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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
[101 Analysis Step 1]
Step 1, of the 2019 Guidance, first looks to whether the claimed invention is directed to a statutory category, namely a process, machine, manufactures, and compositions of mater.
The claim 1 is directed to an apparatus for estimating a traffic volume (i.e. machine) and claim 11 is directed to a method for estimating a traffic volume (i.e. process). Thus, claims 1 and 11 are one of four the statutory categories (Step 1: YES).
[101 Analysis Step 2A, Prong I]
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.
Independent Claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim(s) for the remainder of the 101 rejection. Claim 1 recites:
An apparatus for estimating a traffic volume based on demand for route search, the apparatus comprising:
a processor; and
a storage medium configured to record one or more programs configured to be executable by the processor;
wherein the processor is configured to:
collect a plurality of pieces of route search data;
generate route search demand data based on the collected plurality of pieces of route search data;
correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume; and
estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data to a pre-trained learning model.
The examiner submits that the foregoing bolded limitations(s) constitute a “mental process” because under its broadest reasonable interpretations, the claim covers performance of the limitation in the human mind. For example, “generate…”, “correct…” and “estimate…” in the context of the claim encompasses a person looking at and using the data collected to formulating a judgement and calculation. Accordingly, the claim recites abstract idea.
[101 Analysis Step 2A, Prong II]
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, 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.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An apparatus for estimating a traffic volume based on demand for route search, the apparatus comprising:
a processor; and
a storage medium configured to record one or more programs configured to be executable by the processor;
wherein the processor is configured to:
collect a plurality of pieces of route search data;
generate route search demand data based on the collected plurality of pieces of route search data;
correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume; and
estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data to a pre-trained learning model.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract into a practical applications.
Regarding the additional limitations of “collect a plurality of pieces of route search data” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. Lastly, the “processor” and “storage medium” are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
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 filed, 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 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.
[101 Analysis Step 2B]
Regarding Step 2B of the Revised Guidance, representative independent claims 1 and 11 do 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, the additional elements of using a processor to perform the “generate…”, “correct…” and “estimate…” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “collect…” the examiner submits that these limitations are insignificant extra-solution activities. Hence, the claims are not patent eligible.
Dependent claims 2-10 and 12-20 do not recite any further limitations that cause the claims to be directed towards statutory subject matter. The claims merely recite: abstract idea. Each of the further limitations expound upon the abstract ideas and do not recite additional elements integrating the abstract ideas into a practical application or additional elements that are not well-understood, routine or conventional. Therefore, dependent claims 2-10 and 12-20 are similarly rejected as being directed towards non-statutory subject matter.
Therefore, claims 1-20 is/are ineligible under 35 USC §101.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pub No. US 2022/0327924 A1 to To et. al. (To) in view of Pub No. US 2024/0054889 A1 to Yamada et. al. (Yamada) and further in view of Pub No. WO 2021/244905 A1 to Domokos et. al. (Domokos).
Examiner’s Note: Machine Translation of WO 2021/244905 A1 will be used in the rejection below.
In Reference to Claim 1
To teaches (except for the bolded and italic recitations below):
An apparatus for estimating a traffic volume based on demand for route search, the apparatus comprising:
a processor (10) (see To Fig.1 and paragraphs [0038]-[0039]); and
a storage medium (14) configured to record one or more programs configured to be executable by the processor (10) (see To Fig.1 and paragraphs [0038]-[0039]);
wherein the processor (10) is configured to:
collect a plurality of pieces of route search data (To teaches “In step S101, the CPU 11 functioning as the path candidate generation unit 110 generates the path candidate list R by using the road network data G, the node set V and the node set U, the multiplying factor α, and the observation spot list M as the input”) (see at least To Fig.4 and paragraph [0111]);
generate route search demand data based on the collected plurality of pieces of route search data (To teaches “In step S102, the CPU 11 functioning as the routing matrix generation unit 120 generates the routing matrix A by using the path candidate list R and the observation spot list M as the inputs”) (see at least To Fig.4 and paragraph [0112]);
correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume; and
estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data to a pre-trained learning model (To teaches “In step S103, the CPU 11 functioning as the path specific traffic volume estimation unit 130 estimates the path specific traffic volume matrix X by using the routing matrix A, the observation value data matrix Y, and the reliability matrix W as the inputs” and “the path specific traffic volume estimation device according to the first embodiment estimates the path specific traffic volume on the basis of the observation value data, the observation spot specific reliability data, and the path candidate list. The observation value data is the number of observation objects at each time point at each of the plurality of observation spots. The observation spot specific reliability data is the reliability of the observation value data at each time point at each of the plurality of observation spots. The path candidate list represents a set of path candidates through which the observation objects move. The path specific traffic volume is the traffic volume of the observation objects at each time point at each of the path candidates. According to this, even under a situation where the reliability of the observation value data varies for each time slot or each observation spot, the path specific traffic volume can be precisely estimated”) (see at least To Fig.4 and paragraph [0113] and [0115]).
