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 .
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 enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Claim 1 recites “defining, by a requirement defining machine-learning model, a vehicle application compliance requirement according to an incident cluster among the plurality of incident clusters.” The specification never defines the term “vehicle application compliance requirement,” nor does it give any examples of a vehicle application compliance requirement. It also does not describe the operations performed by a requirement defining machine-learning model that result in defining a vehicle application compliance requirement. And it does not describe any structure of a requirement defining machine-learning model that would make it capable of defining a vehicle application compliance requirement. One skilled in the art would therefore not be able to make or use the claimed invention because they would not know what a vehicle application compliance requirement is or how to design and implement a machine learning model to define a vehicle application compliance requirement.
Claims 2-4, 6-9, 12, 15, and 18-20 also recite “a/the vehicle application compliance requirement,” so they lack enablement in the same manner as described for claim 1.
Claims 5, 10-11, 13-14, and 16-17 depend on claim 1, so they too include the vehicle application compliance requirement and therefore lack enablement as described above.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, it recites “defining, by a requirement defining machine-learning model, a vehicle application compliance requirement according to an incident cluster among the plurality of incident clusters.” As explained above, the term “a vehicle application compliance requirement” is not defined by the specification. The specification does not even provide any examples of a vehicle application compliance requirement. It also does not describe the operations performed by a requirement defining machine-learning model that result in defining a vehicle application compliance requirement. And it does not describe any structure of a requirement defining machine-learning model that would make it capable of defining a vehicle application compliance requirement. The terms “a vehicle application compliance requirement” and “a requirement defining machine-learning model” are therefore indefinite because it is impossible to understand what these elements are, and impossible to know how a requirement defining machine-learning model functions to define a vehicle application compliance requirement. Since these terms are indefinite to the point that they cannot be understood by one skilled in the art, the examiner is unable to examine the present limitation with respect to prior art.
Regarding Claims 2-4, 6-9, 12, 15, and 18, they also recite “the vehicle application compliance requirement,” so they are indefinite as described for claim 1. These claims all recite additional details about the vehicle application compliance requirement or how the vehicle application compliance requirement is used. Since the vehicle application compliance requirement itself is indefinite, these claims are all indefinite to the extent that the examiner is unable to make a meaningful interpretation of their meaning and therefore cannot examine them with respect to prior art.
Further regarding Claims 4-6, they recite limitations that are based on “the plurality of training samples.” The plurality of training samples are prepared “according to the vehicle application compliance requirement,” as recited by claim 3. Since the vehicle application compliance requirement is indefinite, the plurality of training samples are also indefinite; without an ability to understand a clear meaning of the vehicle application compliance requirement, it is impossible to make a meaningful interpretation of the plurality of training samples. As such, claims 3-6 cannot be examined with respect to prior art.
Regarding Claim 10, it recites further details about the vehicle application machine-learning model. The vehicle application machine-learning model depends on (was trained on) the plurality of training samples in claim 4, so the model itself becomes indefinite. The additional limitations on the model recited by the present claim are therefore also indefinite to such an extent that examination under prior art is not possible.
Regarding Claim 11, it further details the preparing step of claim 3, so it too cannot be interpreted in a meaningful way for examination under prior art.
Regarding Claim 13, it recites “wherein the defining is in response to determining that the priority value assigned to the incident cluster exceeds a threshold priority value.” This further details the defining step of claim 1, which is indefinite to the point that it cannot be examined with respect to prior art, as detailed above. As such, the present limitation also cannot be examined with respect to prior art because it is impossible to make a meaningful interpretation of the limitation.
Regarding Claims 14, and 16-17, they depend on claim 1, so they too include the vehicle application compliance requirement and are therefore indefinite for the reasons described above.
Regarding Claims 19 and 20, they recite “defining, by a requirement defining machine-learning model, a vehicle application compliance requirement according to an incident cluster among the plurality of incident clusters.” As detailed for claim 1, this limitation is indefinite to the extent that it cannot be examined with respect to prior art.
