DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. This office action is in response to application with case number 18/741,981 filed on 06/13/2024, in which claims 1-20 are presented for examination.
Priority
3. Acknowledgment is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP23382595.9, filed on 06/15/2023.
Information Disclosure Statement
4. The information disclosure statement(s) (IDS(s)) submitted on 06/13/2024 has/have been received and considered.
Prior Art of Record
5. The Examiner has cited particular paragraphs or columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested of the applicant in preparing responses, to fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. The prompt development of a clear issue requires that the replies of the Applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure (see MPEP §2163.06). Applicant is reminded that the Examiner is entitled to give the Broadest Reasonable Interpretation (BRI) of the language of the claims. Furthermore, the Examiner is not limited to Applicant’s definition which is not specifically set forth in the claims. SEE MPEP 2141.02 [R-07.2015] VI. PRIOR ART MUST BE CONSIDERED IN ITS ENTIRETY, INCLUDING DISCLOSURES THAT TEACH AWAY FROM THE CLAIMS: A prior art reference must be considered in its entirety, i.e., as a whole, including portions that would lead away from the claimed invention. W.L. Gore & Associates, Inc. v. Garlock, Inc., 721 F.2d 1540, 220 USPQ 303 (Fed. Cir. 1983), cert, denied, 469 U.S. 851 (1984). See also MPEP §2123.
Drawings
The drawings are objected to because figures 1-4, 7-11, 13, 15-20, 22, and 24-25 are not of sufficient quality. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
6. 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.
7. Claim(s) 1-20
is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
8. The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
9. Claim(s) 1-18 is/are directed to a method (i.e. a process).
Therefore, claim(s) 1-18 is/are within at least one of the four statutory categories.
10. Claim(s) 19 is/are directed to a non-transitory computer readable medium (i.e. an article of manufacture).
Therefore, claim(s) 19 is/are within at least one of the four statutory categories.
11. Claim(s) 20 is/are directed to an apparatus.
Therefore, claim(s) 20 is/are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
12. Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c).
13. Independent claim(s) 1, 15, 17, and 19-20 include(s) limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]). Claim 1 will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented method comprising:
performing a traffic forecast of vehicles using traffic data of a first time period to generate a first traffic forecast for a target geographical region of a geographical area , the traffic forecast including [mental process/step],
based on the traffic data of the first time period for the target geographical region, generating a seasonal traffic forecast for the target geographical region [mental process/step];
based on traffic data of at least one other geographical region of the geographical area and of the first time period, generating at least one other seasonal traffic forecast for the at least one other geographical region [mental process/step];
based on the traffic data of the target geographical region and the traffic data of the at least one other geographical region, analyzing a mobility flow of the target geographical region and the at least one other geographical region to determine at least one correlation between the target geographical region and the at least one other geographical region [mental process/step]; and
generating the first traffic forecast by adjusting the seasonal traffic forecast for the target geographical region based on the at least one other seasonal traffic forecast and the at least one correlation [mental process/step];
performing a traffic forecast using traffic data of a second time period other than the first time period to generate a second traffic forecast for the target geographical region, wherein the first time period includes the second time period and an additional time period more recent than the second time period [mental process/step];
decomposing the first traffic forecast into components of a first seasonal component, a first trend component, and a first noise component and decomposing the second traffic forecast into components of a second seasonal component, a second trend component, and a second noise component [mental process/step];
comparing the first noise component of the first traffic forecast with the second noise component of the second traffic forecast to detect at least one anomaly, based on comparing at least one deviation between the first noise component and the second noise component to an anomaly threshold [mental process/step]; and
predicting emissions produced by traffic in the target geographical region based on the first traffic forecast, including, when the at least one anomaly is detected, predicting an impact on the emissions of the at least one anomaly [mental process/step].