To does not explicitly teach (bolded and italic recitations above) as to correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data. However, it is known in the art before the effective filing date of the claimed invention to correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data. For example, Yamada teaches correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume (bottleneck capacity) and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data (due to a bottleneck). Yamada further teaches that performing such step provides accurately calculate an amount of change in the predicted value of the required time when the inflow traffic volume is changed (see at least Yamada Figs. 10-11 and 18-19 and paragraphs 82, 84, 91-96, 121-122). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of To to include the step of correct the route search demand data based on an overcrowded road exceeding a marginal traffic volume and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data as taught by Yamada in order to accurately calculate an amount of change in the predicted value of the required time when the inflow traffic volume is changed.
To in view of Yamada teaches of having a model however does not explicitly teach (bolded and italic recitations above) as to use a pre-trained learning model for a training. However, it is known in the art before the effective filing date of the claimed invention to use a pre-trained learning model for a storing data for transportation system. For example, Domokos teaches to use a pre-trained learning model for a storing data for transportation system such as GAN model (see at least Domokos pages 2-3). The substitution of one known element (GAN model as shown in Domokos) for another (model as shown in To in view of Yamada) would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention since the substitution of the GAN model shown in Domokos would have yielded predictable results, namely, storing and gathering data in To in view of Yamada system to use to estimate traffic volume.
In Reference to Claim 2
The apparatus of claim 1 (see rejection to claim 1 above), wherein:
each of the plurality of pieces of route search data includes a road ID of each of the plurality of roads, included in a route searched by a vehicle, and a predicted entry time for each road of the plurality of roads (To teaches “the path candidate generation unit 110 generates a path candidate list R by using road network data G, a node set V and a node set U, a multiplying factor α, and an observation spot list M as inputs. The path candidate list R is a list of the path candidate R.sub.i. It should be noted however that the path candidate R.sub.i is a node string in which a link exists in the road network data G”) (see at least To Fig.2 and paragraph [0067]);
the route search demand data includes a traffic volume for each road of the plurality of roads estimated based on the road ID and the predicted entry time, the traffic volume representing a number of vehicles (To teaches “the path specific traffic volume estimation unit 130 estimates a path specific traffic volume matrix X by using the routing matrix A, the observation value data matrix Y, and the reliability matrix W as the inputs. Then, the path specific traffic volume estimation unit 130 outputs the estimated path specific traffic volume matrix X. The path specific traffic volume matrix X is a matrix using the path specific traffic volume at each time point at each of the path candidates as the element”) (see at least To Fig.2 and paragraph [0093]); and
the marginal traffic volume is a maximum number of vehicles set for each road of the plurality of roads (“the parameters of the traffic model may include a bottleneck capacity of each route in each time”) (see at least Yamada paragraph [0020]).
In Reference to Claim 3
The apparatus of claim 2 (see rejection to claim 2 above), wherein the processor is configured to disperse, with respect to the overcrowded road, an excess demand traffic volume exceeding the marginal traffic volume to an upstream road connected to the overcrowded road, the upstream road being a road connected to the overcrowded road in a reverse direction of a direction of travel (“The parameter setting unit 128 sets the parameters of the traffic model. The parameters of the traffic model are, for example, the free flow travel time and a bottleneck capacity. The parameter setting unit 128 sets the free flow travel time and bottleneck capacity. The bottleneck capacity will be described later. The free flow travel time and the bottleneck capacity are not constant on a time axis. That is, the free flow travel time can be different from time to time (from time period to time period). Similarly, the bottleneck capacity can be different from time to time (from time period to time period). The reasons why the free flow travel time and the bottleneck capacity are different from time to time (from time period to time period) include, for example, for each time period, different lighting intervals of traffic lights, varying number of vehicles turning right or left due to increase or decrease in the number of pedestrians, and varying proportions of large vehicles entering the route”) (see at least Yamada Fig.10 and paragraph [0082]).
