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 .
Priority
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. KR10-2023-0161402, filed on 11/20/2023.
Status of Application
Claims 1-20 are pending.
Claims 1 and 13 are the independent claims.
This Final Office Action is in response to the “Amendments and Remarks” received on 05/11/2026.
Claim Objections
Claim 20 is objected to because of the following informalities: “an auxiliary data” should be rewritten as “auxiliary data”. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim 12 has/have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder communication module coupled with functional language “collect the past speed data and the path navigation data from a probe vehicle” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since Claim 12 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim 12 has/have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
In reviewing the specification, the corresponding structure to communication module is any sort of hardware device ([0056]).
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to an apparatus. Therefore, claim 1 is within at least one of the four statutory categories.
Claim 13 is directed to method. Therefore, claim 13 is within at least one of the four statutory categories.
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.
Claims 1 and 13 include limitations that recite an abstract idea (emphasized below) and Claim 13 will be used as a representative claim for the remainder of the 101 rejections.
Claim 13 recites: A traffic speed prediction method comprising:
collecting, by a processor, past speed data and path navigation data from a probe vehicle from a current time point for road sections;
estimating, by the processor, a path navigation demand based on the path navigation data at the current time point for the road sections;
predicting, by the processor, a future traffic speed using the path navigation demand and the past speed data for the road sections; and
providing, by the processor, the predicted future traffic speed to a vehicle;
wherein the path navigation demand is determined based on a number of times a destination was entered and a path was searched by a user for the road sections.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, the “estimating, predicting, and determined” steps encompass a user to draw conclusions from data. Estimating a path navigation demand based on data is a mental process. Predicting using data is also a mental process. Determining a demand based on numbers and a selection is something that can be done mentally with pen and paper. Accordingly, the claim recites at least one 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”):
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “processor”, the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the “processor” is recited at a high level of generality and merely automates the estimating and predicting steps, therefore acting as a generic computer to perform the abstract idea. Additionally, the processor is claimed generically and is are operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a processor.
In addition to that, the examiner submits that collecting past speed data and path navigation data, as well as providing predicted future traffic speed to a vehicle, using a processor, are insignificant extra-solution activities that merely use a processor to perform the process. In particular, the collecting and providing steps are recited at a high level of generality (i.e. as a general means of gathering and transferring data for use in the estimating and predicting steps), and amounts to mere data gathering/transferring, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a processor or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 13 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, the processor amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of collecting data the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of collecting and providing data is well-understood, routine, and conventional activities because the background recites that the sensors from which the data is acquired/received are all conventional sensors/vehicles. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, claim 13 is not patent eligible.
Further claim 1 is not patent eligible for the same reasons.
Dependent claims 2-12 and 14-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements, if any, in the dependent claims are not sufficient to amount to significantly more than the judicial exception for the same reasons as with Claims 1 and 13.
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, 7-10, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US-20200284594-A1 to Wang et. al. (“Wang”) in view of US-20190316920-A1 to Miyagawa et. al. (“Miyagawa”).
Regarding claim 1, Wang teaches a traffic speed prediction apparatus comprising (Wang claims 1-5):
a processor (Wang [0012] “The navigation system 12 may include a microprocessor or central processing unit (CPU) in communication with various types of computer readable storage devices or media”) configured for estimating a path navigation demand based on current path navigation data (Wang [0019] “The data received from other vehicles may include real-time data from locations on the travel route other than the current location of vehicle 10.”) for road sections (Wang [0021] “The data received via vehicle-to-infrastructure communication 28 may include traffic volume (i.e., the quantity of vehicles operating in a geographical area, which may be estimated by observing the rate at which vehicles enter and/or a leave a geographical area), … roadway incidents, traffic flow rates (vehicles/minute), … and vehicle density (vehicles/mile). The type of vehicle flow may be characterized (e.g., in a 3-phase system that includes jammed, synchronous flow, or free flowing). The real-time data from vehicle-to-infrastructure communication 28 may be utilized by the traffic modeling module 14 alone or in conjunction with any other type of data mentioned”),
and configured to predict a future traffic speed using the path navigation demand and past speed data for the road sections (Wang [0028] – [0029] “determine the travel times through each road segment 42 and intersection 44 on the travel route 34 may include any type of the real-time data, historical data, or any combination thereof… estimated travel time to the destination 38 or to reach each stop along the travel route 34 if there are multiple stops) on the travel route 34 may be based on a statistical distribution of the data, which may be any of the real-time data, historical data, or combination thereof… The statistical distribution may be used to calculate probable traffic speeds through each road segment 42” and Claim 4 “the predicted vehicle speed through the first of the road segments is based on the historical traffic speed data and the real-time speed data.”); and to provide the predicted future traffic speed to a vehicle (Wang claim 4 “the navigation system is further programmed to receive historical traffic speed data of the first of the road segments and the predicted vehicle speed through the first of the road segments is based on the historical traffic speed data and the real-time speed data.”);
and a storage configured to store algorithms and data driven by the processor (Wang [0012]),
wherein the path navigation demand is determined based on a number of times a destination and path was entered by a user for the road sections (Wang [0021] “The data received via vehicle-to-infrastructure communication 28 may include traffic volume (i.e., the quantity of vehicles operating in a geographical area, which may be estimated by observing the rate at which vehicles enter and/or a leave a geographical area)”).