14. The Examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers steps that could be carried out in the human mind. For example, “performing a traffic forecast …,” “… generating a seasonal traffic forecast …,” “… generating at least one other seasonal traffic forecast …,” “… analyzing a mobility flow …,” “generating the first traffic forecast by adjusting the seasonal traffic forecast …,” “performing a traffic forecast using traffic data of a second time period …,” “decomposing the first traffic forecast …,” “comparing the first noise component …,” “predicting emissions produced by traffic …,” step(s) encompass(es) a user making observation, evaluation or judgement about the traffic in an area and the emission as the result of the traffic in the area, could all be carried out in one’s mind. The same user looking at the data collected, could form a simple judgement and predict the emission in an area based on the traffic forecast. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
15. Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
16. In the present case, there are no additional limitations beyond the above-noted abstract idea.
17. 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. see 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.
101 Analysis – Step 2B
18. Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements.
19. As established above claim 1 is representative of all independent claims and therefore claim(s) 15, 17, and 19-20 is/are rejected for the same reason.
20. Dependent claim(s) 2-14, 16 and 18 does/do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-14, 16 and 18 are not patent eligible under the same rationale as provided for in the rejection of 1.
21. Therefore, claim(s) 1-20 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
22. 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.
23. Claim(s) 1-2, 4-14 and 17-20
is/are rejected under 35 U.S.C. 103 as being unpatentable over Rahimi et al. (US-20240311650-A1) in view of Weijermars “Analysis of Urban Traffic Patterns Using Clustering” and further in view of Xu et al. “Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis.”
In regard to claim 1
, Rahimi discloses a computer-implemented method comprising (Rahimi, in at least [0005], discloses a computer-implemented method of using machine learning for time series forecasting):
performing a traffic forecast of vehicles using traffic data of a first time period to generate a first traffic forecast for a target geographical region of a geographical area, the traffic forecast including (Rahimi, in at least in at least Figs. 1A-1B, and [0027], discloses the server computer 115 communicates with one or more data sources 106 via the network(s) 110. The data source(s) 106 compile, store, or otherwise access information associated with time series forecasting and associated time series data [i.e., using traffic data of a first time period]. Time series forecasting is useful in transportation to predict traffic patterns [i.e., performing a traffic forecast of vehicles using traffic data of a first time period to generate a first traffic forecast for a target geographical region of a geographical area], optimize transportation routes, and forecast demand for transportation services),
based on the traffic data of the first time period for the target geographical region, generating a seasonal traffic forecast for the target geographical region (Rahimi, in at least [0050 & 0057], discloses seasonality strength data is extracted from the set of time series data [i.e., based on the traffic data of the first time period for the target geographical region], where the seasonality of a time series is when similar patterns of value variations happen at fixed time intervals, and where the strength of that seasonality measures how reliable this variation is towards the progression of the time series and how much this measurement is used to explain non-trend noise. Generally, high seasonality indicates a bigger continuity of the same patterns. The Prophet model is used to forecast univariate time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality [i.e., generating a seasonal traffic forecast for the target geographical region], as well as holiday effects);
performing a traffic forecast using traffic data of a second time period other than the first time period to generate a second traffic forecast for the target geographical region, wherein the first time period includes the second time period and an additional time period more recent than the second time period (Rahimi, in at least in at least Figs. 1A-1B, and [0027], discloses the data source(s) 106 compile, store, or otherwise access information associated with time series forecasting and associated time series data. Time series forecasting is useful in transportation to predict traffic patterns [i.e., performing a traffic forecast to generate a second traffic forecast for the target geographical region, wherein the first time period includes the second time period and an additional time period more recent than the second time period], optimize transportation routes, and forecast demand for transportation services. Examiner notes, a time series includes data for different time periods. By assuming that the first time period is the period between t=0 and t=2, the first time period includes the second time period between t=0 and t=1 and an additional time period more recent than the second time period, which is the period between t=1 and t=2. That is, performing a traffic forecast using traffic data of a second time period other than the first time period to generate a second traffic forecast for the target geographical region, wherein the first time period includes the second time period and an additional time period more recent than the second time period);
