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 .
DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 filed on April 23, 2026 has been entered.
Status of Claims
Claims 1, 4, 10, 12, and 15 have been amended.
Claims 1-20 are currently pending and have been examined.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1, 10 and 12 recite “a steering angle of the operator executed by the driving system” The specification at [0032] states that the sensor data 250 includes one of a steering angle, not a driving system. In fact, there is no support for a driving system, as claimed and the only driving system mentioned in the background of the specification relates to automatic driving. Examiner fails to find support for the steering angle data being executed by the driving system and fails to find support for a driving system. The dependent claims do not cure the deficiencies. Correct required.
Additionally, claims 1, 4, 10, 12 and 15 recite “identify a gap by the machine learning model associated with the vehicle law and the violation parameter, and update the vehicle law associated with the gap” Examiner has not found support for the machine learning model identifying the gap. The specification at paragraph [0039] discloses the prediction system estimating the vehicle law and the estimates can fill in the gaps. There is no support for updating the law only updating maps as disclosed in paragraph [0049]. The dependent claims do not cure the deficiencies. Correct required.
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.
Step 1:
Claims 12-20 are drawn to methods while claim(s) 1-11 is/are drawn to an apparatus. As such, claims 1-20 are drawn to one of the statutory categories of invention (Step 1: YES).
Step 2A - Prong One:
Claim 12 (representative of independent claim(s) 1 and 10) recites the following steps:
generating a behavior profile of an operator with historical data and estimating a vehicle law, the historical data is acquired about a travel area associated with the operator and the behavior profile includes driving habits, a steering angle of the operator and an unticketed violation during a driving scenario, and the vehicle law is absent in the travel area;
predicting a violation parameter of the vehicle law from the behavior profile and a vehicle maneuver at a location and the location is outside of the travel area;
upon satisfying the violation parameter, outputting feedback by the vehicle about the vehicle law
identifying a gap associated with the vehicle law and the violation parameter, and updating the vehicle law associated with the gap.
These steps, under its broadest reasonable interpretation, encompass a human manually (e.g., in their mind, or using paper and pen) predicting violations of a vehicle law and communicating the feedback to a user (i.e., one or more concepts performed in the human mind, such as one or more observations, evaluations, judgments, opinions), but for the recitation of generic computer components. If one or more claim limitations, under their broadest reasonable interpretation, covers performance of the limitation(s) in the mind but for the recitation of generic computer components, then it falls within the "mental processes" subject matter grouping of abstract ideas.
As such, the Examiner concludes that claim 12 recites an abstract idea (Step 2A - Prong One: YES).
Independent claim(s) 1 and 10 are determined to recite an abstract idea under the same analysis.
Step 2A - Prong Two:
This judicial exception is not integrated into a practical application. The claim(s) recite the additional elements/limitations of:
from a vehicle and a driving system,
a machine learning model
from sensor data of the vehicle,
A prediction system comprising: a memory storing instructions that, when executed by a processor, cause the processor (Claim 1)
A non-transitory computer-readable medium comprising: instructions that when executed by a processor cause the processor (Claim 10)
by the vehicle (Claims 1 and 10)
The requirement to execute the claimed steps/functions listed above is equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. This/these limitation(s) do/does not impose any meaningful limits on producing the abstract idea and therefore do/does not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claim(s) is/are directed to an abstract idea (Step 2A -Prong Two: NO).
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above in "Step 2A - Prong 2", the requirement to execute the claimed steps/functions listed above is equivalent to adding the words "apply it" on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. These limitations therefore do not qualify as "significantly more" (see MPEP 2106.05 (f)).
The Examiner has therefore determined that no additional element, or combination of additional claims elements is/are sufficient to ensure the claim(s) amount to significantly more than the abstract idea identified above (Step 2B: NO).
Regarding Dependent Claims:
Dependent claims 2, 5, 7, 8, 11, 13, 16 and 18-20 fail to include any additional elements and are further part of the abstract idea as identified by the Examiner.