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 “clustering, by a clustering machine-learning model, the plurality of incident samples into a plurality of incident clusters.” Clustering data samples into clusters in a mental process of judgement or evaluation that evaluates the samples and judges which cluster or grouping each sample belongs to. The claim further recites “defining, by a requirement defining machine-learning model, a vehicle application compliance requirement according to an incident cluster among the plurality of incident clusters.” Defining a compliance requirement is a mental process of judgement or evaluation that considers data and decides on a compliance requirement.
This judicial exception is not integrated into a practical application because the only thing produced by the claim is information in the form of clusters of data and a compliance requirement. Nothing practical is performed using these data. The claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional limitation recites “collecting a plurality of incident samples from an Internet, the plurality of incident samples identified by an identification machine-learning model to involve one or more vehicles.” Collecting data is mere data gathering, which is insignificant extra-solution activity and therefore not significantly more than an abstract idea. All of the other elements recited by the claim—a non-transitory computer-readable medium having instructions recorded thereon, one or more processors, a clustering machine-learning model, and a requirement defining machine-learning model—are generic computing components recited at a high degree of generality, so they do not render the claim significantly more than an abstract idea.
Claim 2 recites “defining the vehicle application compliance requirement includes defining a metric type, a metric criterion, and a metric evaluation condition.” Defining a metric type, a metric criterion, and a metric evaluation condition is a mental process of judgement or evaluation that considers information and decides on a metric type, a metric criterion, and a metric evaluation condition. The claim does not recite a practical application and does not include any additional elements.
Claim 3 recites “preparing a plurality of training samples according to the vehicle application compliance requirement.” Preparing training samples is a mental process that can merely consider information and decide which of the information should be included in a set of training samples. The claim does not recite a practical application and does not include any additional elements.
Claim 4 recites “defining an annotation rule according to the vehicle application compliance requirement, labeling a plurality of sensor samples according to the annotation rule, and selecting the training samples from among the plurality of labeled sensor samples.” Defining, labeling, and selecting are all mental processes of judgement or evaluation that consider data and decide on a rule, a label, and a selection of one or more pieces of data. The claim does not recite a practical application and does not include any additional elements.
Claim 5 recites “comprise training a vehicle application machine-learning model with a first portion of the plurality of training samples.” The training is recited at a high degree of generality, so it is considered a generic computing component and therefore not significantly more than an abstract idea. The claim does not recite any additional elements and does not recite a practical application because the only result is a trained model; the model is not used by the claim to perform anything practical.
Claim 6 recites “testing the vehicle application machine-learning model with a second portion of the plurality of training samples according to the vehicle application compliance requirement.” The testing is recited at a high degree of generality, so it is considered a generic computing component and therefore not significantly more than an abstract idea. The claim does not recite any additional elements and does not recite a practical application because the only result is a trained model; the model is not used by the claim to perform anything practical.
Claim 7 recites “determining whether the vehicle application machine-learning model fulfills the vehicle application compliance requirement.” This determination is a mental process that evaluates the machine-learning model and judges whether or not it fulfills the vehicle application compliance requirement. The claim does not recite a practical application and does not include any additional elements.
Claim 8 recites “deploying the vehicle application machine-learning model to a vehicle system in response to determining that the vehicle application machine-learning model fulfills the vehicle application compliance requirement.” Deploying a machine learning model is recited at a high degree of generality, so it is a generic computing component that does not render the claim significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 9 recites “analyzing the output log to validate the vehicle application machine-learning model.” Analyzing a log is a mental process that evaluates data and judges the machine-learning model based on the data. The claim also recites “updating the vehicle application compliance requirement according to a result of the analyzing.” This too is a mental process that considers the analyzing and decides how to update data (the vehicle application compliance requirement). The only additional element is “receiving an output log of the vehicle application machine-learning model from the vehicle system.” Receiving data is mere data gathering, which is insignificant extra-solution activity and therefore not significantly more than an abstract idea. The claim does not recite a practical application because the only thing produced is updated data.