decomposing the first traffic forecast into components of a first seasonal component, a first trend component, and a first noise component and decomposing the second traffic forecast into components of a second seasonal component, a second trend component, and a second noise component (Rahimi, in at least [0042 & 0044 & 0049], discloses a set of configurable techniques or combination of techniques are selected to identify any outliers, including seasonal-trend decomposition [i.e., decomposing the first traffic forecast into components of a first seasonal component and decomposing the second traffic forecast into components of a second seasonal component] using loess (STL), interquartile range (IQR), mean and standard deviation method, and/or another technique. A signal smoothing technique 207 is performed on the set of time series data in order to remove any white noise [i.e., a first noise component and a second noise component] that might exist or any unwanted abrupt change of variance that might impute errors on the forecasting model training. Similar to the outlier replacement functionality, model imputation is used to predict a plausible value for the next iteration, therefore reducing possible noise in the data. Trend strength data is extracted from the set of time series data, where the trend [i.e., first trend component and a second trend component] is a component of a time series that represents low frequency variation of data, which may present as a tendency of data to behave in a certain way. The trend strength measures how well this tendency is maintained throughout the progression of time. Generally, high trend strength means a more stable fixation to the tendency);
comparing the first noise component of the first traffic forecast with the second noise component of the second traffic forecast to detect at least one anomaly, based on comparing at least one deviation between the first noise component and the second noise component to an anomaly threshold (Rahimi, in at least Fig. 2, and [0042], discloses the data preparation stage 205 involves passing a set of time series data through up to three preparation and cleaning steps before features within the set of time series data are extracted and selected. In particular, the set of time series data is initially passed through an outlier removal technique 206 which is configured to ensure that any impossible data or human error is not being fed into the model creation pipeline. Outliers [i.e., deviation between the first noise component and the second noise component] is initially detected by setting a threshold value for a maximum amount of standard deviation presented [i.e., an anomaly threshold], and checking any data points that might be over that margin. Further, any lower and maximum bounds is defined to make sure that all data points respect these ranges. Examiner notes, detecting outliers by setting a threshold value encompasses comparing the first noise component of the first traffic forecast with the second noise component of the second traffic forecast to detect at least one anomaly, based on comparing at least one deviation between the first noise component and the second noise component to an anomaly threshold); and
Rahimi is silent on based on traffic data of at least one other geographical region of the geographical area and of the first time period, generating at least one other seasonal traffic forecast for the at least one other geographical region;
based on the traffic data of the target geographical region and the traffic data of the at least one other geographical region, analyzing a mobility flow of the target geographical region and the at least one other geographical region to determine at least one correlation between the target geographical region and the at least one other geographical region; and
generating the first traffic forecast by adjusting the seasonal traffic forecast for the target geographical region based on the at least one other seasonal traffic forecast and the at least one correlation;
predicting emissions produced by traffic in the target geographical region based on the first traffic forecast, including, when the at least one anomaly is detected, predicting an impact on the emissions of the at least one anomaly.
However, Weijermars teaches based on traffic data of at least one other geographical region of the geographical area and of the first time period, generating at least one other seasonal traffic forecast for the at least one other geographical region (Weijermars, in at least p. 55, and 61, Equations (4-28)-(4-30) discloses locations [i.e., based on traffic data of at least one other geographical region of the geographical area and of the first time period, generating at least one other seasonal traffic forecast for the at least one other geographical region] are grouped according to their seasonal factors. Temporal traffic patterns is analysed by grouping days that show similar daily flow profiles. Spatial traffic patterns is analysed by grouping links that show similar temporal variations. Links are grouped with regard to their average daily flow profile, their weekly and seasonal variations, the influence of weather on their traffic volumes, and with regard to the results of their temporal clusterings. Examiner notes, as mentioned above, the locations are grouped according to their seasonal factors, which means traffic forecast is performed for different locations including at least one other region of the geographical area and of the first time period. As such, Weijermars teaches based on traffic data of at least one other region of the geographical area and of the first time period, generating at least one other seasonal traffic forecast for at least one other geographical region);
