DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Information Disclosure Statements
There are no Information Disclosure Statements (IDS) filed of record.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware of, in the specification.
Status of Application
Claims 1-20 are pending.
Claims 1, 10, and 16 are independent.
Claims 1, 4, 5, 7, 9, 10, and 16 have been amended.
This FINAL Office Action is in response to the “Amendments and Remarks” received on 3/11/2026.
Response to Arguments/Remarks
With respect to Applicant’s remarks filed on 3/11/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
With respect to the previous claim rejections under 35 U.S.C. § 102 and § 103, applicant has amended the independent claim and these amendments have changed the scope of the original application and the Office has supplied new grounds for rejection attached below in the Final office action and therefore the prior arguments are considered moot.
It is the Office’s stance that all of applicant arguments have been considered and the rejections remain.
Final Office Action
CLAIM INTERPRETATION
During examination, claims are given the broadest reasonable interpretation consistent with the specification and limitations in the specification are not read into the claims. See MPEP §2111, MPEP §2111.01 and In re Yamamoto et al., 222 USPQ 934 10 (Fed. Cir. 1984). Under a broadest reasonable interpretation, words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. See MPEP 2111.01 (I). It is further noted it is improper to import claim limitations from the specification, i.e., a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment. See 15 MPEP 2111.01 (II).
A first exception to the prohibition of reading limitations from the specification into the claims is when the Applicant for patent has provided a lexicographic definition for the term. See MPEP §2111.01 (IV). Following a review of the claims in view of the specification herein, the Office has found that Applicant has not provided any lexicographic definitions, either expressly or implicitly, for any claim terms or phrases with any reasonable clarity, deliberateness and precision. Accordingly, the Office concludes that Applicant has not acted as his/her own lexicographer.
A second exception to the prohibition of reading limitations from the specification into the claims is when the claimed feature is written as a means-plus-function. See 35 U.S.C. §112(f) and MPEP §2181-2183. As noted in MPEP §2181, a three prong test is used to determine the scope of a means-plus-function limitation in a claim:
the claim limitation uses the term "means" or "step" or a term used as a substitute for "means" that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function
the term "means" or "step" or the generic placeholder is modified by functional language, typically, but not always linked by the transition word "for" (e.g., "means for") or another linking word or phrase, such as "configured to" or "so that"
the term "means" or "step" or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
The Office has found herein that the claims no longer contain limitations of means or means type language that must be analyzed under 35 U.S.C. §112 (f).
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims under pre-AIA 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of pre-AIA 35 U.S.C. 103(c) and potential pre-AIA 35 U.S.C. 102(e), (f) or (g) prior art under pre-AIA 35 U.S.C. 103(a).
Claims 1-8 are rejected under 35 USC 103 as being unpatentable over Long et al. (United States Patent Publication 2020/0409362) in view of Carver et al. (United States Patent Publication 2020/0286310).
With respect to Claim 1: While Long discloses “A method comprising: identifying a plurality of factors corresponding to an environment” [Long, ¶ 0024, 0026-0027, and 0043-0045];
“and that respectively impact operation of a machine with respect to an ability of the machine to navigate within the environment” [Long, ¶ 0024, 0026-0027, 0033, and 0043-0045 (uses the task monitoring to generate a measure of a current functional load of driver-assistance system 224 on the processing resources available to driver-assistance system 224. In embodiments, a measure, such as a central processing unit utilization generated by the operating system executing the driver-assistance system 224, may establish the current functional load of the driver-assistance system 224. That is, the CPU utilization percentage would be indicative of total resources being consumed and/or available for performing driver-assistance system 224 tasks. In another embodiment, a weighted combination of factors could be utilized, such as w.sub.1*f.sub.1+w.sub.2*f.sub.2+ . . . +w.sub.nf.sub.n, where w.sub.i is a programmatic and/or developer specified weight given to a specific processing resource utilization factor f.sub.i, and where the total is compared to a scale associated with a maximum for allowable processing resource utilization. For example, autonomous driving handoff system 226 may measure the current functional load of driver-assistance system 224 based on a total number of tasks being executed by driver-assistance system 224 to perform autonomous operation of vehicle 202 in view of the total possible number of concurrent tasks the processing environment is capable of processing. As another example, autonomous driving handoff system 226 may measure factors effecting current functional load, such as the tracking types of tasks, thread congestion, pipeline queuing etc., which are either monitorable system performance characteristics exposed to the autonomous driving handoff system 226 by the API of the driver-assistance system, or communicated to the autonomous driving handoff system 226 in messages generated by the driver-assistance system 224. Furthermore, individual measures may be adjusted or weighted based on task type (e.g., weighting pedestrian tracking greater than stationary object tracking), based on operational environment factors (e.g., weighting all tasks greater in rainy conditions than sunny conditions), based on driver (e.g., when driver is known, driver settings, driver characteristics, etc. can cause weighting of tasks, such as weighting tasks for an inexperienced driver and not weighting tasks for an experienced driver), as well as other factors. In embodiments, such weighting may be set programmatically for specific tasks, computational conditions, etc. through API based messaging from the driver-assistance system 224 to the autonomous driving handoff system (e.g., to enable a developer of the specific driver-assistance system to set weighting for