is extra-solution activity, see MPEP 2106.05(g), and is well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well-understood, routine and conventional functions in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 7
Step 2A Prong 1: The claim recites, inter alia:
encrypting the metadata; (This amounts to a mental process of observation, evaluation and judgment wherein a user encrypts data such as using a cipher, can be done with aid of pen and paper.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the additional limitations are:
Using the one or more processing units; transmitting the metadata comprises transmitting the encrypted metadata to the remote server. (The processing units and remote server amount to using generic computer hardware to execute an abstract idea, see MPEP 2106.05(f). The transmitting limitation amounts to receiving/sending data, which is data collection, and as such extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the additional limitations is:
Using the one or more processing units; transmitting the metadata comprises transmitting the encrypted metadata to the remote server. (The processing units and remote server amount to using generic computer hardware to execute an abstract idea, see MPEP 2106.05(f). The transmitting limitation amounts to receiving/sending data, which is data collection, and as such extra-solution activity, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well-understood, routine and conventional functions in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 8
Step 2A Prong 1: The claim recites, inter alia:
determining, based at least in part on the set of inputs, whether a second vehicle is within a threshold distance of the vehicle. (This amounts to a mental process of observation, evaluation and judgment wherein a user determines distance between vehicles from a video. This is supported by instant application in para. [0050] which cites “The set of inputs may include a video stream captured by the one or more cameras, distance information associated with one or more objects included in the video stream (e.g., distances from the vehicle to other nearby vehicles),”)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim does not recite additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the claim does not recite additional limitations.
Claim 9
Step 2A Prong 1: The claim recites, inter alia:
determining, based at least in part on the set of inputs, whether the vehicle is transitioning between a first lane and a second lane included in a video stream of the set of inputs. (This amounts to a mental process of observation, evaluation and judgment wherein a user determines from a video if a car is switching lanes.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim does not recite additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the claim does not recite additional limitations.
Claim 10
Step 2A Prong 1: The claim recites, inter alia:
determining, based at least in part on the set of inputs, whether the vehicle is executing a turn. (This amounts to a mental process of observation, evaluation and judgment wherein a user determines from a video if a car is turning.)
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the claim does not recite additional limitations.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the claim does not recite additional limitations.
Claim 11
Step 2A Prong 1: The claim recites, inter alia:
Claim 11 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the additional limitations are:
transmitting the event report to a remote server associated with the vehicle. (This amount to receiving/sending data, which is data collection, and as such extra-solution activity, see MPEP 2106.05(g). The remote server is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the additional limitations are:
transmitting the event report to a remote server associated with the vehicle. (The remote server is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The limitation of receiving/sending data, which is data collection, and as such extra-solution activity. It is also well-understood, routine and conventional, see MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well-understood, routine and conventional functions in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 12
Step 2A Prong 1: The claim recites, inter alia:
Claim 12 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the additional limitations are:
transmitting data associated with the set of inputs from a first processing unit of the one or more processing units to a second processing unit of the one or more processing units using an optical interconnect between the first processing unit and the second processing unit, wherein identifying the event is based at least in part on transmitting the data. (This amount to receiving/sending data, which is data collection, and as such extra-solution activity, see MPEP 2106.05(g). The use of the processing the first processing unit, second processing unit and optical interconnect is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the additional limitations are:
transmitting data associated with the set of inputs from a first processing unit of the one or more processing units to a second processing unit of the one or more processing units using an optical interconnect between the first processing unit and the second processing unit, wherein identifying the event is based at least in part on transmitting the data. (The use of the first processing unit, second processing unit and optical interconnect is using generic computer hardware to execute the abstract idea, see MPEP 2106.05(f). The remaining limitations to receiving/sending data, which is data collection, and as such is extra-solution activity and is also well-understood, routine and conventional. See MPEP 2106.06(d)(II)(i) wherein it cites “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016)”)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are well-understood, routine and conventional functions in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Claim 13
Step 2A Prong 1: The claim recites, inter alia:
Claim 13 inherits the abstract idea of claim 1.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. Aside from the limitations above, the additional limitations are:
wherein the one or more sensors comprise one or more cameras, one or more light detection and ranging sensors, one or more radar sensors, one or more sonar sensors, an inertial measurement unit, a speedometer, an accelerometer, one or more infrared light detectors, or a combination thereof, (This amount to use generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
and the set of inputs comprise a video stream, distance information associated with one or more objects included in the video stream, location information associated with the one or more objects, a speed of the vehicle, a respective speed of one or more of the one or more objects, an acceleration of the vehicle, infrared light information associated with an environment of the vehicle, or a combination thereof. (This amounts to extra-solution activity and using a particular type of data to be used and/or manipulated, see MPEP 2106.05(g).)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as it is extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Aside from the limitations above, the additional limitations are:
wherein the one or more sensors comprise one or more cameras, one or more light detection and ranging sensors, one or more radar sensors, one or more sonar sensors, an inertial measurement unit, a speedometer, an accelerometer, one or more infrared light detectors, or a combination thereof, (This amount to use generic computer hardware to implement the abstract idea, see MPEP 2106.05(f).)
