DETAILED ACTION
NOTICE OF PRE-AIA OR AIA STATUS
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
STATUS OF CLAIMS
This action is in response to the Applicant’s arguments and amendments filed on 6/19/2026. Applicant amended claims 1, 5, 9, 13 and 17. Claims 1-5 and 7-21 are pending and are examined below.
RESPONSE TO REMARKS AND ARGUMENTS
In regard to the claim objections, Applicant’s amendments filed on 6/19/2026 obviate said objections — accordingly, the claim objections are withdrawn.
In regard to the claim rejections under § 103, Applicant’s arguments and amendments filed on 6/19/2026 have been fully considered but are unpersuasive.
As to claim 1, Applicant argues that neither Kumai nor Burtch disclose or suggest the limitation of “in response to the gap being detected, determine a preferred gap distance for a driver of the transport using a machine learning model executed on contextual data about a weather condition affecting the transport.”
As to Kumai, Applicant argues that Kumai does not disclose or suggest determining a preferred gap distance. Namely, Applicant posits that Kumai’s corrected target inter-vehicle distance is simply a sensor-measured distance at an instant wherein a driver’s manual intervention ended, copied into a target register once two threshold conditions are met. Applicant contests that such constitutes value substitution from a sensor reading, not a preference determination.
As to Burtch, Applicant argues that Burtch describes an artificial neural network (ANN) receiving a data set including weather, but executes the ANN in response to a driver signaling a gap change via input, not in response to sensor-based detection that the gap crossed a threshold. Applicant submits that para. [0053] of Burtch merely puts forth a general statement that weather conditions may play a role in a driver’s subjective preference for gap size.
As to the combination of Kumai and Burtch, Applicant argues that the combination would not arrive at the claimed limitation. As Applicant argued above, Kumai updates its target after a pedal intervention ends, and Burtch runs its ANN only on driver gap input. Hence, Applicant submits that both references tie the operative event to a driver-initiated input. Applicant concludes that therefore the resulting system would execute Burtch’s ANN model in response to driver-initiated manual intervention rather than the claimed sensor-based gap detection.
Examiner respectfully disagrees.
Addressing Kumai, Examiner submits that Kumai does not disclose a mere value substitution from a sensor reading. Rather, Kumai establishes the foundation of the claim: in response to the gap being detected, determine a preferred gap distance for a driver of the transport. Diving in, Kumai para. [0069] expressly contemplates a driver-set target: “[T]he traveling control unit 16 may perform the automatic speed adjustment (speed control) in which a target speed and a target inter-vehicle distance are set as the target values. These target values may be fixed values determined according to the traveling environment or the law, values that are set by the driver, values that vary according to the time or the distance, or values that are set by the travel plan generation unit 15 according to the traveling situation … The automatic speed adjustment is implemented by the actuator 7 that operates based on the control signal output from the traveling control unit 16. The automatic speed adjustment automatically adjusts the speed of the vehicle V even when the driver does not perform the acceleration/deceleration operation … or the brake operation.” Therefore, Kumai’s target inter-vehicle distance is a driver-associated value operated on by an automatic control loop, not an incidental sensor artifact.
Continuing, Kumai further discloses the architecture by which the preferred gap distance is updated upon detection of a deviation. Per para. [0115] and FIG. 6, Kumai determines whether a difference between a target inter-vehicle distance and an actual inter-vehicle distance is equal or larger than a second inter-vehicle distance threshold — if yes, the control architecture corrects the target value. Para. [0116] goes on to state that if no difference is present, no correction is performed; such affirms that Kumai’s processing is conditional, not sequential. Hence, the update of a preferred gap distance is conditioned on, and performed in response to, a detection that a gap deviates from a target beyond a predetermined threshold. Indeed, para. [0010] clarifies that the above condition being met “means that … it is estimated that the intervention operation is not a temporary intervention operation for avoiding an obstacle but is an intervention performed intentionally by the driver to change the target inter-vehicle distance of the travelling control.” The foregoing confirms that the updated value is a preferred gap distance. Therefore, at least Kumai paras. [0010], [0069] and [0116] disclose the BRI of: in response to the gap being detected, determine a preferred gap distance for a driver of the transport.
Addressing Burtch, Examiner acknowledges that Burtch performs its determination of a preferred gap distance upon user input. However, Burtch was not relied upon in the rejection for the trigger of “in response to the gap being detected” — Kumai provides the trigger for performing the determination step in response to said trigger. Note well that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. (See MPEP 2145 IV.)