tasks, operational conditions, etc. for specific driver-assistance systems), weighting may be set according to drive modes of the vehicle (e.g., certain tasks associated with weather condition processes may be weighted differently when traction control functions of the vehicle are being used, certain tasks associated with vehicle trajectory and spacing may be weighted greater or lower based on driver experience characteristics, etc.), weighting may be adjusted and/or readjusted dynamically during operation based on messages generated by the driver-assistance system 224 in response to changes in environment (e.g., changes in type of roadway such as residential or freeway, changes in surrounding objects including detection of pedestrians, changes in weather, changes in driver during a trip, etc.). In embodiments, the developer of the vehicle 202, the operator of the vehicle 202, the developer of the driver assistance system 224, the developer of the autonomous driving handoff system 226, or a combination may adjust weighting and/or adjustment of factors used for determining current functional load experienced by driver-assistance system 224)];
“the machine including one or more processing components and one or more mechanical components separate from the one or more processing components” [Long, ¶ 0024, 0026-0027, 0033, and 0043-0045 (the thresholds may be set by the autonomous driving handoff system 226, the driver-assistance system 224, and/or adjusted based on driving characteristics of the vehicle (e.g., current weather being experienced, driver profile (experienced, new, professional, etc.), current traffic conditions, vehicle type, etc)];
“determining a plurality of performance ability scores respectively corresponding to individual information classes of a plurality of information classes corresponding to the environment” [Long, ¶ 0024, 0026-0027, and 0043-0045 (may establish the current functional load of the driver-assistance system 224. That is, the CPU utilization percentage would be indicative of total resources being consumed and/or available for performing driver-assistance system 224 tasks) and (to analyze the operating environment of vehicle 102)];
“wherein one or more individual performance ability scores of one or more individual information classes quantifies a physical capability of the mechanical components of the machine to navigate physical characteristics of the environment corresponding to the one or more individual information classes and are based at least on respective relationships between one or more physical characteristics of the one or more mechanical components and one or more respective types of factors corresponding to the one or more individual information classes” [Long, ¶ 0024, 0026-0027, 0033, and 0043-0045 (the thresholds may be set by the autonomous driving handoff system 226, the driver-assistance system 224, and/or adjusted based on driving characteristics of the vehicle (e.g., current weather being experienced, driver profile (experienced, new, professional, etc.), current traffic conditions, vehicle type, etc)];
“generating an overall impact score” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Processing logic then adjusts and controls the autonomous vehicle based on satisfaction of at least one threshold (processing block 420). As discussed in greater detail above, the adjustment and/or control can include commanding the driver-assistance system, for example when the first threshold is satisfied, to place operation in more cautious mode (e.g., slow speed, increase room between nearby vehicles, etc.), to re-route the autonomous vehicle (e.g., change driving lanes, exit a roadway), etc. Similarly, the adjustment and/or control can include commanding the driver-assistance system, for example when the second threshold is satisfied, to pull vehicle off road and come to a stop. The adjustment and/or control can also include commanding one or more automated driving system controllers, such as brake assist controllers, lane maintenance system controllers, cruise control controllers, etc. to account for a potential future or imminent condition)];
“the overall impact score conveying information regarding one or more impacts to the machine in the environment based at least the plurality of performance ability scores” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Processing logic then adjusts and controls the autonomous vehicle based on satisfaction of at least one threshold (processing block 420). As discussed in greater detail above, the adjustment and/or control can include commanding the driver-assistance system, for example when the first threshold is satisfied, to place operation in more cautious mode (e.g., slow speed, increase room between nearby vehicles, etc.), to re-route the autonomous vehicle (e.g., change driving lanes, exit a roadway), etc. Similarly, the adjustment and/or control can include commanding the driver-assistance system, for example when the second threshold is satisfied, to pull vehicle off road and come to a stop. The adjustment and/or control can also include commanding one or more automated driving system controllers, such as brake assist controllers, lane maintenance system controllers, cruise control controllers, etc. to account for a potential future or imminent condition)];
“and modifying one or more operations of the machine based at least on the overall impact score” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Processing logic then adjusts and controls the autonomous vehicle based on satisfaction of at least one threshold (processing block 420). As discussed in greater detail above, the adjustment and/or control can include commanding the driver-assistance system, for example when the first threshold is satisfied, to place operation in more cautious mode (e.g., slow speed, increase room between nearby vehicles, etc.), to re-route the autonomous vehicle (e.g., change driving lanes, exit a roadway), etc. Similarly, the adjustment and/or control can include commanding the driver-assistance system, for example when the second threshold is satisfied, to pull vehicle off road and come to a stop. The adjustment and/or control can also include commanding one or more automated driving system controllers, such as brake assist controllers, lane maintenance system controllers, cruise control controllers, etc. to account for a potential future or imminent condition)].
Long does not specifically state mechanical components, rather discloses vehicle types and vehicle capabilities are used for determining the weighted scores for tasks which computes the loads for vehicle control.