and the set of inputs comprise a video stream, distance information associated with one or more objects included in the video stream, location information associated with the one or more objects, a speed of the vehicle, a respective speed of one or more of the one or more objects, an acceleration of the vehicle, infrared light information associated with an environment of the vehicle, or a combination thereof. (This amounts to extra-solution activity and using a particular type of data to be used and/or manipulated, see MPEP 2106.05(g) and does not add meaningful limitations to the claim of identifying and event from input data and generating a report, as it merely cites a particular type of data to be used.)
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are extra-solution activity in combination with generic computer hardware performing generic functions that are implemented to perform the disclosed abstract idea above.
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 1, 8-9, 11, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mondello et al. (US 2019/0302766 A1 – hereinafter Mondello) and further in view of Miller et al. (US 2011/0063099 A1 – hereinafter Miller).
In regards to claim 1, Mondello discloses a method, comprising:
receiving, at a memory system of a vehicle, a set of inputs from one or more sensors of the vehicle; (Mondello para. [0012] teaches receiving vehicle data from sensors wherein it cites “[0012] FIG. 1 illustrates an embodiment of an improved black box data recorder in an autonomous driving vehicle (AVD). In FIG. 1, a black box controller 102 receives a data stream of sensor data from multiple vehicle sensors 104 (e.g., surrounding cameras and other sensors). The vehicle sensor data may further include, but is not limited to camera data, radar data, lidar data, sonar data, laser measurements, tire pressure monitoring, and vehicle operation system data.”)
storing the set of inputs in a volatile memory device of the memory system based at least in part on receiving the set of inputs; (Mondello para. [0013-0014] teaches holding sensor data (input) in in volatile memory wherein it cites “[0013] The received data stream of the sensor data is
initially held in a memory 106. In one embodiment, the memory 106 is volatile, such as a dynamic random-access memory (DRAM), which requires continual power in order to refresh or maintain the data in the memory. [0014] Alternatively or in combination, the sensor data can be held in a cyclic buffer implemented with volatile memory.”)
identifying, by one or more processing units of the vehicle, an event associated with the vehicle based at least in part on the set of inputs; (Mondello para. [0010] cites “In one embodiment, an artificial intelligence (AI) processor analyzes the sensor data stored in the memory to detect an event of interest (e.g., imminent or impending collision or nearby collision involving the respective vehicle or other vehicle) that is about to take place and/or has taken place.”)
using a deep learning device directly coupled with the memory system of the vehicle, the deep learning device for performing one or more operations using a machine learning model and the set of inputs; (Mondello teaches a deep learning device coupled to memory using machine learning model and inputs to perform an operation in para. [0010] cites “In one embodiment, an artificial intelligence (AI) processor analyzes the sensor data stored in the memory to detect an event of interest (e.g., imminent or impending collision or nearby collision involving the respective vehicle or other vehicle) that is about to take place and/or has taken place.” And para. [0017] cites In response to the data from the sensors (104) and/or the movement/status sensors 108, an artificial intelligence (AI) processor 110 analyzes the data stream of sensor data stored in the memory 106 to determine if an event of interest is about to happen and/or has occurred; and in response to the identification of the event based on the analysis of the AP processor 110, the black box controller 102 transfers the sensor data to a non-volatile storage device 112 for storage.“)
However, Mondello does not explicitly disclose generating an event report associated with the event, transmitting the event report to an output device associated with the vehicle, and storing the event report in a non-volatile memory device of the memory system.
