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 02/03/2026 for application number 18/243,575, in which claims 1-20 were originally presented for examination on 09/07/2023. Claims 1-4, 11-14 & 16-19 are currently amended, claim 7 has been previously cancelled, and claim 21 has been added previously as a new claim. Accordingly, claims 1-6 & 8-21 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.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant’s submission(s) filed on 01/16/2026 & 02/03/2026 has/have been entered.
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 01/16/2026 have been fully considered and are addressed as follows:
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, “based on sensor data of an environment of a vehicle and a machine learning model, determining, by a computing system, that a response of a driver of the vehicle is abnormal, wherein the machine learning model is an anomaly detection model trained on training data including a set of sensor data associated with a surrounding of a selected vehicle and a representation of a response of a driver of the vehicle to perform an action based on a set of control area network (CAN) data collected from a bus included in the selected vehicle.” Claims 11 and 16 recite similar claim features. The cited reference fails to disclose at least these instant claim features. … ”
(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 Final Office Action mailed on 11/19/2025. 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 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 1-6 & 8-21 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.
Claims 3-6, 8-10, 13-15 & 18-21 are rejected for incorporating the error(s) of their respective base claims by dependency.
Claim 1 recites the limitation “the response of the driver” in Lines 18-19 (also in claim 3 line 3, claim 4 line 3, and claim 9 line 2). There is insufficient antecedent basis for this limitation in the claim. It is not clear if the said “response of the driver” limitation refers to “a response of a driver of the vehicle” limitation in line 6 or “a response of a driver of the vehicle” limitation in line 11, or if being the same or different driver response. As such for the purpose of examination in this Office Action and as best understood by the Examiner, the said “response of a driver of the vehicle” limitations have been interpreted to be the same driver response.
Claim 11 recites the limitation “the response of the driver” in Line 20 (also in claim 13 line 3, and claim 14 line 14). There is insufficient antecedent basis for this limitation in the claim. It is not clear if the said “response of the driver” limitation refers to “a response of a driver of the vehicle” limitation in line 8 or “a response of a driver of the vehicle” limitation in line 13, or if being the same or different driver response. As such for the purpose of examination in this Office Action and as best understood by the Examiner, the said “response of a driver of the vehicle” limitations have been interpreted to be the same driver response.
Claim 16 recites the limitation “the response of the driver” in Line 18 (also in claim 18 line 4, and claim 19 line 4). There is insufficient antecedent basis for this limitation in the claim. It is not clear if the said “response of the driver” limitation refers to “a response of a driver of the vehicle” limitation in line 7 or “a response of a driver of the vehicle” limitation in line 12, or if being the same or different driver response. As such for the purpose of examination in this Office Action and as best understood by the Examiner, the said “response of a driver of the vehicle” limitations have been interpreted to be the same driver response.
Claims 2-6, 8-10, 12-15 & 17-21 are rejected for incorporating the error(s) of their respective base claims by dependency.
Claim Rejections – 35 USC §101
35 USC §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 14 is rejected under 35 USC §101 because the claimed invention is directed to an abstract idea without significantly more. See MPEP 2106 (III)
The determination of whether a claim recites patent ineligible subject matter is a two-step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), See MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: See MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP 2106.05
Claim 1, a computer-implemented method comprising:
based on sensor data of an environment of a vehicle [pre-solution activity (data gathering) using generic sensors] and a machine learning model [particular technological environment or field of use without telling how it is accomplished], determining, by a computing system [applying the abstract idea using generic computing module], that a response of a driver of the vehicle is abnormal [mental process/step], wherein the machine learning model is an anomaly detection model trained on training data [particular technological environment or field of use without telling how it is accomplished] including a set of sensor data associated with a surrounding of a selected vehicle and a representation of a response of a driver of the vehicle to perform an action based on a set of control area network (CAN) data collected from a bus included in the selected vehicle [pre-solution activity (data gathering) using generic sensors];
determining, by the computing system [applying the abstract idea using generic computing module], the driver has performed abnormal responses that are not in a set of acceptable responses at least a predetermined number of times [mental process/step];
updating, by the computing system [applying the abstract idea using generic computing module], a driver profile associated with the driver based on the performance of the abnormal responses at least the predetermined number of times [mental process/step]; and
causing, by the computing system [applying the abstract idea using generic computing module], a remedial action to be performed based on the response of the driver [insignificant post-solution activity (displaying results of the mental process)].
101 Analysis - Step 1: Statutory category – Yes
The claim recites a method including at least one step. The claim falls within one of the four statutory categories. See MPEP 2106.03
Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III)
The claim recites the limitation of (1) determining … that a response of a driver of a vehicle is abnormal, (2) determining … the driver has performed abnormal responses that are not in a set of acceptable responses at least a predetermined number of times, and (3) updating … a driver profile associated with the driver based on the performance of the abnormal responses at least the predetermined number of times.
This limitation, as drafted, are simple processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of “by a computing system”. That is, other than reciting “a computing system” nothing in the claim elements precludes the step(s) from practically being performed in the mind. For example, but for the “computing system” language, the claim encompasses a person looking at data collected and forming a simple driver abnormal judgement for updating a driver profile. The mere nominal recitation of by a controller does not take the claim limitations out of the mental process grouping. Thus, the claim recites a mental process.