Claim 10 describes the type of output of the vehicle application machine learning model. These outputs (the log and classification results) are merely data. The machine-learning model is described at a high level of generality, so it is still a generic computing component that only produces information. The claim is therefore not significantly more than the mental processes detailed above. It does not recite a practical application or any additional elements.
Claim 11 recites “wherein the preparing includes receiving a sensor data log corresponding to the plurality of sensor samples.” Receiving a sensor log is mere data gathering, which is insignificant extra-solution activity and therefore not significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 12 recites “wherein the vehicle application compliance requirement includes a training set identifier.” An identifier is merely data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 13 recites “assigning a priority value to each incident cluster among the plurality of incident clusters.” Assigning a priority value is a mental process of judgement or evaluation that considers information and decides on a priority value. The claim further recites “wherein the defining is in response to determining that the priority value assigned to the incident cluster exceeds a threshold priority value.” Determining that the priority value exceeds a threshold is a mental process that evaluates the priority value and compares it to a threshold value. The claim does not recite a practical application and does not include any additional elements.
Claim 14 recites details about the priority value, which is merely information and therefore an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 15 recites “wherein the vehicle application compliance requirement is defined further according to constraints of a vehicle type.” Defining information based on other information is a mental process of judgement or evaluation that considers data and decides on a value. The claim does not recite a practical application and does not include any additional elements.
Claim 16 further details the incident samples, which is merely data and therefore an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 17 recites “wherein the collecting includes applying the plurality of incident samples to a large language model.” A large language model is a generic computing component, recited here at a high degree of generality, so it does not render the claim significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 18 recites “wherein the vehicle application compliance requirement includes structured data in a computer-readable format.” This merely specifies additional details about data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 19 recites a method comprising steps that are substantially similar to the operations of claim 1. The present claim therefore recites an abstract idea without significantly more for the same reasons as described for claim 1.
Claim 20 recites a device that performs operations substantially similar to the operations of claim 1. The present claim therefore recites an abstract idea without significantly more for the same reasons as described for claim 1. The additional elements of a device and a controller including circuitry are generic computing components recited at a high degree of generality, so they do not render the claim significantly more than an abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-12, 15, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Szénási, Sándor (“Analysis of historical road accident data supporting autonomous vehicle control strategies,” PeerJ Computer Science 7 (2021): e399; hereinafter “Szénási”).
Regarding Claim 1, Szénási teaches a non-transitory computer-readable medium having instructions recorded thereon that, in response to execution by one or more processors, cause performance of operations (Abstract and Introduction, pp. 1-2—the use of a database and data-mining implies one or more processors executing instructions recorded on a computer readable medium, as does the use of DBSCAN on p. 7) comprising:
collecting a plurality of incident samples from an Internet, the plurality of incident samples identified by an identification machine-learning model to involve one or more vehicles (Introduction, pp. 1-2—the proposed steps include collecting data from an existing accident database, i.e. collecting incident samples using statistical or data-mining methods {e.g. a machine-learning model} that involve one or more vehicles. The Materials and Methods section on pp. 7-8 also describes using elements from the database);
clustering, by a clustering machine-learning model, the plurality of incident samples into a plurality of incident clusters (Materials and Methods, pp. 7-8—DBSCAN is a machine learning model that clusters the plurality of incident samples into a plurality of clusters); and
defining, by a requirement defining machine-learning model, a vehicle application compliance requirement according to an incident cluster among the plurality of incident clusters (as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine this limitation with respect to prior art because the “vehicle application compliance requirement” is indefinite).
Regarding Claim 2, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 1, wherein the defining the vehicle application compliance requirement includes defining a metric type, a metric criterion, and a metric evaluation condition.”
Regarding Claim 3, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 1, wherein the operations further comprise preparing a plurality of training samples according to the vehicle application compliance requirement.”