Carver, which is in the same field of endeavor of controlling vehicles based on vehicle data and environmental data teaches “the machine including one or more processing components and one or more mechanical components separate from the one or more processing components” [Carver, ¶ 0022 and 0024 with Figure 1];
“determining a plurality of performance ability scores respectively corresponding to subsets of factors of the plurality of factors” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The determination as to how much to reduce the speed for either vehicle can be based on the attempted speed 920, a speed limit 985 applicable on the thoroughfare that the vehicle is on, a driver safety score 235A/235B of the driver, a vehicle safety score 240A/240B of the vehicle, an environmental safety score 245 of the environment that the vehicle is driving in)];
“wherein one or more individual performance ability scores are based at least on respective relationships between one or more physical characteristics of the one or more mechanical components and the subsets of factors corresponding thereto” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The vehicle safety score 240A or 240B may be based on anything discussed with respect to the input data 210 of FIG. 2 or the various data sources of FIG. 1, or any of the following: mileage of the vehicle, age of the vehicle, number of owners of the vehicle over time, number of drivers of the vehicle over time, number of accidents that the vehicle has been in, number of repairs made on the vehicle, quantity of fuel remaining in each of one or more fuel storage tanks of the vehicle, type of fuel used by the vehicle (e.g., diesel, leaded gasoline/petrol, unleaded gasoline/petrol, ethanol, hydrogen, natural gas, propane, butane, kerosene, liquefied petroleum gas, electricity, biodiesel, methanol, p-series fuels, or hybrids/combinations thereof), damage incurred by the vehicle, modifications performed to the vehicle, weight being carried by the vehicle, class of vehicle, type of vehicle (e.g., boat, train, airplane, helicopter, sedan automobile, coupe automobile, sports utility vehicle automobile, truck automobile, semi-truck automobile, motorcycle), brand of vehicle, manufacturer of vehicle, distributor of vehicle, purchase date of vehicle, number of component recalls in the vehicle, presence or lack of airbags, presence or lack of heating, presence or lack of air conditioning, presence or lack of fans, tire pressure, quantity of oil, battery charge level of each of one or more batteries, presence or lack of tire chains, presence or lack of four-wheel-drive (4WD) capability by the vehicle, presence or lack of all-wheel-drive (AWD) capability by the vehicle, presence or lack of anti-lock brakes, quality and/or age and/or condition of the brakes, quality and/or age and/or condition of tires and/or tire treads, presence or lack of seat belts, any other indication of vehicle safety or risk discussed herein, whether the vehicle has autonomous driving capabilities, safety record of the vehicle's autonomous driving capabilities, level of driving autonomy, whether the vehicle has autonomous parking capabilities, safety record of the vehicle's autonomous parking capabilities, or combinations thereof)];
“generating an overall impact score” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The determination as to how much to reduce the speed for either vehicle can be based on the attempted speed 920, a speed limit 985 applicable on the thoroughfare that the vehicle is on, a driver safety score 235A/235B of the driver, a vehicle safety score 240A/240B of the vehicle, an environmental safety score 245 of the environment that the vehicle is driving in)];
“the overall impact score conveying information regarding one or more impacts to the machine in the environment based at least on the plurality of performance ability scores” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The determination as to how much to reduce the speed for either vehicle can be based on the attempted speed 920, a speed limit 985 applicable on the thoroughfare that the vehicle is on, a driver safety score 235A/235B of the driver, a vehicle safety score 240A/240B of the vehicle, an environmental safety score 245 of the environment that the vehicle is driving in)];
“and modifying one or more operations of the machine based at least on the overall impact score” [Carver, ¶ 0077, 0086-0091 with Figures 2 and 9 (in certain instances, controller 820 can limit the speed of the vehicle by directly providing speed control to an engine, an electric motor, or other apparatus. Controller 820 may, thus, bypass ECU 830 when limiting the speed of the vehicle)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Carver into the invention of Long to not only include gathering task data based on sensor data, environment data, and vehicle type and capability data for determining processing load, and finally controlling vehicle settings based on these values as Long discloses but to also bring in mechanical vehicle data into the determination of vehicle control such as taught by Carver with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Carver into Long to create a more robust that can not only account for vehicle types and vehicle capabilities, but also mechanical components thus creating a safter and more in-depth safety score thus increasing safety and possible decreasing insurance premiums [Carver,¶ 0058]. Additionally, the claimed invention is merely a combination of old, well known elements gathering data and controlling a vehicle based on vehicle types, environmental data, and user data and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Office Note: The terms in these claims are extremely broad, for example, factors, classes, ability, scores corresponding to classes corresponding to environments, where the scores quantifies a physical capability of mechanical components… The terms are ill defined and most often just that, broad almost meaning anything terms, so that almost everything would read on these scores, classes, components, and abilities. While broad is not indefinite, the Office strongly suggests amending the claims to clearly capture exactly what is being claimed, not just broad terms with associated scores associate with capabilities associated with environments, without any clear definitions.
With respect to Claim 2: Long discloses “The method of claim 1, wherein the one or more operations of the machine are changed in the environment based at least on one or more of the plurality of factors in the environment, or the plurality of performance ability scores” [Long, ¶ 0024, 0026-0027, and 0043-0044, and 0061 with Figure 4].
With respect to Claim 3: Long discloses “The method of claim 2, wherein the one or more operations of the machine include at least one of: forming one or more virtual lanes, adjusting a suspension of the system, increasing speed, decreasing speed, adjusting one or more settings, or adjusting a route” [Long, ¶ 0024, 0026-0027, and 0043-0044, and 0061 with Figure 4].
With respect to Claim 4: Long discloses “The method of claim 1, wherein the overall impact score is additionally determined based at least on data generated using one or more sensors corresponding to the machine” [Long, ¶ 0024, 0026-0027, and 0043-0044, and 0061 with Figure 4];
“and is based at least on a sensitivity factor that dictates how much an individual performance ability score affects the overall impact score, wherein the sensitivity factor is based at least on one or more of the mechanical components of the machine” [Long, ¶ 0024, 0026-0027, and 0043-0044, and 0061 with Figure 4 (weighting may be set according to drive modes of the vehicle (e.g., certain tasks associated with weather condition processes may be weighted differently when traction control functions of the vehicle are being used)].
With respect to Claim 5: While Long discloses “The method of claim 4, wherein the physical characteristics include processing power, vehicle types, and vehicle capabilities ” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4];
Long does not specifically state other possible mechanical physical characteristics.