Miller discloses generating an event report associated with the event, transmitting the event report to an output device associated with the vehicle, and storing the event report in a non-volatile memory device of the memory system. (Miller teaches generating an event report associated with an event in paragraph [0017] wherein it cites “The embodiments of the present invention generally provide, among other things, for a system and method for recording vehicle events and for generating reports corresponding to the recorded vehicle events based on driver status. … The vehicle may provide performance ratings, usage profiles and/or other data with respect to the recorded events to educate the secondary driver to engage in safer driving practices. The recorded vehicle events may include, but not limited to, potential collision events, excessive speed events, belt buckle usage, OCD (e.g., phone) status/usage, and/or videos of exterior or interior portions of the vehicle.”. Miller teaches transmitting the event report to an output device associated with the vehicle wherein it cites in the abstract “The device is further configured to generate a report indicating the recorded vehicle events for transmission…” and in para. [0018] “The recorded events may be assembled and placed into a generated event report. The event report may be visually displayed to the driver within the vehicle or may be wirelessly transmitted to the primary/secondary driver and accessible via a computer to review the results.” Miller teaches storing the event report in memory wherein para. [0056] cites “Older reports can be archived in the device 22, or simply removed from the device 22 and stored in a device such as a personal computer belonging to either the primary or the secondary driver.” and claim 3 cites “3. The apparatus of claim 1 wherein the vehicle interface device is further configured to electronically store the report therein for transmission to a portable storage device.”. Miller also teaches non-volatile memory in para. [0019] wherein it teaches memory devices such as FLASH, ROM, EPROM and EEPROM.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Mondello with the reporting capabilities of the Miller as both references deal with monitoring cars, collecting car data, and detecting events. The benefit of doing so is it creates a more efficient and robust system for monitoring driver performance by not only detecting events, but creating reports about the events, transmitting the report the interest parties (parents of kids driving suggested in the background and para. [0016] of Miller), and recording data over to get a running statistics or driver performance grade as in figs 6-8.
In regards to claim 8, Mondello in view of Miller discloses the method of claim 1, wherein identifying the event comprises: determining, based at least in part on the set of inputs, whether a second vehicle is within a threshold distance of the vehicle. (Mondello para. [0010] cites “In one embodiment, an artificial intelligence (AI) processor analyzes the sensor data stored in the memory to detect an event of interest (e.g., imminent or impending collision or nearby collision involving the respective vehicle or other vehicle) that is about to take place and/or has taken place.” and para. [0023] cites “the AI processor 110 may recognizes objects and in response use predetermined rules to make recording decisions, where the rules specifies what spatial and motion relations among the objects and the vehicle should trigger the recording of the sensor data”, which teaches an event based upon the relationship between the host vehicle and nearby objects/vehicles. Miller para. [0037] cites “The ACC module 42 is generally configured to detect when the vehicle may be on a path that leads to a forward collision (FC). The ACC module 42 is operably coupled to radars (not shown) to detect the presence/proximity of a vehicle that may engage in a forward a collision with the vehicle. …The ACC module 42 may also detect whether the vehicle is in a "tailgating mode" with respect to a vehicle positioned ahead of the current vehicle. The ACC module 42 uses the radars to determine whether the vehicle is in the tailgating mode. It is known in the art to use radars to detect the proximity of the vehicle. … The alert notifies the driver that the vehicle may be too close to the forward vehicle.“ This teaches using radar to determine if a car is too close another forward car and issues a warning to driver. Thus although it does not explicitly mention a threshold distance it implicitly discloses one as it alerts they driver they are too close the vehicle in front of them.)