Regarding Claim 4, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 3, wherein the preparing includes defining an annotation rule according to the vehicle application compliance requirement, labeling a plurality of sensor samples according to the annotation rule, and selecting the training samples from among the plurality of labeled sensor samples.”
Regarding Claim 5, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 4, wherein the operations further comprise training a vehicle application machine-learning model with a first portion of the plurality of training samples.”
Regarding Claim 6, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 5, wherein the operations further comprise testing the vehicle application machine-learning model with a second portion of the plurality of training samples according to the vehicle application compliance requirement.”
Regarding Claim 7, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 6, wherein the operations further comprise determining whether the vehicle application machine-learning model fulfills the vehicle application compliance requirement.”
Regarding Claim 8, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 7, wherein the operations further comprise deploying the vehicle application machine-learning model to a vehicle system in response to determining that the vehicle application machine-learning model fulfills the vehicle application compliance requirement.”
Regarding Claim 9, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 8, wherein the operations further comprise receiving an output log of the vehicle application machine-learning model from the vehicle system, analyzing the output log to validate the vehicle application machine-learning model, and updating the vehicle application compliance requirement according to a result of the analyzing.”
Regarding Claim 10, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 9, wherein the vehicle application machine-learning model is configured for scene classification, the output log includes a plurality of scene classification results, and receiving the output log is in response to the vehicle application machine-learning model classifying a scene as a predetermined class.”
Regarding Claim 11, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 4, wherein the preparing includes receiving a sensor data log corresponding to the plurality of sensor samples.”
Regarding Claim 12, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 1, wherein the vehicle application compliance requirement includes a training set identifier.”
Regarding Claim 15, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 1, wherein the vehicle application compliance requirement is defined further according to constraints of a vehicle type.”
Regarding Claim 18, as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the present claim in terms of prior art, so it cannot be determined if the prior art teaches “The computer-readable medium of claim 1, wherein the vehicle application compliance requirement includes structured data in a computer-readable format.”
Regarding Claim 19, Szénási teaches a method (Materials and Methods, p. 7) comprising:
collecting a plurality of incident samples from an Internet, the plurality of incident samples identified by an identification machine-learning model to involve one or more vehicles (Introduction, pp. 1-2—the proposed steps include collecting data from an existing accident database, i.e. collecting incident samples using statistical or data-mining methods {e.g. a machine-learning model} that involve one or more vehicles. The Materials and Methods section on pp. 7-8 also describes using elements from the database);
clustering, by a clustering machine-learning model, the plurality of incident samples into a plurality of incident clusters (Materials and Methods, pp. 7-8—DBSCAN is a machine learning model that clusters the plurality of incident samples into a plurality of clusters); and
defining, by a requirement defining machine-learning model, a vehicle application compliance requirement according to an incident cluster among the plurality of incident clusters (as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine this limitation with respect to prior art because the “vehicle application compliance requirement” is indefinite).
Regarding Claim 20, Szénási teaches a device (Abstract and Introduction, pp. 1-2—the use of a database and data-mining implies a device comprising a computer that performs the described methods, as does the use of DBSCAN on p. 7) comprising:
a controller including circuitry configured to perform operations including collecting a plurality of incident samples from an Internet, the plurality of incident samples identified by an identification machine-learning model to involve one or more vehicles (Introduction, pp. 1-2—the proposed steps include collecting data from an existing accident database, i.e. collecting incident samples using statistical or data-mining methods {e.g. a machine-learning model} that involve one or more vehicles. The Materials and Methods section on pp. 7-8 also describes using elements from the database),
clustering, by a clustering machine-learning model, the plurality of incident samples into a plurality of incident clusters (Materials and Methods, pp. 7-8—DBSCAN is a machine learning model that clusters the plurality of incident samples into a plurality of clusters), and
defining, by a requirement defining machine-learning model, a vehicle application compliance requirement according to an incident cluster among the plurality of incident clusters (as detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine this limitation with respect to prior art because the “vehicle application compliance requirement” is indefinite).