Carver, which is in the same field of endeavor of controlling vehicles based on vehicle data and environmental data teaches “wherein the physical characteristics include one or more of: one or more dimensions of the machine, a drivetrain of the machine, a sensor configuration of the machine, current energy storage of the machine, energy storage capacity of the machine, a transmission system of the machine, mechanical power of the machine, lights of the machine, cargo capacity of the machine, or current cargo load of the machine.” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The vehicle safety score 240A or 240B may be based on anything discussed with respect to the input data 210 of FIG. 2 or the various data sources of FIG. 1, or any of the following: mileage of the vehicle, age of the vehicle, number of owners of the vehicle over time, number of drivers of the vehicle over time, number of accidents that the vehicle has been in, number of repairs made on the vehicle, quantity of fuel remaining in each of one or more fuel storage tanks of the vehicle, type of fuel used by the vehicle (e.g., diesel, leaded gasoline/petrol, unleaded gasoline/petrol, ethanol, hydrogen, natural gas, propane, butane, kerosene, liquefied petroleum gas, electricity, biodiesel, methanol, p-series fuels, or hybrids/combinations thereof), damage incurred by the vehicle, modifications performed to the vehicle, weight being carried by the vehicle, class of vehicle, type of vehicle (e.g., boat, train, airplane, helicopter, sedan automobile, coupe automobile, sports utility vehicle automobile, truck automobile, semi-truck automobile, motorcycle), brand of vehicle, manufacturer of vehicle, distributor of vehicle, purchase date of vehicle, number of component recalls in the vehicle, presence or lack of airbags, presence or lack of heating, presence or lack of air conditioning, presence or lack of fans, tire pressure, quantity of oil, battery charge level of each of one or more batteries, presence or lack of tire chains, presence or lack of four-wheel-drive (4WD) capability by the vehicle, presence or lack of all-wheel-drive (AWD) capability by the vehicle, presence or lack of anti-lock brakes, quality and/or age and/or condition of the brakes, quality and/or age and/or condition of tires and/or tire treads, presence or lack of seat belts, any other indication of vehicle safety or risk discussed herein, whether the vehicle has autonomous driving capabilities, safety record of the vehicle's autonomous driving capabilities, level of driving autonomy, whether the vehicle has autonomous parking capabilities, safety record of the vehicle's autonomous parking capabilities, or combinations thereof)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Carver into the invention of Long to not only include gathering task data based on sensor data, environment data, and vehicle type and capability data for determining processing load, and finally controlling vehicle settings based on these values as Long discloses but to also bring in mechanical vehicle data into the determination of vehicle control such as taught by Carver with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Carver into Long to create a more robust that can not only account for vehicle types and vehicle capabilities, but also mechanical components thus creating a safter and more in-depth safety score thus increasing safety and possible decreasing insurance premiums [Carver,¶ 0058]. Additionally, the claimed invention is merely a combination of old, well known elements gathering data and controlling a vehicle based on vehicle types, environmental data, and user data and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
With respect to Claim 6: Long discloses “The method of claim 1, wherein at least one performance ability score is determined prior to the machine entering the environment, and at least one other performance ability score is determined while the machine is in the environment” [Long, ¶ 0024, 0026-0027, 0032, and 0043-0044, and 0061 with Figure 4].
With respect to Claim 7: Long discloses “The method of claim 1, wherein the plurality of information classes include three or more of: traffic control; terrain; safety compliance; lighting conditions; traffic flow; weather conditions; or an information level indicating how much information about the environment is available to the machine” [Long, ¶ 0024, 0026-0027, 0032, and 0043-0044, and 0061 with Figure 4 (tasks, weather, traffic, types of road, time of day…)].
With respect to Claim 8: Long discloses “The method of claim 1, wherein the plurality of factors include at least one of high lane numbers, high speed, icing, snow, rain, wind, hail, pedestrians, non-predictable traffic direction, tight turns, rock fall, wildlife warnings, time of day, time of year, elevation change, or no road markings.” [Long, ¶ 0024, 0026-0027, 0032, and 0043-0044, and 0061 with Figure 4 (tasks)].
Claim 9 is rejected under 35 USC 103 as being unpatentable over Long et al. (United States Patent Publication 2020/0409362) in view of Carver et al. (United States Patent Publication 2020/0286310) and in further view of Holt et al. (United States Patent Publication 2014/0282624).
With respect to Claim 9: While Long discloses “The method of claim 1, wherein the generating the overall impact score based at least the plurality of performance ability scores is performed” [Long, ¶ 0024, 0026-0027, 0032, and 0043-0044, and 0061 with Figure 4 (tasks)];
Long does not specifically state using a Minkowski summation for its calculations.
Holt, which is also processing system (which is used on vehicles) teaches “using a Minkowski summation” [Holt, ¶ 0005, 0068, and 0111].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Holt into the invention of Long to not only include gathering task data based on sensor data, and determining processing load, and finally controlling vehicle settings as Long discloses but to also use known summation and math such as the Minkowski summation as taught by Holt with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Holt into Long to create a more robust system that uses known math functions to ease or efficient math problems, such as multi-input single output computations [Holt, ¶ 0111]. Additionally, the claimed invention is merely a combination of old, well known elements gathering data and controlling a vehicle and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Claims 10-15 are rejected under 35 USC 103 as being unpatentable over Long et al. (United States Patent Publication 2020/0409362) in view of Carver et al. (United States Patent Publication 2020/0286310) and in further view of Hitakatsu et al. (United States Patent Publication 2023/0322246).
