DETAILED ACTION
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 .
Status of Claims
This is a Final Rejection office action in response to application Serial No. 17/103.438. Claim(s) 1, 4-9, 12-17, 19-20 have been examined and fully considered. Claim(s) 2-3, 10-11, and 18 were previously cancelled without prejudice or disclaimer of the subject matter that they recite, claim(s) 1, 17, and 20 have been amended. Claim(s 1, 4-9, 12-17, 19-20 are pending in Instant Application.
Response to Arguments/Rejections
Applicant’s arguments, see Remarks, filed 12/08/2025, with respect to the rejection(s) of claim(s) 1, 4-9, 12-17, 19-20 under 35 USC § have been fully considered and are not persuasive. Applicant argues that because claims 1, 11 and 16 recites
“The Office Action at page 4 alleges that the claims recite a mental process because they involve "collecting and analyzing information." Applicants respectfully disagree. Independent claims 1, 17, and 20 have been amended to explicitly recite "obtaining... real-time probe data" and calculating counts based on this probe data that is real-time probe data. Deputy Commissioner Charles Kim recently sent a Memorandum dated August 4, 2025 to Technology Centers 2100, 2600, and 3600 re: "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101" (hereinafter "the August 4, 2025 Memo"). Under the August 4, 2025 Memo at page 2, examiners are instructed that: "A claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)." The claimed subject matter recites processing real-time probe data to calculate a dynamic capacity and a predictive time to congestion. A human mind is not equipped to ingest real-time data streams from networked probes (vehicles), map-match them instantly to lane geometries, and perform the continuous mathematical computation of flow differentials (incoming vs. outgoing) to predict a future event (time to congestion) before that event occurs as claimed. The amended "real-time" limitation renders performance by a human impossible. Therefore, the claims do not recite a mental process.”
Examiner respectfully disagrees. Examiner notes that the step directed to obtaining…wherein the probe data is real-time probe data is determined to be an additional element, which merely amounts to extra-solution data-gathering activities. This step of the system recites at a high level of generality, and amounts to mere data gathering, which is a form of extra-solution activity.
The amendment is not claiming the use the data/information utilized for implementing said abstract idea (i.e. does not improve the computing technology or the transportation technology, e.g., the operation of autonomous driving and automated traffic management , etc.) and is thus does not represent an integration of said abstract idea into a practical application.
Therefore, Examiners maintains the rejection under 35 USC § 101.
A) 35 U.S.C. § 103 of Claims 1, 4-9, 12-17, and 19-20
Per remarks, “The Examiner relies on Mori to teach the claim limitation: “calculate a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects... and a second count of one or more outgoing movable objects..."”.
B) 35 U.S.C. § 103 of Claims 1, 4-9, 12-17, and 19-20
Per remarks, Claims 5, 8-9, 14-15 and 19 stand rejected under 35 U.S.C. § 103 as being unpatentable over Witte in view of Chapman in view of Han in view of Mori and further in view of Xu.
Applicant respectfully traverses the rejection for the reasons set forth below.
Because claims 5, 8-9, 14-15 and 19 depend respectively from claims 1 and 17, they are also patentable over Witte, Chapman, Han, Mori, and Xu alone or in combination for the same reasons as well as for the additional features they recite.
Accordingly, Applicant respectfully requests reconsideration and withdrawal of the rejection.
Applicant remarks, starts Mori Lacks the "Incoming vs. Outgoing"Differential Calculation, and where “AMori fails to cure the deficiencies of Witte, Chapman, and Han regarding the claimed predictive time calculation.”.
Examiner consider Applicant's arguments. Examiner respectfully disagrees. The Applicant is reminded that the claims are given their broadest reasonable interpretation Examiner. With that said, Examiner would like to point to the cited paragraphs [0047]: “In a basic operation, the congestion-level estimation apparatus 200 directly estimates the level of congestion of the subject lane, based on a speed of the monitoring vehicle, and the number of vehicles (vehicle count) overtaken (passed) by or overtaking (passing) the monitoring vehicle per unit time period on the subject lane.”; and [0084]: “FIG. 9 illustrates an example in which there are continuous unit time periods determined that the traffic congestion occurs, and during a period having the continuous unit time periods, it is possible to estimate that the level of congestion is high. The V and N values can be determined by experimentation or the like in which a vehicle passes beside a subject lane already estimated to be congested”.
One ordinary skilled in the art, would interpret that a time element is being determined, while the congestion level is determining both the levels of congestion on the left lane (subject lane) and on the center lane (i.e., incoming and outgoing) as suggest in Mori “monitoring vehicle per unit time period and the number of vehicles overtaking the monitoring vehicle per unit time period are equal to or less than a predetermined threshold value” see, paragraph [0073].
Additionally, the language “predictive time calculation” fails to be disclosed in the limitations of the independent claims. Therefore, Examiners maintains the rejection under 35 USC § 103.
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, 4-9, 12-17, 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Step 1 of the Subject Matter Eligibility Test entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter.
Claim(s) 1, 4-9, 12-17, 19-20 are directed to a system (machine or manufacture), and a method (process), respectively. As such, the claims are directed to statutory categories of invention.
