Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
DETAILED ACTION
This is a non-final Office Action on the merits in response to communications filed by Applicant on April 10, 2025. Claims 53-59 are currently pending and examined below.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 103
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 pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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 53-55, 58-59 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava US2012/0130625 (“Srivastava”) in view of Fowe et al. US2015/0127244 (“Fowe”).
Regarding claim(s) 53. Srivastava discloses a non-transitory computer readable medium containing instructions that, when executed by at least one processor of a computing device, cause the computing device to:
obtain a set of location intervals from a traffic data provider; for each location interval in the set (para. 36, (1) measurement of traffic information is divided into intervals; (2) the current speed (c_i_j) for road i at interval j is set to the default speed input (for example, the posted speed limit);):
select, by at least one processor, a location interval from a map database, the location interval representing a road segment; subdivide, by at least one processor, the respective location interval into a fixed number of sub-segments such that the sub-segments are equal in length (para. 36, (3) for each road segment r_i with crowdsource speed input v_i_j, for time interval t_j, the current speed c_i_j is set to v_i_j; (4) if supplemental inputs are available for c_i_j, take their aggregated sum, supplemental inputs include, but are not limited to, additional crowdsource inputs and other traffic probes;);
identify, by at least one processor, each sub-segment as an offset with respect to the respective location interval, and map, by at least one processor, traffic data to each sub-segment (para. 36-para. 37, [0037] The traffic intensity estimator is implemented according to embodiments of the invention through steps including, but not limited to, the following: (1) for each road segment i: initialize RSC_default[i] and Observed_Speed[i]; (2) at each time step j of time interval t, update RSC: (a) obtain Observed_Speed[i][j] for road segment i at time step j,);
aggregate the mapped traffic data from all the sub-segments of all the location intervals in the set (para. 32, [0032] Referring now to FIG. 3, therein is depicted an embodiment of the invention. Traffic information 301 is obtained from crowdsourcing probes 302 at specific time intervals and from supplemental traffic information sources 303, if available. The information from the crowdsourcing probes 302 and the supplemental traffic information sources 303 are aggregated 304. Embodiments provide that aggregation involves certain functions, including, but not limited to, averaging, averaging after removing outliers, and Gaussian functions. If traffic information sources are not available 305, historical data 306 may be used.);
Srivastava does not explicitly disclose:
process the aggregated traffic data into a defined flow vector format; and transmit the processed flow vectors to a user device over a one-way broadcast and/or a two-way data communications network.
Fowe teaches another traffic segment aggregation. Specifically, process the aggregated traffic data into a defined flow vector format ([0014] FIG. 2 shows an example of aggregation for DLR segment. The six different links are part of the same strand or collection and travel direction, but have different real-time or measured speeds. The index number in the table indicates the sequential position of each road link (e.g., a directed edge in a road network graph with directional traffic flow) on a strand (e.g., a sequence of connected road segments). The less congested links are aggregated into one DLR Segment 1, and the more congested road links are aggregated into another DLR Segment 2. The more congested links includes one link (index 15) that has less congestion, but is included to minimize the number of DLR segments. Depending on the aggregation approach, some links may be included in a same DLR segment as links with different traffic congestion.); and transmit the processed flow vectors to a user device over a one-way broadcast and/or a two-way data communications network ([0083] The processor 82 or another processor calculates travel time, travel speed, or other indicator of congestion for the each of the combined road segments. The traffic information or assigned congestion level is used to determine the congestion for each of the combined road segments. The congestion is reported by transmittal and/or by storage for access by other devices. [0084] The system of FIG. 7 allows the traffic consumers to use less processing power to digest the incoming traffic data. The aggregated DLR segments are reported to the traffic consumers rather than many more separate road segments.).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system and method of Srivastava with the applied teaching of Fowe’s to improve traffic flow management and one of ordinary skill in the art would have been motivated to combine to use a known technique to improve similar devices in the same way since both Srivastava and Fowe disclose a traffic flow management technique using crowdsourcing based on segment aggregation and as such it would be obvious to implement techniques of one in the other.
