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 the Claims
This FINAL action is in response to Applicant's amendment of 03 June 2025. Claims 1, 3-4, 6, 9 and 21 are pending and have been considered as follows. Claims 10-20 are restricted. Claims 2, 5 and 7-8 are cancelled.
Response to Argument
Applicant’s amendments and/or arguments with respect to the rejection of Claims 1, 3-4, 6, 9 and 21 under 35 USC 101 as set forth in the office action of 07 March 2025 have been considered and are NOT persuasive. Specifically, Applicant argues:
The Office Action states that the claims are directed to a "mental process" because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, "identifying...", "determining ...", "classify...", "adjust..." and "generating..." with all the various data in the context of this claim encompasses a person looking at data collected (received, detected, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper.
Furthermore, the claimed steps encompass mental choices or evaluations, and the claimed use of a machine learning model encompasses performing mathematical calculations." Office Action pp. 10-11. The Applicants respectfully disagree.
The claims and application are related to systems and methods where embodiments use historical probe inputs to generate a DDC artifact and then take real-time detection input to monitor the DDC locations in the artifact and use this to publish the content indicating that the DDC event is still happening (on-going) this raises the confidence metric of the DDC event and if there is no real-time data, the DDC artifact will be published to the automotive cloud with a lower confidence. The DDC warning messages published provides smoother navigation and safer route choices for drivers.
As an application that requires real time updating, the processes cannot be practically performed mentally or using pen and paper. As noted in the MPEP, claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. See SRI Int'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019).
As described in the MPEP in MPEP 2106.04(a)(2), examples of claims that do not recite mental processes because they cannot be practically performed in the human mind include: a claim to a method for calculating an absolute position of a GPS receiver and an absolute time of reception of satellite signals, where the claimed GPS receiver calculated pseudoranges that estimated the distance from the GPS receiver to a plurality of satellites, SiRF Tech., 601 F.3d at 1331-33, 94 USPQ2d at 1616-17; a claim to detecting suspicious activity by using network monitors and analyzing network packets, SRI Int'l, 930 F.3d at 1304; a claim to a specific data encryption method for computer communication involving a several-step manipulation of data, Synopsys., 839 F.3d at 1148, 120 USPQ2d at 1481 (distinguishing the claims in TQP Development, LLC v. Intuit Inc., 2014 WL 651935 (E.D. Tex. 2014)); and a claim to a method for rendering a halftone image of a digital image by comparing, pixel by pixel, the digital image against a blue noise mask, where the method required the manipulation of computer data structures (e.g., the pixels of a digital image and a two-dimensional array known as a mask) and the output of a modified computer data structure (a halftoned digital image), Research Corp. Techs., 627 F.3d at 868, 97 USPQ2d at 1280.
The current claims are similar to these examples in that 1) the purported abstract idea is not a process that has typically or practically been performed mentally in the past and 2) the human mind is not equipped to perform the claims limitations such as simulating a deployment and fit of an endovascular device. Each of these tasks in the listed cases that were determined to be subject matter eligible could in theory be performed by a human or using pen and paper given unlimited time (as with any computation). The claims are not related to a mental process because even under the broadest reasonable interpretation, the limitations cannot be and would not be practically performed in the human mind. For example, the steps of "acquiring real-time probe data for the location; identifying the location as experiencing a dangerous driving condition based on the dangerous driving condition 8
artifact; determining that the dangerous driving condition is ongoing based on the real-time probe data; and publishing a dangerous driving condition event warning when the confidence value exceeds a predefined threshold" are all steps that are not performed in the human mind.
The claims cannot be performed in the human mind as the steps cover a technical concept that can only be performed by a machine.
In addition, as previously argued, the claimed process is patent eligible under 35 U.S.C. 101 as it is tied to a particular machine. The Courts have held that "[a] claimed process is surely patent-eligible under § 101 if: (1) it is tied to a particular machine or apparatus, or (2) it transforms a particular article into a different state or thing." Bilski 545 F.3d at 954. The Courts have also held that the machine-or-transformation test has a further aspect: "the use of a specific machine or transformation of an article must impose meaningful limits on the claim's scope to impart patent-eligibility." Id. at 961 (see also Prometheus Labs., Inc. v. Mayo Collaborative Servs., 581 F.3d 1336, 1342-43 (Fed. Cir. 2009). The claims in question satisfy this test. The Courts have defined a "machine" as "a concrete thing, consisting of parts, or of certain devices and combination of devices. This includes every mechanical device or combination of mechanical powers and devices to perform some function and produce a certain effect or result." In re Ferguson, 558 F.3d 1359, 1364 (Fed.
