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
Applicant’s arguments with respect to claims 1-5 have been considered but are moot because the arguments do not apply to the new rejection made below.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1 and 5 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Askeland (US 10275662.)
Regarding claim 1, “A data collecting device comprising” Askeland teaches (5:10-15 and Fig. 7 step 702) FIGS. 1 and 2, the vehicle 110 includes a pair of camera arrays 134, one or more of which may include one or more imagers 136. The imagers 136 of the camera arrays 134 may be arranged in a predetermined pattern, for example, in order to provide a desired area of coverage of the surface 112 on which the vehicle 110 travels.
As to “a processor configured to: determine whether snow lies around a vehicle” Askeland teaches (12:29-41 and Fig. 7 step 704) to identify one or more material characteristics of the surface 112, such as, for example, the type of material, the surface characteristics of the material (e.g., characteristics such as smooth or abrasive), and/or whether the surface 112 is fully or partially coated with, for example, dirt, water, snow, and/or ice; (6:66-7:60) implemented using processors.
As to “set a type of feature to be detected for each of a region on an image where the processor determines that snow lies and a region on the image where the processor determines that snow does not lie; detect a feature of the set type for each of the region on the image where the processor determines that snow lies and the region on the image where the processor determines that snow does not lie” Askeland teaches (Fig. 7 step 706) correlating material data with friction-related data; (14: 6-31) Based on the content of the image data 400, the image interpreter 410 determines the material data 416 that most closely matches the image data 400 from a discrete image. For example, the image interpreter 410 receives the image 1 information, analyzes it, and determines that the image data 400 for the image 1 information most closely correlates to dry pavement from the material data 416. The image interpreter 410 further determines the friction coefficient 414 that corresponds to the correlating material data 416 of dry pavement. Dry pavement may have a friction coefficient 414 of about 0.90. Similarly, for image data 400 corresponding to the image 7 information, the image interpreter 410 correlates the discrete image 7 information with the material data 416 corresponding to hard-packed snow. The friction coefficient 414 corresponding to hard-packed snow is about 0.20, and thus, for this example, the friction-related data 411 would be 0.20; (12:29-41) determining whether the surface is fully or partially coated with, for example, dirt, water, snow, and/or ice; (6:44-65) for different units of surface.
As to “and generate probe data representing the detected features” Askeland teaches (16:9-46 and Fig. 5) a reporting module 514 configured to send correlations from the correlation module 412 (e.g., from the correlation table 415) to the network 516, so that the correlations may be shared with other vehicles in a fleet of the vehicles; (Fig. 7) step 722 communicating updates to server.
As to “wherein the image represents an area around the vehicle generated by a camera mounted on the vehicle.” Askeland teaches (5:10-15) FIGS. 1 and 2, the vehicle 110 includes a pair of camera arrays 134, one or more of which may include one or more imagers 136. The imagers 136 of the camera arrays 134 may be arranged in a predetermined pattern, for example, in order to provide a desired area of coverage of the surface 112 on which the vehicle 110 travels.
Regarding claim 5, its rejection is similar to claim 1.
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(s) 2-3, is/are rejected under 35 U.S.C. 103 as being unpatentable over Askeland in view of Shashua et al. (US 20170336794, hereinafter Shashua.)
Regarding claim 2, Askeland does not teach “The data collecting device according to claim 1, wherein the processor sets a three-dimensional structure on or around a road as the type of feature to be detected, for the region on the image where the processor determines that snow lies around the vehicle, and sets a predetermined feature including an on-surface structure formed along the surface of a road or the ground around the road as the type of feature to be detected, for the region on the image where the processor determines that snow does not lie around the vehicle.” However, Shashua teaches (¶0087) a system for navigating a vehicle on a road with snow covering at least some lane markings and road edges may include at least one processor programmed to: receive from an image capture device, at least one environmental image forward of the vehicle, including areas where snow covers at least some lane markings and road edges; identify, based on an analysis of the at least one image, at least a portion of the road that is covered with snow and probable locations for road edges bounding the at least a portion of the road that is covered with snow; (¶0857-¶0859) landmark based determination of a current position relative to the target trajectory to determine a direction of travel for the vehicle, to navigate a vehicle in snow; (¶0088) the analysis of the at least one image may include identifying at least one tire track in the snow. The analysis of the at least one image may include identifying a plurality of trees along an edge of the road. The analysis of the at least one image may include recognizing a change in curvature at a surface of the snow. The recognized change in curvature may be determined to correspond to a probable location of a road edge. The feature may correspond to an edge of a tire track. The feature may correspond to an edge of the road. The at least one processor may be further programmed to cause the vehicle to navigate between determined edges of the road. The at least one processor may be further programmed to cause the vehicle to navigate by at least partially following tire tracks in the snow; (¶0015, ¶0028-¶0029) The autonomous vehicle road navigation model may include identification of at least one landmark, including a position of the at least one landmark based on image analysis. The position of the at least one landmark may be determined based on position measurements performed using sensor systems associated with the plurality of vehicles. The position measurements may be averaged to obtain the position of the at least one landmark. The at least one landmark may include at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, or a lamppost. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the road surface detection system as taught by Askeland with the road edge determination and lane marking determination as taught by Shashua for the benefit of increasing vehicle safety of an autonomous/ego vehicle (¶0341.)