Wang does not teach that the path navigation demand is determined based on a number of times a destination and path was searched by a user. However, Miyagawa teaches that the path navigation demand is determined based on a number of times a destination and path was searched by a user (Miyagawa [0242] – [0243] “the management apparatus 1 may predict a congestion state of predetermined public transportation by using OD information included in search requests from a large number of user IDs. … ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the apparatus of Wang to incorporate the teachings of Miyagawa such that the path navigation demand is determined based on a number of times a destination and path was searched by a user. Doing so would allow users to avoid crowded locations (Miyagawa [0243]).
Regarding claim 7, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 1. Wang further discloses that the processor is further configured for estimating a traffic speed for each road section according to a number of path navigation for each path navigation request time point for each road section, and wherein the number of path navigation includes a number of times destination was entered by the user for each road section and a path was navigated (Wang [0020] “The real-time data from vehicle-to-vehicle communication 26 may also include probabilistically weighted route lists. The algorithm in the traffic modeling module 14 may utilize the route list information to anticipate the routes that other vehicles may be travelling on to adjust the estimated time of arrival calculation.”).
Regarding claim 8, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 1. Wang further discloses that the processor is further configured to predict the future traffic speed by further reflecting auxiliary data including at least one of weather, day of a week, time of a day, season information, or a combination thereof (Wang [0016] “Real-time weather data may be transmitted from the sensors 24 of the vehicle 10 to the traffic modeling module 14. The vehicle 10 may collect weather information (e.g., rain, fog, or snow) from the immediate vicinity via the sensors 24 to estimate the impacts on travel speed or potential waiting times at specific locations, such as an intersection.”).
Regarding claim 9, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 8. Wang further discloses that the processor includes: an attention model with the past speed data and the path navigation data as inputs of the attention model (Wang [0028] “determine the travel times through each road segment 42 and intersection 44 on the travel route 34 may include any type of the real-time data, historical data, or any combination thereof.”); an embedding layer for embedding the auxiliary data (Wang [0028] – [0029]); and a prediction model for predicting the future traffic speed using outputs of the attention model and the embedding layer (Wang [0028] – [0029] “estimated travel time to the destination 38 or to reach each stop along the travel route 34 if there are multiple stops) on the travel route 34 may be based on a statistical distribution of the data, which may be any of the real-time data, historical data, or combination thereof… The statistical distribution may be used to calculate probable traffic speeds through each road segment 42” and Claim 4 “the predicted vehicle speed through the first of the road segments is based on the historical traffic speed data and the real-time speed data.”).
