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 Objections
Claims 2, 6, 7, 10, and 11 objected to because of the following informalities:
Claim 2: “…prior vehicle statistical distribution…” should read – …prior vehicle speed statistical distribution… –
Claim 6: “…a variational interference method.” should read – …a variational inference method.”
Claim 7: “…the observed vehicle seed data…” should read – …the observed vehicle speed data… –
Claim 10: “…the estimate travel time…” should read – …the estimated travel time… –
Claim 11: “…transmit an origin location, a destination location and a proposed time of travel to the system; receive an estimated travel time…” should read – …transmit the origin location, the destination location and the proposed time of travel to the system; receive the estimated travel time… –
Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-5, and 7-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hyun (Korean Patent Publication KR20220055822).
Regarding claim 1, Hyun teaches a system for estimation of vehicle speed along a stretch of road, the system comprising: one or more processors (processor(s)) (Page 7 line 9 The computer program includes C/C++, C#, JAVA, Python, which can be read by a processor (CPU) of the computer through the device interface of the computer in order for the computer to read the program and execute the methods implemented as a program); a memory accessible to the processor(s), the memory comprising program code executable by the processor(s) (Page 7 line 6 The method for calculating the expected passing speed described above may be implemented in the form of a computer program written to perform each step in a computer and recorded in a computer-readable recording medium.) to: compute a prior vehicle speed statistical distribution (prior distribution) based on historical vehicle speed data along the road (Page 5 line 13 In Equation 5, 'Prior' is prior information, and the probability of the speed to be predicted at the past time t .sub.A + δt using the historical average speed information of the section between node B and node C, that is, link 2, is normally distributed. is expressed as is the historical average speed, is a parametric function that has an inverse relationship with the expected required time δt and has t.sub.A as a variable.); receive observed vehicle speed data relating to real-time or near real-time vehicle traffic along the road (Page 4 line 30 In the next step S20 , the first passing speed distribution may be calculated by modeling real-time speed values passing through the second link at the predetermined time point t.sub.A as a likelihood function.); perform Bayesian fusion of the prior distribution and the observed vehicle speed data by representing the prior distribution as a conjugate to a likelihood function to determine a posterior distribution (Page 5 line 21 Referring back to FIG. 1 , the estimated passing speed distribution of the second link may be calculated by substituting the calculated first passing speed distribution and the output second passing speed distribution in Bayesian inference in step S40. Using Bayesian inference, we can express the exact probability by multiplying the first passing speed distribution and the second passing speed distribution. Equation 6 below shows general Bayesian inference.); determine a vehicle speed estimate based on the posterior distribution (Page 5 line 28 In Equation 6, 'Post' is the normalization process by multiplying the expected speed distribution function (first passing speed distribution) found from the current real-time speed data and the expected speed distribution function calculated from the past speed data (the second passing speed distribution) It means a function that makes a probability distribution again.).
Regarding claim 3, Hyun teaches the system of claim 1 as set forth above. Hyun further teaches wherein the prior distribution is computed based on historical vehicle data for a time of the day and a day of the week associated with a time of estimation of the vehicle speed (Page 3 line 32 In particular, in consideration of at least any one or more factors such as the time t .sub.A and year, month, day, day, time, day of the week, holiday status, season and weather among a plurality of past speed data, the t.sub.A +.sub. Past speed data highly related to the time δt can be read from the memory.).
Regarding claim 4, Hyun teaches the system of claim 1 as set forth above. Hyun further teaches wherein the processor(s) performs Bayesian fusion of the prior distribution and the observed vehicle speed data by computing hyperparameters, the hyperparameters comprising variance of historical vehicle speed data; wherein the computed hyperparameters define a closed-form solution as a Normal-Normal conjugate pair of the prior and posterior distributions to perform the Bayesian fusion (Page 6 line 25 That is, the larger the variance, the more likely it is to have a lot of speed around the mean value. In other words, it can be seen that the greater the variance, the lower the accuracy of the second passing speed distribution, which is the past information. Accordingly, the dispersion value of the second passing speed distribution may have an inverse relationship with the expected required time δt. Variance value when the estimated time required (δt) is large will be small, and the variance value when the estimated time required (δt) is small will grow That is, the larger the expected required time δt, the more reliable the past speed information than the current real-time speed information.).
Regarding claim 5, Hyun teaches the system of claim 4 as set forth above. Hyun further teaches wherein a weight allocated to the observed vehicle speed data during the fusion is proportional to a count of samples of received observed vehicle speeds (Page 5 line 37 On the other hand, as can be seen in Equation 7, the 'prior' distribution function has an inverse relationship with the expected required time δt and has t.sub.A as a variable. is included as a parameter. This is information indicating how reliable the expected velocity distribution calculated from the past data is. In other words ' The setting of (t.sub.A, δt)' is a factor in determining which information is weighted among the real-time speed information of the current time and the past speed information.).