based on the traffic data of the target geographical region and the traffic data of the at least one other geographical region, analyzing a mobility flow of the target geographical region and the at least one other geographical region to determine at least one correlation between the target geographical region and the at least one other geographical region (Weijermars, in at least p. 55-57, discloses Grouping locations according to their seasonal factors has been subject of research in literature on AADT (Average Annual Daily Traffic) estimation on the basis of short term traffic counts [i.e., based on the traffic data of the target geographical region and the traffic data of the at least one other geographical region]. The shape of the daily flow profile might differ between seasons, also in this case both the height and the shape of the daily flow profile are taken into account. Analogue to the analysis of differences in weekly variations, the working day flow profile of each month is compared to the average working day flow profile. Locations are also grouped according to the influence of rain on traffic volumes. Traffic volumes are compared for dry and for wet periods using matched pair analysis. Spatial variations in temporal traffic patterns can also be analysed by comparing clustering results for locations. First, it can be analysed to what extent the resulting clusters at different locations correspond. In that case, the sets of days in the resulting clusters are compared for different locations [i.e., analyzing a mobility flow of the target geographical region and the at least one other geographical region to determine at least one correlation between the target geographical region and the at least one other geographical region]); and
generating the first traffic forecast by adjusting the seasonal traffic forecast for the target geographical region based on the at least one other seasonal traffic forecast and the at least one correlation (Weijermars, in at least Fig. 8.4, and p. 130, discloses a new profile is created of which the traffic volumes are 0.8*the average traffic volume of the Monday and Tuesday cluster + 0.2*the average traffic volume of the Thursday cluster [i.e., generating the first traffic forecast by adjusting the seasonal traffic forecast for the target geographical region based on the at least one other seasonal traffic forecast and the at least one correlation]);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi in view of Weijermars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – generating forecast – and the combination would provide for taking adequate traffic management measures to facilitate mobility whilst minimizing its negative side effects (Weijermars, see at least p. 2).
Rahimi, as modified by Weijermars, is silent on predicting emissions produced by traffic in the target geographical region based on the first traffic forecast, including, when the at least one anomaly is detected, predicting an impact on the emissions of the at least one anomaly.
However, Xu teaches predicting emissions produced by traffic in the target geographical region based on the first traffic forecast, including, when the at least one anomaly is detected, predicting an impact on the emissions of the at least one anomaly (Xu, in at least p. 91, teaches vehicle emission models have been developed to predict traffic-induced emissions [i.e., predicting emissions produced by traffic in the target geographical region based on the first traffic forecast] at macroscopic, mesoscopic, and microscopic levels. Mesoscopic models provide emission estimates according to average link speeds. These models can take into account spatial and temporal variability across the network although they cannot represent explicitly the vehicle behaviour. They are able to predict vehicular emissions at a second-by-second resolution by taking vehicle operating conditions as inputs including acceleration, deceleration, idling, and cruising [i.e., including, when the at least one anomaly is detected, predicting an impact on the emissions of the at least one anomaly])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as modified by Weijermars, in view of Xu with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – modeling and analysis – and use a model for predicting traffic-induced emissions and the combination would provide for calibrating the simulation in order to improve the model’s ability to replicate traffic conditions observed (Xu, see at least p. 93).
In regard to claim 2
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Weijermars teaches wherein the traffic data comprises data obtained from sensors in the target geographical region (Weijermars, in at least p. 14, discloses traffic data collection systems use intrusive and non-intrusive sensors. Intrusive sensors are those that involve placement on top of or in the lane to be monitored, e.g. inductive loop detectors, magnetic sensors, pneumatic tubes and Weight In Motion (WIM) sensors. Non-intrusive sensors do not interfere with traffic either during installation or operation and include infrared sensors, radars and video image detection. Besides these road based sensors, also vehicles or road users serve as a data source [i.e., wherein the traffic data comprises data obtained from sensors in the target geographical region]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as already modified Weijermars by Xu, in view of Weijermars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – generating forecast – and the combination would provide for taking adequate traffic management measures to facilitate mobility whilst minimizing its negative side effects (Weijermars, see at least p. 2).
In regard to claim 4
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Weijermars teaches wherein the target geographical region and the at least one other geographical region are respective regions of an urban area or a town or city (Weijermars, in at least p. 15-16, discloses pneumatic loop detectors are easy to install and remove and are therefore appropriate for short term traffic counts throughout a city. A relatively new source of urban traffic data [i.e., wherein the target geographical region and the at least one other geographical region are respective regions of an urban area or a town or city] are floating car data that are collected by GPS or GSM based systems).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as already modified Weijermars by Xu, in view of Weijermars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – generating forecast – and the combination would provide for taking adequate traffic management measures to facilitate mobility whilst minimizing its negative side effects (Weijermars, see at least p. 2).
