DETAILED ACTION
Remarks
This office action is issued in response to communication filed on 5/8/2026. Claims 21-42 are pending in this Office Action.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Objection to the abstract has been withdrawn in response to the new abstract with less than 150 words.
In view of applicant’s arguments and amendments, the 101 abstract idea rejection has been withdrawn.
Applicant’s amendments fail to overcome the double patenting rejection. Accordingly, the examiner maintains the double patenting rejection.
Applicant’s amendments overcome the 112 rejection of claim 42. Accordingly, the rejections of claim 42 under 112 (a) and (b) have been withdrawn.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claims 21,32,39 is rejected on the ground of nonstatutory obviousness type double patenting as being unpatentable over claims 1-5 and 9-13 of US Patent 11,518,391 B1hereinafter “391 patent”. Although the claims at issue are not identical, they are not patentably distinct from each other because all the elements of the instant application claims 21,32,39 are to be found in the claims 1-5 and 9-13 of the 391 patent. Claims 21,32,39 is also rejected on the ground of nonstatutory obviousness type double patenting as being unpatentable over claims 1-5 and 9-13 of US Patent 11,518,392 B1 for the same rationale.
Instant Application (18/216,183)
US Patent 11,518,391 B1
21. (Currently Amended) A system comprising one or more processors and one or more computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising:
determining potential distracted driving events based at least on driving event records involving phone usage during vehicle operation;
clustering, using a clustering algorithm, the potential distracted driving events into clusters based at least on similarities of features of the potential distracted driving events,
wherein the potential distracted driving events are unlabeled prior to clustering ;
transmitting the clusters to a user computing device for manual input to qualify the clusters as either vehicle operator clusters or passenger clusters;
receiving qualified clustered data from the user computing device qualifying the clusters as either the vehicle operator clusters or the passenger clusters;
training a machine-learning model based at least on the qualified clustered data;
analyzing user data using the machine-learning model, as trained, to identify distracted driving events and passenger events represented by the user data; and generating a driver profile for a user associated with the user data based at least on the distracted driving events, as identified.
1. A distracted driving analysis system for identifying distracted driving events, the distracted driving analysis system comprising at least one processor in communication with at least one memory device, wherein the at least one processor is programmed to: receive a plurality of driving event records, each driving event record of the plurality of driving event records including phone usage by a user that occurred within a time period of a driving event associated with the user, wherein at least one of the plurality of driving event records is labeled as an actual distracted driving event or a passenger event; divide the plurality of driving event records into at least two clusters based at least in part upon common features of one or more driving event records of the plurality of driving event records and the label of the at least one driving event record by processing the plurality of driving event records with a semi-supervised machine learning algorithm; generate a trained model based at least in part upon the at least two clusters including cluster labels; process a new driving event using the trained model; assign the new driving event to one of the at least two clusters using the trained model; and based at least in part upon the cluster labels for the assigned cluster, determine whether the new driving event is an actual distracted driving event or a passenger event.
2. The system of claim 1, wherein the at least one processor is further programmed to: receive a feature input from a user computer device, the feature input indicate the common features of the driving event records to be analyzed using the semi-supervised machine learning algorithm; display information related to the at least two clusters to a user through the user computer device; and receive the cluster labels from the user computer device, the cluster labels indicating whether the driving event records in each cluster represent actual distracted driving events or passenger events.
3. The system of claim 1, wherein the at least one processor is further programmed to: receive a new driving event associated with a second user; determine, using the trained model, whether the new driving event is an actual distracted driving event or a passenger event; and assign a category to the distracted driving event as an actual distracted driving event or a passenger event.
4. The system of claim 3, wherein the at least one processor is further programmed to assign a confidence level to the category assigned to the new driving event.
5. The system of claim 4, wherein the at least one processor is further programmed to generate a driver profile for the second user, wherein the driver profile includes the categorized new driving event.