In regards to claim 9, Mondello in view of Miller discloses the method of claim 1, wherein identifying the event comprises:
determining, based at least in part on the set of inputs, whether the vehicle is transitioning between a first lane and a second lane included in a video stream of the set of inputs. (Mondello para. [0012] getting inputs from sensors and surrounding cameras wherein it cites “In FIG. 1, a black box controller 102 receives a data stream of sensor data from multiple vehicle sensors 104 (e.g., surrounding cameras and other sensors). The vehicle sensor data may further include, but is not limited to camera data, radar data, lidar data, sonar data, laser measurements, tire pressure monitoring, and vehicle operation system data.”. Mondello para. [0017] teaches identifying events based on sensor data wherein it cites “In response to the data from the sensors (104) and/or the movement/status sensors 108, an artificial intelligence (AI) processor 110 analyzes the data stream of sensor data stored in the memory 106 to determine if an event of interest is about to happen and/or has occurred;…”) Miller paragraph [0041] teaches using a forward pointing camera to determine whether the vehicle is changing or exiting its lane wherein it cites “A lane departure warning (LDW) module 44 is operably coupled to the device 22. The LDW module 44 uses a forward pointing camera (not shown) to determine what side of the vehicle is deviating from a lane or crossing over the lane to issue a warning.”. Miller para. [0046] teaches detecting exiting or entering a lane either the left or right side and this is an event, wherein it cites “In general, the device 22 uses the signal LDW and the signal BSM to monitor for space management events. For example, the LDW module 44 is configured to trigger and event if the vehicle departs from either a left or right side of the lane and the BSM module 46 provides an alert to notify the driver that a vehicle is in the detection zone.”)
In regards to claim 11, Mondello in view of Miller disclose the method of claim 1, further comprising:
transmitting the event report to a remote server associated with the vehicle. (Miller para. [0054] teaches transmitting a report to the server associated with the vehicle wherein it cites “The device 22 may generate the report that details various recorded events and profiles for the secondary driver and transmit the same over a signal REPORT to the APIM 50. The APIM 50 may transmit the report over an output link 53 via the driver's OCD 54 to be uploaded into a server (not shown) so that the primary driver can review the same over a computer.”)
In regards to claim 13, Mondello in view of Miller discloses the method of claim 1, wherein the one or more sensors comprise one or more cameras, one or more light detection and ranging sensors, one or more radar sensors, one or more sonar sensors, an inertial measurement unit, a speedometer, an accelerometer, one or more infrared light detectors, or a combination thereof, (Mondello para. [0012] teaches receiving vehicle data from sensors wherein it cites “[0012] FIG. 1 illustrates an embodiment of an improved black box data recorder in an autonomous driving vehicle (AVD). In FIG. 1, a black box controller 102 receives a data stream of sensor data from multiple vehicle sensors 104 (e.g., surrounding cameras and other sensors). The vehicle sensor data may further include, but is not limited to camera data, radar data, lidar data, sonar data, laser measurements, tire pressure monitoring, and vehicle operation system data.”) and the set of inputs comprise a video stream, distance information associated with one or more objects included in the video stream, location information associated with the one or more objects, a speed of the vehicle, a respective speed of one or more of the one or more objects, an acceleration of the vehicle, infrared light information associated with an environment of the vehicle, or a combination thereof. (Miller figure 1 and 2 teaches video module element 47, para. [0017] teaches vides of exterior of the vehicle, para. [0047] teaches video module sending video of events. Fig. 1 and 2 also teaches vehicle speed which is “VEH_SPEED”. )
Claim 14 is the non-transitory computer-readable medium storage embodiment of claim 1 with similar limitations, and as such is rejected using the same reasoning found in claim 1. The only difference is claim 14 cites a non-transitory computer-readable medium is disclosed in para. [0044] and claim 19 of Mondello.
Claim 20 is the apparatus embodiment of claim 1 with similar limitations, and as such is rejected using the same reasoning found in claim 1. The only difference is claim 20 cites a controller associated with a memory device which is disclosed in para. [0038] of Mondello which cites “The black box controller 102 and/or a separate controller of the storage device 112 may run firmware to perform operations responsive to communications.”.
Claims 2-6, 10, 15-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mondello et al. (US 2019/0302766 A1 – hereinafter Mondello) in view of Miller et al. (US 2011/0063099 A1 – hereinafter Miller) as applied to claim 1 above, and further in view of Kolouri – US 2019/0332109 A1.