In regard to claim 5
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Weijermars teaches wherein the traffic data of the target geographical region comprises at least one type of traffic data from among any of:
a number of the vehicles inside the target geographical region;
a number of each type of a plurality of types of the vehicles inside the target geographical region;
a location of each of vehicle of the vehicles inside the target geographical region;
a direction of travel of each vehicle of the vehicles inside the target geographical region;
a speed of each vehicle of the vehicles inside the target geographical region;
an average speed of each vehicle of the vehicles inside the target geographical region;
a minimum and/or maximum speed of each vehicle of the vehicles inside the target geographical region;
a level of congestion inside the target geographical region;
a number of traffic jams inside the target geographical region;
a level of use of a road network inside the target geographical region;
a maximum transit capacity inside the target geographical region; and
an identifier of each vehicle of the vehicles inside the target geographical region (Weijermars, in at least p. 15, discloses pneumatic tubes are often used for short term traffic counts [i.e., a number of the vehicles inside the target geographical region]. A pneumatic tube is a hollow rubber tube that detects vehicles by the change in air pressure in the tube. Every vehicle axes that passes the loop is recorded by an air switch).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as already modified Weijermars by Xu, in view of Weijermars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – generating forecast – and the combination would provide for taking adequate traffic management measures to facilitate mobility whilst minimizing its negative side effects (Weijermars, see at least p. 2).
In regard to claim 6
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Weijermars teaches wherein generating the seasonal traffic forecast of the target geographical region comprises generating, based on the traffic data of the first time period, regressors that define a seasonal aspect of the traffic data of the first time period (Weijermars, in at least p. 130, discloses the estimation of missing or future traffic volumes using temporal traffic patterns consists of two steps. First, it has to be determined to what cluster day d belongs. Subsequently, the missing or future traffic volume has to be estimated [i.e., wherein generating the seasonal traffic forecast of the target geographical region comprises generating, based on the traffic data of the first time period]. After determination of the most appropriate cluster, the missing or future traffic volume has to be estimated. The most straightforward method is to use the average traffic volume over all days within the cluster for time period t. More advanced methods combine the average traffic volume of the cluster with available measurements on day d and/or upstream locations by applying a regression [i.e., regressors that define a seasonal aspect of the traffic data of the first time period] or ARIMA model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as already modified Weijermars by Xu, in view of Weijermars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – generating forecast – and the combination would provide for taking adequate traffic management measures to facilitate mobility whilst minimizing its negative side effects (Weijermars, see at least p. 2).
In regard to claim 7
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Weijermars teaches further comprising analyzing the mobility flow of the target geographical region and a plurality of other geographical regions to determine the at least one other geographical region relevant for the traffic forecast of the target geographical region (Weijermars, in at least p. 55-57, discloses Grouping locations according to their seasonal factors has been subject of research in literature on AADT (Average Annual Daily Traffic) estimation on the basis of short term traffic counts. Since it is concluded from the previous chapter that also the shape of the daily flow profile might differ between seasons, also in this case both the height and the shape of the daily flow profile are taken into account. Analogue to the analysis of differences in weekly variations, the working day flow profile of each month is compared to the average working day flow profile. Locations are also grouped according to the influence of rain on traffic volumes. Traffic volumes are compared for dry and for wet periods using matched pair analysis. Spatial variations in temporal traffic patterns is also analysed by comparing clustering results for locations. First, it is analysed to what extent the resulting clusters at different locations correspond. In that case, the sets of days in the resulting clusters are compared for different locations [i.e., analyzing the mobility flow of the target geographical region and a plurality of other geographical regions to determine the at least one other geographical region relevant for the traffic forecast of the target geographical region]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as already modified Weijermars by Xu, in view of Weijermars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – generating forecast – and the combination would provide for taking adequate traffic management measures to facilitate mobility whilst minimizing its negative side effects (Weijermars, see at least p. 2).