With respect to Claim 10: While Long discloses “A computing system comprising: one or more processers to perform operations comprising” [Long, ¶ 0021-0027 and 0043]:
“identifying a plurality of factors corresponding to an environment” [Long, ¶ 0024, 0026-0027, and 0043];
“the plurality of factors being respectively based at least on a mechanical configuration of a system and an amount of impact caused to operation of the system” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Processing logic then adjusts and controls the autonomous vehicle based on satisfaction of at least one threshold (processing block 420). As discussed in greater detail above, the adjustment and/or control can include commanding the driver-assistance system, for example when the first threshold is satisfied, to place operation in more cautious mode (e.g., slow speed, increase room between nearby vehicles, etc.), to re-route the autonomous vehicle (e.g., change driving lanes, exit a roadway), etc. Similarly, the adjustment and/or control can include commanding the driver-assistance system, for example when the second threshold is satisfied, to pull vehicle off road and come to a stop. The adjustment and/or control can also include commanding one or more automated driving system controllers, such as brake assist controllers, lane maintenance system controllers, cruise control controllers, etc. to account for a potential future or imminent condition)];
“determining a plurality of performance ability scores respectively corresponding to subsets of the plurality of factors” [Long, ¶ 0024, 0026-0027, and 0044 (loads)];
“as respectively impacted by the corresponding subsets of the plurality of factors” [Long, ¶ 0024, 0026-0027, and 0044 (loads)];
“generating an overall impact score” [Long, ¶ 0024, 0026-0027, and 0043-0044, and 0061 with Figure 4];
“the overall impact score conveying information regarding one or more impacts to the system in the environment based at least on a magnitude of one or more of the plurality of performance ability scores” [Long, ¶ 0024, 0026-0027, and 0043-0044, and 0061 with Figure 4];
“the overall impact score being based at least on a sensitivity factor that dictates how much an individual performance ability score affects the overall impact score” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Processing logic then adjusts and controls the autonomous vehicle based on satisfaction of at least one threshold (processing block 420). As discussed in greater detail above, the adjustment and/or control can include commanding the driver-assistance system, for example when the first threshold is satisfied, to place operation in more cautious mode (e.g., slow speed, increase room between nearby vehicles, etc.), to re-route the autonomous vehicle (e.g., change driving lanes, exit a roadway), etc. Similarly, the adjustment and/or control can include commanding the driver-assistance system, for example when the second threshold is satisfied, to pull vehicle off road and come to a stop. The adjustment and/or control can also include commanding one or more automated driving system controllers, such as brake assist controllers, lane maintenance system controllers, cruise control controllers, etc. to account for a potential future or imminent condition)];
wherein the sensitivity factor is based at least on one or more of: the mechanical configuration of the system; one or more characteristics of the environment; or a severity of the one or more impacts on the system” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Processing logic then adjusts and controls the autonomous vehicle based on satisfaction of at least one threshold (processing block 420). As discussed in greater detail above, the adjustment and/or control can include commanding the driver-assistance system, for example when the first threshold is satisfied, to place operation in more cautious mode (e.g., slow speed, increase room between nearby vehicles, etc.), to re-route the autonomous vehicle (e.g., change driving lanes, exit a roadway), etc. Similarly, the adjustment and/or control can include commanding the driver-assistance system, for example when the second threshold is satisfied, to pull vehicle off road and come to a stop. The adjustment and/or control can also include commanding one or more automated driving system controllers, such as brake assist controllers, lane maintenance system controllers, cruise control controllers, etc. to account for a potential future or imminent condition)];
“and modifying one or more operations of the system based at least on the overall impact score corresponding to the one or more sub-areas in the map” [Long, ¶ 0024, 0026-0027, and 0043-0044, and 0061 with Figure 4].
Long does not specifically state mechanical components, rather vehicle types and vehicle capabilities are used for determining the weighted scores for tasks which computes the loads for vehicle control or specifically state generating a map, rather Long discloses using sensor data, map data, and location data for help in vehicle control which is based on map and location data.
Carver, which is in the same field of endeavor of controlling vehicles based on vehicle data and environmental data teaches “the plurality of factors being respectively based at least on a mechanical configuration of a system and an amount of impact caused to operation of the system” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The vehicle safety score 240A or 240B may be based on anything discussed with respect to the input data 210 of FIG. 2 or the various data sources of FIG. 1, or any of the following: mileage of the vehicle, age of the vehicle, number of owners of the vehicle over time, number of drivers of the vehicle over time, number of accidents that the vehicle has been in, number of repairs made on the vehicle, quantity of fuel remaining in each of one or more fuel storage tanks of the vehicle, type of fuel used by the vehicle (e.g., diesel, leaded gasoline/petrol, unleaded gasoline/petrol, ethanol, hydrogen, natural gas, propane, butane, kerosene, liquefied petroleum gas, electricity, biodiesel, methanol, p-series fuels, or hybrids/combinations thereof), damage incurred by the vehicle, modifications performed to the vehicle, weight being carried by the vehicle, class of vehicle, type of vehicle (e.g., boat, train, airplane, helicopter, sedan automobile, coupe automobile, sports utility vehicle automobile, truck automobile, semi-truck automobile, motorcycle), brand of vehicle, manufacturer of vehicle, distributor of vehicle, purchase date of vehicle, number of component recalls in the vehicle, presence or lack of airbags, presence or lack of heating, presence or lack of air conditioning, presence or lack of fans, tire pressure, quantity of oil, battery charge level of each of one or more batteries, presence or lack of tire chains, presence or lack of four-wheel-drive (4WD) capability by the vehicle, presence or lack of all-wheel-drive (AWD) capability by the vehicle, presence or lack of anti-lock brakes, quality and/or age and/or condition of the brakes, quality and/or age and/or condition of tires and/or tire treads, presence or lack of seat belts, any other indication of vehicle safety or risk discussed herein, whether the vehicle has autonomous driving capabilities, safety record of the vehicle's autonomous driving capabilities, level of driving autonomy, whether the vehicle has autonomous parking capabilities, safety record of the vehicle's autonomous parking capabilities, or combinations thereof)];