Cir. 2009) (quoting In re Nuijten, 500 F.3d 1346, 1355 (Fed. Cir. 2007)) (internal quotation marks omitted).
The one or more sensors are a machine and are integral to each of the claims at issue.
It is clear that the methods at issue could not be performed without the use of the one or more sensors; for example, without the data provided by the one or more sensors it would be impossible to detect the dangerous driving conditions which is the precise goal of the claims.
There is no further processing / analysis of the data without the one or more sensors . E.g., there is no associated analog process of the steps laid out in the method. The presence of the one or more sensors further places a meaningful limit on the scope of the claims. In order for the addition of a machine to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed method to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved more quickly, i.e., through the utilization of a computer for performing calculations. Again, this is not a situation in which there is a method that can be performed without a machine. There is no detecting without the use of the digital data provided by the one or more sensors. The use of a one or more sensors is essential to the operation of the claimed methods.
The claims are not directed to an abstract idea. The Applicants respectfully request the rejections be withdrawn.
The Office Action further asserts that the additional "limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the acquiring step from using probe sensors are recited at a high level of generality (i.e. as a general means of receiving information a for use in the determining and other steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Inputting and Outputting is recited at a high level of generality, and thus is insignificant extra-solution activity. Furthermore, the "publishing" step is merely insignificant extra-solution activity as it involves sending the data which would be a well- understood, routine, and conventional functions. In addition, the use of a machine learning model merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h)." Office Action pg. 13. The Applicants respectfully disagree.
In prong two of the 2019 PEG, the question is whether the judicial exception is integrated into a practical application. "Under the procedure, if a claim recites a judicial exception (a law of nature, a natural phenomenon, or an abstract idea), it must then be analyzed to determine whether the recited judicial exception is integrated into a practical application of that exception." 84 Fed. Reg. 53. As covered in the MPEP at § 2105.05(a), to be integrated into a practical application, "a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art."10
The claims provide an application of a concept to a new and useful end. The disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Finally, the specification identifies a technical problem and explains the details of an unconventional technical solution expressed in the claims and identifies technical improvements realized by the claims over the prior art.
The technical problem, as described in the specification, that the invention is attempting to solve is how to identify and provide charting, verifying, tracking, or otherwise identifying the dangerous driving conditions, or the dynamic events which dangerous driving conditions cause. This is difficult in large roadway systems often involving thousands of miles of roads. Notification systems may rely on annotated but unverified reports from vehicles that describe the dangerous driving condition in detail. While these types of systems may be useful for many drivers, more intelligent systems are frequently expected and required by drivers or autonomous vehicles. Operators expect and demand both ideal and safe routes and accurate arrival times. The older systems of acquiring and publishing reports is not sufficient to provide these levels of service. The Application provides improved reporting for intelligent traffic systems. Intelligent traffic systems perform analysis on probe reports and other data in order to provide safe and accurate routing solutions for the movement of people, goods, and vehicles. Accurate routing capabilities may be based on enterprise-grade maps and location data and can adapt routes to real-world circumstances in real-time. These intelligent traffic systems may provide accurate estimated time of arrivals (ETAs), routing, and maneuvers that take into account real-time congestion and incidents.