In regards to claim 2, Mondello in view of Miller disclose the method of claim 1 further comprising: generating, by the one or more processing units, an environment of the vehicle using the set of inputs, wherein identifying the event is further based at least in part on the inputs. (Mondello para. [0020-0023] teaches using an AI processor including an AI model that processes input data from sensor, which senses the environment using sensors (para. [0012] cites “The vehicle sensor data may further include, but is not limited to camera data, radar data, lidar data, sonar data, laser measurements, tire pressure monitoring, and vehicle operation system data.”) to determine imminent or impending collision, nearby collision, impact involving the vehicles, etc. It also teaches AI process recognizing objects and in response use rules to make decision, wherein the rules specifies spatial and motion relations among objects and the vehicle. Thus, it recognizes the environment of the vehicle using inputs and identifying events based on the environment.)
However, Mondello in view of Miller does not explicitly disclose generating a model of the environment using input or sensor data.
Kolouri discloses generating a model of the environment using input or sensor data. (Kolouri para. [0070] teaches computer vision system that synthesizes and process sensor data to predict the presence, location, classification, and/or path of objects and features in the environment of the vehicle. Para. [0076-0077] teaches obtaining environment sensor data from radar, lidar, GPS, optical cameras, thermal cameras, etc., wherein this sensor data describes surrounding actors such as vehicles, pedestrians, bicyclists and animals. It includes data such as position, heading direction, distance, velocity and acceleration. Para. [0078] teaches tokenizing this data and using it training and controlling vehicle actions. Para. [0089] teaches a Deep RNN provides information about surroundings of vehicle and para. [0090] teaches using the data to provide a canonical representation for all the actors in the scene, wherein the has current path and future expected paths of actors, thus it also determines events of actors based on the canonical representation (model associated with an environment of the vehicle using the set of inputs).)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Mondello in view of Miller with the canonical representation of the Kolouri as Mondello and Kolouri have various vehicle sensors such as camara data, radar data, lidar data, vehicle operation data, etc. It provides the benefit of fusing the sensor data of various sensors to create a model of the environment around the vehicle for making decision and/or controlling the vehicle based on the current scene and what is predicted to happen thus avoiding possible collisions.
In regard to claim 3, Mondello in view of Miller in view of Kolouri disclose the method of claim 2, further comprising:
generating, by the deep learning device, an indication of a first action associated with the event; and (Mondello para. [0010] teaches an event cites “In one embodiment, an artificial intelligence (AI) processor analyzes the sensor data stored in the memory to detect an event of interest (e.g., imminent or impending collision or nearby collision involving the respective vehicle or other vehicle) that is about to take place and/or has taken place.” and Kolouri para. [0090] teaches generating the canonical representation of the environment including current and future expect paths of actors, then in Kolouri para. [0091] inputs the canonical representation to a DNN to generate vehicle control (actions) recommendations. Kolouri para. [0093] teaches detailed recommendations for vehicle actions wherein it cites “include detailed recommendations as to various vehicle control parameters (e.g., including magnitudes and/or directions, as appropriate, for steering, braking, acceleration, and so on).”)
determining an evaluation of a second action executed by the vehicle based at least in part on the indication of the first action, (Kolouri para. [0096-0098] teaches observing the actual operator actions such as (e. g., engagement of a steering wheel, accelerator pedal, brake pedal, and the like) and/or the results of such action (e.g., the resulting position, heading, velocity, acceleration, deceleration, and the like, and/or changes thereof)) and then comparing the actual action with recommended actions. This is similar to the evaluation of a second action of the instant application as para. [0054] of instant application cites “In some examples, the event report may include an evaluation of a second action executed by the vehicle 205 using the indication of the first action associated with the event. For example, the memory system may compare the action taken by the vehicle with the recommended action, and determine a score based on how closely the action taken by the vehicle matches the recommended action. In some examples, the event report may include the indication of the recommended action associated with the event generated by the machine learning model, a comparison between the action taken by the vehicle and the recommended action, a comparison between the action taken by the vehicle and a similar action taken by another driver, or any combination thereof.”, thus comparing the actual action to a recommended action is an evaluation of the second action.)