If the claim recites a statutory category of invention, the claim requires further analysis in Step 2A. Step 2A of the Subject Matter Eligibility Test is a two-prong inquiry. In Prong One, examiners evaluate whether the claim recites a judicial exception.
Claim 1 recites abstract limitations, including those indicated in bold below:
A system for determining dynamic road capacity data, the system comprising:
at least one non-transitory memory configured to store computer executable instructions; and at least one processor configured to execute the computer executable instructions to:
obtain a set of input data comprising at least probe data, historical capacity dynamic pattern data associated with at least one lane of a road segment of a region, wherein the probe data is real-time probe data;
calculate road capacity data of the at least one lane of the road segment, based on the probe data and one or more map attributes, wherein the road capacity data is a maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time;
calculate a count of one or more movable objects in the at least one lane of the road segment, based on the probe data;
calculate a ratio between the count of the one or more movable objects and the maximum number of movable objects on the at least one lane of the road segment that can pass a given point in unit time;
determine a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state;
calculate a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period;
determine dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data, and the traffic condition of the at least one lane of the road segment; and
transmit a report of the dynamic road capacity data to a backend server.
Claim(s) 17 and 20 recite abstract limitations analogous to those identified above with respect to claim 1.
These limitations, as drafted, are a process that, under its broadest reasonable interpretation, cover performance of the limitations in the mind, or by a human using pen and paper, and therefore recite mental processes. More specifically, other than reciting “ a computer” nothing in the claim element precludes the aforementioned steps from practically being performed in the human mind, or by a human using pen and paper. The mere recitation of a generic computer does not take the claim out of the mental process grouping. Thus, the claim recites an abstract idea.
If the claim recites a judicial exception in step 2A Prong One, the claim requires further analysis in step 2A Prong Two. In step 2A Prong Two, examiners evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
Claim 1 recites additional elements that are underlined below:
A system for determining dynamic road capacity data, the system comprising:
at least one non-transitory memory configured to store computer executable instructions; and at least one processor configured to execute the computer executable instructions to:
obtain a set of input data comprising at least probe data, historical capacity dynamic pattern data associated with at least one lane of a road segment of a region, wherein the probe data is real-time probe data;
calculate road capacity data of the at least one lane of the road segment, based on the probe data and one or more map attributes, wherein the road capacity data is a maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time;
calculate a count of one or more movable objects in the at least one lane of the road segment, based on the probe data;
calculate a ratio between the count of the one or more movable objects and the maximum number of movable objects on the at least one lane of the road segment that can pass a given point in unit time;
determine a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state;
calculate a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period;
determine dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data, and the traffic condition of the at least one lane of the road segment; and
transmit a report of the dynamic road capacity data to a backend server.
Claim 17 recites additional elements that are underlined below:
A method for determining dynamic road capacity data, the method comprising: obtaining, by one or more processors, a set of input data comprising at least probe data and historical capacity dynamic pattern data associated with at least one lane of a road segment of a region, wherein the probe data is real-time probe data;
calculating, by the one or more processors, a road capacity data of the at least one lane of the road segment, based on the probe data and one or more map attributes, wherein the road capacity data is a maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time;
calculating, by the one or more processors, a count of one or more movable objects in the at least one lane of the road segment, based on the probe data; calculating, by the one or more processors, a ratio between the count of the one or more movable objects and the maximum number of movable objects on the at least one lane of the road segment that can pass a given point in unit time;
determining, by the one or more processors, a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state;
calculating, by the one or more processors, a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period;
determining, by the one or more processors, the dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data and the traffic condition of the at least one lane of the road segment; and transmitting, by the one or more processors, a report of the dynamic road capacity data to a backend server.
Claim 20 recites additional elements that are underlined below:
A computer programmable product comprising a non-transitory computer readable medium having stored thereon computer executable instructions, which when executed by one or more processors, cause the one or more processors to carry out operations for determining dynamic road capacity data, the operations comprising:
obtaining, by the one or more processors, a set of input data comprising at least probe data and historical capacity dynamic pattern data associated with at least one lane of a road segment of a region, wherein the probe data is real-time probe data;
calculating, by the one or more processors, a road capacity data of the at least one lane of the road segment, based on the probe data and one or more map attributes, wherein the road capacity data is a maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time;
calculating, by the one or more processors, a count of one or more movable objects in the at least one lane of the road segment, based on the probe data; calculating, by the one or more processors, a ratio between the count of the one or more movable objects and the maximum number of movable objects on the at least one lane of the road segment that can pass a given point in unit time;
determining, by the one or more processors, a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state;
calculating, by the one or more processors, a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period;
determining, by the one or more processors, the dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data and the traffic condition of the at least one lane of the road segment; and transmitting, by the one or more processors, a report of the dynamic road capacity data to a backend server.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of one or more processor; and a backend server of the claimed components are recited at a high level of generality and are merely invoked as tools to perform the abstract idea. In addition, each of these additional limitations indicate a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application (see MPEP 2106.05(h)).