Dependent claims 3, 4, 6, 9, 14, 15 and 17 include additional limitations that are part of the abstract idea except for:
radar enforcement, and a speed camera
machine learning model with information from a sensor system of the vehicle,
output by the driving system,
using data from a sensor, and the data includes one of a steering angle, a braking frequency, a pulse rate, a respiratory rate, electrodermal activity, pupil dilation, and body temperature associated with the operator.
using image data about a scene
a transportation apparatus
wherein the feedback is generated by a voice assistant within the vehicle
downloaded by the vehicle from a server
a voice assistant
The additional elements of the dependent claims are equivalent to adding the words ''apply it'' on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. The claims are ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (9,928,735) in view of Phillips (2019/0009788) and Aoude et al. (2019/0287401)
Claims 1, 10 and 12
Wang discloses a prediction system comprising:
a memory storing instructions that, when executed by a processor, cause the processor to (Wang [Column 5 Lines 56-61]):
A non-transitory computer-readable medium comprising: instructions that when executed by a processor cause the processor to (Wang [Column 11 Lines 9-16]):
predicting a violation parameter of the vehicle law (Wang [Column 35 Lines 5-10]) See at least “The at least one mobile platform (e.g. 300) may cross correlates an identified current location of the transportation vehicle ( e.g. vehicle 800) with a time and a location of the historical traffic violation citations and traffic rules to predict a likelihood of receiving a traffic violation citation at the identified current location of the transportation vehicle with a time.”
and an unticketed violation during a driving scenario, and the vehicle law is absent in the travel area (Wang [Column 26 Lines 4-8]). See “notifications are sent based on violations with time restrictions. The time restrictions is a time frame where a certain driving actions are not allowed at a certain location, which is used to help the system produce time sensitive alerts when applicable.” Where a restriction is not a law.
the location is outside of the travel area; upon satisfying the violation parameter using the vehicle, outputting feedback by the vehicle about the vehicle law. (Wang [Column 1 Lines 18-22][Column 2 Lines 52-57]). See at least “Exemplary embodiments of the present invention provide systems and methods for avoidance of traffic violation citations by alerting a user of a potential traffic violation in a current location of the user or any location requested by the user [and the location is outside of the travel area;] on the basis of the analysis of the historic traffic violation citation data of the location.” See also [Column 3 Lines 20-25] “Exemplary embodiments of the present invention cross- correlate an identified current location of the users with time and location of the issued traffic violation citations and the traffic rules to predict a likelihood of receiving a traffic violation citation at the user's identified or current location, time and date.”
identifying a gap associated with the vehicle law and the violation parameter, and updating the vehicle law associated with the gap (Wang [Column 5 Lines 41-43]). See “The historical data is updated and complimented by real-time crowdsourced data provided by the users.” Wang does not explicitly disclose updating a vehicle law, however the specification lacks support for this limitation, as well as, appropriate disclosure on how a law is updated. Wang teaches updating historical data regarding traffic violations.
Wang discloses predicting a potential traffic violation based on current and past location. Wang does not teach driving habits of the user as a factor in the prediction. Phillips teaches:
generate a behavior profile of an operator with historical data from a vehicle and a driving system, the historical data is acquired about a travel area associated with the operator and the behavior profile includes driving habits, a steering angle of the operator executed by the driving system (Phillips [0028][0050]) See [0028] “The unique driver profile is created from the accepted plural signals including time and geo-location data based on driving habits of the driver.” And [0050 “a driver's driver habits are captured with a portable vehicle on-board diagnostics (OBD) apparatus. The OBD is used alone…” See [0220] for historical data…” the network device stores in a database associated with the network device the created driver profile as a known driver profile for the identified driver.” See Table 1, #6 for steering wheel positioning determined by signal. See 112 above for more information.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a prediction system to provide for storing and updating traffic violation citation data for a plurality of users to avoid traffic violations., as taught by Wang, using specific operator historical data to aid in the prediction, as taught by Phillips, to create more accurate driver profiles (Phillips [0026]).
Wang nor Phillips explicitly disclose the maneuver of an operator. Aoude teaches:
estimating a vehicle law, (Aoude [0089]). See at least “The sensors may include, but are not limited to, cameras, radars, lidars, ultrasonic detectors or any other hardware that can sense or infer from sensed data the distance to, speed, heading, location, or combinations of them, among other things, of a ground transportation entity. Sensor fusion is performed using aggregations or combinations of data from two or more sensors.”
using a machine learning model from the behavior profile and a vehicle maneuver at a location from sensor data of the vehicle, (Aoude [0057][0058]). See [0057] “The data captured by these sensors can be used to model the patterns of motion, behaviors, and intentions of the entities...” See [0058] “Based on the direct use of current sensor data[vehicle maneuver] and on the results of applying the artificial intelligence and machine learning to the current sensor data, the system produces early warnings such as alerts of dangerous situations.” Where the behavior profile is taught [0076] “
by the machine learning model (Aoude [0006])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a prediction system to provide for storing and updating traffic violation citation data for a plurality of users to avoid traffic violations and using specific operator data., as taught by Wang and Phillips, the current maneuvering of a driver, as taught by Aoude, to help avoid collisions in ground transportation (Aoude [0005]).