In Reference to Claim 4
The apparatus of claim 3 (see rejection to claim 3 above), wherein the processor is configured to: repeatedly calculate a process of dispersing the excess demand traffic volume to the upstream road according to a preset percentage; and end the process of dispersing the excess demand traffic volume to the upstream road when a total sum of excess demand traffic volumes for respective roads, after dispersion is performed, is less than or equal to a certain percentage of a total sum of marginal traffic volumes for respective roads (“Further, a.sub.k(t−T.sup.−.sub.k,t) indicates the traffic volume of vehicles flowing into the route k from the reference point S at the time that is the free flow travel time T.sup.−.sub.k,t before the time t. In this traffic model, it is assumed that vehicles travel from the reference point S to the predetermined point P (bottleneck) in the free flow travel time. Therefore, a.sub.k(t−T.sup.−.sub.k,t) corresponds to the traffic volume of vehicles arriving at the bottleneck (predetermined point P) at the time t. Similarly, A.sub.k(t−T.sup.−.sub.k,t) indicates the accumulated traffic volume of vehicles flowing into the route k from the reference point S at the time that is the free flow travel time T.sup.−.sub.k,t before the time t. A.sub.k(t−T.sup.−.sub.k,t) corresponds to the accumulated traffic volume of vehicles arriving at the bottleneck (predetermined point P) at the time t”) (see at least Yamada Fig.10 and paragraph [0089]).
In Reference to Claim 5
The apparatus of claim 3 (see rejection to claim 3 above), wherein the route search demand data and the marginal traffic volume are represented by a matrix (“Herein, a T-row J-column matrix in which the observation spot specific people count observation value data Y.sub.t, j is set as each element is represented as an observation value data matrix Y. The observation value data matrix Y refers to data in which traffic volume is temporally and spatially measured in a certain granularity and aggregated. As a method of obtaining the observation value data matrix Y, a method of measuring traffic volume by counting the number of passing vehicles or people by using, for example, a camera, the number of people passing through a gate, an infrared sensor, and the like is exemplified”) (see at least To Fig.2 and paragraph [0061]).
In Reference to Claim 6
The apparatus of claim 3 (see rejection to claim 3 above), wherein the processor is configured to perform dispersion according to : Dover,tprop=Pprop,t*Dover,t, where Dover,tprop is a matrix representing a dispersion volume for an excess demand traffic volume for each road at a specific point in time t, Pprop,t is a matrix representing a dispersion percentage at the specific point in time t, and Dover,t is a matrix representing an excess demand traffic volume for each road at the specific point in time t (“Where <W′, Y′> represents an inner product. In addition, since the number of people is nonnegative, the objective function L is minimized so as to satisfy a restriction condition X.sub.t, i′≥0. In other words, an (I×T) dimensional vector X′ that satisfies Y′=HX′ is obtained”) (see at least To Fig.2 and paragraph [0104]).
In Reference to Claim 7
The apparatus of claim 6 (see rejection to claim 6 above), wherein Pprop,t is obtained by: Pprop,t=α*diagUover,t*I+1-α*A, where α is a constant, Uover,t is a unit matrix of a matrix representing an excess demand traffic volume for each road at the specific point in time t, diag() is a function turning Uover,t into a diagonal matrix, I is a unit matrix, and A is a matrix representing a connection relationship between roads according to the direction of travel (To in view of Yamada and further in view of Domokos does not disclose the specific formula however it is generally considered to be within the ordinary skill in the art to adjust, vary, select or optimize the numerical parameters or values in a particular system depending on the system requirements. Further, where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation where a particular parameter is recognized as a result-effective variable (See MPEP 2144)).