“determining a plurality of performance ability scores respectively corresponding to subsets of the plurality of factors as respectively impacted by the corresponding subsets of the plurality of factors” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The vehicle safety score 240A or 240B may be based on anything discussed with respect to the input data 210 of FIG. 2 or the various data sources of FIG. 1, or any of the following: mileage of the vehicle, age of the vehicle, number of owners of the vehicle over time, number of drivers of the vehicle over time, number of accidents that the vehicle has been in, number of repairs made on the vehicle, quantity of fuel remaining in each of one or more fuel storage tanks of the vehicle, type of fuel used by the vehicle (e.g., diesel, leaded gasoline/petrol, unleaded gasoline/petrol, ethanol, hydrogen, natural gas, propane, butane, kerosene, liquefied petroleum gas, electricity, biodiesel, methanol, p-series fuels, or hybrids/combinations thereof), damage incurred by the vehicle, modifications performed to the vehicle, weight being carried by the vehicle, class of vehicle, type of vehicle (e.g., boat, train, airplane, helicopter, sedan automobile, coupe automobile, sports utility vehicle automobile, truck automobile, semi-truck automobile, motorcycle), brand of vehicle, manufacturer of vehicle, distributor of vehicle, purchase date of vehicle, number of component recalls in the vehicle, presence or lack of airbags, presence or lack of heating, presence or lack of air conditioning, presence or lack of fans, tire pressure, quantity of oil, battery charge level of each of one or more batteries, presence or lack of tire chains, presence or lack of four-wheel-drive (4WD) capability by the vehicle, presence or lack of all-wheel-drive (AWD) capability by the vehicle, presence or lack of anti-lock brakes, quality and/or age and/or condition of the brakes, quality and/or age and/or condition of tires and/or tire treads, presence or lack of seat belts, any other indication of vehicle safety or risk discussed herein, whether the vehicle has autonomous driving capabilities, safety record of the vehicle's autonomous driving capabilities, level of driving autonomy, whether the vehicle has autonomous parking capabilities, safety record of the vehicle's autonomous parking capabilities, or combinations thereof)];
“generating an overall impact score” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The determination as to how much to reduce the speed for either vehicle can be based on the attempted speed 920, a speed limit 985 applicable on the thoroughfare that the vehicle is on, a driver safety score 235A/235B of the driver, a vehicle safety score 240A/240B of the vehicle, an environmental safety score 245 of the environment that the vehicle is driving in)];
“the overall impact score conveying information regarding one or more impacts to the system in the environment based at least on a magnitude of one or more of the plurality of performance ability scores” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The determination as to how much to reduce the speed for either vehicle can be based on the attempted speed 920, a speed limit 985 applicable on the thoroughfare that the vehicle is on, a driver safety score 235A/235B of the driver, a vehicle safety score 240A/240B of the vehicle, an environmental safety score 245 of the environment that the vehicle is driving in)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Carver into the invention of Long to not only include gathering task data based on sensor data, environment data, and vehicle type and capability data for determining processing load, and finally controlling vehicle settings based on these values as Long discloses but to also bring in mechanical vehicle data into the determination of vehicle control such as taught by Carver with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Carver into Long to create a more robust that can not only account for vehicle types and vehicle capabilities, but also mechanical components thus creating a safter and more in-depth safety score thus increasing safety and possible decreasing insurance premiums [Carver,¶ 0058]. Additionally, the claimed invention is merely a combination of old, well known elements gathering data and controlling a vehicle based on vehicle types, environmental data, and user data and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Hitakatsu, which is also a vehicle control system that gathers data and controls vehicles teaches “generating a map of the environment including one or more sub-areas and one or more overall impact scores corresponding to the one or more sub-areas” [Hitakatsu, ¶ 0007-0016].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Kapoor into the invention of Long to not only include gathering task data based on sensor data, and determining processing load, and finally controlling vehicle settings as Long discloses but to also create maps of new data, which can affect vehicle control and impact as taught by Hitakatsu with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Hitakatsu into Long to create a system that can help other vehicles by updating the data [Hitakatsu, ¶ 0011]. Additionally, the claimed invention is merely a combination of old, well known elements gathering data and controlling a vehicle and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
Office Note: The terms in these claims are extremely broad, for example, factors, classes, ability, scores corresponding to classes corresponding to environments, where the scores quantifies a physical capability of mechanical components… The terms are ill defined and most often just that, broad almost meaning anything terms, so that almost everything would read on these scores, classes, components, and abilities. While broad is not indefinite, the Office strongly suggests amending the claims to clearly capture exactly what is being claimed, not just broad terms with associated scores associate with capabilities associated with environments, without any clear definitions.
With respect to Claim 11: Long discloses “The computing system of claim 10, wherein the one or more operations of the system are changed in the environment based at least on one or more of the plurality of factors in the environment, or the plurality of performance ability scores” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
With respect to Claim 12: Long discloses “The computing system of claim 11, wherein the one or more operations of the system include at least one of: forming one or more virtual lanes, adjusting a suspension of the system, increasing speed, decreasing speed, adjusting one or more settings, or adjusting a route” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
With respect to Claim 13: Long discloses “The computing system of claim 10, wherein the overall impact score is additionally determined based at least on data generated using one or more sensors corresponding to the system” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
With respect to Claim 14: While Long discloses “The method of claim 4, wherein the mechanical configuration of the system includes processing power, vehicle types, and vehicle capabilities ” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4];
Long does not specifically state other possible mechanical configurations taken into account.