Claims 2, 11 and 13
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Wang further teaches:
wherein the instructions to satisfy the violation parameter further include instructions to detect that the vehicle maneuver violates the vehicle law, and the vehicle law applies outside of the travel area and the vehicle law is absent within the travel area (Wang [Column 16 Lines 7-17]). See at least “This function is useful for the users who drive across state/country lines and are unfamiliar with the RRLC from the different states/countries…., the system of the mobile application sends alerts for different rules or a summary of differing traffic rules to the user according to the geolocation as it changes.”
Claims 3 and 14
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Wang further teaches:
wherein the instructions to predict the violation parameter further include instructions to infer enforcement of the vehicle law by factoring one of a safety zone, radar enforcement, and a speed camera associated with the location (Wang [Column 28 Lines 62-65]). See at least “some areas in which a vehicle may only travel at 5 mph are indicated by extra-large signs with words in a larger font. The system of the application also sends notifications to the users for these special speed zone areas.” See also [Column 38 Lines 14-23] “providing notification of a potential traffic violation alert in a geographic area, the method comprising the steps of: storing a plurality of the traffic related information including at least one of bus lane cameras, bus lane locations, speed cameras locations, school zone locations and traffic light cameras (900); receiving a geocoded location through a location identifier (902); inferring traffic ticket specific information (904); using an internal clock mechanism to identify current time and date (908)…”
Claims 4 and 15
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Wang further teaches:
wherein a rule within the vehicle law is updated using the gap (Wang [Column 5 Lines 41-43]). See “The historical data is updated and complimented by real-time crowdsourced data provided by the users.” Wang does not explicitly disclose updating a vehicle law, however the specification lacks support for this limitation, as well as, appropriate disclosure on how a law is updated. Wang teaches updating historical data regarding traffic violations.
Modified Aoude further teaches:
wherein the instructions to predict the violation parameter further include instructions to infer a confidence level of the operator for the vehicle maneuver and travel within the location using the machine learning model with information from a sensor system of the vehicle, and the information includes one of the steering angle output by the driving system, a braking frequency, a pulse rate, a respiratory rate, electrodermal activity, pupil dilation, and body temperature associated with the operator, (Aoude [0089][0184][0214]). See [0106] The system “can also use kinematic sensors to measure the reaction and behavior of the driver and, from that, infer the quality of driving..” See [0089] inferring. See [0184] “Data on road users can be collected using (a) entity data broadcast by each entity itself about its current state, through a BSM or a PSM for instance; and (b) sensors installed externally on infrastructure or on vehicles, such as doppler radars, ultrasonic sensors, vision or thermal cameras, lidars, and others.”
Although the limitation has been addressed in view of prior art, the Examiner notes that the particular calculation to infer a confidence level in an operator (i.e. “wherein the confidence level indicates a differentiation between driving in the location with anxiety and a medical condition about the operator from the pulse rate and the respiratory rate wherein driving in the location outside of the travel area is associated with the pulse rate being an elevated value and the respiratory rate being a constant value.” as claimed) is considered non-functional descriptive material, of which does not explicitly alter or impact the steps of the method in such a way as to establish a new and unobvious functional relationship with the method as claimed. As such, the non-functional descriptive material limitation can be given little to no patentable weight. See MPEP 2111.05. The functional limitation is inferring the quality of driving of the operator [confidence level]. The reference cited teaches this. Appropriate correction is required.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a prediction system to provide for storing and updating traffic violation citation data for a plurality of users to avoid traffic violations., as taught by Wang and Phillips, the state of a driver, as taught by Aoude, to help avoid collisions in ground transportation (Aoude [0005]).
Claims 5 and 16
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Aoude further teaches:
wherein the instructions to predict the violation parameter further include instructions to compute a probability of a collision associated with the location (Aoude [0108]). See at least “predict the next action or reaction of the driver or user of the vehicle or other ground transportation entity or vulnerable road user, but also be able to predict the intent and future trajectories and associated near-miss or collision risks due to other vehicles, ground transportation entities and vulnerable road users nearby… The risk is computed by the SOBE based on the probability of the various future predicted trajectories of the nearby vehicle… If the risk of collision is higher than a certain threshold, then the warning is displayed to the driver of the host vehicle.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a prediction system to provide for storing and updating traffic violation citation data for a plurality of users to avoid traffic violations., as taught by Wang and Phillips, the probability of a collision associated with the location, as taught by Aoude, to help avoid collisions in ground transportation (Aoude [0005]).