Regarding the determining steps, examiner submits that these limitations represent extra-solution data-gathering activities. The determining steps from the sensors are recited at a high level of generality (i.e. as a general means of gathering data for use in the updating/defining/etc. steps), and amounts to mere data gathering, which is a form of extra-solution activity.
Also, the obtain(ing) and calculating limitations, examiner submits that these limitations represent extra-solution data-gathering activities. These steps of the system recites at a high level of generality, and amounts to mere data gathering, which is a form of extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
If the additional elements do not integrate the exception into a practical application in step 2A Prong Two, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
As discussed above, one or more processor; and a backend server amount to mere instructions to apply the exception. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, update, or generate) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. As discussed above, these elements also amount to merely indicating a field of use or technological environment in which to apply a judicial exception, which does not amount to significantly more than the exception itself. (see MPEP 2106.05(h)).
Regarding the receiving steps, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). See, for example, [0009], [0013], etc. In addition, the Symantec, TLI, OIP Techs. and buySAFE court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is here).
The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015).
The various metrics/limitations of claims 4-9, 12-16 and 19 merely narrow the previously recited abstract idea limitations and introduce additional abstract limitations that are directed to mental processes and mathematical concepts. For the reasons described above with respect to claims 1 and 17, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 4, 6-7, 12-13, 16-17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Witte et al. (US 2016/0223348; previously recorded), hereinafter, referred to as “Witte” in view of in view of Chapman et al. (US 20170004706; previously recorded), hereinafter, referred to as “Chapman”, and in view of Han (CN105336183B; the NPL citations are based on the provided English Translation) hereinafter, referred to as “Han”, and in further view of Mori et al. (Pub. No.: US 2023/0154312), hereinafter, referred to as “Mori”.
Regarding [claim 1], Witte discloses a system for determining dynamic road capacity data (see at least Abstract: and Paragraph [0150]: “As a first step of this process the navigation device receives current capacity data for road segments within a predetermined area of the device's current location from a server—step 400. The server is a server of a real-time traffic information system”), the system comprising:
at least one non-transitory memory configured to store computer executable instructions (see at least Paragraph [0095]: “a computer software carrier could be a non-transitory physical storage medium such as a ROM chip, CD ROM or disk, or could be a signal Such as an electronic signal over wires, an optical signal or a radio signal Such as to a satellite or the like. The present invention provides a machine readable medium containing instructions which when read by a machine cause the machine to operate”); and
at least one processor configured to execute the computer executable instructions (see at least Paragraph [0095]: “when used to operate a system or apparatus comprising data processing means causes, in conjunction with said data processing means, said apparatus or system to carry out the steps”) to:
obtain a set of input data comprising at least probe data (see at least Paragraph [0074]: “The current capacity data is prefer ably based on live capacity data for the or each segment. The current capacity data may be obtained in any suitable manner by the server using one or more sources of data e.g. positional data (or probe data)”, historical capacity dynamic pattern data associated with at least one lane of a road segment (see at least Paragraph [0063]: “the current capacity data for a segment may in fact be based at least in part, or even entirely, upon historical capacity data for the segment, provided that it has been verified, directly or indirectly, that this appropriately reflects current conditions. For example, a navigation device may store historical capacity data for segments, preferably that is time dependent”) of a region, wherein the probe data is real-time probe data (see, Paragraphs [0112]: “The GPS system is implemented when a device, specially equipped to receive GPS data …Implementing geometric triangulation, the receiver utilizes the three known positions to determine its own two-dimensional position relative to the satellites. This can be done in a known manner. Additionally, acquiring a fourth satellite signal allows the receiving device to calculate its three dimensional position by the same geometrical calculation in a known manner. The position and velocity data can be updated in real time on a continuous basis by an unlimited number of users.”; and [0141]: “ The skilled person will appreciate that other periods are possible and may be substantially any of the following periods: 15 minutes, 30 minutes, hourly, every 2 hours, every 5 hours, every 12 hours, every 2 days, weekly, or any time in between these. Indeed, in such embodiments the processor 202 may be arranged to upload the record of the whereabouts on a substantially real time basis, although this may inevitably mean that data is in fact transmitted from time to time with a relatively short period between the transmissions and as such may be more correctly thought of as being pseudo real time.”);
calculate road capacity data of the at least one lane of the road segment, based on the probe data (see at least Paragraph [0074]: “The current capacity data is preferably based on live capacity data for the or each segment. The current capacity data may be obtained in any suitable manner by the server using one or more sources of data e.g. positional data (or probe data), vehicle to vehicle (V2V) data, road loops, third party data, etc. The source of data may be one or more live (or real-time) traffic feeds”)and one or more map attributes (see at least Paragraph [0152]: “it is envisaged that the server may transmit absolute current capacity values for the segments, which the navigation device may use to derive relative current capacity data for use in the methods of the invention based on maximum capacity data stored for the segments by the navigation device, or otherwise derived by the device using attributes of the segments in the electronic map data of the device”)…
Witte does not explicitly teach
…wherein the road capacity data is a maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time;
calculate a count of one or more movable objects in the at least one lane of the road segment, based on the probe data;
calculate a ratio between the count of the one or more movable objects and the maximum number of movable objects on the at least one lane of the road segment that can pass a given point in unit time;
determine a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state;
calculate a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period;
determine dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data, and the traffic condition of the at least one lane of the road segment; and transmit a report of the dynamic road capacity data to a backend server.