Regarding claim(s) 54. Srivastava in view of Fowe’s further teaches wherein the location interval is a Traffic Management Channel (TMC) segment (Srivastava: Para. 29, non-limiting examples, data consumers could be a traffic management service, while visualized data may be in the form of a colorized map used by the public through a web portal or mobile device. A map according to an embodiment may visualize high congestion with red, medium congestion with yellow, low congestion with green, and unknown with gray.).
Regarding claim(s) 55. Srivastava in view of Fowe’s further teaches wherein said traffic data includes one or more of speed, traffic flow, incidents or construction events (Srivastava: [0030] Embodiments of the invention provide for data inputs, including, but not limited to, time interval, time epoch, road network, and vehicle speed. The term "time interval" as used herein is intended to be construed broadly so as to encompass, by way of example and without limitation, the basic granularity of time tracked by the system.).
Regarding claim(s) 58. Srivastava in view of Fowe’s further teaches where the lane elements are designated by a color or other visual iconographic scheme (Srivastava: Para. 29, non-limiting examples, data consumers could be a traffic management service, while visualized data may be in the form of a colorized map used by the public through a web portal or mobile device. A map according to an embodiment may visualize high congestion with red, medium congestion with yellow, low congestion with green, and unknown with gray).
Regarding claim(s) 59. Srivastava in view of Fowe’s further teaches wherein the traffic data includes speed buckets for all road classes (Srivastava: [0026] As depicted in FIG. 1, the edge 102 between nodes N1 106 and N2 107 has a speed limit 111 of 40 m.p.h. and a reported speed 115 of 5 m.p.h. According to embodiments, this traffic information is used to calculate the road speed capacity (RSC) 119, which may be calculated by: (speed limit-reported speed)/(speed limit). Thus, for the edge 102 between Nodes N1 106 and N2 107, the RSC 121, expressed as a percentage, is: (40-5)/(40).times.100=87.5%.)
Claims 56-57 are rejected under 35 U.S.C. 103 as being unpatentable over Srivastava US2012/0130625 (“Srivastava”) in view of Fowe et al. US2015/0127244 (“Fowe”) and further in view of Slavin et al. US2014/0278052 (“Slavin”).
Regarding claim(s) 56, 57. Srivastava in view of Fowe’s is silent to wherein the location interval includes lane elements and wherein the lane elements are designated as one or more of: main roadbed, High Occupancy Vehicle (HOV) or other express lane; right hand junction lane, left hand junction lane, or exit only lane.
Slavin teaches another traffic management system and method and additionally wherein the location interval includes lane elements and wherein the lane elements are designated as one or more of: main roadbed, High Occupancy Vehicle (HOV) or other express lane; right hand junction lane, left hand junction lane, or exit only lane (Srivastava: [0056-0057] One element of the lane-level navigation system is the inclusion of a lane-level route optimizer which can be used in addition to or instead of a link-level optimizer. Given the origin, destination and vehicle type and preference (e.g., high occupancy vehicle (HOV), car vs. truck, value of time, etc.), the link-level optimizer computes candidate paths represented by a sub-network or a veritable "hammock" of link sequences connecting an origin and a destination. A hammock can be thought of as a network of links and nodes emanating from a single link at the origin, or the vehicle's current position, and converging at a single link at the
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the system and method of Srivastava in view of Fowe with the applied teaching of Slavin to improve traffic flow management and one of ordinary skill in the art would have been motivated to combine to use a known technique to improve similar devices in the same way since both Srivastava in view of Fowe and Slavin disclose a traffic flow management technique using crowdsourcing based on segment aggregation and as such it would be obvious to implement techniques of one in the other.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRUC M DO whose telephone number is (571)270-5962. The examiner can normally be reached on 9AM-6PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramón Mercado, Ph.D. can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TRUC M DO/Primary Examiner, Art Unit 3658