Claims 6 and 17
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Aoude further teaches:
wherein the instructions to estimate the vehicle law further include instructions to extract and classify traffic symbols using the machine learning model with image data about a scene surrounding the vehicle (Aoude [0176]). See at least “sensor data (e.g., speed and distance from radar units, images and video from cameras) is collected and stored locally at the RSE in preparation, in some implementations, to be transferred to a remote computer that is powerful enough to build an AI model of the behavior of the different entities of the intersection using this collected data.”
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a prediction system to provide for storing and updating traffic violation citation data for a plurality of users to avoid traffic violations., as taught by Wang and Phillips, using image data to estimate vehicle law, as taught by Aoude, to prevention of a violation in traffic situations at intersections. (Aoude [0004]).
Claims 7 and 18
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Wang further teaches:
wherein the behavior profile is portable to a transportation apparatus other than the vehicle (Wang [Figure 1][Column 6 Lines 40-50]). Where the user profile database configured to store the user information is on the mobile device and the figure shows that the mobile device can port to the internet.
Wang nor Phillips disclose information exchange. Aoude teaches:
and the behavior profile is downloaded by the vehicle from a server (Aoude [0179]). See “When the machine learning (AI) model is completed at the server, it is downloaded to the Road Side Equipment through the Internet, for example.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included in the method of a prediction system to provide for storing and updating traffic violation citation data for a plurality of users to avoid traffic violations., as taught by Wang and Phillips, using image data to estimate vehicle law, as taught by Aoude, to prevention of a violation in traffic situations at intersections (Aoude [0004]).
Claims 8 and 19
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Wang further teaches:
wherein the behavior profile includes driving violations by the operator within the travel area (Wang [Column 16 Lines 7-17]). See at least “This function is useful for the users who drive across state/country lines and are unfamiliar with the RRLC from the different states/countries. The system of the mobile application has the user's driver license information as the user enters and store the information of the driver license with the state/country who issued the driver license when registering a user profile. Using the location from where the user's driver license was issued with a user's current geolocation data, the system of the mobile application sends alerts for different rules or a summary of differing traffic rules to the user according to the geolocation as it changes.”
Claims 9 and 20
Modified Wang, Phillips and Aoude disclose the limitations above. Modified Wang further teaches:
wherein the feedback is generated by a voice assistant within the vehicle (Wang [Column 22 Lines 7-9]). See “In addition to other indicators or other formats, such as colors, shapes, voice notification, text message are used to identify violations with time restrictions.”
Response to Arguments
Applicant's arguments with respect to 35 USC 101 have been fully considered but they are not persuasive.
Applicant argues: First, under Step 2A/Prong Two, amended claim I now clearly integrates the prediction
system into a practical application at least with… Unlike Electric Power Group, LLC v. Alstom S.A., at least these features are more than collecting and analyzing information.
Examiner maintains the previous response. In combination, the steps disclose a sequence of operations that include storing data (operator behavior data and traffic laws), receiving an input (GPS location data), retrieving stored data (operator data and traffic laws) in response to the input and displaying the retrieved data. The only arguable inventive aspect of this set of steps is the particulars of the information processed. Apart from such particulars as is known of those of ordinary skill , the claimed combination of operations amounts to a generic, routine and conventional sequence of generic, routine and conventional operations of a computer system. Further the combination of operations automates a mental process that could be performed by a “human analog.” For example, a human being could watch monitor their speed and the speed limit of their current location and knowing that they have a tendency to drive faster, remind themselves to slow down. Automation of a mental process has been held insufficient to add significantly more to an abstract idea (see July 2015 Update at pp, 7, 11, note 24 discussing Benson, Bancorp, and Cybersourse). For that additional reason, in combination, the claimed operations of the computer system fail to add significantly more to the abstract idea.
None of the claims (independent or dependent) effects an improvement to another technology or technical field; nor does any of the claims amount to an improvement to the function of system.
Accordingly, Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself.
Applicant argues: Amended claim 1 has a technical improvement that clearly integrates into a practical vehicle application at least with to "generate a behavior profile of an operator with historical data from a vehicle and a driving system, and estimate a vehicle law," "predict a violation parameter of the vehicle law using a machine learning model from the behavior profile and a vehicle maneuver at a location from sensor data of the vehicle," and "identify a gap by the machine learning model associated with the vehicle law and the violation parameter, and update the vehicle law associated with the gap" as recited in amended 1.
