Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments/Amendments
The amendment, filed 12/19/2025 in response to the Non-Final Office Action mailed on 10/02/2025 has been entered.
Claims 1-9 are currently pending in U.S. Patent Application No. 18/375,579.
Applicant’s remarks filed 12/19/2025 have been fully considered but are moot because the new grounds of rejection regarding the amended limitation no longer relies on the combination of references presented in the Non-Final Rejection. A change in scope necessitated by the Applicant’s amendments has led to an updated search revealing new art.
Claim Objections
Claims 1-4, 6, and 8 are objected to because of the following informalities:
Claim 1 (and corresponding claims 3-4) recite the following limitation “determine whether detect an abnormal condition…”, which grammatically does not read correctly. The Examiner recommends that the language here be reconsidered (e.g., “determine whether an abnormal condition…”).
Claim 2 (and corresponding claims 6 and 8) recite “the processor modifies the normal range so as to approximate-the distributions”, where it is unclear whether the hyphen is intentional.
Appropriate correction is required.
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, 3, and 4 are rejected as being unpatentable over Tong et al. (US 2019/0057261; hereinafter “Tong”) in view of Ali et al. (“Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis”, DOI: https://doi.org/10.1016/j.iatssr.2020.07.002, Publication Year: 2020; hereinafter “Ali”) in view of Jhunjhunwala (“A continuously updating k-means algorithm”, https://medium.com/data-science/a-continuously-updating-k-means-algorithm-89584ca7ee63, Publication Year: 2019; hereinafter “Jhunjhunwala”).
Regarding Claim 1, Tong discloses an anomaly detection device comprising:
a processor configured to ([0033-0035], Tong discloses a computing device including a processor.):
extract a normal travel feature indicating a condition of a road surface by inputting an image representing surroundings of the vehicle into a feature extractor that has been trained to extract the normal travel feature ([0025], [0048-0049], Fig. 3, Tong teaches taking an input image and obtaining a feature vector through a convolutional neural network. The Examiner notes Tong’s feature vector is indicative of “a condition of a road surface” since the Tong’s feature vector is downstream input into a classifier to determine a condition of a road surface.),
([0049], Tong teaches classifying a feature vector representing a road surface condition as a dry road condition (i.e., a normal travel feature), compared to a wet road condition or a snow covered road condition (i.e., abnormal traveling conditions where a driver would not be able to travel as they normally would travel a dry road). The Examiner notes that the collection or group of feature vectors defines the claimed “distribution”.).
Tong does not disclose identify a normal travel period during which a vehicle is able to travel normally, based on sensor signals indicating motion of the vehicle, determine whether detect an abnormal condition in which the vehicle is unable to travel normally, when the normal travel feature is outside a normal range that is a tolerable range in which the vehicle is able to travel normally, and in response to determining that the normal travel feature is outside the normal range, modify the normal range, based on a distribution of normal travel features (italicized for context).
Ali discloses identify a normal travel period during which a vehicle is able to travel normally, based on sensor signals indicating motion of the vehicle (Fig. 4, Ali discloses analyzing various motion data (speed, acceleration/deacceleration, and yaw rate) to identify normal driving periods and risky driving periods.), determine whether detect an abnormal condition in which the vehicle is unable to travel normally, when the normal travel feature is outside a normal range that is a tolerable range in which the vehicle is able to travel normally (3.2. Cluster analysis, Ali discloses performing K-mean clustering analysis on an input data feature X (note Equation 2 where input data feature X is a combination of different motion characteristics) to classify the input data as risky (i.e., abnormal condition) or normal driving (i.e., normal travel feature). The Examiner notes that in K-mean clustering, the input data is assigned to the cluster with the closest mean value, such that input data feature X would be classified as risky/abnormal condition if the data feature is outside of the range of the normal driving cluster and is closer to the risky/abnormal driving cluster.),
Tong and Ali are considered to be analogous to the claimed invention as they are in the same field of applying machine learning methods to vehicle driving data. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tong such that it incorporates Ali’s method for identifying normal travel periods, and furthermore utilize Ali’s K-means algorithm to cluster the features obtained by Tong into normal and risky/abnormal features. The motivation for this combination being the ability to apply an unsupervised learning algorithm to efficiently determine normal and abnormal driving behaviors.