Regarding claim 3, “The data collecting device according to claim 2, wherein the processor detects the three-dimensional structure by inputting the image into a first classifier that has been trained to detect the three-dimensional structure from the image, for the region on the image where the processor determines that snow lies around the vehicle, and detects the predetermined feature by inputting the image into a second classifier that has been trained to detect the predetermined feature from the image, for the region on the image where the processor determines that snow does not lie around the vehicle.” Shashua further teaches (¶0367, ¶0425) the small data objects may include digital signatures, which are derived from a digital image (or a digital signal) that was obtained by a sensor (e.g., a camera) onboard a vehicle traveling along the road segment. The digital signature may be created to be compatible with a classifier function that is configured to detect and to identify the road feature from the signal that is acquired by the sensor. For example, for a road feature that is detectable by a camera onboard a vehicle, and where the camera system onboard the vehicle is coupled to a classifier which is capable of distinguishing the image data corresponding to that road feature as being associated with a particular type of road feature, for example, a road sign. Examiner note: see rejection for claim 2 that describes landmark/feature detection for when there is snow cover and when there isn’t. See also (¶0406.)
Claim(s) 4, is/are rejected under 35 U.S.C. 103 as being unpatentable over Maston (US 20200042953) in view of Buentello et al. (US 11145202, hereinafter Buentello.)
Regarding claim 4, “A data collection instruction device comprising: a processor configured to: determine whether snow lies in a predetermined region” Maston teaches (¶0004) the subject disclosure pertains to road maintenance analytics. Data can be received from a variety of sources including one or more vehicle mounted sensors, network-accessible services and equipment operators. Road condition can be determined or inferred based on a least a subset of the data, such as road temperature, among others. A treatment recommendation can be determined or inferred based on the road condition and a myriad of other factors such as weather conditions, residual treatment material remaining on roads, as well as staff, material, and equipment availability, among other things. For example, a treatment recommendation can be to plow a snow covered road; (¶0025 and ¶0018) using sensors mounted on plow to determine road surface conditions.
As to “transmit a collection instruction to a snowplow via a communication device instructing the snowplow to collect probe data representing a feature of a predetermined type in the predetermined region based on a determination that snow lies in the predetermined region” Maston teaches (¶0028) artificial neural network or other machine learning mechanism for finding an optimal treatment for a road. Continuous learning can also be employed to monitor performance, retrain, and redeploy to ensure a quality recommendation. In one instance, continuous machine learning can be employed with respect to determining a treatment recommendation based on comparison of post treatment results to expected results. In other words, effectiveness of a recommended treatment can be captured and utilized to improve a machine learning mechanism associated recommending treatments; (¶0026) if the road condition indicates a street is covered with snow, the treatment component 220 can recommend plowing the street; (¶0049) service truck/plow data is received.
Maston does not teach “and transmit the collection instruction to a vehicle other than a snowplow via the communication device instructing the vehicle to collect the probe data based on a determination that snow does not lie in the predetermined region.” However, Buentello teaches (12:42-48) determinations of significant flooding in the roadway by the road hazard detection system that are reported to the remote road hazard detection server may trigger the deployment of drones to the affected area to verify and produce more precise estimates of flood location, depth, flow rate, and dimensions and to take further images of the flooding. Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the navigation system that detects snow as taught by Maston with the drone deployment as taught by Buentello for the benefit of determining the extent of the hazard without damaging the vehicle (by driving through flood) and for determining the extent of the hazard.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fajardo (US 20190361149) – (Fig. 9) Truck positions map showing plow tracking from the snow sensor; (Fig. 11) Road Condition Map from processed data showing major roads that have been cleared by plows and displaying road safety conditions; (FIG. 14) Snow Sensor picture data from snow plow service showing sensor unit's picture for users to view snow conditions at plows location.
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 extension fee 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 date of this final action.
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/Frank Johnson/Primary Examiner, Art Unit 2425