In Reference to Claim 8
The apparatus of claim 6 (see rejection to claim 6 above), wherein the plurality of specific points in time have a predetermined time interval (“FIG. 3 illustrates an example of the observation value data matrix Y and the reliability matrix W. In the observation value data matrix Y, the observation value data Y.sub.1, j (j=1, 2, . . . , J) observed in the observation time slot (observation period) between t=0 and t=1 at the observation spot M.sub.j (j=1, 2, . . . , J) is set as an element of a (1, j) component. Similarly, in the observation value data matrix Y, the observation value data Y.sub.2, j (j=1, 2, . . . , J) observed in the observation time slot between t=1 and t=2 at the observation spot M.sub.j (j=1, 2, . . . , J) is set as an element of a (2, j) component. Subsequently too, similarly, in the observation value data matrix Y, the observation value data Y.sub.t, j (j=1, 2, . . . , J) observed in the observation time slot between t=T−1 and t=T at the observation spot M.sub.j (j=1, 2, . . . , J) is set as the element of the (T, j) component”) (see at least To Fig.2 and paragraph [0063]).
In Reference to Claim 9
The apparatus of claim 1 (see rejection to claim 1 above), wherein the learning model includes a generative adversarial network (GAN) including a generator and a discriminator (see at least Domokos pages 2-3).
In Reference to Claim 10
The apparatus of claim 9 (see rejection to claim 9 above), wherein the processor is configured to train the discriminator using estimated traffic volume data, the estimated traffic volume data generated by the generator based on corrected route search demand data and actual traffic volume data, and then to train the generator in a direction of deceiving the trained discriminator (see at least Domokos pages 2-3).
In Reference to Claim 11
To teaches (except for the bolded and italic recitations below):
A method for estimating a traffic volume based on demand for route search, the method comprising:
Collecting, by a processor, a plurality of pieces of route search data (To teaches “In step S101, the CPU 11 functioning as the path candidate generation unit 110 generates the path candidate list R by using the road network data G, the node set V and the node set U, the multiplying factor α, and the observation spot list M as the input”) (see at least To Fig.4 and paragraph [0111]);
generating route search demand data based on the collected plurality of pieces of route search data (To teaches “In step S102, the CPU 11 functioning as the routing matrix generation unit 120 generates the routing matrix A by using the path candidate list R and the observation spot list M as the inputs”) (see at least To Fig.4 and paragraph [0112]);
correcting the route search demand data based on an overcrowded road exceeding a marginal traffic volume; and
estimating an actual traffic volume for a plurality of roads by applying the corrected route search demand data to a pre-trained learning model (To teaches “In step S103, the CPU 11 functioning as the path specific traffic volume estimation unit 130 estimates the path specific traffic volume matrix X by using the routing matrix A, the observation value data matrix Y, and the reliability matrix W as the inputs” and “the path specific traffic volume estimation device according to the first embodiment estimates the path specific traffic volume on the basis of the observation value data, the observation spot specific reliability data, and the path candidate list. The observation value data is the number of observation objects at each time point at each of the plurality of observation spots. The observation spot specific reliability data is the reliability of the observation value data at each time point at each of the plurality of observation spots. The path candidate list represents a set of path candidates through which the observation objects move. The path specific traffic volume is the traffic volume of the observation objects at each time point at each of the path candidates. According to this, even under a situation where the reliability of the observation value data varies for each time slot or each observation spot, the path specific traffic volume can be precisely estimated”) (see at least To Fig.4 and paragraph [0113] and [0115]).
To does not explicitly teach (bolded and italic recitations above) as to correcting the route search demand data based on an overcrowded road exceeding a marginal traffic volume and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data. However, it is known in the art before the effective filing date of the claimed invention to correcting the route search demand data based on an overcrowded road exceeding a marginal traffic volume and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data. For example, Yamada teaches correcting the route search demand data based on an overcrowded road exceeding a marginal traffic volume (bottleneck capacity) and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data (due to a bottleneck). Yamada further teaches that performing such step provides accurately calculate an amount of change in the predicted value of the required time when the inflow traffic volume is changed (see at least Yamada Figs. 10-11 and 18-19 and paragraphs 82, 84, 91-96, 121-122). Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of To to include the step of correcting the route search demand data based on an overcrowded road exceeding a marginal traffic volume and estimate an actual traffic volume for a plurality of roads by applying the corrected route search demand data as taught by Yamada in order to accurately calculate an amount of change in the predicted value of the required time when the inflow traffic volume is changed.