Tong in view of Ali does not teach in response to determining that the normal travel feature is outside the normal range, modify the normal range, based on a distribution of normal travel features (italicized for context).
Jhunjhunwala discloses in response to determining that the normal travel feature is outside the normal range, modify the normal range, based on a distribution of normal travel features (italicized for context) (Improved k-means algorithm, Jhunjhunwala discloses a process of integrating new input data (i.e., feature data) to a K-means clustering model. If a new input feature is outside of the threshold value of a cluster (i.e., in response to determining a feature is outside a normal range, where the cluster is a distribution of data features) a new cluster is made with the new feature (i.e., modify the normal range). The Examiner notes the similarities between the disclosure provided by Jhunjhunwala and the Applicant’s Fig. 4, specifically the modified normal ranges indicated by 430.).
Tong in view of Ali and Jhunjhunwala are considered to be analogous to the claimed invention as they are in the same field of applying K-means algorithms to data to perform clustering. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tong in view of Ali such that it incorporated Jhunjhunwala’s method of applying new feature data to K-means thresholds, such that the normal travel features obtained by Tong in view of Ali are used to modify the normal travel feature clusters using the method disclosed by Jhunjhunwala. The motivation for this combination being the ability to actively incorporate new data to update thresholds.
Claims 3 and 4 are the method and non-transitory recording medium claims
corresponding to claim 1, and are similarly rejected (see Fig. 3, [0039-0040], Tong).
Claims 2, 6, and 8 are rejected as being unpatentable over Tong in view of Ali in view of Jhunjhunwala in view of Hitomi et al. (US 2019/0193741; hereinafter “Hitomi”).
Regarding Claim 2, Tong in view of Ali in view of Jhunjhunwala teaches the anomaly detection device according to claim 1, further comprising (Improved k-means algorithm, Jhunjhunwala discloses a process of integrating new input data (i.e., feature data) to a K-means clustering model. If a new input feature is outside of the threshold value of a cluster (i.e., in response to determining a feature is outside a normal range, where the cluster is a distribution of data features) a new cluster is made with the new feature (i.e., modify the normal range). The Examiner notes that the process of generating a new cluster and the preexisting cluster is analogous to approximating the distributions of the reference features (i.e., the preexisting cluster) and the normal cluster (i.e., the new cluster).).
Tong in view of Ali in view of Jhunjhunwala does not teach a memory configured to store reference features, wherein: each reference feature represents a preset normal condition of the road surface and a preset normal range is determined based on the reference features.
Hitomi discloses a memory configured to store reference features, wherein: each reference feature represents a preset normal condition of the road surface and a preset normal range is determined based on the reference features ([0062], [0078], Hitomi discloses a reference DB which stores reference features representing normal road conditions. The Examiner notes Fig. 3 which defines the range given by the normal road conditions (defined by distribution P).).
Tong in view of Ali in view of Jhunjhunwala and Hitomi are considered to be analogous to the claimed invention as they are in the same field of applying analyzing vehicle travel data for determining normal and abnormal conditions. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Tong in view of Ali in view of Jhunjhunwala such that it incorporates the reference DB disclosure provided by Hitomi as a means to perform the normal range modification (as taught by Tong in view of Ali in view of Jhunjhunwala). The motivation for this combination being the ability to store and utilize historical data in order to make determinations on normal and abnormal data.
Claims 6 and 8 are the method and non-transitory recording medium claims
corresponding to claim 2, and are similarly rejected (see Fig. 3, [0039-0040], Tong).
Allowable Subject Matter
Claims 5, 7, and 9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/PROMOTTO TAJRIAN ISLAM/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669