However, in the same field of endeavor, Chapman teaches
…wherein the road capacity data is a maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time (see at least Paragraph [0021]: “Moreover, road traffic conditions may be measured and represented in one or more of a variety of ways, Such as in absolute terms (e.g., average speed: Volume of traffic for an indicated period of time; average occupancy time of one or more traffic sensors or other locations on a road. Such as to indicate the average percentage of time that a vehicle is over or otherwise activating a sensor; one of multiple enumerated levels of road congestion, such as measured based on one or more other traffic condition measures, etc.) and/or in relative terms (e.g., to represent a difference from typical or from maximum).”);
calculate a count of one or more movable objects in the at least one lane of the road segment of a region (see at least Paragraph [0018]: “obtained road traffic condition information data may include multiple data samples provided by mobile data sources ( e.g., vehicles), data readings from road-based traffic sensors (e.g., loop sensors embedded in road pavement), and data from other data sources. The data may be analyzed in various manners to facilitate determination of traffic condition characteristics of interest, such as estimated average traffic speed and estimated total volume of vehicles, and to enable such traffic condition determinations to be performed in a real-time or near-real time manner (e.g., within a few minutes of receiving the underlying data samples and/or readings)”), based on the probe data (see at least Paragraph [0026]: “Such groups of data samples may then be further processed in order to determine other travel-related information, such as a heading for each data sample (e.g. by calculating the angle between the position of a data sample and the position of a prior and/or subsequent data sample) and/or a speed for each data sample (e.g., by calculating the distance between the position of a data sample and the position of a prior and/or subsequent data sample, and by dividing the distance by the corresponding time).”);
calculate a ratio between the count of the one or more movable objects and the maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time (see at least Paragraph [0096]: “the probabilistic determination may further use information about the a priori probability of the number of such vehicles and the a priori probability of a particular arrival rate. In step 730, the routine then infers the total volume of all vehicles passing through the road segment during the period of time. Such as based on the determined number of vehicles and information about what percentage of the total number of vehicles are vehicles that provide data samples, and further assesses a confidence interval for the inferred total volume. In step 740, the routine then infers the percentage occupancy for the road segment during the period of time based on the inferred total volume”);
determine a traffic condition of the at least one lane of the road segment, based on the ratio (see at least Paragraph [0018]: “determining traffic conditions (e.g., traffic flow and/or average traffic speed) for various portions of a road network in a particular geographic area, based at least in part on obtained data samples. The assessed data may then be utilized in order to perform other functions related to analyzing, predicting, forecasting, and/ or providing traffic-related information”)…
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte by combining wherein the road capacity data is a maximum number of movable objects on the least one lane of the road segment that can pass a given point in unit time; calculate a count of one or more movable objects in the at least one lane of the road segment, based on the probe data; calculate a ratio between the count of the one or more movable objects and the maximum number of movable objects on the at least one lane of the road segment that can pass a given point in unit time; determine a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state; calculating a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state; determine dynamic road capacity data based on the road capacity data, the count of the one or more movable objects… as taught by Chapman. One would be motivated to make this modification in order to convey may partition the data samples into two or more groups for further processing, such as to produce improved accuracy or resolution of processing (e.g., by calculating distinct average speeds that more accurately reflect the speeds of various traffic flows) as well as additional information of interest (e.g., the speed differential between HOV traffic and non HOV traffic) (see at least Paragraph [0047]) .
Neither Witte nor Chapman does not expressly teaches
determine a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state;
calculate a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period;
determine dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data, and the traffic condition of the at least one lane of the road segment; and transmit a report of the dynamic road capacity data to a backend server.
However, Han teaches
determine a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state (see at least Paragraph [0079]: “present invention, generally, when it is confirmed that a road section is congested, traffic control based on the road section's traffic capacity is performed. It can be determined that congestion occurs on the road section when it is determined that the arrival rate of the intersection downstream of the road section is greater than the traffic capacity of the road section. As for how to determine the arrival rate of the downstream intersection and the traffic capacity of the road section” and [0082]-[0087]);
…
determine dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data, and the traffic condition of the at least one lane of the road segment (see at least Paragraph [0135]: “the traffic capacity of the upstream intersection of the road section may be the sum of the traffic capacity of the upstream intersection in the straight phase and the traffic capacity of the upstream intersection in the left turn phase. Correspondingly, in the embodiment of the present invention, the traffic capacity of the downstream intersection of the road section is the sum of the traffic capacities of all relevant phases corresponding to the downstream intersection. Specifically, in the embodiment of the present invention, the traffic capacity of the upstream intersection of the road section may be the sum of the traffic capacity of the upstream intersection in the straight phase and the traffic capacity of the upstream intersection in the left turn phase” and [0136]-[0138]); and transmit a report of the dynamic road capacity data to a backend server (see Paragraphs [0070]: “The traffic data collected by the cross-section sensing detector includes the vehicle flow and saturation flow at the entrance, and the data output collection interval can be set to once every 5 minutes”; [0071]: “The traffic data collected by the cross-section sensing detector includes the vehicle flow and saturation flow at the entrance, and the data output collection interval can be set to once every 5 minutes. The cross-section overflow detector is generally arranged on a lane on the inner side of the middle of the road, and is 50-70 meters away from the upstream intersection of the road section. The cross-section overflow detector can detect whether the queue of the road section is overflowing, so as to assist in the judgment of the congestion status”; [0121]-[0131]; and [0176]-[0178]).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to incorporating by determining a traffic condition of the at least one lane of the road segment as taught by Han, and determining dynamic road capacity data combining as Witte and Chapman. One would be motivated to make this modification in order to alleviate congestion by increasing the cycle time of the road section and improving the traffic capacity of the intersection (see at Paragraph [0004]).
