DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to Amendments and Remarks filed on 07/06/2026 for application number 18/243,575, in which claims 1-20 were originally presented for examination on 09/07/2023.
Claim(s) 1, 3, 4, 8, 11, 13, 14, 16, 18 & 19 is/are currently amended, claim(s) 2, 7, 9, 12 & 17 is/are cancelled, and claim(s) 21-25 has/have been added as new claim(s). Accordingly, claim(s) 1, 3-6, 8, 10, 11, 13-16 & 18-25 is/are currently pending.
Priority
Acknowledgment is made of applicant’s claim this application to be CON of application No. 17/735,823 filed on 05/03/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/04/2023 has been received and considered.
Examiner Notes
Examiner cites particular paragraphs (or columns and lines) in the references as applied to Applicant’s claims for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP §2163.06. Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) to the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. See MPEP §2111.01.
Response to Arguments
Arguments filed on 07/06/2026 have been fully considered and are addressed as follows:
Regarding the claim rejections under 35 USC §112(b): The rejections of claims for lack of antecedent basis are withdrawn, as the amended claims filed on 07/06/2026 recite proper antecedent basis. The rejections of claims for being indefinite are withdrawn for the reasons recited in the Non-Final office action dated 04/07/2026, and outlined below. In additions, Applicant’s amendment may necessitate the new ground of rejection under §112(b) presented below.
Regarding the claim rejections under 35 USC §101: The rejection(s) of claim(s) for being directed to a judicial exception without significantly more, is/are withdrawn, as the amended claims filed on 07/06/2026 has/have overcome the rejection as recited in the Non-Final Office Action mailed on 04/07/2026.
Regarding the claim rejections under 35 USC §102(a)(1): Applicant’s arguments regarding the rejections of the claims as being clearly anticipated by the prior art of Fung (PG Pub. No. US-2016/0152233-A1)have been fully considered. However, those arguments are not persuasive.
Applicant asserts that:
“Without conceding the correctness of the rejections, the claims have been amended to advance prosecution. As amended, claim 1 recites, inter alia, “determining, based on the sensor data, an acceptable response time range within which the driver is to perform an action with the vehicle; determining an actual response time for the driver to perform the action based on control area network (CAN) data collected from a CAN bus included in the vehicle and not based on the sensor data, such that the actual response time is determined earlier than would be determinable from the sensor data alone; and determining that the actual response time is outside the acceptable response time range." Claims 11 and 16 recite similar claim features. The cited” (see Remarks pages 7-10; emphasis added)
The examiner respectfully disagrees. Examiner notes that Applicant’s arguments are all focusing on new limitations added to the amended base claims 1, 11 & 16 apparently to overcome the current anticipation rejection under §102(a)(1) as recited in the Non-Final Office Action mailed on 04/07/2026.
Those arguments are rendered moot in light of the new grounds of rejection outlined below, which were necessitated by the applicant’s amendment, i.e., Applicant’s arguments and amendments have been addressed in the new rejection outlined below.
For at least the foregoing reasons, and the rejections outlined below, the prior art rejections are maintained.
Claim Objections
37 CFR 1.75 (b) More than one claim may be presented provided they differ substantially from each other and are not unduly multiplied. MPEP§1.75
Claim(s) 21-22 is/are objected to because of the following informalities:
Claim 22 is/are objected under 37 CFR 1.75(b) as being a substantial duplicate of claim 21.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3-6, 8, 10, 11, 13-16 & 18-25 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
The term “acceptable responses” in claims 1, 2, 11, 12, 16 & 17 is a relative term which renders the claim indefinite. The term “acceptable” is not defined by the claim, Specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
The term “substantially 360 degree” in claims 1, 11 & 16 is a relative term(s) which renders the claim indefinite, wherein the term “substantially” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 3-6, 8, 10, 13-15 & 18-25 are rejected for incorporating the error(s) of their respective base claims by dependency.
