DETAILED CORRESPONDENCE
This Office action is in response to the remarks filed 12/31/2025.
Claim Status
Claims 1-20 are pending.
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 .
Response to Amendment
In light of the amendments to claims 19 and 20 , the 35 USC § 101 rejections has been withdrawn.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 5/21/2026 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 12/31/2025 have been fully considered but they are not persuasive.
On page 8 of 12 through 9 of 12, Applicant alleges “...that amended Claim 1 does not recite an abstract idea because it recites a technical solution for collision-avoidance in autonomous driving that cannot be performed mentally.” The Examiner disagrees.
In response, the rejection is not an over expansion of a mental process because the “obtaining” steps are mere data gathering. While the “processing” and “determining” steps are simply observation and/or evaluations performed by a human driver to predict the driving behavior of targeted vehicles. Contrary to Applicant’s arguments the claims are directed to a simple comparison of data and not a complex topological data. Thus, by looking at two set of driving data and predicting a risk of collision using a target model is a known ability for a person skilled in the art. Furthermore, a driving behavior to be performed by the first vehicle is not a positive recitation of controlling the vehicle.
Therefore, the claims recite a mental process and not a mathematical concept. The 35 USC § 101 rejection is being maintained because claim 1 recites a mental concept.
Secondly, the terms of collision-avoidance in autonomous driving being argued is not within the claim 1. Furthermore, even if the collision-avoidance in autonomous driving is recited. Claim 1 still lacks a controlling element based on the predicted driving behavior that is performed by the first vehicle. Additionally, it is improper to purport language from the specification into the claims, to narrow the interpretation.
On page 10 of 12 through 11 of 12 Applicant alleges that "Nguyen has not been shown to teach or suggest any “in response to detecting that the first driving data and the second driving data fall within a preset value range, processing, by the first vehicle, the first driving data and the second driving data by using a target model, to obtain a predicted driving behavior of the first vehicle" as recited in amended Claim 1.” The Examiner disagrees.
In response, Applicant has formulated an argument on the basis of the newly amended claim language of “in response to detecting that the first driving data and the second driving data fall within a preset value range, processing, by the first vehicle, the first driving data and the second driving data by using a target model, to obtain a predicted driving behavior of the first vehicle”; however, the Examiner has not been given a fair opportunity to consider the amendments. Furthermore, this newly amended language has necessitated a new ground of rejections.
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.
Where applicant acts as his or her own lexicographer to specifically define a term of a claim contrary to its ordinary meaning, the written description must clearly redefine the claim term and set forth the uncommon definition so as to put one reasonably skilled in the art on notice that the applicant intended to so redefine that claim term. Process Control Corp. v. HydReclaim Corp., 190 F.3d 1350, 1357, 52 USPQ2d 1029, 1033 (Fed. Cir. 1999). The term “non-transitory computer program product” in claim 20 is used by the claim to mean “storage medium,” while the accepted meaning is “non-transitory computer readable storage medium .” The “non-transitory computer program product” is indefinite because the specification does not clearly redefine the term.
If support is found within the specification, Applicant is advised to amend the claim(s) to recite “A non-transitory computer readable medium comprising a computer program product comprising machine readable instructions that, when executed by a processor, performs: [the claimed functions]”, or equivalent language. see MPEP 2106.03 (I).
In other words, the computer program product (i.e. software) must be embodied in a tangible, physical form such as a non-transitory computer readable medium. A claim directed toward a non-transitory computer readable medium would comprise an article of manufacture and thus fall within one of the four categories of patent eligible subject matter. See MPEP 2106.03 (I).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites:
A driving behavior determining method, wherein the method implemented by a first vehicle, the method comprises:
obtaining, by the first vehicle, first driving data of the first vehicle and second driving data of a second vehicle;
obtaining, by the first vehicle, based on the first driving data and the second driving data, a driving behavior set in which the first vehicle has no risk of collision with the second vehicle;
in response to detecting that the first driving data and the second driving data fall within a preset value range, processing, by the first vehicle, the first driving data and the second driving data by using a target model, to obtain a predicted driving behavior of the first vehicle; and
in response to detecting that the driving behavior set comprises the predicted driving behavior, determining, by the first vehicle, the predicted driving behavior as a driving behavior to be performed by the first vehicle.
