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 action is in response to the claims filed on 02/18/2026. Wherein, claims 1, 4, 11, and 18 are amended and claims 3 and 15 are cancelled. Claims 1, 2, 4-14, and 16-20 are rejected.
Response to Arguments
Applicant's arguments, see REMARKS, filed 02/18/2026, with respect to the rejection of claims 1-20 under 35 USC § 101 have been fully considered and are persuasive. Therefore, the previous rejections, under 35 USC § 101 are withdrawn.
Applicant’s arguments with respect to the rejection(s) of claim(s) 1-3 and 5-20 under 35 USC § 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Arcot et al.
Applicant’s arguments with respect to claim(s) 4, rejected under 35 USC § 103, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
With respect to prior claim 3, now amended into independent claim 1, the Applicant argues:
According to embodiments of amended Claim 1, the reliability index of the short-term traffic pattern data (from historical probe data) is assessed to determine if it is of a reliable accuracy. If the short-term traffic pattern data is sufficiently reliable, it is used to generate traffic data for the road segment for the times associated with the first period of time. If the short-term traffic pattern data is not sufficiently reliable, real-time probe data associated with the first period of time is used to generate the traffic data. The processing of real-time probe data is computationally expensive, such that the use of reliable historical data is preferable when it is sufficiently reliable. This ensures that real-time probe data is employed more sparingly and only when necessary for accuracy.
The Office Action, in the rejection of Claim 3, has alleged that Lewis teaches this feature citing to paragraph [0038]. However, Applicant asserts that the disclosure of Lewis has been misinterpreted. Lewis recites the assignment of weights based on a confidence in reliability of data. This is calculated as a score. Lewis recites:
When source A is assigned a weight of 1 and source B is assigned a weight of 0.2, and for a given epoch of the historical data there are two data points from source A and three probes from source B, a score or probe count for the epoch may be 2.6. The score or probe count is a measure of the confidence of the data used to generate the free-flow traffic model or the congested traffic model.
Lewis considers reliability; however, Lewis only does so to value probe data and determines a "measure of confidence of the data," but fails to disclose how such confidence is employed. Lewis fails to teach or suggest the use of short-term traffic pattern data (from historical probe data) if the confidence is above a threshold, and using real-time probe data if the historical probe data is determined to not be sufficiently reliable as found in embodiments of Claim 1. As such, Lewis cannot reasonably be interpreted to anticipate amended Claim 1.
Examiner cordially disagrees.
Lewis is comparing weights of the of the probe data to determine which data should be considered as more reliable during the time period. ¶ [0038] provides one embodiment, but others are also provided in ¶ [0055]-[0060].
Ultimately the coefficient, or weight, of the probe data is assigned a value between 0 and 1. This step “determines a reliability index of the traffic pattern data and a reliability of the short-term traffic pattern data” as required by previously claimed 3. Further, if a probe has a coefficient or 0 they will not be used when generating the traffic data. Therefore, in response to the reliability index of the traffic pattern being below a predetermined value, i.e., below any number greater than 0, the traffic data is generated using the real-time probe data of other probes which were also received during the first period of time. Thus, Lewis discloses the entirety of previous claim 3 and those amendments of claim 1 which were taken from claim 3.
Examiner agrees that Lewis does not explicitly teach generating traffic data for the road segment using historical traffic pattern data for the times associated with the first period of time in response to the reliability index of the traffic data being above a predetermined value. However, a new rejection is presented below in view of Arcot wherein the combination of Lewis and Arcot teach the entirety of amended claim 1.
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.