Regarding claim 7, Hyun teaches the system of claim 1 as set forth above. Hyun further teaches wherein the fusion of the prior and the observed vehicle seed data is performed by modelling the prior and the posterior vehicle speed distributions as normal distributions (Page 3 line 42 Here, D = {X.sub.1, X.sub.2, ??, X.sub.n} represents a data set. is the assumed distribution is a parameter of In the case of a normal distribution, the mean and variance are indicated. and the assumed distribution is the parameter.).
Regarding claim 8, Hyun teaches the system of claim 1 as set forth above. Hyun further teaches wherein the Bayesian fusion is performed in real-time or near real-time as the observed vehicle speed data is received (Page 5 line 21 Referring back to FIG. 1 , the estimated passing speed distribution of the second link may be calculated by substituting the calculated first passing speed distribution and the output second passing speed distribution in Bayesian inference in step S40. Using Bayesian inference, we can express the exact probability by multiplying the first passing speed distribution and the second passing speed distribution. Equation 6 below shows general Bayesian inference.).
Regarding claim 9, Hyun teaches a system for estimation of travel time, the system comprising: one or more processors (processor(s)) (Page 7 line 9 The computer program includes C/C++, C#, JAVA, Python, which can be read by a processor (CPU) of the computer through the device interface of the computer in order for the computer to read the program and execute the methods implemented as a program); a memory accessible to the processor(s), the memory comprising program code executable by the processor(s) (Page 7 line 6 The method for calculating the expected passing speed described above may be implemented in the form of a computer program written to perform each step in a computer and recorded in a computer-readable recording medium.) to: receive an origin location, a destination location (Page 3 line 2 The expected passing speed calculation method according to the present specification is a method applicable to a so-called navigation system, and it is assumed that a user inputs a departure point and a destination to the navigation system.) and a proposed time of travel from a computing device (Page 3 line 32 In particular, in consideration of at least any one or more factors such as the time t .sub.A and year, month, day, day, time, day of the week, holiday status, season and weather among a plurality of past speed data, the t.sub.A +.sub. Past speed data highly related to the time δt can be read from the memory.); determine a travel route between the origin location and the destination location (Page 3 line 7 The navigation system may generate an entire route by connecting a plurality of nodes selected from a source to a destination.), the travel route comprising at least one road segment (Page 3 line 6 A plurality of nodes may be selected between the source and the destination. A path connecting a node to a node is called a 'link'.); estimate the travel time by estimating a road segment travel time for each segment and adding the road segment travel times (Page 3 line 8 In this case, it is possible to calculate the estimated speed for passing through each link and calculate the estimated time required for the entire route by summing them all up.); wherein the road segment travel time is estimated by: computing a prior vehicle speed statistical distribution (prior distribution) based on historical vehicle speed data along the road segment (Page 5 line 13 In Equation 5, 'Prior' is prior information, and the probability of the speed to be predicted at the past time t .sub.A + δt using the historical average speed information of the section between node B and node C, that is, link 2, is normally distributed. is expressed as is the historical average speed, is a parametric function that has an inverse relationship with the expected required time δt and has t.sub.A as a variable.); receiving observed vehicle speed data relating to real-time or near real-time vehicle traffic along the road segment (Page 4 line 30 In the next step S20 , the first passing speed distribution may be calculated by modeling real-time speed values passing through the second link at the predetermined time point t.sub.A as a likelihood function.); performing Bayesian fusion of the prior distribution and the observed speed data by representing the prior distribution as a conjugate to a likelihood function to determine a posterior distribution (Page 5 line 21 Referring back to FIG. 1 , the estimated passing speed distribution of the second link may be calculated by substituting the calculated first passing speed distribution and the output second passing speed distribution in Bayesian inference in step S40. Using Bayesian inference, we can express the exact probability by multiplying the first passing speed distribution and the second passing speed distribution. Equation 6 below shows general Bayesian inference.); determining an estimated vehicle speed estimate based on the posterior distribution (Page 5 line 28 In Equation 6, 'Post' is the normalization process by multiplying the expected speed distribution function (first passing speed distribution) found from the current real-time speed data and the expected speed distribution function calculated from the past speed data (the second passing speed distribution) It means a function that makes a probability distribution again.); determining the road segment travel time based on the vehicle speed estimate and the road segment length (Page 4 line 23 Referring to FIG. 1, in step S10, the estimated time required to reach the Node B through the first link at the predetermined time point t.sub.A (hereinafter, 'expected time required (δt)') can be calculated.).
Regarding claim 10, Hyun teaches the system of claim 9 as set forth above. Hyun further teaches wherein the processor(s) is further configured to transmit the estimate travel time to the computing device (Page 7 line 18 In addition, when the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the above functions, the code uses the communication module of the computer to determine how to communicate with any other computer or server remotely. It may further include a communication-related code for whether to communicate and what information or media to transmit and receive during communication.).