The claims and application build upon the intelligent traffic systems by detecting and monitoring locations with potential for dangerous driving conditions. Recurring dangerous driving conditions on the road may be present due to several factors. Embodiments are not only able to identify these locations but also to be able to detect in real-time if/when the cause of the dangerous driving condition is ongoing. Embodiments use metrics that indicate a driver or vehicle is experiencing a dangerous driving condition e.g., sudden breaking, jerking, sudden lane changes, swerving, etc. The road segments with the higher numbers of recurring dangerous driving condition events for various time epochs are identified and analyzed by the mapping system to identify the causes of this dangerous driving condition events. The mapping system categorizes the event for navigation impacts either routing or ETA or safety warnings. In addition, over time, dangerous driving conditions evolve or change (or disappear). Obstructions are removed, hazards are fixed, roadway configurations are corrected, etc. A pothole that exists at a first time may be fixed at a second time so that when a vehicle traverses the location at a third time there no longer is a dangerous driving condition. As noted in the specification, "[d]uring the classification/categorization process, the mapping system 121 may provide a confidence value for the dangerous driving condition that relates to change that the dangerous driving condition actually exists. With a lot of probe reports that indicate certain actions, the mapping system 121 may be more confident in its classification than if there was only a few or only one probe report that indicated certain actions. Over time as more actions are detected and the metrics increase, the mapping system 121 may become more confident in its classification. Similarly, if subsequent probe reports do not indicate certain actions, the mapping system 121 may be less confident that the dangerous driving condition still exists. In this way, both real-time warnings and updates may be provided by the mapping system 121 as the roadway evolves over time." Para [0076].
Accordingly, the claims are integrated into a practical application of the exception and are subject matter eligible. As opposed to claiming a result or resulting system, the claims recite specific steps/operations which accomplish a desired result. The Applicants argue that the USPTO and §101 do not preclude protection for innovation in the field of intelligent traffic systems. As such, the claims are integrated into a practical application of the exception and are subject matter eligible. The Applicants respectfully request the rejections of claims 1, 3, 4, 6, 9, and 21 be withdrawn.
The Examiners Response:
Examiner has carefully considered Applicant’s amendments and arguments and respectfully disagrees. Regarding the amended claims, the claims still recite mental processes in the form of the limitations of “identifying…”, “determining…”, and “increasing…” which are all steps that encompasses a person looking at data collected (received, detected, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. The claims as of right now do not include limitations that a human could not perform as a mental process given the appropriate data from external sources. In addition, “acquiring…” is merely data gathering, which is a form of insignificant extra-solution activity. Furthermore, “publishing…” is merely uploading data to a cloud through a network and is merely insignificant extra-solution activity.
Conventional sensors, such as the specification, are generally not considered a particular machine, since they are used for data gathering and as such are used for insignificant extra-solution activity. As a result, the claims are not tied to a particular machine as the sensors are conventional and the mental steps are performed with a generic computer processor.
Finally, the improvement being claimed in traffic systems is not persuasive as current human reporters perform such tasks and are able to perform the task with large amounts of data, and the current claims do not claim any such improvement as argued. Furthermore, an improvement to an abstract idea does not lead to the judicial exception being integrated into a practical application, See Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a *new* abstract idea is still an abstract idea"). As such, even in combination, these additional elements, under broadest reasonable interpretation, do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Applicant’s amendments and/or arguments with respect to the rejection of Claims 1, 3-4, 6, 9 and 21 under 35 USC 103 as set forth in the office action of 07 March 2025 have been considered but are moot because the new ground(s) of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. For clarification, a new art was brought to focus on the amended limitations that adjusting the confidence level.