Moving on, Examiner respectfully disagrees with Applicant’s characterization of Burtch regarding the machine learning model executing on contextual data about a weather condition affecting the transport. While Applicant points solely to Burtch para. [0053], this paragraph alone does not carry the whole weight of the rejection. Looking at the entire picture, Burtch teaches: determine a preferred gap distance for a driver of the transport using a machine learning model executed on contextual data about a weather condition affecting the transport (“Weather conditions (rain, ice, etc.) may also play a role in the driver’s preference for gap size—for instance, a bigger gap may be preferred when braking and visibility are impaired.” ¶ 53. “The method 500 further includes, at 512, providing the data set to an artificial neural network 400 in response to the input being sensed. Said another way, when the driver signals a desired change in gap using the ACC gap input 210, various data (e.g., weather conditions, vehicle 102, 106 velocities, etc.) is sent to the ANN 400.” Emphasis added; ¶ 54. “The ANN 400 may then use this data, collected over numerous instances, to calculate a change in the desired distance.” ¶ 55. “The method 500 further includes, at 516, changing the requested distance based on at least one of the input from the driver and the calculated change in the desired distance by the ANN 400.” ¶ 57.). Summarizing, at least Burtch paras. [0053]-[0057] teach that an ANN configured to determine a preferred gap distance for a driver of the transport, wherein the ANN executes on contextual data about a weather condition affecting the transport.
Addressing the combination of Kumai and Burtch, a skilled artisan would have arrived at the claimed limitation in view of the combination of Kumai and Burtch. Namely, a skilled artisan would have turned to Burtch to enhance Kumai with a reasonable expectation of success because Burtch is useful for “present[ing] an ACC system that reduces the need for changes in the desired time gap by the driver,” especially in view of “weather conditions.” (Burtch, ¶¶ 5, 53.) In more detail, Examiner confirms Applicant’s understanding that Kumai’s target correction unit 22 would execute Burtch’s ANN to perform the target value correction processing described at Kumai, para. [0115]. Such would have predictable results with a reasonable expectation of success given Burtch’s above-outlined usefulness in the context of ACC in addition to the fact that Burtch’s output desired distance would align with Kumai’s adjusted target inter-vehicle distance. However, it is important to note that the MPEP guides: “a determination of obviousness based on teachings from multiple references does not require an actual, physical substitution of elements.” (MPEP 2145 III.) Thus, Examiner maintains that Kumai-Burtch arrives at the claimed invention without requiring an actual, physical substitution of elements as Applicant appears to suggest at Remarks, p. 9.
Importantly, Examiner respectfully disagrees with Applicant’s notion that triggering the ANN in Kumai-Burtch would be based on a driver-initiated manual intervention. As shown above, Kumai determines a preferred gap distance for a driver of the transport in response to the gap being detected as required by claim 1 — Kumai’s update at S64 is conditioned on, and performed in response to, a detection that a gap deviates from a target beyond a predetermined threshold (See Kumai, ¶ 115). Applicant’s argument appears to be based on the impression that the entire process of S60-S64 is initiated by a driver-initiated operation. However, this supposed distinguishment is narrower than what the broadest reasonable interpretation (BRI) of the claim encompasses; the claim does not require that the “in response to the gap being detected” step exclude an antecedent condition. In other words, Applicant’s argument conflates what starts Kumai’s routine with what causes the correction within Kumai’s routine. Thus, Examiner maintains that Kumai reads on the claimed trigger for performing a determination of a preferred gap distance for a driver of a transport.
Accordingly, the claim rejections under § 103 are maintained.
CLAIM REJECTIONS—35 U.S.C. § 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 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.
Claim(s) 1, 7, 9 15 and 17 is/are rejected under § 103 as being unpatentable over Kumai et al. (US20170001642A1; “Kumai”) in view of Burtch et al. (US20190092168A1; “Burtch”).