To in view of Yamada teaches of having a model however does not explicitly teach (bolded and italic recitations above) as to use a pre-trained learning model for a training. However, it is known in the art before the effective filing date of the claimed invention to use a pre-trained learning model for a storing data for transportation system. For example, Domokos teaches to use a pre-trained learning model for a storing data for transportation system such as GAN model (see at least Domokos pages 2-3). The substitution of one known element (GAN model as shown in Domokos) for another (model as shown in To in view of Yamada) would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention since the substitution of the GAN model shown in Domokos would have yielded predictable results, namely, storing and gathering data in To in view of Yamada system to use to estimate traffic volume.
In Reference to Claim 12
The method of claim 11 (see rejection to claim 11 above), wherein:
each of the plurality of pieces of route search data includes a road ID of each of the plurality of roads, included in a route searched by a vehicle, and a predicted entry time for each road of the plurality of roads (To teaches “the path candidate generation unit 110 generates a path candidate list R by using road network data G, a node set V and a node set U, a multiplying factor α, and an observation spot list M as inputs. The path candidate list R is a list of the path candidate R.sub.i. It should be noted however that the path candidate R.sub.i is a node string in which a link exists in the road network data G”) (see at least To Fig.2 and paragraph [0067]);
the route search demand data includes a traffic volume for each road of the plurality of roads estimated based on the road ID and the predicted entry time, the traffic volume representing a number of vehicles (To teaches “the path specific traffic volume estimation unit 130 estimates a path specific traffic volume matrix X by using the routing matrix A, the observation value data matrix Y, and the reliability matrix W as the inputs. Then, the path specific traffic volume estimation unit 130 outputs the estimated path specific traffic volume matrix X. The path specific traffic volume matrix X is a matrix using the path specific traffic volume at each time point at each of the path candidates as the element”) (see at least To Fig.2 and paragraph [0093]); and
the marginal traffic volume is a maximum number of vehicles set for each road of the plurality of roads (“the parameters of the traffic model may include a bottleneck capacity of each route in each time”) (see at least Yamada paragraph [0020]).
In Reference to Claim 13
The method of claim 12 (see rejection to claim 12 above), wherein the correcting includes dispersing, with respect to the overcrowded road, an excess demand traffic volume exceeding the marginal traffic volume to an upstream road connected to the overcrowded road, the upstream road being a road connected to the overcrowded road in a reverse direction of a direction of travel (“The parameter setting unit 128 sets the parameters of the traffic model. The parameters of the traffic model are, for example, the free flow travel time and a bottleneck capacity. The parameter setting unit 128 sets the free flow travel time and bottleneck capacity. The bottleneck capacity will be described later. The free flow travel time and the bottleneck capacity are not constant on a time axis. That is, the free flow travel time can be different from time to time (from time period to time period). Similarly, the bottleneck capacity can be different from time to time (from time period to time period). The reasons why the free flow travel time and the bottleneck capacity are different from time to time (from time period to time period) include, for example, for each time period, different lighting intervals of traffic lights, varying number of vehicles turning right or left due to increase or decrease in the number of pedestrians, and varying proportions of large vehicles entering the route”) (see at least Yamada Fig.10 and paragraph [0082]).
In Reference to Claim 14
The method of claim 13 (see rejection to claim 13 above), wherein the correcting further includes: repeatedly calculating a process of dispersing the excess demand traffic volume to the upstream road according to a preset percentage; and ending the process of dispersing the excess demand traffic volume to the upstream road when a total sum of excess demand traffic volumes for respective roads, after dispersion is performed, is less than or equal to a certain percentage of a total sum of marginal traffic volumes for respective roads (“Further, a.sub.k(t−T.sup.−.sub.k,t) indicates the traffic volume of vehicles flowing into the route k from the reference point S at the time that is the free flow travel time T.sup.−.sub.k,t before the time t. In this traffic model, it is assumed that vehicles travel from the reference point S to the predetermined point P (bottleneck) in the free flow travel time. Therefore, a.sub.k(t−T.sup.−.sub.k,t) corresponds to the traffic volume of vehicles arriving at the bottleneck (predetermined point P) at the time t. Similarly, A.sub.k(t−T.sup.−.sub.k,t) indicates the accumulated traffic volume of vehicles flowing into the route k from the reference point S at the time that is the free flow travel time T.sup.−.sub.k,t before the time t. A.sub.k(t−T.sup.−.sub.k,t) corresponds to the accumulated traffic volume of vehicles arriving at the bottleneck (predetermined point P) at the time t”) (see at least Yamada Fig.10 and paragraph [0089]).