, wherein the event report comprises the evaluation. (Miller para. [0123 and 0136] and figures 6-7 shows the event report and it would have been obvious to include evaluation of actions in the report as part of the driver’s performance.)
In regards to claim 4, Mondello in view of Miller in view of Kolouri disclose the method of claim 2, further comprising:
generating, by the machine learning model, an indication of a recommended action associated with the event; and (Kolouri para. [0090] teaches generating the canonical representation of the environment including current and future expect paths of actors, then in Kolouri para. [0091] inputs the canonical representation to a DNN to generate vehicle control (actions) recommendations. Kolouri para. [0093] teaches detailed recommendations for vehicle actions wherein it cites “include detailed recommendations as to various vehicle control parameters (e.g., including magnitudes and/or directions, as appropriate, for steering, braking, acceleration, and so on).”)
transmitting the indication of the recommended action to a display component of the vehicle. (Miller paragraph [0020] teaches a vehicle interface display which displays information and warnings; Miller para. [0037] teaches forward collision detected and proves warning so driver can take corrective action, while Miller does not explicitly disclose displaying a recommended action it would have been obvious to do as Miller gives warnings on the displays for user to take corrective actions and Kolouri discloses recommended action, it would be obvious to display recommended action as corrective actions to avoid the detected collision of Miller and Mondello.)
In regards to claim 5, Mondello in view of Miller in view of Kolouri disclose the method of claim 2, wherein generating the model comprises:
identifying one or more second vehicles included in a video stream, the video stream included in the set of inputs; and (Mondello para. [0012] teaches video stream as input wherein it cites “In FIG. 1, a black box controller 102 receives a data stream of sensor data from multiple vehicle sensors 104 ( e.g., surrounding cameras and other sensors). The vehicle sensor data may further include, but is not limited to camera data, radar data, lidar data, sonar data, laser measurements, tire pressure monitoring, and vehicle operation system data”, and Modello para. [0023] teaches the AI processor recognizing objects from data and using spatial and motion relations amount the objects (other vehicles or people) and the vehicle. Kolouri para. [0070] cites “Kolouri para. [0070] teaches computer vision system that synthesizes and process sensor data to predict the presence, location, classification, and/or path of objects and features in the environment of the vehicle 10…the computer vision system 74 can incorporate information from multiple sensors, including but notlimited to cameras, lidars, radars, and/or any number of other types of sensors.”)
identifying respective speeds of the one or more second vehicles using one or more parameters included in the set of inputs. (Kolouri para. [0073] teaches identifying a second car and its speed using input wherein it cites “In various embodiments, for an autonomous vehicle 10 with multi-sensory inputs, at a given time stamp, the tokenized sensor inputs identify the actors (e.g. cars, pedestrians, cyclists, and so on) and numerical descriptions for the actors including their coordinates with respect to the self (i.e. self-driving vehicle 10), their speed, their acceleration, heading angle, for example as described in greater detail further below in connection with step 506 of the process 500 of FIG. 5.”)
In regards to claim 6, Mondello in view of Miller disclose the method of claim 1 further comprising: identifying data associated with an environment of the vehicle based at least in part on identifying the event; and (Mondello teachings identifying an event and identifying an object in the environment of the vehicle and using rules related to spatial and motion of the object in relation to vehicle in para. [0023] wherein it cites “For example, the AI processor 110 may recognizes objects and in response use predetermined rules to make recording decisions, where the rules specifies what spatial and motion relations among the objects and the vehicle should trigger the recording of the sensor data…”) transmitting the report to a remote server associated with the vehicle. (Miller para. [0054] teaches transmitting a report to the server associated with the vehicle wherein it cites “The device 22 may generate the report that details various recorded events and profiles for the secondary driver and transmit the same over a signal REPORT to the APIM 50. The APIM 50 may transmit the report over an output link 53 via the driver's OCD 54 to be uploaded into a server (not shown) so that the primary driver can review the same over a computer.” and Miller para. [0030] teaches recording metadata of event and reporting it, wherein it cites “The device 22 receives the signal NEAR_MISS and correlates the detected events noted thereon with other factors such as but not limited to, date/time, speed, number of passengers, buckle status for driver and passenger located throughout the vehicle and/or phone status at the time the potential collision event occurred.”. Thus it transmits the report to a remote server.)