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.
Claim 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Szénási, as applied to claim 1, above, in view of Walthers et al. (U.S. 2018/0322509, hereinafter “Walthers”).
Regarding Claim 13, Szénási does not specifically teach wherein the operations further comprise assigning a priority value to each incident cluster among the plurality of incident clusters, wherein the defining is in response to determining that the priority value assigned to the incident cluster exceeds a threshold priority value. However, Walthers teaches assigning a priority value to each incident cluster among the plurality of incident clusters (¶ [0023]—high priority clusters are identified among the incident clusters, thus assigning a priority value to each incident cluster). As detailed above with respect to 35 U.S.C. 112, the examiner is unable to examine the limitation “wherein the defining is in response to determining that the priority value assigned to the incident cluster exceeds a threshold priority value” in terms of prior art.
All of the claimed elements were known in Szénási and Walthers and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the priority assignment of Walthers with the clusters of Szénási to yield the predictable result of wherein the operations further comprise assigning a priority value to each incident cluster among the plurality of incident clusters. One would be motivated to make this combination for the purpose of ensuring that a greater response is given to more important or more hazardous clusters by assigning a priority to those clusters.
Regarding Claim 14, Szénási/Walthers teaches wherein the priority value is based on a size of the incident cluster (Walthers, ¶ [0023]—a ranking {priority} of clusters takes into account a number of incident reports included in a cluster).
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Szénási, as applied to claim 1, above, in view of Will, IV et al. (U.S. 2019/0072968, hereinafter “Will”).
Regarding Claim 16, Szénási does not specifically teach wherein the plurality of incident samples include natural language text. However, Will teaches a plurality of incident samples that includes natural language text (fig. 2; ¶ [0041] – [0042]).
All of the claimed elements were known in Szénási and Will and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the natural language text of Will with the incident samples of Szénási to yield the predictable result of wherein the plurality of incident samples include natural language text. One would be motivated to make this combination for the purpose of improving system flexibility by enabling it to use and understand unstructured reports that include natural language.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Szénási in view of Will, as applied to claim 16, above, and further in view of Grigorev, Artur, et al. (“Automatic accident detection, segmentation and duration prediction using machine learning,” IEEE Transactions on Intelligent Transportation Systems 25.2 (2023): 1547-1568; hereinafter “Grigorev”).
Regarding Claim 17, Szénási/Will does not specifically teach wherein the collecting includes applying the plurality of incident samples to a large language model. However Grigorev teaches collecting includes applying the plurality of incident samples to a large language model (Algorithm, pp. 4-5—ChatGPT, a large language model, is applied to collected incident reports {i.e. samples}).
All of the claimed elements were known in Szénási/Will and Grigorev and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the large language model of Grigorev with the incident samples of Szénási/Will to yield the predictable result of wherein the collecting includes applying the plurality of incident samples to a large language model. One would be motivated to make this combination for the purpose of improving traffic conditions by generating swift, informed, and adaptable incident response plans (Grigorev, Introduction).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This art includes:
Nayak et al. (U.S. 2023/0033672) teaches a classifier machine learning model that uses clustering to determine traffic violation hotspots from vehicle data
Madduri et al. (U.S. 2019/0147737) teaches a system that collects structured and unstructured accident data, clusters the data, and takes corrective action to reduce vehicle accidents
Duralti et al. (U.S. 2023/0322247) teaches a driver assistance system that help drivers to acquire and implement region-specific driving habits
Santos, Daniel, et al. (“Machine learning approaches to traffic accident analysis and hotspot prediction,” Computers 10.12 (2021): 157) teaches a system that collects accident data, generates clusters, and predicts accident hotspots
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAL W SCHNEE whose telephone number is (571) 270-1918. The examiner can normally be reached M-F 7:30 a.m. - 6:00 p.m.
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/HAL SCHNEE/Primary Examiner, Art Unit 2129
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