In Reference to Claim 15
The method of claim 13 (see rejection to claim 13 above), wherein the route search demand data and the marginal traffic volume are represented by a matrix (“Herein, a T-row J-column matrix in which the observation spot specific people count observation value data Y.sub.t, j is set as each element is represented as an observation value data matrix Y. The observation value data matrix Y refers to data in which traffic volume is temporally and spatially measured in a certain granularity and aggregated. As a method of obtaining the observation value data matrix Y, a method of measuring traffic volume by counting the number of passing vehicles or people by using, for example, a camera, the number of people passing through a gate, an infrared sensor, and the like is exemplified”) (see at least To Fig.2 and paragraph [0061]).
In Reference to Claim 16
The method of claim 13 (see rejection to claim 13 above), wherein the dispersing includes performing dispersion according to: Dover,tprop=Pprop,t*Dover,t, where Dover,tprop is a matrix representing a dispersion volume for an excess demand traffic volume at a specific point in time t, Pprop,t is a matrix representing a dispersion percentage at the specific point in time t, and Dover,t is a matrix representing an excess demand traffic volume at the specific point in time t (“Where <W′, Y′> represents an inner product. In addition, since the number of people is nonnegative, the objective function L is minimized so as to satisfy a restriction condition X.sub.t, i′≥0. In other words, an (I×T) dimensional vector X′ that satisfies Y′=HX′ is obtained”) (see at least To Fig.2 and paragraph [0104]).
In Reference to Claim 17
The method of claim 16 (see rejection to claim 16 above), wherein Pprop,t is obtained according to: Pprop,t=α*diagUover,t*I+1-α*A, where α is a constant, Uover,t is a unit matrix of a matrix representing an excess demand traffic volume at the specific point in time t, diag() is a function turning Uover,t into a square matrix, and A is a matrix representing a connection relationship between roads according to the direction of travel (To in view of Yamada and further in view of Domokos does not disclose the specific formula however it is generally considered to be within the ordinary skill in the art to adjust, vary, select or optimize the numerical parameters or values in a particular system depending on the system requirements. Further, where the general conditions of a claim are disclosed in the prior art, it is not inventive to discover the optimum or workable ranges by routine experimentation where a particular parameter is recognized as a result-effective variable (See MPEP 2144)).
In Reference to Claim 18
The method of claim 16 (see rejection to claim 16 above), wherein the plurality of specific points in time have a predetermined time interval (“FIG. 3 illustrates an example of the observation value data matrix Y and the reliability matrix W. In the observation value data matrix Y, the observation value data Y.sub.1, j (j=1, 2, . . . , J) observed in the observation time slot (observation period) between t=0 and t=1 at the observation spot M.sub.j (j=1, 2, . . . , J) is set as an element of a (1, j) component. Similarly, in the observation value data matrix Y, the observation value data Y.sub.2, j (j=1, 2, . . . , J) observed in the observation time slot between t=1 and t=2 at the observation spot M.sub.j (j=1, 2, . . . , J) is set as an element of a (2, j) component. Subsequently too, similarly, in the observation value data matrix Y, the observation value data Y.sub.t, j (j=1, 2, . . . , J) observed in the observation time slot between t=T−1 and t=T at the observation spot M.sub.j (j=1, 2, . . . , J) is set as the element of the (T, j) component”) (see at least To Fig.2 and paragraph [0063]).
In Reference to Claim 19
The method of claim 11 (see rejection to claim 11 above), wherein the learning model includes a generative adversarial network (GAN) including a generator and a discriminator (see at least Domokos pages 2-3).
In Reference to Claim 20
The method of claim 19 (see rejection to claim 19 above), further comprising: training the discriminator using estimated traffic volume data, the estimated traffic volume data generated by the generator based on corrected route search demand data, and actual traffic volume data, and then training the generator in a direction of deceiving the trained discriminator (see at least Domokos pages 2-3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pub No. US 2020/0033151 A1 to Seshadri (Seshadri) teaches predicting traffic volume within the routes.
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/BRANDON D LEE/Primary Examiner, Art Unit 3662 December 31, 2025