Claim Rejections - 35 USC §102
In the event the determination of the status of the application as subject to AIA 35 USC §102 and §103 (or as subject to pre-AIA 35 USC §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 the appropriate paragraphs of 35 USC §102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-6, 8, 10, 11, 13-16 & 18-25 are rejected under 35 USC §102(a)(1) as being clearly anticipated by PG Pub. No. US-2016/0152233-A1 by Fung et al. (hereinafter “Fung”), which is found in the IDS submitted on 12/04/2023
As per claim 1, Fung discloses a computer-implemented method (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, discloses a method for responding to driver behavior) comprising:
based on sensor data of an environment of a vehicle and a machine learning model, determining, by a computing system, that a driving response of a driver of the vehicle is abnormal, wherein
the sensor data indicates a substantially 360 degree surrounding of the vehicle,
the machine learning model is an anomaly detection model trained on training data collected from a plurality of vehicles including a first vehicle and a second vehicle,
the training data includes, for each of the first vehicle and the second vehicle, a set of sensor data associated with a surrounding of that vehicle and a representation of a response of a driver of that vehicle to perform an action based on a set of control area network (CAN) data collected from a CAN bus included in that vehicle (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 [reproduced here for convenience] & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, discloses a method for responding to driver behavior, wherein both vehicles and drivers are monitored [i.e., sensor data of an environment of a vehicle] for accommodating driver’s slow reaction time, attention lapse and alertness [i.e., a response of a driver of the vehicle is abnormal].
Fung further discloses the ECU 150 receives signals from numerous sensors, devices, systems and any known systems for detecting objects traveling around a vehicle [implies a set of control area network (CAN) data collected from a bus included in the selected vehicle], e.g., one or more sensors, such as a camera, lidar or radar capable of detecting the presences and location of various one or more objects (including other vehicles) within the vicinity of the vehicle [i.e., a set of sensor data associated with a surrounding of a selected vehicle].
Fung also discloses a machine learning method or pattern recognition algorithm is used to determine the driver's centering habits [i.e., a machine learning model], wherein the response system 199 learns the driver’s centering habits, i.e., the centering habits of a driver [i.e., a representation of a response of a driver of the vehicle to perform an action] is detected by response system 199 and learned [i.e., the machine learning model is an anomaly detection model trained on training data], wherein any machine learning method or pattern recognition algorithm is used to determine the driver's centering habits), and
the determining the driving response is abnormal comprises:
determining, based on the sensor data, an acceptable response time range within which the driver is to perform an action with the vehicle (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, discloses determining a minimum reaction time for vehicle recovery and/or for avoiding collision, receives vehicle operating information, then determining, in step 3260, an initial threshold setting from the minimum reaction time and the vehicle operating information);
determining an actual response time for the driver to perform the action based on control area network (CAN) data collected from a CAN bus included in the vehicle and not based on the sensor data, such that the actual response time is determined earlier than would be determinable from the sensor data alone (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, discloses detecting and/or learning the driver’s centering habits [i.e., an actual response time for the driver to perform the action], wherein a machine learning method or pattern recognition algorithm is used to determine the driver’s centering habits. Fung further discloses assessing the driver's slower reaction time, attention lapse and/or alertness [i.e., an actual response time for the driver to perform the action], wherein the response system 199 determines a minimum reaction time for vehicle recovery and/or for avoiding collision, receiving vehicle operating information [i.e., data collected from a CAN bus included in the vehicle and not based on the sensor data], then determining an initial threshold setting from the minimum reaction time and the vehicle operating information); and
determining that the actual response time is outside the acceptable response time range (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, discloses the response system 199, in step 2938, determines if the rate of brake pressure increase exceeds the activation threshold, and assessing the driver's slower reaction time, attention lapse and/or alertness, wherein the response system 199 determines a minimum reaction time for vehicle recovery and/or for avoiding collision, receives vehicle operating information, then determining, in step 3260, an initial threshold setting from the minimum reaction time and the vehicle operating information);
determining, by the computing system, the driver has performed abnormal responses driving responses that each fall outside a corresponding acceptable response time range at least a predetermined number of times (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, discloses the response system 199 learns the driver’s centering habits, i.e., the centering habits of a driver are detected by response system 199 and learned, wherein a machine learning method or pattern recognition algorithm is used to determine the driver’s centering habits [implies driver has performed abnormal responses that are not in a set of acceptable responses at least a predetermined number of times]. Fung further discloses the response system 199, in step 2938, determines if the rate of brake pressure increase exceeds the activation threshold, and assessing the driver's slower reaction time, attention lapse and/or alertness [i.e., the driver has performed abnormal responses that are not in a set of acceptable responses at least a predetermined number of times], wherein the response system 199 determines a minimum reaction time for vehicle recovery and/or for avoiding collision, receives vehicle operating information, then determining, in step 3260, an initial threshold setting from the minimum reaction time and the vehicle operating information);