As to independent claim 1, Kumai discloses an apparatus comprising:
a processor (“ECU 10A” – ¶ 55.) configured to:
detect that a gap distance between a lead transport and a transport traveling behind the lead transport is outside a predetermined threshold based on sensor data received from the transport (“The external situation recognition unit 13 recognizes … the inter-vehicle distance[] between the vehicle V and the preceding vehicle based on the detection result of the external sensor 1.” ¶ 61. “The difference determination unit 21 performs the difference determination processing (S62) to determine whether the difference between the target inter-vehicle distance and the inter-vehicle distance … is equal to or larger than the second inter-vehicle distance threshold. If it is determined that the difference is equal to or larger than the second inter-vehicle distance threshold, the target correction unit 22 performs the target value correction processing (S64) to correct the target value of the correction candidate.” ¶ 115 and FIG. 6.);
in response to the gap being detected, determine a preferred gap distance for a driver of the transport (“[T]he traveling control unit 16 may perform the automatic speed adjustment (speed control) in which a target speed and a target inter-vehicle distance are set as the target values. These target values may be fixed values determined according to the traveling environment or the law, values that are set by the driver, values that vary according to the time or the distance, or values that are set by the travel plan generation unit 15 according to the traveling situation … The automatic speed adjustment is implemented by the actuator 7 that operates based on the control signal output from the traveling control unit 16. The automatic speed adjustment automatically adjusts the speed of the vehicle V even when the driver does not perform the acceleration/deceleration operation … or the brake operation.” ¶ 69. “The difference determination unit 21 performs the difference determination processing (S62) to determine whether the difference between the target inter-vehicle distance and the inter-vehicle distance … is equal to or larger than the second inter-vehicle distance threshold. If it is determined that the difference is equal to or larger than the second inter-vehicle distance threshold, the target correction unit 22 performs the target value correction processing (S64) to correct the target value of the correction candidate.” ¶ 115 and FIG. 6. “[I]f the above condition is satisfied, this vehicle traveling control device corrects the target inter-vehicle distance to the inter-vehicle distance recognized when the driver's intervention operation is ended, allowing the speed control to be performed according to the driver's intention.” ¶ 10. Note: Kumai determines whether a difference between a target inter-vehicle distance and an actual inter-vehicle distance is equal or larger than a second inter-vehicle distance threshold — if yes, the control architecture corrects the target value. Furthermore, Kumai’s target inter-vehicle distance is a driver-preferred value operated on by an automatic control loop.); and
control a speed of the transport via a cruise control function based on the preferred gap distance (“The device restarts the speed control using … the corrected target inter-vehicle distance.” Abstract. See also ¶ 68 for discussion of the speed control.).
Kumai fails to explicitly disclose: determine a preferred gap distance between the transport and the lead transport for a driver of the transport using a machine learning model executed on contextual data about a weather condition affecting the transport.
Nevertheless, Burtch teaches: determine a preferred gap distance between a transport and a lead transport for a driver of the transport using a machine learning model executed on contextual data about a weather condition affecting the transport (“Weather conditions (rain, ice, etc.) may also play a role in the driver’s preference for gap size—for instance, a bigger gap may be preferred when braking and visibility are impaired.” ¶ 53. “The method 500 further includes, at 512, providing the data set to an artificial neural network 400 in response to the input being sensed. Said another way, when the driver signals a desired change in gap using the ACC gap input 210, various data (e.g., weather conditions, vehicle 102, 106 velocities, etc.) is sent to the ANN 400.” Emphasis added; ¶ 54. “The ANN 400 may then use this data, collected over numerous instances, to calculate a change in the desired distance.” ¶ 55. “The method 500 further includes, at 516, changing the requested distance based on at least one of the input from the driver and the calculated change in the desired distance by the ANN 400.” ¶ 57.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumai to include the feature of: determine a preferred gap distance between a transport and a lead transport for a driver of the transport using a machine learning model executed on contextual data about a weather condition affecting the transport, as taught by Burtch, with a reasonable expectation of success because these features are useful for “present[ing] an ACC system that reduces the need for changes in the desired time gap by the driver,” especially in view of “weather conditions.” (Burtch, ¶¶ 5, 53.) Indeed, modifying Kumai to implement Burtch’s ANN would have predictable results with a reasonable expectation of success given Burtch’s above-outlined usefulness in the context of ACC in addition to the fact that Burtch’s output desired distance would align with Kumai’s adjusted target inter-vehicle distance.
Independent claims 9 and 17 are rejected for at least the same reasons as claim 1 as the claims recite similar subject matter but for minor differences.
As to claims 7 and 15, Kumai fails to explicitly disclose: determine the preferred gap distance using the machine learning model on sensor data about an environment of the transport.