The Examiner notes that this claim of a technical improvement is not representative of an "actual" improvement to the technology itself, but at best is an improvement to the business method or abstract idea itself. In fact, Applicant can provide no tangible findings that there was actually anything different and/or improved in the instant system compared to prior "conventional systems", other than a mere allegation and unsubstantiated, conclusory statement that the instant invention improves existing systems. However, the Examiner respectfully notes that the features of the claimed invention does not represent an improvement, it is merely performing operations with a device. The Applicant cannot point to anything that was specifically done either in the claimed subject matter, the specification, or provided reasoning to show how this is significantly more or provides an improvement to the technology of the conventional system implementation. Moreover, the Examiner respectfully notes that the needed "improvement" in terms of patent eligibility is not one resulting from programming a generic processor to perform a different (or even improved) function, but rather a specific and actual improvement to the machine itself is needed. Based on these findings of fact, the Examiner contends the claims are indeed directed towards an abstract idea and Applicant's arguments to the contrary are considered to be non-persuasive.
Applicant Argues: the Office Action ignores that the claims recite that "the historical data is acquired about a travel area associated with the operator and the behavior profile includes driving habits, a steering angle of the operator and an unticketed violation during a driving scenario."
Respectfully, the claims represent mere data gathering in conjunction with a law of nature or abstract idea. The final step of outputting a notification about the predicted violation is not a technical improvement. There is not enough disclosure to support the claims being more than gathering data about a user’s driving habits, analyzing the data against area laws and displaying the result of the analyzing to a user. The claims stop short of any real prevention of the violation. The unticketed violation is not detailed with sufficient information to support how that information is relevant to the behavior profile and it also is not clear why vehicle law that is absent in the travel area would be relevant to receive a notification to the user if they aren’t violating any laws. If the user frequently performs u-turns, and the vehicle law of u-turns being illegal is absent in the travel area, there would be no prediction of violation parameter because there is no violation.
Applicant Argues: Enfish… the technical improvement of the prediction system includes preemptively and automatically preventing "illegal maneuvers through informing the operator about a vehicle law outside a travel area using a behavior profile and maneuver handling for a location, thereby improving driving experiences and enhancing navigation guidance" using notifications.
Examiner respectfully disagrees. The claims are not directed to preventing illegal maneuvers, the claims are merely alerting the user to the potential of a violation by outputting a notification. The driver could ignore the notice and still perform the illegal maneuver. Furthermore sending notification does not represent a technical improvement. See TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48 which is directed to gathering and analyzing information using conventional techniques and displaying the result, which is the same analysis used in the instant application.
Applicant Argues: Amended claim 1 also prevents a vehicle system from overactively warning "an operator about a vehicle law that varies from state-to-state, province-to-province [and] becomes ineffective from an operator ignoring alerts.
Examiner respectfully disagrees. Preventing a vehicle from overactively warning a user is at best a business practice improvement but does not represent a technical improvement. Furthermore the prevention of what is deemed ineffective is subjective as each user responds differently to the volume of alerts. Preventing overactive alerts is an intended use, not a technical improvement.
Applicant Argues: As such, the technical improvements in the specification and claimed features are similar to the facts in Desjardins at least because the noted improvements in paragraphs [0017] and [0040] are technically captured in amended claim 1.
Applicant argues that the claims are analogous to Ex parte Desjardins by allegedly providing a prediction system that preemptively prevents illegal maneuvers. This is not persuasive as the functioning of the claims do not provide a technical improvement. The claims are not directed to actually preventing illegal maneuvers, but rather providing notifications so that the user can make a better judgment call or personal decision. This is not a technical improvement. The claims merely represent data gathering and a broadly cited machine learning model to predict violation parameters. As such the rejection is maintained.
Applicant's amendment with respect to 35 USC 112 rejection of the previous action have been fully considered however the additional amendments required that the 112 rejection be maintained and updated.
Applicant's arguments with respect to 35 USC 103 have been fully considered but they are not persuasive.
Applicant Argues: Bender in these paragraphs and elsewhere is completely unrelated to using event data to predict a violation parameter of a vehicle law. In fact, Bender does not at all discuss how vehicle events
caused by a vehicle operator relate to vehicle laws…. Bender in paragraph [0074] or related concepts elsewhere cannot possibly discuss "an unticketed violation during a driving scenario, and the vehicle law is
absent in the travel area" as recited by amended claim 1
Examiner agrees and has relied on newly cited Phillips for the teaching.
Applicant Argues: Wang and Bender clearly do not discuss to "identify a gap by the machine learning model associated with the vehicle law and the violation parameter, and update the vehicle law associated with the gap" as recited by amended claim 1.
Examiner has relied on Wang to teach updating the history of the violation parameter.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RASHIDA R SHORTER whose telephone number is (571)272-9345. The examiner can normally be reached Monday- Friday from 9am- 530pm.
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, Jessica Lemieux can be reached at (571) 270-3445. 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.
/RASHIDA R SHORTER/Primary Examiner, Art Unit 3626
Prosecution Insights