updating, by the computing system, a driver profile associated with the driver based on the performance of the abnormal driving responses at least the predetermined number of times (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, also discloses the response system 199 learns the driver’s centering habits, i.e., the centering habits of a driver is detected by response system 199 and learned [i.e., updating, by the computing system, a driver profile associated with the driver based on the performance of the abnormal responses at least the predetermined number of times], wherein any machine learning method or pattern recognition algorithm is used to determine the driver's centering habits [i.e., a driver profile, See Applicant’s Specification, in at least ¶29, wherein “The driver profile 120 can include information associated with the driver of the vehicle 100, such the driver's habits”]); and
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Fung’s Fig. 62 [emphasis added]
causing, by the computing system, a remedial action to be performed based on the abnormal driving response, the remedial action comprising automatically changing the vehicle from operating in a manual mode to operating in a semi-autonomous mode or a fully-autonomous mode (Fung, in at least title, abstract, Fig(s). 1, 5, 41, 42, 47, 61, 62 & 74, and ¶¶87, 93-119, 124, 134, 142, 229-233, 241, 272-276, 285 & 300, discloses a method for responding to driver behavior, wherein both vehicles and drivers are monitored for accommodating driver’s slow reaction time, attention lapse and alertness [i.e., the response of a driver]. Fung further discloses automatically adjusting the operation of one or more vehicle systems [i.e., a remedial action to be performed] in response to the assessed driver behavior [i.e., based on the response of the driver], modifying one or more vehicle systems automatically in order to mitigate against hazardous driving situations [i.e., a remedial action to be performed], wherein Fung’s driver behavior response system receives information about the state of a driver and automatically adjust the operation of one or more vehicle systems. Fung also discloses the response system 199 controls the electronic stability control system 222, the antilock brake system 224, the brake assist system 226 and the brake pre-fill system 228 [i.e., a remedial action to be performed] in a manner that compensates for the potentially slower reaction time of the driver [i.e., based on the response of the driver], wherein the response system 199 activates, in step 2940, a modulator pump and/or valves to automatically increase the brake pressure, i.e., activates brake assist, which allows for an increase in the amount of braking force applied at the wheels, wherein the motor vehicle includes provisions for increasing vehicle stability to reduce the likelihood of hazardous driving conditions while or when a driver is drowsy).
As per claim 2, Cancelled
As per claim 3, Fung discloses the computer-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated. Fung further discloses comprising:
providing, by the computing system, a training dataset in the training data that includes the sensor data of the environment of the vehicle and the driving response of the driver to the machine learning model; and
updating, by the computing system, the machine learning model based on the training dataset (Fung, in at least Fig(s). 1 & 62 and ¶276, discloses the response system 199 learns the driver’s centering habits, i.e., the centering habits of a driver can be detected by response system 199 and learned [implies updating, by the computing system, the machine learning model], wherein any machine learning method or pattern recognition algorithm is used to determine the driver's centering habits).
As per claim 4, Fung discloses the computer-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated. Fung further discloses comprising:
determining, by the computing system, a speed limit based on the driver profile associated with the driver of the vehicle, wherein the determining that the driving response of the driver of the vehicle is abnormal is based on the speed limit (Fung, in at least Fig. 56 and ¶¶143, 152 & 263-265, discloses the maximum speed at which low speed follow system 230 operates could be modified according to the level of drowsiness. Likewise, the on/off setting or the maximum speed at which cruise control system 232 can be set may be modified in proportion to the level of drowsiness. Fung further discloses the response system 199 determines the body state index of the driver, sets the low speed follow status based on the body state index of the driver. For example, look-up table 3850 shows an exemplary relationship between body state index and the low speed follow status).
As per claim 5, Fung discloses the computer-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Fung further discloses wherein the remedial action includes a decrease of a speed of the vehicle to less than a predetermined threshold less than a speed limit associated with a location of the vehicle (Fung, in at least Fig. 74 and ¶¶143, 152, 263-265 & 296-300, discloses the maximum speed at which low speed follow system 230 operates could be modified according to the level of drowsiness. Likewise, the on/off setting or the maximum speed at which cruise control system 232 can be set may be modified in proportion to the level of drowsiness. Fung further discloses response system 199 receives the speed, location and/or bearing of the target vehicle as well as the host vehicle. Fung further discloses a process for setting a first time to collision threshold and a second time to collision threshold. In step 4580, response system 199 determines a minimum reaction time for avoiding a collision, wherein response system 199 determines the body state index of the driver).