Nevertheless, Burtch teaches: determine a preferred gap distance using a machine learning model on sensor data about an environment of the transport (An “artificial neural network” — which interprets “sensor system data (e.g., from various sensors)” — may change and hence determine a desired “distance between vehicles” – see at least Abstract. See also ¶¶ 34 and 35. “The ANN 400 may then use this data, collected over numerous instances, to calculate a change in the desired distance.” ¶ 55. “The method 500 further includes, at 516, changing the requested distance based on at least one of the input from the driver and the calculated change in the desired distance by the ANN 400.” ¶ 57.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumai to include the feature of: determine a preferred gap distance using a machine learning model on sensor data about an environment of the transport, as taught by Burtch, with a reasonable expectation of success because these features are useful for “present[ing] an ACC system that reduces the need for changes in the desired time gap by the driver,” especially in view of “weather conditions.” (Burtch, ¶¶ 5, 53.) Indeed, modifying Kumai to implement Burtch’s ANN would have predictable results with a reasonable expectation of success given Burtch’s above-outlined usefulness in the context of ACC in addition to the fact that Burtch’s output desired distance would align with Kumai’s adjusted target inter-vehicle distance.
Claim(s) 2, 10 and 18 is/are rejected under § 103 as being unpatentable over Kumai and Burtch as applied to claim 1 — further in view of Sellschopp (US20180222442A1; “Sellschopp”).
As to claims 2, 10 and 18, the combination of Kumai and Burtch fails to explicitly disclose: remotely activate the cruise control function.
Nevertheless, Sellschopp teaches: remotely activate the cruise control function (“Motor vehicle 12 comprises a control device 16 that is designed to establish a wireless connection 18 with vehicle-external server device 14. Conversely, the vehicle-external server device is designed to establish wireless connection 18. Vehicle-external server device 14 is used to activate and deactivate a variety of vehicle-internal functions 20.” ¶ 25. “The vehicle-internal functions can, for example, be a cruise control system, a cruise control system for maintaining distance between vehicles.” ¶ 9.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kumai and Burtch to include the feature of: remotely activate the cruise control function, as taught by Sellschopp, with a reasonable expectation of success because remote control of a vehicle is a well-known and routine feature in the vehicle control art. Sellschopp lists several advantages of remote control, such as ensuring authorized use, deactivating erroneous functionality, etc. (See Sellschopp, ¶¶ 14-20.)
Claim(s) 3, 11, 14 and 19 is/are rejected under § 103 as being unpatentable over Kumai and Burtch as applied to claim 1 — further in view of Li et al. (US20160249180A1; “Li”).
As to claims 3, 11 and 19, the combination of Kumai and Burtch fails to explicitly disclose: remotely trigger the transport to change speed to achieve the preferred gap distance.
Nevertheless, Li teaches: remotely trigger a transport to change speed to achieve a preferred gap distance (“Control center 102 may generate the remote driving commands for the respective vehicle 101 in accordance with the previously collected environmental information, and further transmit the remote driving commands to the vehicle 101. For example, when the distance sensors on the vehicle 101 indicate that a distance between vehicle 101 and another vehicle in front of the vehicle 101 is less than a predetermined threshold value set as a safe following distance, control center 102 may remotely transmit a driving command to the respective vehicle 101 so that the processors on the vehicle 101 apply the brake to reduce the speed of the vehicle 101.” ¶ 35.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kumai and Burtch to include the feature of: remotely trigger a transport to change speed to achieve a preferred gap distance, as taught by Li, with a reasonable expectation of success because this feature is useful for providing a “driving assistance service.” (See Li, ¶ 3.)
As to claim 14, the combination of Kumai and Burtch fails to explicitly disclose: remotely controlling the speed.
Nevertheless, Li teaches: remotely controlling the speed (“Control center 102 may generate the remote driving commands for the respective vehicle 101 in accordance with the previously collected environmental information, and further transmit the remote driving commands to the vehicle 101. For example, when the distance sensors on the vehicle 101 indicate that a distance between vehicle 101 and another vehicle in front of the vehicle 101 is less than a predetermined threshold value set as a safe following distance, control center 102 may remotely transmit a driving command to the respective vehicle 101 so that the processors on the vehicle 101 apply the brake to reduce the speed of the vehicle 101.” ¶ 35.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kumai and Burtch to include the feature of: remotely trigger a transport to change speed to achieve a preferred gap distance, as taught by Li, with a reasonable expectation of success because this feature is useful for providing a “driving assistance service.” (See Li, ¶ 3.)
Claim(s) 4, 12 and 20 is/are rejected under § 103 as being unpatentable over Kumai and Burtch as applied to claim 1 — further in view of Wigard et al. (US20190184993A1; “Wigard”).
As to claims 4, 12 and 20, the combination of Kumai and Burtch fails to explicitly disclose: determine the preferred gap distance based on traffic data.