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, 3-4, 6, 9 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a method. Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for detecting dangerous driving conditions, the method comprising:
acquiring, by one or more sensors comprising ranging circuitry and at least one of a camera, a weather sensor, or an occupant sensor, lane-level map matched probe data for a location;
identifying dangerous driving events in the lane-level map matched probe data on one or more lane locations at the location;
determining a reoccurring lane location that exhibits a reoccurring dangerous driving event;
inputting the reoccurring dangerous driving event into a machine trained model configured to classify causes of dangerous driving conditions;
and outputting by the machine trained model, a classification for a cause of the reoccurring dangerous driving condition and a confidence value for the classification, the confidence value representative of a measure of a confidence of the machine trained model that the classified cause of the reoccurring dangerous driving condition exists;
generating and storing a dangerous driving condition artifact for the reoccurring dangerous driving condition that includes at least the classification for the cause of the reoccurring dangerous driving condition and the confidence value;
acquiring real-time probe data for the location;
identifying the location as experiencing a dangerous driving condition based on the dangerous driving condition artifact;
determining that the dangerous driving condition is ongoing based on the real-time probe data;
increasing the confidence value of the dangerous driving condition artifact;
and publishing a dangerous driving condition event warning when the confidence value exceeds a predefined threshold
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “identifying…”, “determining …”, “classify…”, “generating…” and “increasing…” with all the various data in the context of this claim encompasses a person looking at data collected (received, detected, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. Furthermore, the claimed steps encompass mental choices or evaluations, and the claimed use of a machine learning model encompasses performing mathematical calculations. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, 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. The courts have indicated that additional elements 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.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for detecting dangerous driving conditions, the method comprising:
acquiring, by one or more sensors comprising ranging circuitry and at least one of a camera, a weather sensor, or an occupant sensor, lane-level map matched probe data for a location;
identifying dangerous driving events in the lane-level map matched probe data on one or more lane locations at the location;
determining a reoccurring lane location that exhibits a reoccurring dangerous driving event;
inputting the reoccurring dangerous driving event into a machine trained model configured to classify causes of dangerous driving conditions;
and outputting by the machine trained model, a classification for a cause of the reoccurring dangerous driving condition and a confidence value for the classification, the confidence value representative of a measure of a confidence of the machine trained model that the classified cause of the reoccurring dangerous driving condition exists;
generating and storing a dangerous driving condition artifact for the reoccurring dangerous driving condition that includes at least the classification for the cause of the reoccurring dangerous driving condition and the confidence value;
acquiring real-time probe data for the location;
identifying the location as experiencing a dangerous driving condition based on the dangerous driving condition artifact;
determining that the dangerous driving condition is ongoing based on the real-time probe data;
increasing the confidence value of the dangerous driving condition artifact;
and publishing a dangerous driving condition event warning when the confidence value exceeds a predefined threshold
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the acquiring step from using probe sensors are recited at a high level of generality (i.e. as a general means of receiving information a for use in the determining and other steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Inputting and Outputting is recited at a high level of generality, and thus is insignificant extra-solution activity. Furthermore, the “publishing” step is merely insignificant extra-solution activity as it involves sending the data to a cloud server over a network which would be a well‐understood, routine, and conventional functions. In addition, the use of a machine learning model merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
As discussed above, the broadest reasonable interpretation of the mental process steps is that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. The recitation of “machine learning model” merely indicates a field of use or technological environment in which the judicial exception is performed. This type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of acquiring information are a well-understood, routine and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processor is anything other than a conventional computer. See Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. As discussed in Step 2A, Prong Two above, the recitation of “outputting” is recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. Furthermore, the additional element of “machine learning model” in the claim limitations is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Hence, the claim is not patent eligible.
Dependent claims 3-4, 6, 9 and 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 additional elements that do not integrate the judicial exception into a practical application. These dependent claims merely define terms in the independent claim or have additional steps such as “determining…”, and “performing…”. In addition, the “performing” step as currently claimed is insignificant extra-solution activity recited at a high level of generality. Therefore, dependent claims 3-4, 6, 9 and 21 are not patent eligible.
Therefore, claims 1, 3-4, 6, 9 and 21 are ineligible under 35 USC §101.
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.
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.
Claims 1, 3, 6, 9 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Pelleg (US 20210201666) in view of Xu (US 20170352262) in further view of Tel-Or (US 20220254249) in further view of Camp (US 20190147736 A1).
Regarding Claim 1, Pelleg teaches A method for detecting dangerous driving conditions, the method comprising (see at least [¶06]):
identifying dangerous driving events in the lane-level map matched probe data on one or more lane locations at the location (Identifying dangerous driving events, such as swerving braking and bumps, and associating those events with locations of roads/lanes with a probe/device location. see at least [¶026-031 & 042-046]);
determining a reoccurring lane location that exhibits a reoccurring dangerous driving events (Determining the location of reoccurring lanes/roads that have reoccurring dangerous driving events/anomalies. see at least [¶038-039 & 048-050]);
inputting the reoccurring dangerous driving events into a machine trained model configured to classify causes of dangerous driving conditions (Input data from local devices can be inputted into an ANN to classify the cause and type of a dangerous driving event/condition. see at least [¶061-064]);
and outputting by the machine trained model, a classification for a cause of the reoccurring dangerous driving condition (An ANN is able to output a classification for the cause of a reoccurring dangerous driving event/condition. see at least [¶061-064]);
Pelleg does not explicitly teach acquiring, by one or more sensors comprising ranging circuitry and at least one of a camera, a weather sensor, or an occupant sensor, lane-level map matched probe data for a location.