As per claim 6, Fung discloses the computer-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Fung further discloses wherein the remedial action includes an alert that indicates the driver has performed an abnormal response and indicates an action for the driver to perform (Fung, in at least Fig(s). 5, 30 & 31 and ¶¶141, 208 & 212, discloses the impact of response system 199 on each vehicle system is described as either "control" type or "warning" type. The control type indicates that the operation of a vehicle system is modified by the control system. The warning type indicates that the vehicle system is used to warn or otherwise alert a driver. Fung further discloses methods of alerting a drowsy driver using visual, audible and tactile feedback for a driver).
As per claim 7, Cancelled
As per claim 8, Fung discloses the computer-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated. Fung further discloses comprising:
determining, by the computing system, that a second driving response of a second driver of a second vehicle is normal based on second sensor data and the machine learning model; and
preventing, by the computing system, performance of a remedial action based on the second driving response of the second driver (Fung, in at least Fig(s). 1 & 62 and ¶¶138, 164, 172, 179, 200 & 276, discloses response system 199 determines the driver state to be normal or drowsy. In other cases, the driver state may range over three or more states ranging between normal and very drowsy (or even asleep). In this step, response system 199 may use any information received during step 402, including information from any kinds of sensors or systems.
Fung further discloses that during normal operation, EPS system 160 functions to assist a driver in turning steering wheel 1304. However, in some situations, it may be beneficial to reduce this assistance. Fung also discloses the response system 199 learns the driver’s centering habits, i.e., the centering habits of a driver can be detected by response system 199 and learned, wherein any machine learning method or pattern recognition algorithm is used to determine the driver's centering habits. Fung, in at least Fig. 66 and ¶¶285-286, further discloses response system 199 receives object information, e.g., other vehicles information within the vicinity of the vehicle, and determines the location and bearing of a tracked object. response system 199 sets a zone threshold that is determined using the body state index of the driver as well as information about the tracked object).
As per claim 9, Cancelled
As per claim 10, Fung discloses the computer-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated. Fung further discloses comprising:
performing, by the computing system, an assessment of the driver based on the machine learning model (Fung, in at least Fig(s). 1 & 62 and ¶276, discloses the response system 199 learns the driver’s centering habits, i.e., the centering habits of a driver can be detected by response system 199 and learned, wherein any machine learning method or pattern recognition algorithm is used to determine the driver's centering habits).
As per claim 12, Cancelled
As per claim 17, Cancelled
As per claims 11, 13-15, 21, 22 & 24; the claims are directed towards systems that recite similar limitations and/or steps performed by the methods of claims 1, 3-6 & 8, respectively. The cited portions of Fung used in the rejections of claims 1, 3-6 & 8 discloses the same steps performed by the system of claims 11, 13-15, 21, 22 & 24. Therefore, claims 11, 13-15, 21, 22 & 24 are rejected under the same rationales used in the rejections of claims 1, 3-6 & 8 as outlined above.
As per claims 16, 18-20, 23 & 25; the claims are directed towards non-transitory computer-readable storage mediums that recite similar limitations and/or steps performed by the methods of claims 1, 3-6 & 8, respectively. The cited portions of Fung used in the rejections of claims 1, 3-6 & 8 discloses the same steps performed by the instruction included in computer-readable storage mediums of claims 16, 18-20, 23 & 25. Therefore, claims 16, 18-20, 23 & 25 are rejected under the same rationales used in the rejections of claims 1, 3-6 & 8 as outlined above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See previously mailed PTO-892 forms.
Chalfant et al. (US-2009/0210257-A1) discloses a driver feedback system that includes a server for receiving a communication of a driving characteristic from a sensor located on the vehicle and forwarding the received driving characteristic to the driver evaluation module to update a dynamic driver profile.
Lassoued et al. (US-2019/0102689-A1) discloses monitoring risk associated with operating a vehicle by a processor, wherein one or more behavior parameters of an operator of a vehicle may be learned in relation to the vehicle, one or more alternative vehicles, or a combination thereof using one or more sensing devices for a journey. Lassoued further discloses risk associated with the one or more learned behavior parameters for the journey may be assessed. Lassoued also discloses vehicle operator profiles for each operator of a vehicle associated with the driving risk assessment system.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tarek Elarabi whose telephone number is (313)446-4911. The examiner can normally be reached on Monday thru Thursday; 6:00 AM - 4:00 PM EST.
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/Tarek Elarabi/Primary Examiner, Art Unit 3661
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