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 21-42 are rejected under 35 U.S.C. 103 as being unpatentable over Daniels.,( US Patent 11,590,982 B1,hereinafter “Daniels”) and further in view of Sicconi et al.,(US Patent Application Publication 2019/0213429 A1, hereinafter “Sicconi”)
As to claim 21, Daniel teaches a system comprising one or more processors and one or more computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations (See Daniel Fig.2) comprising:
determining potential distracted driving events based at least on driving event records involving phone usage during vehicle operation;( Daniels col 2, lines 41-45 teaches a vehicle event recorder is able to record sensor data which can be used to determine events related to a driver that characterize the driver’s behavior while operating the vehicle)
clustering, using a clustering algorithm, the potential distracted driving events into clusters based at least on similarities of features of the potential distracted driving events, (Daniel col 8, lines 60-67 teaches received vehicle event data is filtered and categorized for various criteria. The examiner interprets that “categorized” teaches or suggests “clustering algorithm” )
wherein the potential distracted driving events are unlabeled prior to clustering ( Daniel col 8, lines 60-67 teaches received vehicle event data is further processed into a form suitable for efficient human review)
transmitting the clusters to a user computing device for manual input to qualify the clusters as either vehicle operator clusters or passenger clusters; (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data)
receiving qualified clustered data from the user computing device qualifying the clusters as either the vehicle operator clusters or the passenger clusters; (Daniel col 9, lines 15-23 teaches after reviewer labeling, the determined event data labels and associated vehicle event data are transmitted for storage in database . The examiner interprets “or” as optional and therefore “passenger clusters” is not required by the claim )
training a machine-learning model based at least on the qualified clustered data; ( Daniel col 9, lines 24-30 teaches model trainer builds a model by training a machine learning model , a neural network model or any other appropriate model)
Daniel fails to expressly teach analyzing user data using the machine-learning model, as trained, to identify distracted driving events and passenger events represented by the user data ; generating a driver profile for a user associated with the user data based at least on the distracted driving events, as identified. (Sicconi par [0037] teaches system uses machine vision to analyze head pose, eye gaze and eye lid closing patterns to flag possible distraction and unsafe conditions. Deep learning is used to create personalized models of driving habits and experience )
However, Sicconi teaches analyzing user data using the machine-learning model, as trained, to identify distracted driving events and passenger events represented by the user data ; generating a driver profile for a user associated with the user data based at least on the distracted driving events, as identified. (Sicconi par [0037] teaches system uses machine vision to analyze head pose, eye gaze and eye lid closing patterns to flag possible distraction and unsafe conditions. Deep learning is used to create personalized models of driving habits and experience . Sicconi par [0062] teaches Feature extraction from visual clues (attention, distraction, drowsiness, drunkenness, face identification, problematic interactions between driver and passenger(s));
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teaching of Daniels and Chan to achieve the claimed invention. One would have been motivated to make such combination to help keeping drivers safe and reduce car insurance costs.(Sicconi par [0033])
As to claim 22, Daniels and Sicconi teach the system of claim 21, wherein the driving event records comprise historical driving data associated with operation of a vehicle and historical phone usage data associated with usage of a mobile computing device. (Daniels col 41-60 teaches recording sensor data including using cell phone )
As to claim 23, Daniels and Sicconi teach the system of claim 22, wherein the historical driving data comprise at least one of data collected by a vehicle sensor, data collected by a vehicle operating system, or data collected by the mobile computing device. (Daniels col 41-60 teaches recording sensor data including using cell phone )
As to claim 24, Daniels and Sicconi teach the system of claim 22, wherein the historical phone usage data comprise at least one of application-related data, texting data, or general phone usage data. (Daniels col 41-60 teaches recording sensor data including using cell phone )
As to claim 25, Daniels and Sicconi teach the system of claim 22, wherein determining the potential distracted driving events based at least in part upon timestamps within the historical driving data and the historical phone usage data. (Sicconi par [0075]-[0086] teaches plurality of phone related distraction conditions. Sicconi par [0116] teaches using machine learning to created models used to interprets the multiplicity of data collected in the car for making real time decision )
As to claim 26, Daniels and Sicconi teach the system of claim 21, wherein the features comprise at least one of acceleration data, speedometer data, braking data, tap and swipe data, or texting data. (Daniels col 4, lines 62-67 teaches sensor data includes accelerometer, GPS and others)
As to claim 27, Daniels and Sicconi teach the system of claim 21, wherein the features input includes compound features including at least one of a combination of two or more data types or a relationship between two or more data types. (Daniels col 4, lines 62-67 teaches sensor data includes accelerometer, GPS and others)
As to claim 28, Daniels and Sicconi teach the system of claim 21, wherein transmitting the clusters further comprises transmitting information about respective features common to the potential distracting driving events within each of the clusters to enable the manual input. (Daniels col 9, lines 15-25 teaches raw, filtered , categorized and/or otherwise processed vehicle event data is sent to a set of reviewers to be annotated with labels that identify , describe and /or characterize the events in the vehicle event data)
As to claim 29, Daniels and Sicconi teach the system of claim 21, wherein the operations further comprise generating an insurance policy for the user based on the driver profile of the user.(Sicconi par [0171] teaches policy premium pricing by the hour of coverage and based on driving behavior)
As to claim 30, Daniels and Sicconi teach the system of claim 21, wherein the operations further comprise determining a confidence level that the user data indicates a distracted driving event or a passenger event. (Sicconi par [0056] teaches the system monitors the attention level of the driver against a personalized behavior model and permissible thresholds )
As to claim 31, Daniels and Sicconi teach the system of claim 21, the driver profile comprises a driver score calculated based at least on the distracted driving events and the passenger events. ( Sicconi par [0056] teaches the system monitors the attention level of the driver against a personalized behavior model and permissible thresholds compatible with driving risk computed from the driving context)
Claims 32-33, 34, 35,36,37 and 38 merely recite a computer method performed by the system of claims 21-22 , 25, 26,28,29 and 31 respectively. Accordingly, Daniels and Sicconi teach every limitation of claims 32-33,34, 35,36,37 and 38 as indicates in the above rejection of claims 21-22 , 25, 26,28,29 and 31 respectively.
Claims 39, 40,41 and 42 merely recite a non-transitory computer readable comprising instructions executed by the processor of the system claims 21 , 28,29 and 31 respectively. Accordingly, Daniels and Sicconi teach every limitation of claims 39, 40,41 and 42 as indicates in the above rejection of claims 21 , 28,29 and 31 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Chen et al. US Patent 11,155,269 B1 discloses a system and method for determining distracted drivers associated with driving route based on postures of vehicle occupants. Rau., US Patent 10,392,022 B1 discloses a system and method to identify anomalous driving behaviors using unsupervised machine learning.
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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/HIEN L DUONG/Primary Examiner, Art Unit 2147
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