Regarding claim 11, Hyun teaches the system of claim 10 as set forth above. Hyun further teaches wherein the computing device further comprises: one or more computing device processor (computing device processor(s)) (Page 7 line 9 The computer program includes C/C++, C#, JAVA, Python, which can be read by a processor (CPU) of the computer through the device interface of the computer in order for the computer to read the program and execute the methods implemented as a program); a memory accessible to the computing device processor(s), the memory comprising program code executable by the computing device processor(s) (Page 7 line 6 The method for calculating the expected passing speed described above may be implemented in the form of a computer program written to perform each step in a computer and recorded in a computer-readable recording medium.) to: transmit an origin location, a destination location and a proposed time of travel to the system; receive an estimated travel time from the system (Page 3 line 2 The expected passing speed calculation method according to the present specification is a method applicable to a so-called navigation system, and it is assumed that a user inputs a departure point and a destination to the navigation system.).
Regarding claim 12, the claim is commensurate in scope with claim 1 with the exception that claim 12 is directed to a method rather than a system. Therefore, the same prior art can be applied to claim 12 as was applied to claim 1.
Regarding claim 13, the claim is commensurate in scope with claim 9 with the exception that claim 13 is directed to a method rather than a system. Therefore, the same prior art can be applied to claim 13 as was applied to claim 9.
Regarding claim 14, Hyun teaches the method of claim 12 as set forth above. Hyun further teaches one or more non-transitory computer-readable storage media storing instructions that when executed by one or more processors cause the one or more processors to perform the method of claim 12 (Page 7 line 6 The method for calculating the expected passing speed described above may be implemented in the form of a computer program written to perform each step in a computer and recorded in a computer-readable recording medium.).
Claim Rejections - 35 USC § 103
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) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hyun in view of Min (U.S. Patent Application Publication 2015/0348410).
Regarding claim 2, Hyun teaches the system of claim 1 as set forth above. However, Hyun does not teach wherein the fusion of the prior distribution and the observed vehicle speed data comprises exponential smoothing of the prior vehicle statistical distribution to incorporate the observed vehicle speed data.
Min, in the same field of endeavor, teaches a system for estimating traffic flow. The system performs exponential smoothing on the historical traffic flow data as a part of estimating traffic flow (Paragraph 0058 At step 330, the arrival time estimation apparatus 700 calculates travel times according to a moving average, exponential smoothing, and a service pattern by using the measured travel times of the plurality of vehicles 10. The calculated travel times, which are calculated according to the moving average, the exponential smoothing, and the service pattern of the plurality of vehicles 10, may be calculated for each predefined section.).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to have modified Hyun with the teachings of Min which teaches performing exponential smoothing on the historical traffic flow data as a part of estimating traffic flow in order to more accurately predict travel times of transportation vehicles (See Min Paragraph 0020 Moreover, according to embodiments described herein, transportation passengers can use services while accurately estimating the time it will take to use a transportation vehicle.).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hyun in view of Kuusinen (U.S. Patent Application Publication 2019/0202656).
Regarding claim 6, Hyun teaches the system of claim 1 as set forth above. However, Hyun does not teach wherein the processor(s) infers a distribution of the observed vehicle speed data using Markov chain Monte Carlo (MCMC) method or a variational interference method.
Kuusinen, in the same field of endeavor, teaches a system for estimating traffic flow. The system creates a distribution of traffic flow data using a Markov chain Monte Carlo method (Paragraph 0031 When the statistical traffic model is trained with the long-term historical data, the historical data is used to find either the optimal parameters (using, for example, maximum likelihood or maximum a posteriori methods) or a distribution over parameters possibly represented as a sample of possible parameter values (using, for example, Markov chain Monte Carlo).).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention and with a reasonable expectation of success, to have modified Hyun with the teachings of Kuusinen which teaches creating a distribution of traffic flow data using a Markov chain Monte Carlo method in order to better predict traffic flow by analyzing historical traffic flow data (See Kuusinen Paragraph 0029 The following description illustrates a solution that aims to improve the quality of traffic forecasts in an elevator system, and thus the performance of the elevator system. The solution produces a traffic forecast both based on obtained historical or long-term origin-destination (OD) passenger counts associated with a long time period (for example, days, weeks, months or years) and recent or short-term origin-destination counts associated with a time period close to the current time, for example, the current day.).
Conclusion
The prior art made of the record and not relied upon is considered pertinent to
applicant’s disclosure.
Dudar – U.S. Patent Application Publication 2016/0300408
KOKKALA – U.S. Patent Application Publication 2021/0253394
Thies geb. Behrendt – U.S. Patent Application Publication 2024/0019298
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/PATRICK DANIEL MOHL/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