Carver, which is in the same field of endeavor of controlling vehicles based on vehicle data and environmental data teaches “wherein the physical characteristics include one or more of: one or more dimensions of the machine, a drivetrain of the machine, a sensor configuration of the machine, current energy storage of the machine, energy storage capacity of the machine, a transmission system of the machine, mechanical power of the machine, lights of the machine, cargo capacity of the machine, or current cargo load of the machine.” [Carver, ¶ 0086-0091 with Figures 2 and 9 (The vehicle safety score 240A or 240B may be based on anything discussed with respect to the input data 210 of FIG. 2 or the various data sources of FIG. 1, or any of the following: mileage of the vehicle, age of the vehicle, number of owners of the vehicle over time, number of drivers of the vehicle over time, number of accidents that the vehicle has been in, number of repairs made on the vehicle, quantity of fuel remaining in each of one or more fuel storage tanks of the vehicle, type of fuel used by the vehicle (e.g., diesel, leaded gasoline/petrol, unleaded gasoline/petrol, ethanol, hydrogen, natural gas, propane, butane, kerosene, liquefied petroleum gas, electricity, biodiesel, methanol, p-series fuels, or hybrids/combinations thereof), damage incurred by the vehicle, modifications performed to the vehicle, weight being carried by the vehicle, class of vehicle, type of vehicle (e.g., boat, train, airplane, helicopter, sedan automobile, coupe automobile, sports utility vehicle automobile, truck automobile, semi-truck automobile, motorcycle), brand of vehicle, manufacturer of vehicle, distributor of vehicle, purchase date of vehicle, number of component recalls in the vehicle, presence or lack of airbags, presence or lack of heating, presence or lack of air conditioning, presence or lack of fans, tire pressure, quantity of oil, battery charge level of each of one or more batteries, presence or lack of tire chains, presence or lack of four-wheel-drive (4WD) capability by the vehicle, presence or lack of all-wheel-drive (AWD) capability by the vehicle, presence or lack of anti-lock brakes, quality and/or age and/or condition of the brakes, quality and/or age and/or condition of tires and/or tire treads, presence or lack of seat belts, any other indication of vehicle safety or risk discussed herein, whether the vehicle has autonomous driving capabilities, safety record of the vehicle's autonomous driving capabilities, level of driving autonomy, whether the vehicle has autonomous parking capabilities, safety record of the vehicle's autonomous parking capabilities, or combinations thereof)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Carver into the invention of Long to not only include gathering task data based on sensor data, environment data, and vehicle type and capability data for determining processing load, and finally controlling vehicle settings based on these values as Long discloses but to also bring in mechanical vehicle data into the determination of vehicle control such as taught by Carver with a reasonable expectation of success. One would be motivated to incorporate aspects of the cited prior art Carver into Long to create a more robust that can not only account for vehicle types and vehicle capabilities, but also mechanical components thus creating a safter and more in-depth safety score thus increasing safety and possible decreasing insurance premiums [Carver,¶ 0058]. Additionally, the claimed invention is merely a combination of old, well known elements gathering data and controlling a vehicle based on vehicle types, environmental data, and user data and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the results of the combination would have been predictable.
With respect to Claim 15: Long discloses “The computing system of claim 10, wherein the system is comprised in at least one of: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets; a system for performing deep learning operations; a system for presenting at least one of augmented reality content, virtual reality content, or mixed reality content; a system for hosting one or more real-time streaming applications; a system implemented using an edge device; a system implemented using a robot; a system for performing conversational AI operations; a system for implementing one or more large language models (LLMs); a system for generating synthetic data; a system incorporating one or more virtual machines (VMs ); a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 16-20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Long et al. (United States Patent Publication 2020/0409362).
With respect to Claim 16: Long discloses “One or more processors comprising processing circuitry to perform operations, the operations comprising: identifying a plurality of factors corresponding to an environment” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4];
“the plurality of factors respectively impacting operation of a system” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Long, ¶ 0024, 0026-0027, 0033, and 0043-0045 (uses the task monitoring to generate a measure of a current functional load of driver-assistance system 224 on the processing resources available to driver-assistance system 224. In embodiments, a measure, such as a central processing unit utilization generated by the operating system executing the driver-assistance system 224, may establish the current functional load of the driver-assistance system 224. That is, the CPU utilization percentage would be indicative of total resources being consumed and/or available for performing driver-assistance system 224 tasks. In another embodiment, a weighted combination of factors could be utilized, such as w.sub.1*f.sub.1+w.sub.2*f.sub.2+ . . . +w.sub.nf.sub.n, where w.sub.i is a programmatic and/or developer specified weight given to a specific processing resource utilization factor f.sub.i, and where the total is compared to a scale associated with a maximum for allowable processing resource utilization. For example, autonomous driving handoff system 226 may measure the current functional load of driver-assistance system 224 based on a total number of tasks being executed by driver-assistance system 224 to perform autonomous operation of vehicle 202 in view of the total possible number of concurrent tasks the processing environment is capable of processing. As another example, autonomous driving handoff system 226 may measure factors effecting current functional load, such as the tracking types of tasks, thread congestion, pipeline queuing etc., which are either monitorable system performance characteristics exposed to the autonomous driving handoff system 226 by the API of the driver-assistance system, or communicated to the autonomous driving handoff system 226 in messages generated by the driver-assistance system 224. Furthermore, individual measures may be adjusted or weighted based on task type (e.g., weighting pedestrian tracking greater than stationary object tracking), based on operational environment factors (e.g., weighting all tasks greater in rainy conditions than sunny conditions), based on driver (e.g., when driver is known, driver settings, driver characteristics, etc. can cause weighting of tasks, such as weighting tasks for an inexperienced driver and not weighting tasks for an experienced driver), as well as other factors. In embodiments, such weighting may be set programmatically for specific tasks, computational conditions, etc. through API based messaging from the driver-assistance system 224 to the autonomous driving handoff system (e.g., to enable a developer of the specific driver-assistance system to set weighting for tasks, operational conditions, etc. for specific driver-assistance systems), weighting may be set according to drive modes of the vehicle (e.g., certain tasks associated with weather condition processes may be weighted differently when traction control functions of the vehicle are being used, certain tasks associated with vehicle trajectory and spacing may be weighted greater or lower based on driver experience characteristics, etc.), weighting may be adjusted and/or readjusted dynamically during operation based on messages generated by the driver-assistance system 224 in response to changes in environment (e.g., changes in type of roadway such as residential or freeway, changes in surrounding objects including detection of pedestrians, changes in weather, changes in driver during a trip, etc.). In embodiments, the developer of the vehicle 202, the operator of the vehicle 202, the developer of the driver assistance system 224, the developer of the autonomous driving handoff system 226, or a combination may adjust weighting and/or adjustment of factors used for determining current functional load experienced by driver-assistance system 224)];