As Han teaches the traffic, along with the continuous increase of the demand, a large number of road sections can be in a congestion state, to improve the traffic capacity of the intersection by enlarging the period duration of the road section to relieve the congestion, however, Han does explicitly teach …calculate a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period…
Addition and/or in the alternative, Mori teaches
…
determine a traffic condition of the at least one lane of the road segment, based on the ratio, wherein the traffic condition is a non-congestion state (see, Paragraph ]0041]: “a congestion-level estimation system 100 according to an embodiment of the present invention. As illustrated in FIG. 1, the congestion-level estimation system 100 includes a peripheral state acquisition unit 110, a monitoring vehicle state acquisition unit 120, and a congestion-level estimation apparatus 200. The congestion-level estimation apparatus 200 includes an acquired information storage unit 170, a video analysis unit 130, a congestion-level estimation unit 140, a data storage unit 150, and an output unit 160. Note that the monitoring vehicle state acquisition unit 120 may be referred to as "acquisition unit", the video analysis unit 130 may be referred to as "count unit", and the congestion-level estimation unit 140 may be referred to as "estimation unit". The congestion-level estimation apparatus 200 may be referred to as "state estimation apparatus".”);
calculate a time for the at least one lane of the road segment to reach a congestion state from the non-congestion state based on a first count of one or more incoming movable objects on the at least one lane of the road segment per a certain time period and a second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period (see Figure 9; Paragraph [0043]-[0046] and [0047]: “In a basic operation, the congestion-level estimation apparatus 200 directly estimates the level of congestion of the subject lane, based on a speed of the monitoring vehicle, and the number of vehicles (vehicle count) overtaken (passed) by or overtaking (passing) the monitoring vehicle per unit time period on the subject lane.”; and [0084]: “FIG. 9 illustrates an example in which there are continuous unit time periods determined that the traffic congestion occurs, and during a period having the continuous unit time periods, it is possible to estimate that the level of congestion is high. The V and N values can be determined by experimentation or the like in which a vehicle passes beside a subject lane already estimated to be congested”);
determine dynamic road capacity data based on the road capacity data, the count of the one or more movable objects, the historical capacity dynamic pattern data, and the traffic condition of the at least one lane of the road segment (see, Paragraph [0047]-[0049]); and transmit a report of the dynamic road capacity data to a backend server (see, Figure 12; Paragraph [0094]: “In detecting the vehicle from the frame image, the video analysis unit 130 determines coordinates (upper left XY coordinates and lower right XY coordinates) corresponding to a rectangle surrounding the vehicle, and stores the image of the vehicle and the coordinates of the rectangle, as an object recognition result, into the data storage unit 150” and [0120]: “The video analysis unit 130 stores the counted value, together a time (start time of the unit time period, for example), and an average speed of the monitoring vehicle in a unit time period, for each frame image during a unit time period (for example, 10 seconds), into the data storage unit 150. The video analysis unit 130 may additionally store the location information of the monitoring vehicle at the corresponding time into the data storage unit 150.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify a technique allowing for estimation of a wide range of a state related to congestion of vehicles traveling on a road as taught by Mori. One would be motivated to make this modification in order to convey an object thereof is to provide a technique allowing for estimation of a wide range of a state related to congestion of vehicles traveling on a road (see, Paragraph [0009]).