Claim(s) 1-3 and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis (US 2015/0120174 A1, “Lewis”) in view of Arcot et al. (US 2014/0032091 A1, “Arcot”)
Regarding claims 1, 11, and 18, Lewis discloses traffic volume estimation and teaches:
An apparatus (FIG. 6 illustrates an exemplary server 125 of the system of FIG.1 – See at least ¶ [0061]) comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to at least: (The server 125 includes a processor 300, a communication interface 305, and a memory 301. Additional, fewer, or different components may be used. The memory includes computer program code for one or more programs and the processor 300 is configured to cause the server 125 to perform the programs – See at least ¶ [0061])
receive historical probe data associated with a road segment of a plurality of road segments of a road network within a geographic region; (The lookup table (e.g., free flow lookup table) may be generated from historical data collected on each road segment and averaged within individual time epochs – See at least ¶ [0053])
generate, from the historical probe data, traffic pattern data and short-term traffic pattern data for a plurality of periods of time for the road segment, (Example sizes for time epochs include 15 minutes, 30 minutes, 1 hour, or another value. In the example of 15 minute epochs, the traffic data is formatted into 96-dimensional vectors, in which each of the 96 components describe traffic for a different 15 minute epoch. For example, a daily traffic vector having 96 components may be defined as x = (X 1. . . . . x n), where n=96. The values contained in the vector may be speeds for a given epoch. For example, the first element of the vector is the average speed for time between 0:00 a.m. to 0:15 a.m., and the 50th element of the vector is the average speed for time between 12:15 p.m. and 12:30 p.m. Other vectors may be used. In another example, some days may have only two time epochs: rush hour and not rush hour – See at least ¶ [0049]) wherein the traffic pattern data comprises traffic patterns that are consistent with historical traffic patterns along the road segment during a first period of time of the plurality of periods of time, (A model selection 51 is determined as a function of the speed data 50, the time data 30, and the location data 40. The speed data 50 may be compared to a threshold value to determine whether a free flow model 31 or a congestion model 33 should be used. As an example, when the speed data 50 is less than the threshold value, the congestion model 33 may be used. When the speed data 50 is more than the threshold value, the free flow model 31 may be used – See at least ¶ [0047]) and wherein short-term traffic pattern data comprises traffic patterns that fluctuate relative to historical traffic patterns along the road segment during a second period of time; (The congestion algorithm may estimate probe quantity independent of the epoch and based on the velocity in other epochs over the extended period of time. The congestion algorithm includes a congestion lookup table that associate a velocity derived from the probe data to a traffic density. In one example, another comparison is made. The congestion algorithm may identify a time epoch from the probe data, access a threshold Velocity based on the time epoch, and compare the threshold velocity to the velocity derived from the probe data. When the velocity from the probe data exceeds the threshold velocity, the congestion algorithm may revert to the free flow algorithm – See at least ¶ [0065] Here, the system identifies a congested area at one time, i.e., a first time period, based on the historical data and real-time data for a segment at a specific time and selects a congested model. Then at another time period, i.e., a second time period, it determines that the even though the data may be consistent with a congestion there may be other reasons for the slow down, i.e., the traffic pattern data comprises patterns that fluctuate with relative historical traffic patterns, and may revert back to the free-flow model selection.)
receive real-time probe data associated with the road segment of the road network (In one example, for new observations, a single probe point or multiple probe points may be collected to give a real-time estimate of current traffic velocity. From the velocity and time, either the algorithm for free-flow traffic or the algorithm for congestion traffic is selected. From either algorithm, the average number of probe points for this road segment at this time and traffic conditions is estimated – See at least ¶ [0037]) during times associated with the first period of time and times associated with the second period of time; (Example sizes for time epochs include 15 minutes, 30 minutes, 1 hour, or another value. In the example of 15 minute epochs, the traffic data is formatted into 96-dimensional vectors, in which each of the 96 components describe traffic for a different 15 minute epoch. For example, a daily traffic vector having 96 components may be defined as x = (X 1. . . . . x n), where n=96. The values contained in the vector may be speeds for a given epoch. For example, the first element of the vector is the average speed for time between 0:00 a.m. to 0:15 a.m., and the 50th element of the vector is the average speed for time between 12:15 p.m. and 12:30 p.m. Other vectors may be used. In another example, some days may have only two time epochs: rush hour and not rush hour – See at least ¶ [0049])