“and being respectively based at least one or more individual characteristics corresponding to the system” [Long, ¶ 0024-0027, and 0043-0045, and 0061 with Figure 4 (Examples of such scenarios include: incomplete sensor data that does not enable driver-assistance system to generate a sufficient model of vehicle's 102 surroundings, a scenario is encountered by vehicle 102 that driver-assistance system does not recognize or cannot react to, or vehicle processor overload that does not enable driver-assistance system 124 to perform adequate operational environment analysis)];
“wherein the plurality of factors include an information level about the environment indicating how much information about the environment is available to the system” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Long, ¶ 0024, 0026-0027, 0033, and 0043-0045 (uses the task monitoring to generate a measure of a current functional load of driver-assistance system 224 on the processing resources available to driver-assistance system 224. In embodiments, a measure, such as a central processing unit utilization generated by the operating system executing the driver-assistance system 224, may establish the current functional load of the driver-assistance system 224. That is, the CPU utilization percentage would be indicative of total resources being consumed and/or available for performing driver-assistance system 224 tasks. In another embodiment, a weighted combination of factors could be utilized, such as w.sub.1*f.sub.1+w.sub.2*f.sub.2+ . . . +w.sub.nf.sub.n, where w.sub.i is a programmatic and/or developer specified weight given to a specific processing resource utilization factor f.sub.i, and where the total is compared to a scale associated with a maximum for allowable processing resource utilization. For example, autonomous driving handoff system 226 may measure the current functional load of driver-assistance system 224 based on a total number of tasks being executed by driver-assistance system 224 to perform autonomous operation of vehicle 202 in view of the total possible number of concurrent tasks the processing environment is capable of processing. As another example, autonomous driving handoff system 226 may measure factors effecting current functional load, such as the tracking types of tasks, thread congestion, pipeline queuing etc., which are either monitorable system performance characteristics exposed to the autonomous driving handoff system 226 by the API of the driver-assistance system, or communicated to the autonomous driving handoff system 226 in messages generated by the driver-assistance system 224. Furthermore, individual measures may be adjusted or weighted based on task type (e.g., weighting pedestrian tracking greater than stationary object tracking), based on operational environment factors (e.g., weighting all tasks greater in rainy conditions than sunny conditions), based on driver (e.g., when driver is known, driver settings, driver characteristics, etc. can cause weighting of tasks, such as weighting tasks for an inexperienced driver and not weighting tasks for an experienced driver), as well as other factors. In embodiments, such weighting may be set programmatically for specific tasks, computational conditions, etc. through API based messaging from the driver-assistance system 224 to the autonomous driving handoff system (e.g., to enable a developer of the specific driver-assistance system to set weighting for tasks, operational conditions, etc. for specific driver-assistance systems), weighting may be set according to drive modes of the vehicle (e.g., certain tasks associated with weather condition processes may be weighted differently when traction control functions of the vehicle are being used, certain tasks associated with vehicle trajectory and spacing may be weighted greater or lower based on driver experience characteristics, etc.), weighting may be adjusted and/or readjusted dynamically during operation based on messages generated by the driver-assistance system 224 in response to changes in environment (e.g., changes in type of roadway such as residential or freeway, changes in surrounding objects including detection of pedestrians, changes in weather, changes in driver during a trip, etc.). In embodiments, the developer of the vehicle 202, the operator of the vehicle 202, the developer of the driver assistance system 224, the developer of the autonomous driving handoff system 226, or a combination may adjust weighting and/or adjustment of factors used for determining current functional load experienced by driver-assistance system 224)];
“determining a plurality of performance ability scores respectively corresponding to subsets of the plurality of factors” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4];
“the plurality of performance ability scores indicating respective impacts associated with performance of one or more operations corresponding to the system as respectively impacted by the corresponding subsets of the plurality of factors” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4];
“generating an overall impact score, the overall impact score conveying information regarding one or more impacts to the system in the environment based at least on a magnitude of one or more of the plurality of performance ability scores” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4 (Processing logic then adjusts and controls the autonomous vehicle based on satisfaction of at least one threshold (processing block 420). As discussed in greater detail above, the adjustment and/or control can include commanding the driver-assistance system, for example when the first threshold is satisfied, to place operation in more cautious mode (e.g., slow speed, increase room between nearby vehicles, etc.), to re-route the autonomous vehicle (e.g., change driving lanes, exit a roadway), etc. Similarly, the adjustment and/or control can include commanding the driver-assistance system, for example when the second threshold is satisfied, to pull vehicle off road and come to a stop. The adjustment and/or control can also include commanding one or more automated driving system controllers, such as brake assist controllers, lane maintenance system controllers, cruise control controllers, etc. to account for a potential future or imminent condition)];
“and modifying one or more operations of the system based at least on the overall impact score” [Long, ¶ 0024, 0026-0027, 0032, and 0043-0045, and 0061 with Figure 4].
Office Note: The terms in these claims are extremely broad, for example, factors, classes, ability, scores corresponding to classes corresponding to environments, where the scores quantifies a physical capability of mechanical components… The terms are ill defined and most often just that, broad almost meaning anything terms, so that almost everything would read on these scores, classes, components, and abilities. While broad is not indefinite, the Office strongly suggests amending the claims to clearly capture exactly what is being claimed, not just broad terms with associated scores associate with capabilities associated with environments, without any clear definitions.
With respect to Claim 17: Long discloses “The one or more processors of claim 16, wherein the one or more operations of the system are changed in the environment based at least on one or more of the plurality of factors in the environment, or the plurality of performance ability scores” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
With respect to Claim 18: Long discloses “The one or more processors of claim 17, wherein the one or more operations of the system include at least one of: forming one or more virtual lanes, adjusting a suspension of the system, increasing speed, decreasing speed, adjusting one or more settings, or adjusting a route” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
With respect to Claim 19: Long discloses “The one or more processors of claim 16, wherein the overall impact score is additionally determined based at least on data generated using one or more sensors corresponding to the system” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
With respect to Claim 20: Long discloses “The one or more processors of claim 19, wherein the overall impact score is determined, re-determined, or confirmed when the system enters the environment” [Long, ¶ 0024, 0026-0027, and 0043-0045, and 0061 with Figure 4].
Prior Art (Not relied upon)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached form 892.
Conclusion
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/JESS WHITTINGTON/Primary Examiner, Art Unit 3666c
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