However, Xu teaches acquiring, by one or more sensors comprising ranging circuitry and at least one of a camera, a weather sensor, or an occupant sensor, lane-level map matched probe data for a location (Obtaining lane-level matched probe data for various locations with probe vehicles, with the vehicles having ranging circuitry and onboard cameras. see at least [¶037-042, 049-50 & 060]).
Xu would be in a similar field as it also deals in the area of probe data and traffic jams. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Pelleg to use the technique of acquiring, by one or more sensors comprising ranging circuitry and at least one of a camera, a weather sensor, or an occupant sensor, lane-level map matched probe data for a location as taught by Xu. Doing so would lead to improved driver safety and reduce congestion by providing users with traffic information (see at least [¶02]).
Pelleg and Xu do not explicitly teach and a confidence value for the classification, the confidence value representative of a measure of a confidence of the machine trained model that the classified cause of the reoccurring dangerous driving condition exists; generating and storing a dangerous driving condition artifact for the reoccurring dangerous driving condition that includes at least the classification for the cause of the reoccurring dangerous driving condition and the confidence value.
However, Tel-Or does teach and a confidence value for the classification, the confidence value 1representative of a measure of a confidence of the machine trained model that the classified cause of the reoccurring dangerous driving condition exists (A confidence value/likelihood is obtained for the classification of the traffic condition, with the confidence value/likelihood indicating the confidence of the machine learning model identifying that the traffic condition is currently on-going. see at least [¶041-044 & 048-053]),
generating and storing a dangerous driving condition artifact for the reoccurring dangerous driving condition that includes at least the classification for the cause of the reoccurring dangerous driving condition and the confidence value (An artifact/traffic condition is generated which indicates a location of the traffic condition (which could be ongoing/reoccurring) and cause of a reoccurring dangerous driving event determined from the obtained confidence value/likelihood. see at least [¶041-044 & 048-053]);
Tel-Or would be in a similar field as it also deals in the area of traffic event classification. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Pelleg and Xu to use the technique of a confidence value for the classification, the confidence value representative of a measure of a confidence of the machine trained model that the classified cause of the reoccurring dangerous driving condition exists; generating and storing a dangerous driving condition artifact for the reoccurring dangerous driving condition that includes at least the classification for the cause of the reoccurring dangerous driving condition and the confidence value as taught by Tel-Or. Doing so would lead to improved driver response when detecting and updating traffic conditions (see at least [¶042]).
Pelleg, Xu and Tel-Or do not explicitly teach acquiring real-time probe data for the location; identifying the location as experiencing a dangerous driving condition based on the dangerous driving condition artifact; determining that the dangerous driving condition is ongoing based on the real-time probe data; increasing the confidence value of the dangerous driving condition artifact; and publishing a dangerous driving condition event warning when the confidence value exceeds a predefined threshold.
However, Camp does teach acquiring real-time probe data for the location (Acquiring real-time probe data from a location that is exhibiting road event conditions. see at least [¶030, 043, 071 & 0102]);
identifying the location as experiencing a dangerous driving condition based on the dangerous driving condition artifact (Identifying that a road location is experiencing a dangerous driving/road condition based on the road event. see at least [¶047-051]);
determining that the dangerous driving condition is ongoing based on the real-time probe data; increasing the confidence value of the dangerous driving condition artifact (Determining that the dangerous driving/road condition is ongoing based on obtaining real-time probe data that increased the confidence value/metric of the road event. see at least [¶049-051]);
and publishing a dangerous driving condition event warning when the confidence value exceeds a predefined threshold (Publishing the dangerous driving/road condition warning/alert when the confidence value/metric exceeds a predefined threshold. see at least [¶048-052]).
Camp would be in a similar field as it also deals in the area of reporting road events and conditions. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Pelleg, Xu and Tel-Or to use the technique of acquiring real-time probe data for the location; identifying the location as experiencing a dangerous driving condition based on the dangerous driving condition artifact; determining that the dangerous driving condition is ongoing based on the real-time probe data; increasing the confidence value of the dangerous driving condition artifact; and publishing a dangerous driving condition event warning when the confidence value exceeds a predefined threshold as taught by Camp. Doing so would lead to improved notification for control of a vehicle to evade a road event condition (see at least [¶067-069]).