determine a reliability index of the traffic pattern data and reliability index of the short-term traffic pattern data; (The one or more sources may be prioritized to impact the traffic volume estimation 35 differently. For example, a first coefficient may be assigned to the probe data from a first source, and a second coefficient may be assigned to the probe data from the second source. The estimated probe quantity may be calculated as a function of the first coefficient and the second coefficient or the traffic volume is estimated from the first coefficient and the second coefficient. The coefficients may be a fractional or decimal value between 0 and 1. The coefficients may be assigned as a function of the sampling rate of the respective probe data. For example, mobile device may report probe data multiple times on the same road segments, and different types of mobile devices may have different sampling rates. In one example, the probe data includes a device identifier indicative of the manufacturer of the mobile device, the operating of the mobile device, or the mobile application that collects probe data. The coefficient for the probe data may be assigned as a function of one or more components of the device identifier – See at least ¶ [0058]-[0059])
generate the traffic data for the road segment using the real-time probe data received during the times associated with the first period of time (At act S103, the processor 300 selects a free flow algorithm or a congestion algorithm from the probe data. The processor 300 may compare the velocity value from the probe data to a congestion threshold in order to select the algorithm. The congestion threshold may be received from a user or predetermined. The congestion threshold may be variable as a function of time of day, time of year, weather, or another factor. The congestion threshold may be a function of the road segment (e.g., different road segments may have different congestion thresholds) – See at least ¶ [0063]) in response to the reliability index of the traffic pattern data being below a predetermined value; and (In one example, probe points are weighted depending on the sources when building the model for free-flow traffic, the model for congested traffic, or both. When counting probes, proves from source A may be associated with a first weight and probes from source B may be associated with a second weight, the weights may be assigned based on confidence (e.g., a fleet of trucks may produce more reliable data than mobile phones) or quantity of the probes. When source A is assigned a weight of 1 and source B is assigned a weight of 0.2, and for a given epoch of the historical data there are two data points from source A and three probes from source B, a score or probe count for the epoch may be 2.6. The score or probe count is a measure of the confidence of the data used to generate the free-flow traffic model or the congested traffic model – See at least ¶ [0038]; If the score for the historical data is not high enough, i.e., below a predetermined value, then the free-flow data is used – See at least ¶ [0056])
generate real-time traffic data for the road segment using the real-time probe data received during the times associated with the second period of time, (The congestion algorithm may estimate probe quantity independent of the epoch and based on the velocity in other epochs over the extended period of time. The congestion algorithm includes a congestion lookup table that associate a velocity derived from the probe data to a traffic density. In one example, another comparison is made. The congestion algorithm may identify a time epoch from the probe data, access a threshold Velocity based on the time epoch, and compare the threshold velocity to the velocity derived from the probe data. When the velocity from the probe data exceeds the threshold velocity, the congestion algorithm may revert to the free flow algorithm – See at least ¶ [0065]) wherein the traffic data and the real-time traffic data facilitate at least one of navigational guidance or at least semi- autonomous vehicle control along the road segment. (However, as traffic volume increases in the upstream segments, the controller 200 may determine that the downstream segment with the closure will likely experience traffic delays. The controller 200 may generate a message for display 211 to instruct the driver to choose an alternative route or otherwise warn the driver regarding the upcoming traffic congestion that may soon form – See at least ¶ [0070])
Lewis does not explicitly teach generate the traffic data for the road segment using the historical traffic pattern data being above a predetermined value. However, Arcot discloses trend based predictive traffic and teaches:
generate traffic data for the road segment using historical traffic pattern data for the times associated with the first period of time (The disclosed embodiments combine TP data and RT data over a particular period of time, referred to as the “evaluation window”, immediately preceding a time frame for which a predicted traffic speed is desired, referred to as the “prediction window”. Generally, in application, the evaluation window will be a period time just prior to the current time and the prediction window will be a period of time just after the current time. As will be discussed, the duration of the evaluation window is implementation dependent and may be of a suitable duration so as to envelop the time intervals for which the TP and RT data is statistically relevant to the desired predicted traffic speeds. As discussed above, the duration of the prediction window may be undefined or otherwise dynamic and of a duration which extends until the predicted speed values converge with the TP speed values. Alternatively, the duration of the prediction window may be extend over a duration for which a confidence in the accuracy of the predicted speed values exceeds a defined threshold – See at least ¶ [0014]-[0017]) in response to the reliability index of the traffic pattern data being above a predetermined value; (The disclosed embodiments combine TP data and RT data over a particular period of time, referred to as the “evaluation window”, immediately preceding a time frame for which a predicted traffic speed is desired, referred to as the “prediction window”. Generally, in application, the evaluation window will be a period time just prior to the current time and the prediction window will be a period of time just after the current time. As will be discussed, the duration of the evaluation window is implementation dependent and may be of a suitable duration so as to envelop the time intervals for which the TP and RT data is statistically relevant to the desired predicted traffic speeds. As discussed above, the duration of the prediction window may be undefined or otherwise dynamic and of a duration which extends until the predicted speed values converge with the TP speed values. Alternatively, the duration of the prediction window may be extend over a duration for which a confidence in the accuracy of the predicted speed values exceeds a defined threshold – See at least ¶ [0014]-[0017])
In summary, Lewis discloses providing different weights to data depending on the reliability of the collected data, i.e., the reliability of both the historical and real-time probe data. Further, Lewis discloses utilizing collected data to generate traffic data using historical pattern data for the road segment if the collected probe data is below a reliability threshold. Lewis does not explicitly teach generate the traffic data for the road segment using the historical traffic data in response to the reliability index of the traffic pattern data being above a predetermined value. However, Arcot discloses trend based predictive traffic and teaches using collected data based on the recentness of the data. Thus, if the historical data is too old, i.e., the reliability index of the traffic pattern data is below some threshold, then only real-time probe data is use, however, if the historical data isn’t old, i.e., the reliability index of the traffic pattern data is above some threshold, then that data may be used in the analysis.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the traffic volume estimation of Lewis to provide for the trend based predictive traffic, as taught in Arcot, to provide improved traffic speed predictions, subsequent to an occurrence (or resolution) of previously unknown, recent, unanticipated, unique and/or temporary events or conditions, until such time as the effect of the event or condition recedes/ dissipates and/or the TP data is updated to account therefore, i.e. until the predicted traffic speeds converge with the expected traffic speeds. (At Arcot ¶ [0016])
Regarding claim 2, Lewis further teaches:
wherein causing the apparatus to generate real-time traffic data for the road segment using the real-time probe data received during the times associated with the second period of time comprises causing the apparatus to filter out real-time probe data associated with the first period of time associated with the traffic pattern data. (The algorithm for congested traffic examines time intervals (e.g., epochs) in a predetermined time duration (e.g., a year). All time intervals may be used or the time intervals may be filtered based on a predetermined factor (e.g., time, weather, events, traffic on the roads) – See at least ¶ [0033])
Regarding claims 5, 12, and 19, Lewis further teaches:
wherein the probe data associated with the road segment is map-matched to the road segment of the plurality of road segments. (From the location data 40 a geographic database (e.g., database 123) may be queried to determine a road segment 41. The geographic database may store locations of opposing nodes for the road segment 41. The closest road segment to the geographic coordinates may be selected. In another example, the coordinates should be within a predetermined distance (e.g., 1 meter, 10 feet) to the road segment. An algorithm called a map-matcher may match the road segments to the location data – See at least ¶ [0046])
Regarding claims 6, 13, and 20, Lewis further teaches:
determine at least one speed category for the road segment for each of the plurality of periods of time, (the system identifies the road segment as either free flow, i.e., a first speed category, or congested conditions, i.e., second speed category – See at least ¶ [0021]) wherein the traffic pattern data comprises probe data for time periods of the plurality of periods of time that corresponds with the at least one speed category for a corresponding historical time period, (The algorithm for free-flow traffic uses historical data for individual time of day and days of the week from the historical data in which traffic was in free flow. For example, the historical traffic data for which velocity was at the free flow velocity is organized by epoch (e.g., all Tuesdays at 2:00 p.m. with free-flow traffic are grouped together). The average number of probe points for each group of free-flow epochs is calculated – See at least ¶ [0035]) and the short-term traffic pattern data comprises probe data for time periods of the plurality of periods of time that does not correspond with the at least one speed category for a corresponding historical time period. (the short term traffic data can indicate a congested condition, i.e., does not correspond with the speed category of the free flow category – See at least ¶ [0037])
Regarding claims 7 and 14, Lewis further teaches:
wherein the at least one speed category is determined based, at least in part, on at least one of a road type or a road functional class. (The threshold value for the model selection 51 may be chosen based on functional classification of the road segment. For example, interstate highways may be considered congested at higher speeds than state highways or local roads – See at least ¶ [0050])
Regarding claim 8 and 15, Lewis further teaches:
wherein the traffic pattern data for the road segment and the short-term traffic pattern data for the road segment each comprise a reliability index. (In one example, probe points, i.e., real-time data, are weighted, i.e., a reliability index, depending on the sources when building the model for free-flow traffic, the model for congested traffic, or both – See at least ¶ [0038])
Regarding claims 9 and 16, Lewis further teaches:
wherein the reliability index is an indication of confidence that the traffic pattern data or the short-term traffic pattern data is an accurate reflection of historical stability of traffic patterns on the road segment. (t The weights may be assigned based on confidence (e.g., a fleet of trucks may produce more reliable data than mobile phones) or quantity of the probes. When Source A is assigned a weight of 1 and source B is assigned a weight of 0.2, and for a given epoch of the historical data there are two data points from source A and three probes from source B, a score or probe count for the epoch may be 2.6. The score or probe count is a measure of the confidence of the data used to generate the free-flow traffic model or the congested traffic model. The calculations may be repeated for multiple or all time epochs – See at least ¶ [0038])
Regarding claims 10 and 17, Lewis further teaches:
wherein causing the apparatus to generate the traffic pattern data and the short-term traffic pattern data comprises aggregating continuous periods of time for the road segment for each of the traffic pattern data and the short-term traffic pattern data. (Traffic on a given stretch of road can be characterized by a density (p) in (vehicles/unit distance), traffic flow (q) in (vehicles/unit time), and Velocity (V) in (distance/unit time). The distance may be miles, kilometers, meters or another length unit. The time may be hours, minutes, seconds or another time unit. Traffic flow, density, and velocity are related accord to the equation q=pv – See at least ¶ [0018])
Claim(s) 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lewis in view of Arcot, as applied to claim 3, and in further view of Pan et al. (US 2016/0189044 A1, “Pan”).
Regarding claim 4, the combination of Lewis and Arcot does not explicitly teach in response to the reliability index of the traffic pattern data being below the predetermined value, the apparatus is further caused to regenerate the traffic pattern data for the first period of time. However, Pan discloses traffic prediction using real-world transportation data and teaches:
in response to the reliability index of the traffic pattern data being below the predetermined value, the apparatus is further caused to regenerate the traffic pattern data for the first period of time. (Towards that end, a decision-tree model can be trained that selects between ARIMA and HAM to forecast the speed at individual time stamps. In this model, the decision parameter and threshold are denoted as λ and Θ, respectively. For each time stamp, we choose between ARIMA and HAM based on the trained value of λ. If λ < Θ we choose ARIMA, otherwise, we choose HAM. The value of w is calculated based on the rate of the overall predication error between HAM and ARIMA at t – See at least ¶ [0052]; Examiner notes that the pattern is first determined by HAM and then the error, i.e., reliability index, is compared to the ARIMA, i.e., a recalculation based on historical data, and if the error is great then the system uses the recalculated data.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the traffic volume estimation of Lewis and Arcot to provide for the traffic prediction using real-world transportation data, as taught in Pan, to save customers substantial amounts of time and money. (At Pan ¶ [0004])
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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/C.L.C./Examiner, Art Unit 3662
/ANISS CHAD/Supervisory Patent Examiner, Art Unit 3662
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