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 concept.
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:
in response to detecting that the driving behavior set comprises the predicted driving behavior, determining, by the first vehicle, the predicted driving behavior as a driving behavior to be performed by the first vehicle.
Thus, claim 1 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 the additional elements of:
A driving behavior determining method, wherein the method comprises:
obtaining, by the first vehicle, first driving data of the first vehicle and second driving data of a second vehicle [data input—a pre-solution activity];
obtaining, by the first vehicle, based on the first driving data and the second driving data, a driving behavior set in which the first vehicle has no risk of collision with the second vehicle [data input—a pre-solution activity];
in response to detecting that the first driving data and the second driving data fall within a preset value range, processing, by the first vehicle, the first driving data and the second driving data by using a target model, to obtain a predicted driving behavior of the first vehicle [data gathering—a post-solution activity]
These claims does not use the judicial exception 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 exception. The additional limitation is no more than mere data gathering and data outputting.
Accordingly, even in combination, this additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Respectively, dependent claims 2-9 and 11-18 as a whole, do not integrate the recited judicial exception into a practical application.
Regarding claim 10. Claim 10 is an apparatus that stores method of claim 1; therefore, claim 10 is rejected under the same rationale of claim 1.
Step 2B evaluation: Inventive Concept: - No
In Step 2B of the 2019 PEG, the claim(s) 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 claims 2-9 and 11-18 amount to no more than mere data gathering step, data manipulation, insignificant extra solution activity and/or data output. The same analysis applies here in 2B, i.e., data manipulation and/or data output 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, MPEP 2106.0S(f).
Independent claims 19 (a storage medium) and 20 (a computer program product) are similar in scope to claim 1, and are therefore rejected under the same rationale as detailed above in regard to claim 1.
Thus, these claims are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Nguyen in view of Othmezouri et al., US 2012/0143488 hereinafter “Othmezouri”.
Claims 1, 10, 19 and 20. Nguyen teaches a driving behavior determining method, wherein the method implemented by a first vehicle, the method comprises:
obtaining, by the first vehicle, first driving data of the first vehicle and second driving data of a second vehicle (see at least item 500 in fig. 5 teaches about first vehicle, item 502 in fig. 5 teaches about the second vehicle);
obtaining, by the first vehicle, based on the first driving data and the second driving data, a driving behavior set in which the first vehicle has no risk of collision with the second vehicle (see at least col. 3, lns. 35-52 reads on this element as such—“ For example, it may be determined that the driver departed the lane because of distraction from talking on a cell phone. Vehicle event recorder 102 comprises a model for determining a lane departure behavior based at least in part on a combined sensor data set (e.g., the model was trained to provide an indication of whether the driving behavior is bad based on a combined set of sensor inputs). A combined sensor data set comprises a data set comprising data from at least a first sensor and a second sensor. When a lane departure event is determined, the vehicle event recorder receives first sensor data and second sensor data, combines them to determine a combined sensor data set, and determines lane departure behavior (e.g., whether a high likelihood of the lane departure event is associated with bad behavior) based at least in part on the combined sensor data using a model. Then, a bad behavior indication is stored in a database system in response to the lane departure behavior indicating a bad behavior”);
in response to detecting that the first driving data and the second driving data fall within a preset value range, processing, by the first vehicle, the first driving data and the second driving data by using a target model, to obtain a predicted driving behavior of the first vehicle (Taken together the cited sections reads on this element: col. 4, lns. 2-6 teaches “[v]ehicle event recorder 102 comprises a system for receiving and processing sensor data. Processing sensor data comprises filtering data, identifying patterns in data, detecting events, etc.” col. 3, lns. 15-35 teaches “When the vehicle event recorder 102 determines a lane departure event, it begins a process for identifying whether an inappropriate driving behavior is associated with the lane departure event. This association can be used to determine whether the lane departure event was potentially caused by an inappropriate driving behavior. A lane departure event comprises a determination that the vehicle departed from its correct position in the lane. For example, the vehicle swerved off center, the vehicle failed to track a corner correctly, the vehicle hit the lane marker, etc. A lane departure event may be a simple minor driver error, or it may be caused by an inappropriate behavior that can be corrected” While col. 3, lns. 5-14 describe prediction of a driving behavior as such—“ When the vehicle event recorder determines, based on sensor data from a sensor, that a lane departure event has occurred, it is advantageous to determine whether the lane departure event occurred as a result of a bad behavior. The system for identifying an inappropriate driving behavior is able to utilize a model trained on manually labeled sensor data sets to determine bad behavior based on a combined data set (e.g., a data set created by combining sensor data from two or more vehicle sensors).”); and
in response to detecting that the driving behavior set comprises the predicted driving behavior, determining, by the first vehicle, the predicted driving behavior as a driving behavior to be performed by the first vehicle (col. 6, ln. 61- col. 7, ln. 35 reads on this element as such—“ A determination is then made whether the lane departure is a result of a bad behavior (e.g., using a handheld cell phone, using a hands free cell phone, eating, drinking, drowsy, falling asleep, spacing out, etc.). The vehicle event recorder is configured to store a bad behavior indication in a database system in response to the lane departure behavior indicating a bad behavior.”).