Regarding Claim 3, Pelleg, Xu, Tel-Or and Camp teach all of the limitations of Claim 1 as shown above, Furthermore, Pelleg teaches wherein the dangerous driving event comprise at least one of sudden breaking, sudden deceleration, jerky motion, a sudden lane change, sinuosity, or an isolated zero speed cluster in a probe trajectory (Dangerous Lane/road events can include sudden breaking, swerves and road bumps. see at least [¶026-029, 031]).
Regarding Claim 6, Pelleg, Xu, Tel-Or and Camp teach all of the limitations of Claim 1 as shown above, Furthermore, Xu teaches wherein the machine trained model is configured to input a plurality of probe reports for the respective location for a respective time epoch (A collection of probe reports over a predetermined period of time are able to be inputted into a machine learning classifier. see at least [¶040]).
Xu would be in a similar field as it also deals in the area of probe data and traffic jams. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Pelleg, Tel-Or and Camp to use the technique of having the machine trained model is configured to input a plurality of probe reports for the respective location for a respective time epoch as taught by Xu. Doing so would lead to improved driver safety and reduce congestion by providing users with traffic information (see at least [¶02]).
Regarding Claim 9, Pelleg, Xu, Tel-Or and Camp teach all of the limitations of Claim 1 as shown above, Furthermore, Pelleg teaches wherein the cause comprises at least one of a poor road surface, a dangerous curves or intersection, a road obstruction, a difficult maneuver, or a share exit ramp (The cause can involve poor road surfaces, potholes and road obstructions. see at least [¶03, 030 & 046]).
Regarding Claim 21, Pelleg, Xu, Tel-Or and Camp teach all of the limitations of Claim 1 as shown above, furthermore, Camp teaches performing, by a vehicle, a maneuver or route change based on the published dangerous driving condition event warning (Performing a maneuver or route change based on the published road event condition by a vehicle (can be autonomous or non-autonomous). see at least [¶067-069]).
Camp would be in a similar field as it also deals in the area of reporting road events and conditions. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Pelleg, Xu and Tel-Or to use the technique of performing, by a vehicle, a maneuver or route change based on the published dangerous driving condition event warning as taught by Camp. Doing so would lead to improved notification for control of a vehicle to evade a road event condition (see at least [¶067-069]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Pelleg (US 20210201666) in view of Xu (US 20170352262) in further view of Tel-Or (US 20220254249) in further view of Camp (US 20190147736 A1) in further view of Golov (US 20190382029).
Regarding Claim 4, Pelleg, Xu, Tel-Or and Camp teach all of the limitations of Claim 1 as shown above, Pelleg, Xu, Tel-Or and Camp do not explicitly teach wherein determining comprises identifying a lane location that exceed a predefined threshold of dangerous driving events for a time period.
However, Golov does teach determining comprises identifying a lane location that exceed a predefined threshold of dangerous driving events for a time period (Determining that a lane/roadway location exceeds a predefined threshold of dangerous braking events for a time period. see at least [¶06, 034, 038-039, 041 & 049]).
Golov would be in a similar field as it also deals in the area of determining road conditions. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Pelleg, Xu, Tel-Or and Camp to use the technique of determining comprises identifying a lane location that exceed a predefined threshold of dangerous driving events for a time period as taught by Golov. Doing so would lead to improved evasion of unsafe driving locations (see at least [¶041]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/MOISES GASCA ALVA/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667
1 A Confidence Score is a number between 0 and 1 that represents the likelihood that the output of a Machine Learning model is correct and will satisfy a user’s request. The output of all Machine Learning (ML) systems is composed of one or multiple predictions. For example, YouTube ML will predict which video(s) you want to see next; Uber ML will predict the ETA (estimated time of arrival) for a ride. Each prediction has a Confidence Score. The higher the score, the more confident the ML is that the prediction will satisfy the user’s request. - https://medium.com/voice-tech-global/machine-learning-confidence-scores-all-you-need-to-know-as-a-conversation-designer-8babd39caae7#:~:text=A%20Confidence%20Score%20is%20a,will%20satisfy%20the%20user's%20request.