However Mondello in view of Miller does not explicitly disclose identifying metadata of a model associated with an environment of the vehicle; and
Kolouri discloses identifying metadata of a model associated with an environment of the vehicle based. (Kolouri teaches identifying metadata of the environment related the vehicle in para. [0073] cites “In various embodiments, for an autonomous vehicle 10 with multi-sensory inputs, at a given time stamp, the tokenized sensor inputs identify the actors (e.g. cars, pedestrians, cyclists, and so on) and numerical descriptions for the actors including their coordinates with respect to the self (i.e. self-driving vehicle 10), their speed, their acceleration, heading angle, for example as described in greater detail further below in connection with step 506 of the process 500 of FIG. 5.” and para. [0077] cites “[0077] In various embodiments, the sensing module 410 obtains sensor data pertaining to other vehicles, pedestrians, bicyclists, animals, and/or other objects that may be in proximity to the vehicle 10 and/or a path thereof, and parameters pertaining to such objects (e.g., object type, position, heading angle, distance from the host vehicle 10, velocity, acceleration, and so on). Also in certain embodiments, the sensing module 410 receives sensor data as inputs 405, and provides the sensor data as outputs 415 to the processing module 420 ( e.g., via the communication system 36 of FIG. 1). In certain embodiments, the sensing module 410 provides pre-processed, tokenized sensor data pertaining to various "actors" (e.g., other vehicles, pedestrians, bicyclists, animals, and/or other objects) that may be in proximity to the vehicle 10 and/or a path thereof; as well as observational data pertaining to an operator of the vehicle 10 (e.g., as to steering, accelerating, decelerating, braking, and/or other actions for the vehicle 10 based on operator actions), and provides the tokenized sensor data and the operational observation data as outputs 415 to the processing module 420.”. )
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Mondello in view of Miller with the teachings of Kolouri as Mondello and Kolouri have various vehicle sensors such as camara data, radar data, lidar data, vehicle operation data for monitoring vehicle operations. It provides the benefit of fusing the sensor data of various sensors to create a model of the environment around the vehicle for making decision and/or controlling the vehicle based on the current scene and what is predicted to happen thus avoiding possible collisions.
In regards to claim 10, Mondello in view of Miller discloses the method of claim 1, but does not explicitly disclose wherein identifying the event comprises: determining, based at least in part on the set of inputs, whether the vehicle is executing a turn.
Kolouri discloses determining, based at least in part on the set of inputs, whether the vehicle is executing a turn. (Kolouri para. [0083] teaches getting input information from sensors includes radars and camera, para. [0089] teaches a vehicle performing a turn, para. [0100] teaches the system controls of initialing a turn.)
It would have been obvious to one of ordinary skill in the art before earliest effective filing date of the claimed invention to modify the teachings of the Mondello in view of Miller with that of Kolouri in order to allow for determining when a vehicle is executing a turn as all the reference deal with monitor/observing car actions and surroundings. The benefit of doing so it allow for better control and prediction of action for vehicles as it can monitor a plurality of actions taking place.
In regard to claim 15, it is the non-transitory computer-readable medium embodiment of claim 2 with similar limitations as claim 2 and thus is rejected using the same reasoning found in claim 2.
In regard to claim 16, it is the non-transitory computer-readable medium embodiment of claim 3 with similar limitations as claim 3 and thus is rejected using the same reasoning found in claim 3.
In regard to claim 17, it is the non-transitory computer-readable medium embodiment of claim 4 with similar limitations as claim 4 and thus is rejected using the same reasoning found in claim 4.
In regard to claim 18, it is the non-transitory computer-readable medium embodiment of claim 5 with similar limitations as claim 5 and thus is rejected using the same reasoning found in claim 5.