As to [claim 4]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 1. Han teaches wherein the time for the at least one lane of the road segment to reach the congestion state from the non-congestion state is calculated as:
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wherein t is the time taken for the one or more movable objects to reach the congestion state from the non-congestion state, wherein n is congestion capacity threshold for the at least one lane of the road segment, wherein m is current count of one or more movable objects on the at least one lane of the road segment, wherein n >= m; wherein x is the first count of one or more incoming movable objects on the at least one lane of the road segment per the certain time period, and wherein y is the second count of one or more outgoing movable objects on the at least one lane of the road segment per the certain time period (see at least Paragraphs [00]: “For example, cross-section detectors may be deployed in road sections and traffic data may be collected through the cross-section detectors. The cross-section detector may include a cross-section sensing detector and a cross-section overflow detector. he cross-section sensing detector can be arranged at a position 30m away from the stop line in each lane. The traffic data collected by the cross-section sensing detector includes the vehicle flow and saturation flow at the entrance, and the data output collection interval can be set to once every 5 minutes. The cross-section overflow detector is generally arranged on a lane on the inner side of the middle of the road, and is 50-70 meters away from the upstream intersection of the road section. The cross-section overflow detector can detect whether the queue of the road section is overflowing, so as to assist in the judgment of the congestion status. As shown in FIG1 , it is a schematic diagram of the layout of a cross-section detector provided in an embodiment of the present invention. In Figure 1, the section sensing detector 101 is arranged at a position 30m away from the stop line in each lane, and the section overflow detector 102 is arranged on a lane on the inner side of the middle of the road and 50-70 meters away from the upstream intersection of the road section, and two are arranged in one lane”; [0050]: “It is worth noting that in order to prevent vehicles with large delays from being misjudged as vehicles entering or exiting the road section, the vehicle information of the downstream intersection is generally matched with the vehicle information of the upstream intersection within a preset time period. The preset time period is determined by the maximum delay. Under normal circumstances, the maximum delay between upstream and downstream will not exceed 1 hour, so it is recommended to take 1 hour within the preset time period. In summary, within a cycle time, the number of vehicles entering the section at the entrance and exit between the upstream intersection and the downstream intersection of the road section can be: the number of vehicles detected by the electric alarm detector at the downstream intersection of the section within a cycle time, but not detected by the electric alarm detector at the upstream intersection of the section; within a cycle time, the number of vehicles leaving the section at the entrance and exit between the upstream intersection and the downstream intersection of the road section can be: the number of vehicles detected by the electric alarm detector at the upstream intersection of the section within a cycle time, but not detected by the electric alarm detector at the downstream intersection of the section.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte in view of Chapman by wherein t is the time taken for the one or more movable objects to reach the congestion state from the non-congestion state as taught by Han. One would be motivated to make this modification in order to convey an improved method of traffic congestion estimation using signal phase and timing data from traffic signals at intersections and probe data from vehicles traversing said intersections (see at least Paragraph [0004]).
As to [claim 6]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 1. Witte discloses wherein the at least one processor (“the processor 202”) is further configured to: obtain event data associated with the at least one lane of the road segment of the region (see at least Paragraph [0140]: “the processor 202 may be programmed to store the GPS data when the device passes a road junction, a change of road segment, or other such event”); and calculate incoming lane capacity data and outgoing lane capacity data associated with the road segment (see at least Paragraph [0067]: “receiving data indicative of a current capacity of at least some of the segments of the navigable network. These embodiments are preferred as they provide the ability to transmit live current capacity data for use in the methods of the invention, Such that the flow values determined may be based at least in part upon live capacity data. The step of receiving the data is preferably carried out by a navigation device. The device preferably also carries out the steps of generating the plurality of routes, determining the relative flow values of the routes, and selecting a given one of the routes. The at least some of the segments for which current capacity data is received preferably include at least Some, or all, of those segments whose current capacity data is used in obtaining the flow values for each of the plurality of routes in accordance with the invention”) based on the probe data (see at least Paragraph [0074]: “The current capacity data may be obtained in any suitable manner by the server using one or more sources of data e.g. positional data (or probe data), vehicle to vehicle (V2V) data, road loops, third party data, etc. The source of data may be one or more live (or real-time) traffic feeds”) and the event data (see at least Paragraphs [0065]: “Thus the current capacity is indicative of the relative current capacity of the segment. The “relative capacity' as used herein therefore refers to the capacity of a segment relative to a maximum capacity of the segment. The maximum capacity of a segment may correspond to a threshold capacity value above which the segment is deemed to be congested. The maximum capacity may be determined empirically or theoretically, e.g. based upon the number of lanes, number of traffic light cycles, road category etc. of the segment. The maximum capacity of a segment may be an attribute of the segment, e.g. associated with electronic map data indicative of the segment”).
As to [claim 7]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 6. Witte discloses wherein the event data comprises weather data, holiday data, and music festivals data (see at least Paragraph [0153]: “the navigation device may be arranged to store time-dependent historic capacity values for road segments represented by the electronic map. Each road segment may be associated with multiple capacity values in respect of different timeslots, e.g. days of the week, peak or off-peak periods, weekends, night times, etc. The server may be arranged to transmit current capacity data to the device only if the current capacity value based on live data differs from the historic value by a predetermined amount. If no new current capacity value is received by the device from the server, the device will use the stored historic capacity value for the segment that is applicable to the time for which the generated route is required, i.e. a current time. Thus the current capacity data used by the navigation device may be based in part on historic capacity values, and in part on received live capacity values. The device may first convert the historic data to relative capacity data if needed”).