Nguyen is silent on the term range. Yet, Othmezouri teaches first driving data and the second driving data fall within a preset value range ([0039]-[0041] teaches “the system comprises a first sensor which is adapted for measuring trajectory, speed, steering, forward and/or lateral acceleration of the first and/or second traffic object or second traffic participant. Preferably, the first sensor comprises a light indicator and is further adapted for detecting traffic lights and/or traffic signs.” While [0074] teaches “the driving assistance or vehicle or traffic control system or a vehicle guidance assistance system preferably evaluates risk of collision and comprises a behavior estimator 1 adapted for estimating actual and/or future behavior of a first traffic participant and of an object such as of a second traffic participant, respectively, wherein the behavior estimator 1 is further adapted for estimating a trajectory to be taken by the first traffic participant and a trajectory to be taken by the object, e.g. the second traffic participant. A risk estimator 4 is adapted for determining risk of collision of the first traffic participant relative to the object, e.g. second traffic participant by calculating information, which comprises an output probability value, adapted for risk assessment of collision of the first traffic participant relative to the object, e.g. second traffic participant.” While [0079] teaches “An input to the model is the ego-vehicle trajectory 5, indicated in FIG. 1, which is obtained by odometry using vehicle sensors for speed, steering, forward and/or lateral acceleration, potentially combined with ego-motion estimation from the optical flow in the camera image, possibly also by gyros and/or accelerometers adapted for determining position, velocity and so on. Another input is a target tracker 8, which preferably serves for detection and/or tracking of all objects that might be involved in an accident, e.g. the tracking of pedestrians, or other vehicles' position and/or orientation in a range of, for instance, about 100 m according to a preferred embodiment of the invention.” Taken together the cited sections reads on this element.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to incorporate the teaching of Othmezouri with the invention Nguyen because such modification would provide an indication of an inappropriate driving behavior (col. 3, lns. 23-26: Nguyen).
Claims 2 and 11. Nguyen teaches the method according to claim 1 and Nguyen further teaches, wherein the method further comprises:
in response to detecting that the driving behavior set does not comprise the predicted driving behavior, determining, from the driving behavior set, the driving behavior to be performed by the first vehicle (Taken together the cited section reads on this element: col. 3, lns. 5-14 describe prediction of a driving behavior as such—“ When the vehicle event recorder determines, based on sensor data from a sensor, that a lane departure event has occurred, it is advantageous to determine whether the lane departure event occurred as a result of a bad behavior. The system for identifying an inappropriate driving behavior is able to utilize a model trained on manually labeled sensor data sets to determine bad behavior based on a combined data set (e.g., a data set created by combining sensor data from two or more vehicle sensors).”).