In regard to claim 19, it is the non-transitory computer-readable medium embodiment of claim 6 with similar limitations as claim 6 and thus is rejected using the same reasoning found in claim 6.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mondello et al. (US 2019/0302766 A1 – hereinafter Mondello) in view of Miller et al. (US 2011/0063099 A1 – hereinafter Miller) in view of Kolouri et al. (US 2019/0332109 A1 – hereinafter Kolouri) and further in view of Kusko et al. (US 2022/0027501 A1 – hereinafter Kusko).
In regard to claim 7, Mondello in view of Miller in view of Kolouri disclose the method of claim 6, but does not explicitly encrypting the metadata and transmitting it to a server.
Kusko et al. discloses encrypting data and transmitting it to a server. (Kusko abstract teaches preserving data privacy for autonomous vehicles. Para. [0060] teaches encrypting data in vehicle memory and vehicle operations severs and transmitted the data over a network to a server wherein it cites “ the users privacy choices are also prearranged, but can be altered via the user-to-company account; (iii) selected data sources are encrypted in the vehicles memory and vehicle operators servers; (iv) retrieval of the encrypted data is accomplished through user authentication within the users account; (v) uses content selective encryption as a means of controlling user personal data; (vi) does not control the cars systems in any way, instead causes selected data to be encrypted to support personal user data privacy; (vii) a change to a vehicles destination would be handled through the vehicle operators systems after the user authenticates to that system and selects a destination change; (viii) individuals have control over location and sensor based data collected using the exemplar of the individual as a client of autonomous vehicles; (ix) selectively ‘hides’ personal information including location; (x) ensures basic vehicle information needed to be tracked by the vehicle owner is not significantly impacted, for instance by obscuring precise location information; (xi) the vehicle operator has sufficient information to aid in the servicing of the vehicle while the clients exact location is protected; (xii) implements methods of securing a user's privacy; and (xiii) describes a methodology of encrypting data transmitted over a network or within a vehicle.”)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Mondello in view of Miller in view of Kolouri with that of Kusko to encrypt data associated with autonomous vehicles as Mondello, Kolouri and Kusko deal with autonomous vehicles and the benefit it perseveres user privacy.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Mondello et al. (US 2019/0302766 A1 – hereinafter Mondello) in view of Miller et al. (US 2011/0063099 A1 – hereinafter Miller) and further in view of Costin et al. (US 2022/0057798 A1 – hereinafter Costin).
In regard to claim 12, Mondello in view of Miller disclose the method of claim 1, but does not explicitly disclose further comprising:
transmitting data associated with the set of inputs from a first processing unit of the one or more processing units to a second processing unit of the one or more processing units using an optical interconnect between the first processing unit and the second processing unit, wherein identifying the event is based at least in part on transmitting the data.
Costin discloses transmitting data associated with the set of inputs from a first processing unit of the one or more processing units to a second processing unit of the one or more processing units using an optical interconnect between the first processing unit and the second processing unit, wherein identifying the event is based at least in part on transmitting the data. (Costin para. [0040] teaches SoCs receive the same inputs through an exchange of data between controllers and that a sensor-data distributor forwards aggregated input data to another controller. Para. [0055-0057] and fig. 3 teaches data in SoC 322 of the primary ECU 320 goes the repeater 329, then the converter 354, then the SoC 352 of the Backup ECU 350, which goes the repeater 359, thus going from a first processing unit to a second processing unit. Then para. [0059] teaches the communication links between the elements in figure 3 can be implemented over a data communication medium including fiber optic cable, which is an optical interconnect.)
It would have been obvious to one of ordinary skill in the art before the earliest effective filing date of the claimed invention to modify the teachings of the Mondello in view of Miller with that Costin in order to allow for passing information between processing units using an optical interconnect as all the Mondello and Costin both deal with monitoring the surroundings of autonomous vehicles. Using the optical interconnect allows for high bandwidth for sensor fusion of the various sensors such as cameras, LiDAR, radar, and other sensors and increased reliability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAULINHO E SMITH whose telephone number is (571)270-1358. The examiner can normally be reached Mon-Fri. 10AM-6PM CST.
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, Abdullah Kawsar can be reached at 571-270-3169. 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.
/PAULINHO E SMITH/Primary Examiner, Art Unit 2127
Prosecution Insights