Step 2A Prong two evaluation: Practical Application - No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites additional elements or steps of (a) by a/the computing system, (b) based on sensor data of an environment of a vehicle and (c) a machine learning model including a set of sensor data associated with a surrounding of a selected vehicle and a representation of a response of a driver of the vehicle to perform an action based on a set of control area network (CAN) data collected from a bus included in the selected vehicle, and (d) causing, by the computing system, a remedial action to be performed based on the response of the driver.
The “providing … training data”, i.e., the “based on sensor data of a plurality of vehicles and responses of a plurality of drivers of the plurality of vehicles”, and the “based on sensor data of an environment of the vehicle” element(s)/ step(s) is/are recited at a high level of generality (i.e. as a general means of gathering vehicle and driver information for use in the “determining” step(s)), and amount to mere data gathering, which is a form of insignificant extra-solution activity.
The “by a/ the computer system” step(s) merely describes how to generally and merely automates the determining step(s), therefore acting as a generic computer to perform the abstract idea and/ or “apply” the otherwise mental judgements using a generic or general-purpose processor, i.e. a computer. The computer system, i.e., “at least one processor” and “memory”, is recited at a high level of generality and is merely automates the determining step(s).
The “machine learning model” limitations also recited at a high level of generality, and amounts to mere linking use of a judicial exception to a particular technological environment or field of use without telling you how it is accomplished.
The “causing … remedial action to be performed …” step also recited at a high level of generality (i.e., as a general means of alerting or displaying results from the determining step(s) and/or updating), and amounts to mere post solution displaying, See Specification ¶34, i.e., visual alert to the driver, which is a form of insignificant extra-solution activity.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B evaluation: Inventive concept - No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. See MPEP 2106.05(f).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra- solution activity in Step 2A should be re-evaluated in Step 2B. Here, the “providing training data”, i.e., the “sensor data of a plurality of vehicles and responses of a plurality of drivers of the plurality of vehicles”, the “sensor data of an environment of the vehicle”, the “machine learning model” and the “computer system” element(s) was/were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
The background section of Applicant’s Specification recites that “known systems use sensor data indicating only a particular surrounding region of a vehicle”, “known systems rely solely on sensor data when monitoring behavior, such as images and/or video captured by a camera”, See Specification at least ¶3.
The said model (110) is a conventional software model that can be not only a machine learning (ML) model, but also an artificial intelligence (AI) model, an analytical model, a mathematical model or any combination thereof, See Specification at least ¶30, wherein the Specification does not provide any indication that the said model is anything other than a conventional AI/ ML model. The additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer system cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B, MPEP 2106.05(f). Accordingly, a conclusion that the providing training/ sensor data step(s) and the computing system elements is well-understood, routine, conventional activity is supported under Berkheimer. Thus, the claim is ineligible.
Independent system and/or non-transitory computer-readable storage medium of claims 11 and/or 16, respectively, recite similar step limitations performed by the method of claim 1. Therefore, claims 11 & 16 are rejected under the same rationales used in the rejections of claim 1 as outlined above.
Dependent claims 2-6, 8-10, 12-15 & 17-21 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application and amounts to mere input and/or output data manipulation. Therefore, dependent claims 2-6, 8-10, 12-15 & 17-21 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Thus, claims 1-6 & 8-21 are ineligible under 35 USC §101.
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 1-6 & 8-21 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 response of a driver of the vehicle is abnormal, wherein the machine learning model is an anomaly detection model trained on training data including a set of sensor data associated with a surrounding of a selected vehicle and a representation of a response of a driver of the vehicle to perform an action based on a set of control area network (CAN) data collected from a bus included in the selected 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 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. Fung also 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]);
determining, by the computing system, the driver has performed abnormal responses that are not in a set of acceptable responses 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);
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Fung’s Fig. 62 [emphasis added]
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 (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
causing, by the computing system, a remedial action to be performed based on the response of the driver (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, Fung discloses the computer-implemented method of claim 1, accordingly, the rejection of claim 1 above is incorporated.
Fung further discloses wherein the set of acceptable responses is determined based in part on the driver profile (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., a driver profile], i.e., the centering habits of a driver is 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 [i.e., a driver profile]).
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 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 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 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 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, 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, the response of the driver of the vehicle based on a control area network bus in the vehicle (Fung, in at least Fig(s). 50 and ¶¶ 106 & 248, discloses collision warning system 234 retrieves the heading, position and speed of an approaching vehicle, wherein this information could be received from the approaching vehicle through a wireless network, such as a DSRC network).
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 claims 11-15 & 21; the claims are directed towards systems that recite similar limitations and/or steps performed by the methods of claims 1-6, respectively. The cited portions of Fung used in the rejections of claims 1-6 discloses the same steps performed by the system of claims 11-15 & 21. Therefore, claims 11-15 & 21 are rejected under the same rationales used in the rejections of claims 1-6 as outlined above.
As per claims 16-20; the claims are directed towards non-transitory computer-readable storage mediums that recite similar limitations and/or steps performed by the methods of claims 1-5, respectively. The cited portions of Fung used in the rejections of claims 1-5 discloses the same steps performed by the instruction included in computer-readable storage mediums of claims 16-20. Therefore, claims 16-20 are rejected under the same rationales used in the rejections of claims 1-5 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.
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/Tarek Elarabi/Primary Examiner, Art Unit 3661