Claims 3 and 12. Nguyen in view of Othmezouri teaches the method according to claim 1 and further teaches, wherein the target model is obtained through training based on third driving data, and
is constructed based on the third driving data (Taken together the cited section reads on this element: col. 3, lns. 5-14 describe prediction of a driving behavior as such—“When the vehicle event recorder determines, based on sensor data from a sensor, that a lane departure event has occurred, it is advantageous to determine whether the lane departure event occurred as a result of a bad behavior. The system for identifying an inappropriate driving behavior is able to utilize a model trained on manually labeled sensor data sets to determine bad behavior based on a combined data set (e.g., a data set created by combining sensor data from two or more vehicle sensors).” While col. 2, ln. 60-col. 3, ln. 14 reads on this element as such—“The vehicle event recorder is additionally configured to train the model. Training the model comprises providing the model with a set of labeled data sets. For example, a labeled data set comprises a data set determined by combining a first sensor data and a sensor data to determine a combined data set. The combined data set is manually processed to determine a label. For example, the combined data set is viewed by a human reviewer. The human reviewer applies one or more labels to the combined data set. The labels comprise bad behavior indication types, for example, using a handheld cell phone, using a hands free cell phone, eating, drinking, drowsy, falling asleep, or spacing out. If necessary, the set of labeled data sets comprises a large number of data sets.” Additionally, col. 6, lns. 43-49 reads on this element as such—“Processor 304 additionally comprises model builder 308 for building a model, (e.g., a lane departure behavior model). In various embodiments, model builder 308 builds a model by training a machine learning model or a neural network. Model builder 308 builds the model utilizing sensor data 312 associated with sensor data labels 314. The built model is transmitted to a vehicle event recorder, via the communications network and interface 302.”).
Claims 4 and 13. Nguyen teaches the method according to claim 3 and further teaches,
wherein the method further comprises: in response to detecting that at least one of the first driving data or the second driving data falls outside the preset value range, determining, from the driving behavior set, the driving behavior to be performed by the first vehicle (col. 7, lns. 7-49 teaches modifying event detection threshold which implies that the collected data is does match a preset value range. Fig. 5 illustrates combining the first and second sensor data. Fig. 6 illustrates the concept of modifying. Fig. 7 illustrates the concept of data parameters and data statistics. While Fig. 8 illustrates combining the sensor data set. Thus, taken together the cited sections reads on this element).
Claims 5 and 14. Nguyen in view of Othmezouri teach the method according to claim 1 and further teaches, wherein the processing the first driving data and the second driving data by using a target model, to obtain a predicted driving behavior of the first vehicle comprises; however, Nguyen does not explicitly recite the term of distribution probabilities.
Yet, Othmezouri teaches processing the first driving data and the second driving data by using the target model, to obtain distribution probabilities of a plurality of candidate driving behaviors ([0045] reads on this element as such—[t]he first sub-problem is that of estimating the probability that a vehicle has executed a certain behavior in the past based on observations, such as the distance of the vehicle to its border, and any information obtainable from a vehicle which provides an indication as to the behavior. According to a preferred embodiment of the invention, a variant of the HMM is adapted for estimating a probability distribution over behaviors for each vehicle. The second sub-problem preferably provides a probabilistic distribution adapted for expressing the trajectory execution of a vehicle for each possible behavior of the same vehicle. The trajectories are then analyzed to determine the risk of collision. Thus, the riskiness of the behavior can be assessed for a time period within the time horizon. This assessment of the riskiness of the behavior is preferably communicated to the driver, operator, pilot or captain of the first traffic vehicle in a positive way, i.e. emphasizing how risk-free the vehicle has been driven.); and
in response to detecting that a distribution probability of a target driving behavior in the plurality of candidate driving behaviors meets a preset condition ([0047] reads on this element as such—[t]he first sub-problem is that of estimating the probability that a vehicle is executing a certain behavior based on observations, such as the distance of the vehicle to its border, and any information obtainable from a vehicle which provides an indication as to future behavior of which the turning indicator lights of the vehicles are only one embodiment. According to a preferred embodiment of the invention, a variant of the HMM is adapted for estimating a probability distribution over behaviors for each vehicle. The second sub-problem preferably provides a probabilistic distribution adapted for expressing the trajectory execution of a vehicle for each possible behavior of the same vehicle. The trajectories are then analyzed to determine the risk of collision.),
determining the target driving behavior as the predicted driving behavior of the first vehicle, wherein the distribution probability of the target driving behavior is the largest in the distribution probabilities of the plurality of candidate driving behaviors
([0078] reads on this element as such—“An evaluation of risk is given by a probabilistic model of the possible future evolution of a vehicle, preferably by the probability distribution over behaviors from driving behavior recognition and/or driving behavior realization. A value for the risk of collision can be calculated based on this probabilistic model. This value of risk can be communicated to the driver or operator of the vehicle, e.g. through a display, through auditory or tactile communicators or by any other means. Also, the risk values over a period of time can be collected and stored, e.g. in a suitable memory, and a representative risk behavior can be communicated to the driver or operator after elapse of a time period, e.g. 30 minutes. This is preferably done in such a way that the reporting to the driver or operator is positive for low-risk behavior. In this way the driver or operator is motivated to maintain low-risk behavior but is, if necessary, warned of risky behavior.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to incorporate the teaching of Othmezouri with the invention Nguyen because such modification would provide an indication of an inappropriate driving behavior (col. 3, lns. 23-26: Nguyen).