As to [claim 12]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 11. Witte further discloses wherein the at least one processor is further configured to:
obtain map data of the region (see at least Paragraph [0146]: “the mass data storage 160 also contains map data. Such map data provides information about the location of road segments, points of interest and other such information that is generally found on map”); and update the map data of the region based on the report of the dynamic road capacity data (see at least Paragraph [0149]: “The navigation device stores an electronic map comprising data representative of a network of road segments. The steps of the method are implemented while the vehicle, with which the navigation device is associated, travels along a predetermined route to a destination. When the device, and hence vehicle, reaches a particular location along the route, it is determined that a remainder of the route is affected by congestion. The navigation device may receive traffic updates from a server via a telecommunications system, or by other means, such as via a radio broadcast system”).
As to [claim 13]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 12. Witte further discloses wherein the at least one processor is further configured to transmit congestion risk warning message to one or more end user vehicles in the at least one road segment based on the updated map data (see at least Paragraph [0079]: “current capacity data is transmitted and/or received for use in the methods described herein, the data which is received may or may not be of the same form that is used in obtaining the flow values for the routes. For example, where relative current capacity data is used in flow value determination, absolute current capacity data may be received, and converted to relative current capacity data for use in the method. Thus, the received and used current capacity data are both indicative of a current capacity of the navigable segments, but may be indicative of different forms of current capacity”).
As to [claim 16]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 1. Neither Witte, Chapman, or Han teaches
wherein the at least one processor is further configured to map match the probe data to the at one of the road segment.
However, Han teaches
wherein the at least one processor is further configured to map match the probe data to the at one of the road segment (see at least Paragraph [0074]: “traffic flow data such as the number of vehicles passing through each road section can be calculated by matching the license plate number of each vehicle collected from upstream and downstream with the time each vehicle passes the stop line. For a non-closed road section, that is, a road section with an entrance and exit between the upstream intersection and the downstream intersection of the road section, for example, there is an entrance and exit of a unit or a community in the road section, it is also necessary to estimate the number of vehicles entering the road section through the entrance and exit between the upstream intersection and the downstream intersection of the road section, and the number of vehicles leaving the road section.”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte, Chapman, and Han by combining wherein the at least one processor is further configured to map match the probe data to the at one of the road segment as taught by Han. One would be motivated to make this modification in order to alleviate congestion by increasing the cycle time of the road section and improving the traffic capacity of the intersection (see at Paragraph [0004]).
As to [claim 17]: recites analogous limitations that are present in claim 1, therefore claim 17 would be rejected for the same reasons above. Witte discloses a method for determining dynamic road capacity data, the method comprising…
As to [claim 20]: recites analogous limitations that are present in claim 1, therefore claim 20 would be rejected for the same reasons above.
Claim(s) 5, 8-9, 14-15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Witte, Chapman, Han and Mori, and in view of in view in of Xu et al. (US 2018/0151064; previously recorded), hereinafter, referred to as "Xu".
As to [claim 5]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 4. Neither Witte, Chapman, nor Han or Mori teaches wherein to determine the dynamic road capacity data, the at least one processor is further configured to calculate a current deficit of the count of one or more movable objects accommodated in the at least one lane of the road segment to reach the congestion state from the non-congestion state.
However, Xu teaches
wherein to determine the dynamic road capacity data, the at least one processor is further configured to calculate a current deficit of the count of one or more movable objects accommodated in the at least one lane of the road segment to reach the congestion state from the non-congestion state (see at least Paragraph [0043]: “The output from the traffic processing engine may be an estimate of the current travel speed for a given road segment (e.g., road link). Based on this travel speed for a road segment, the road condition (e.g., road congestion) can be estimated to be free flow (e.g., no traffic congestion), queueing ( e.g. traffic stopped due to traffic signals), or stationary (e.g., heavy traffic congestion), among other levels of congestion. From a user perception perspective, travel speed along a particular road segment that is equal to or lower than a queueing speed may be conventionally considered as road congestion which may be depicted graphically on a map interface as yellow or red to indicate the level of traffic slowing. However, traffic speed along a particular road segment may not always be indicative of a level of traffic congestion”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte, Chapman, Han and Mori by combining wherein the at least one processor is further configured to map match the probe data to the at one of the road segment as taught by Xu. One would be motivated to make this modification in order to convey an improved method of traffic congestion estimation using signal phase and timing data from traffic signals at intersections and probe data from vehicles traversing said intersections (see at least Paragraph [0004]).
As to [claim 8]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 7. Xu teaches wherein the determined traffic condition is further based on the event data (see at least Paragraph [0045]: “Traffic congestion may occur and begin to accumulate as a result of traffic volume exceeding available road capacity, particularly when an accident happens, times of peak volume (e.g., rush hour, sporting events, etc.), and during construction or maintenance of roadways. In general, traffic conditions may be provided by a navigation system service provider using probe data and sensor technologies”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte, Chapman, and Han by combining traffic condition is further based on the event data as taught by Xu. One would be motivated to make this modification in order to convey an improved method of traffic congestion estimation using signal phase and timing data from traffic signals at intersections and probe data from vehicles traversing said intersections (see at least Paragraph [0004]).