In regard to claim 8
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Weijermars teaches wherein the traffic data comprises information about events occurring in the target geographical region (Weijermars, in at least p. 23, discloses the traffic data is combined with calendar data, weather data, data on road works and events [i.e., wherein the traffic data comprises information about events occurring in the target geographical region] and accident data in order to explain variations in measured traffic volumes), and
wherein the computer-implemented method further comprises analyzing the traffic data of the target geographical region to obtain at least one correlation between the traffic data and at least one event (Weijermars, in at least p. 48, 51, discloses in case of small clusters with an abnormal daily flow profile it is investigated whether road works, events or accidents are on the basis of the cluster [i.e., analyzing the traffic data of the target geographical region to obtain at least one correlation between the traffic data and at least one event]. Cluster analysis is used for the analysis of spatial variations in traffic patterns by grouping locations that show similar patterns. Subsequently, it is investigated what (spatial) factors are on the basis of the resultant groups. Analogue to the analysis of differences in weekly variations, the working day flow profile of each month is compared to the average working day flow profile), and
wherein generating the first traffic forecast by adjusting the seasonal traffic forecast for the target geographical region based on the at least one other seasonal traffic forecast and the at least one correlation further comprises adjusting the seasonal traffic forecast for the target geographical region based on the at least one correlation between the traffic data and at least one external event and based on a predicted at least one event (Weijermars, in at least Fig. 8.4, and p. 125, 130, discloses the advantage of cluster analysis is that it enables a better estimation of the actual traffic volumes on a certain type of day and a certain location. Moreover, regular variations as well as the effect of (lengthy) road works, recurrent events and infrastructural and land use developments on traffic volumes can be monitored. A new profile is created of which the traffic volumes are 0.8*the average traffic volume of the Monday and Tuesday cluster + 0.2*the average traffic volume of the Thursday cluster [i.e., generating the first traffic forecast by adjusting the seasonal traffic forecast for the target geographical region based on the at least one other seasonal traffic forecast and the at least one correlation further comprises adjusting the seasonal traffic forecast for the target geographical region based on the at least one correlation between the traffic data and at least one external event and based on a predicted at least one event]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as already modified Weijermars by Xu, in view of Weijermars with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – generating forecast – and the combination would provide for taking adequate traffic management measures to facilitate mobility whilst minimizing its negative side effects (Weijermars, see at least p. 2).
In regard to claim 9
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, wherein decomposing the first traffic forecast into first seasonal, trend, and noise components and decomposing the second traffic forecast into second seasonal, trend, and noise components comprises using a Seasonal Trend Decomposition with LOESS, STL, technique (Rahimi, in at least [0042 & 0044 & 0049], discloses a set of configurable techniques or combination of techniques are selected to identify any outliers, including seasonal-trend decomposition [i.e., decomposing the first traffic forecast into first seasonal component and composing the second traffic forecast into second seasonal component] using loess (STL) [i.e., using a Seasonal Trend Decomposition with LOESS, STL, technique], interquartile range (IQR), mean and standard deviation method, and/or another technique. A signal smoothing technique 207 is performed on the set of time series data in order to remove any white noise [i.e., first noise component and second noise component] that might exist or any unwanted abrupt change of variance that might impute errors on the forecasting model training. Similar to the outlier replacement functionality, model imputation is used to predict a plausible value for the next iteration, therefore reducing possible noise in the data. Trend strength data is extracted from the set of time series data, where the trend [i.e., first trend component and second trend component] is a component of a time series that represents low frequency variation of data, which may present as a tendency of data to behave in a certain way. The trend strength measures how well this tendency is maintained throughout the progression of time. Generally, high trend strength means a more stable fixation to the tendency).
In regard to claim 10
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, wherein comparing at least one deviation between the first noise component and the second noise component to an anomaly threshold comprises comparing a percentage deviation to the anomaly threshold (Rahimi, in at least Fig. 2, and [0042], discloses the data preparation stage 205 involves passing a set of time series data through up to three preparation and cleaning steps before features within the set of time series data are extracted and selected. In particular, the set of time series data is initially passed through an outlier removal technique 206 which is configured to ensure that any impossible data or human error is not being fed into the model creation pipeline. Outliers [i.e., deviation between the first noise component and the second noise component] is initially detected by setting a threshold value for a maximum amount of standard deviation presented [i.e., an anomaly threshold comprises comparing a percentage deviation to the anomaly threshold], and checking any data points that might be over that margin. Further, any lower and maximum bounds is defined to make sure that all data points respect these ranges. Examiner notes, detecting outliers by setting a threshold value encompasses comparing at least one deviation between the first noise component and the second noise component to an anomaly threshold comprises comparing a percentage deviation to the anomaly threshold).