Nevertheless, Wigard teaches: determine a preferred gap distance based on traffic data (“The other information used to determine optimum intervehicle distance may include information regarding whether adjacent vehicles are autonomous vehicles or non-autonomous vehicles. Alternatively or additionally, the additional information may include road conditions, traffic/congestion, weather, and/or the like.” See at least ¶ 19.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kumai and Burtch to include the feature of: determine a preferred gap distance based on traffic data, as taught by Wigard, with a reasonable expectation of success because this feature is useful for “facilitat[ing] autonomous, driverless vehicles as well as enhanc[ing] the driver-based vehicle experience.” (Wigard, ¶ 2.)
Claim(s) 5 and 13 is/are rejected under § 103 as being unpatentable over Kumai and Burtch as applied to claim 1 — further in view of Mallinger (US20180265085A1; “Mallinger”).
As to claims 5 and 13, the combination of Kumai and Burtch fails to explicitly disclose: wherein the processor is further configured to receive data indicating a change in the weather condition; and in response, control the cruise control to change the speed of the transport based on the change in the weather.
Nevertheless, Mallinger teaches: receive data indicating a change in the weather condition; and in response, control a cruise control to change a speed of a transport based on the change in the weather (In the disclosed method, operation 110 comprises determining “whether weather conditions are changing” and then proceeding to make “a determination of what a new vehicle following speed should be.” See at least ¶ 17 and FIG. 2.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kumai and Burtch to include the feature of: receive data indicating a change in the weather condition; and in response, control a cruise control to change a speed of a transport based on the change in the weather, as taught by Mallinger, with a reasonable expectation of success because this feature is useful for “broaden[ing] the intelligence of an adaptive cruise control system to leverage … real time data in a manner to … improve the utility of present day adaptive cruise control systems.” (Mallinger, ¶ 4.)
Claim(s) 8, 16 and 21 is/are rejected under § 103 as being unpatentable over Kumai and Burtch as applied to claim 1 — further in view of Scofield et al. (US20170068245A1; “Scofield”).
As to claims 8, 16 and 21, the combination of Kumai and Burtch fails to explicitly disclose: generate a digital twin of the transport; and transfer the preferred gap distance to another transport via the digital twin.
Nevertheless, Schofield teaches:
generate a digital twin of the transport (“The remote preference provider component 602 may provide the driving profile 604 to the first automated driving component 606.” ¶ 39. Note: A driving profile 604 meets the broadest reasonable interpretation (BRI) of a digital twin because it is a digital representation of possible states/actions of a vehicle.) and
transfer the preferred gap distance to another transport via the digital twin (“The automated driving component 302 may be configured to generate an operational tailgate parameter 306 for the autonomous vehicle 312 based upon the tailgating preference 304. The operational tailgate parameter 306 may determine a tailgate threshold distance 310. The tailgate threshold distance 310 may be a distance maintained between the autonomous vehicle 312 and a second vehicle 314 during operation of the autonomous vehicle 312.” ¶ 36. See also ¶ 39 wherein the a driving profile containing the tailgate parameter is transferred to a transport.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Kumai and Burtch to include the features of: generate a digital twin of the transport; and transfer the preferred gap distance to another transport via the digital twin, with a reasonable expectation of success, because this feature is useful for providing users the “ability to control various aspects of how the autonomous vehicle operates, such as those users that have been accustomed a certain driving style, route, driving speed, etc.” (Schofield, ¶ 2.)
CONCLUSION
The following prior art made of record and not relied upon pertains to Applicant’s disclosure. Stählin (DE102013220693A1) discloses: an ACC system that determines a minimum inter-vehicle distance as a function of prevailing weather and lighting conditions detected by on-board sensors and applies a weather-dependent factor to the otherwise weather-independent minimum distance. The adjustment is performed automatically by the system based on the detected conditions, with the driver’s selectable distance options constrained to values at or above the weather-adjusted minimum. (See at least ¶¶ 4, 6.)
This action is final. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire three months from the mailing date of this action. In the event a first reply is filed within two months of the mailing date of this final action and the advisory action is not mailed until after the end of the three-month shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than six months from the date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Mario C. Gonzalez whose telephone number is (571) 272-5633. The Examiner can normally be reached M–F, 10:00–6:00 ET.
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If attempts to reach the Examiner by telephone are unsuccessful, the examiner’s supervisor, Fadey S. Jabr, can be reached on (571) 272-1516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.C.G./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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