Claims 6 and 15. Nguyen in view of Othmezouri teaches the method according to claim 5; however, Nguyen does not recite distribution probability. Yet, Othmezouri teaches wherein the method further comprises: in response to detecting that the distribution probability of the target driving behavior does not meet the preset condition, determining, from the driving behavior set, the driving behavior to be performed by the first vehicle ([0094] reads on this element as such—“[p]redicting a vehicle motion is preferably performed in at least two steps: Firstly, the positional observations of vehicles are mapped into the canonical space 17 via LSCM. Secondly, the prediction using the GP in canonical space 17 is then performed. Finally, but optionally, the probability distribution over prediction in canonical space 17 is mapped back to original space using the inverse first step. However, the last step is preferably optional, i.e. one might determine everything in canonical space 17.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to incorporate the teaching of Othmezouri with the invention Nguyen because such modification would provide an indication of an inappropriate driving behavior (col. 3, lns. 23-26: Nguyen).
Claims 7 and 16. Nguyen in view of Othmezouri teaches the method according to claim 5; however, Nguyen does not recite distribution probability. Yet, Othmezouri teaches wherein the preset condition is that the distribution probability of the target driving behavior is greater than or equal to a preset first threshold, or a variance determined based on the distribution probability of the target driving behavior is less than or equal to a preset second threshold ([0091] teaches variance while [0092] teaches the concept of covariance).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to incorporate the teaching of Othmezouri with the invention Nguyen because such modification would provide an indication of an inappropriate driving behavior (col. 3, lns. 23-26: Nguyen).
Claims 8 and 17. Nguyen in view of Othmezouri teaches the method according to claim 5; however, Nguyen does not recite distribution probability. Yet, Othmezouri teaches wherein the driving behavior set comprises at least one candidate driving behavior of the plurality of candidate driving behaviors (fig. 4 and fig. 5 best illustrates at least one candidate driving behavior of the plurality of candidate driving behaviors).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to incorporate the teaching of Othmezouri with the invention Nguyen because such modification would provide an indication of an inappropriate driving behavior (col. 3, lns. 23-26: Nguyen).
Claims 9 and 18. Although Othmezouri teaches the method according to claim 8. Nguyen further teaches wherein the method further comprises:
obtaining, based on the first driving data and the second driving data, at least one score of at least one candidate driving behavior comprised in the driving behavior set (fig. 6 illustrates driver score); and
the determining, from the driving behavior set, the driving behavior to be performed by the first vehicle comprises:
determining, from the driving behavior set, a candidate driving behavior with a highest score as the driving behavior to be performed by the first vehicle(fig. 6 illustrates driver score).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed to incorporate the teaching of Othmezouri with the invention Nguyen because such modification would provide an indication of an inappropriate driving behavior (col. 3, lns. 23-26: Nguyen).
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 ANA D THOMAS whose telephone number is (571)272-8549. The examiner can normally be reached Monday - Friday 8 - 5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya Burgess can be reached at 571-272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.D.T/Examiner, Art Unit 3661
/RUSSELL FREJD/Primary Examiner, Art Unit 3661
Prosecution Insights