In regard to claim 11
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Xu teaches wherein predicting the emissions comprises computing an emission amount based on at least one type of information from among information including:
information indicating average emissions of a vehicle based on speed;
a number of the vehicles from the traffic forecast; and
an average speed of the vehicles from the traffic forecast (Xu, in at least p. 93, teaches emission models that account for variations in speed [i.e., information indicating average emissions of a vehicle based on speed] profiles provide more accurate results than models which only consider an average speed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the application to modify Rahimi, as already modified by Weijermars and Xu, in view of Xu with a reasonable expectation of success, as both inventions are directed to the same field of endeavor – modeling and analysis – and use the vehicle speed for predicting emission and the combination would provide for calibrating the simulation in order to improve the model’s ability to replicate traffic conditions observed (Xu, see at least p. 93).
In regard to claim 12
, Rahimi, as modified by Weijermars and Xu, teaches the computer-implemented method as claimed in claim 1, accordingly the rejection of claim 1 is incorporated.
Further, Weijermars teaches wherein the traffic data is in form of sets of traffic data corresponding to a plurality of geographical regions, respectively, the plurality of geographical regions including the target geographical region and the at least one other geographical region, and wherein the computer-implemented method comprises performing a parameter determination process comprising:
clustering the sets of traffic data based on similarity to each other to generate a plurality of clusters of sets of traffic data (Weijermars, in at least p. 42-42, and equations (4.1)-(4.4), discloses Clustering is the unsupervised classification of patterns (e.g. observations) into groups (clusters). Unsupervised classification means that patterns are grouped on the basis of the data solely. When analysing spatial variations or temporal variations on network level, the definition and representation of a pattern has to be adjusted. For the analysis of temporal traffic patterns at a link, let us define a cluster [i.e., clustering the sets of traffic data] as a group of days that show similar daily traffic profiles at link l [i.e., based on similarity to each other to generate a plurality of clusters of sets of traffic data]. A pattern thus is defined as a daily traffic profile at link l. Cluster analysis requires a pattern to be defined mathematically, by a number of features);
for each cluster of the plurality of clusters, selecting as a respective representative set of traffic data, a set of traffic data of a cluster among the plurality of clusters which is most similar to an average of the sets of traffic data of the cluster (Weijermars, in at least p. 46, discloses the resultant clusters is described by their average daily flow profiles [i.e., for each cluster of the plurality of clusters , selecting as a respective representative set of traffic data, a set of traffic data of a cluster among the plurality of clusters which is most similar to an average of the sets of traffic data of the cluster]. The average daily flow profile of cluster k, PlDk is determined by averaging the traffic volumes q over all days in the cluster); and
based on a representative set of traffic data among the respective representative set of traffic data corresponding to the target geographical region, performing mobility analysis to determine optimal parameters for the seasonal traffic forecast for the target geographical region and, based on a representative set of traffic data among the respective representative set of traffic data corresponding to the at least one other geographical region, performing mobility analysis to determine optimal parameters for the at least one other seasonal traffic forecast for the at least one other geographical region (Weijermars, in at least p. 43, discloses the optimal aggregation level depends on the amount and frequency of short-term variations. When the aggregation level is too low, differences between days can be due to difference in the number of green periods or other random short term variations in traffic volumes. When the aggregation level is too high on the other hand, differences in time of peak periods or peak volumes might be missed. The optimal aggregation level is determined by analysing the daily flow profiles on different aggregation levels [i.e., based on a representative set of traffic data among the respective representative set of traffic data corresponding to the target geographical region, performing mobility analysis to determine optimal parameters for the seasonal traffic forecast for the target geographical region and, based on a representative set of traffic data among the r