As to [claim 9]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 1. Witte further discloses wherein to determine the dynamic road capacity data, the at least one processor is further configured to:
retrieve the historical capacity dynamic pattern data for the at least one lane of the road segment (see at least Paragraph [0081]: “the maximum capacity data for each navigable segment may be stored data, and the method may comprise a client device, e.g. a navigation device, storing electronic map data indicative of each navigable segment, and retrieving the stored maximum capacity data for use in obtaining the relative current capacity data for the segment”); and …
Neither Witte nor Chapman nor Han or Mori does not explicitly teach
…generate a traffic pattern profile based on the historical capacity dynamic pattern data, the count of one or more movable objects and the traffic condition of the at least one lane of the road segment.
However, in the same field of endeavor, Xu teaches
…generate a traffic pattern profile based on the historical capacity dynamic pattern data, the count of one or more movable objects and the traffic condition of the at least one lane of the road segment (see at least Paragraph [0043]: “The traffic processing engine receives the probe data, and may perform a map-matching process of the probe data to align the probe data with map data describing the road segment geometry. The output from the traffic processing engine may be an estimate of the current travel speed for a given road segment (e.g., road link). Based on this travel speed for a road segment, the road condition (e.g., road congestion) can be estimated to be free flow (e.g., no traffic congestion), queueing (e.g. traffic stopped due to traffic signals), or stationary ( e.g., heavy traffic congestion), among other levels of congestion”; and [0063]: “the current capacity data for a segment may in fact be based at least in part, or even entirely, upon historical capacity data for the segment, provided that it has been verified, directly or indirectly, that this appropriately reflects current conditions. For example, a navigation device may store historical capacity data for segments, preferably that is time dependent. The device may receive updates of “live' capacity data only in relation to segments for which the actual current capacity differs from the expected capacity based on the relevant historical data by a predetermined amount”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte, Chapman, Han and Mori by combining …generate a traffic pattern profile based on the historical capacity dynamic pattern data, the count of one or more movable objects and the traffic condition of the at least one lane of the road segment as taught by Xu. One would be motivated to make this modification in order to convey an improved method of traffic congestion estimation using signal phase and timing data from traffic signals at intersections and probe data from vehicles traversing said intersections (see at least Paragraph [0004]).
As to [claim 14]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 12. Witte nor Chapman or Han does not explicitly teach wherein the at least one processor is further configured to recommend driving strategies to one or more vehicles driving upstream of the at least one lane of the road segment based on the updated map data.
However, in the same field of endeavor, Xu teaches
wherein the at least one processor is further configured to recommend driving strategies to one or more vehicles driving upstream of the at least one lane of the road segment based on the updated map data (see at least Paragraph [0044]: “road segments approaching intersections may have traffic traveling below the posted speed limits due to a red traffic signal, though this slowed traffic speed may not be indicative of congestion on the road segment, but instead due to the signal phase and timing of a traffic light of the intersection. When considering traffic control on arterial roads, intersections play a critical role in traffic flow management. An intersection having a traffic signal may provide movement control strategies to maximize vehicle capacity and safety on roads associated with the intersection. Each intersection may have its own assigned signal and phase timing, which may or may not be related to other intersections nearby to coordinate traffic flow”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte, Chapman, Ishikawa and Han by combining wherein the at least one processor is further configured to recommend driving strategies to one or more vehicles driving upstream of the at least one lane of the road segment based on the updated map data as taught by Xu. One would be motivated to make this modification in order to convey an improved method of traffic congestion estimation using signal phase and timing data from traffic signals at intersections and probe data from vehicles traversing said intersections (see at least Paragraph [0004]).
As to [claim 15]: the combination of Witte, Chapman, Han and Mori teaches the system of claim 1. Neither Witte, Chapman, nor Han or Mori teaches wherein the map attributes include upstream road segment data for an upstream road segment connected to the at least one road segment and downstream road segment data for a downstream road segment connected to the at least one road segment.
Xu teaches wherein the map attributes include upstream road segment data (see at least Paragraph [0065]: “As shown, a plurality of paths are identified through an intersection at 610, such as through map artifact data describing road segment geometry 110 of FIGS. 3 and 4.”) for an upstream road segment connected to the at least one road segment and downstream road segment data for a downstream road segment connected to the at least one road segment (see at least Paragraph [0044]: “When considering traffic control on arterial roads, intersections play a critical role in traffic flow management. An intersection having a traffic signal may provide movement control strategies to maximize vehicle capacity and safety on roads associated with the intersection. Each intersection may have its own assigned signal and phase timing, which may or may not be related to other intersections nearby to coordinate traffic flow”).
Accordingly, it would have been obvious to one of ordinary skill in the art before the filing of the invention to further modify Witte, Chapman, Han and Mori by combining the map attributes include upstream road segment data as taught by Xu. One would be motivated to make this modification in order to convey an improved method of traffic congestion estimation using signal phase and timing data from traffic signals at intersections and probe data from vehicles traversing said intersections (see at least Paragraph [0004]).
As to claim 19: recites analogous limitations that are present in claim 5, therefore claim 19 would be rejected for the same reasons above.
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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/B.U./Examiner, Art Unit 3663 /JAMES M MCPHERSON/Examiner, Art Unit 3663