Regarding claim 10, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 8. Wang further discloses that the processor is further configured:
to select at least one road section for data collection (Wang [0028] “The type of data that is utilized to determine the travel times through each road segment 42 and intersection 44 on the travel route 34”), and
to determine whether the past speed data, the path navigation data, and the auxiliary data are normally collected from a probe vehicle in the at least one selected road section (Wang [0028] “For Example, the real-time traffic speed data transmitted from other vehicles 40, when available, on a particular road segment 42 may be weighted heavier than historical data, or the real-time data may be the only data considered, when estimating the travel time through the particular segment 42. Another example may include estimating the travel time through the particular segment 42 using historical data alone, if real-time traffic speed data transmitted from other vehicles 40 is not available.”),
to reselect another road section in response to a case where any of the past speed data, the path navigation data, and the auxiliary data is not collected normally (Wang [0015] “However, the accuracy of estimating the traffic speed may decrease when real-time data from vehicle sensors is utilized to estimate traffic speed at locations on the travel route other than the current location. Therefore, the real-time data from the vehicle sensors 24 may be weighted so that it has an increased affect in estimating travel times through portions or segments of the travel route that are closer to the current location and a decreased affect in estimating travel times through portions or segments of the travel route that are further away from the current location. The real-time data from the vehicle sensors 24 may be weighted based on the distance data relative to other vehicles in the front of and/or behind the vehicle 10, and the speed limit of the road.”).
Regarding claim 12, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 1. Wang further discloses including: a communication module operably connected to the processor (Wang ref 10 “exemplary vehicle”) and configured to collect the past speed data (Wang [0025] “The historical data may be stored on a data file system located on the vehicle 10 or may be located remotely and transmitted to the vehicle 10 via wireless communication”) and the path navigation data from a probe vehicle (Wang [0015] “he real-time data may be transmitted from sensors 24 of the vehicle 10 to the traffic modeling module 14 to estimate the traffic speed on the travel route.”).
Claim(s) 2-3, 5-6, 11, 13-15, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Miyagawa, further in view of WO-2016174745-A1 (“WO745”).
Regarding claim 2, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 1. Wang as modified by Miyagawa does not teach that the processor is further configured to generate a path navigation demand table based on 3D data including the road sections, a path navigation request time point for each road section, and an arrival time point for each road section. However, WO745 teaches that the processor is further configured to generate a path navigation demand table based on 3D data including the road sections, a path navigation request time point for each road section, and an arrival time point for each road section (WO745 Fig. 8A-8C and Description “display example of a start point O, an end point D, a departure date, a departure time, and a route indicating evaluation conditions set via the input / output unit 108 or the communication interface unit 101 … The point A and the point B are the position information of the stops existing on the route OD, and are stored in the map data storage unit 110 or the timetable storage unit 114 as route information. … The predicted arrival time display 803 is a display output example of the predicted arrival time at each point calculated by the arrival time prediction unit 105 based on the evaluation condition of the evaluation condition display 802.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of WO745 to Wang as modified by Miyagawa such that the processor is further configured to generate a path navigation demand table based on 3D data including the road sections, a path navigation request time point for each road section, and an arrival time point for each road section. Doing so would allow users to view results of the processing and more visually recognize the estimated arrival times (WO745 Description).
Regarding claim 3, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 1. Wang as modified by Miyagawa does not teach that the processor is further configured to generate a path navigation demand table including a number of vehicles scheduled to arrive for each arrival time point for each road section based on a road section, a time point of a path navigation request for each road section, and an arrival time point for each road section. However, WO745 teaches that the processor is further configured to generate a path navigation demand table including a number of vehicles scheduled to arrive for each arrival time point for each road section based on a road section, a time point of a path navigation request for each road section, and an arrival time point for each road section (WO745 Fig. 8A-8C & 12 and Description “display example of a start point O, an end point D, a departure date, a departure time, and a route indicating evaluation conditions set via the input / output unit 108 or the communication interface unit 101 … The predicted arrival time display 803 is a display output example of the predicted arrival time at each point calculated by the arrival time prediction unit 105 based on the evaluation condition of the evaluation condition display 802. … display output of the excess traffic volume of each road section constituting the predetermined route.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of WO745 to Wang as modified by Miyagawa such that the processor is further configured to generate a path navigation demand table including a number of vehicles scheduled to arrive for each arrival time point for each road section based on a road section, a time point of a path navigation request for each road section, and an arrival time point for each road section. Doing so would allow users to view results of the processing and more visually recognize the estimated arrival times (WO745 Description).
Regarding claim 5, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 3. WO745 further discloses that the processor is further configured to determine that the future traffic speed will decrease as the number of vehicles scheduled to arrive for each arrival time point for each road section increases (WO745 Fig. 4 and Description “the speed and traffic of the mixed traffic represented by the second curve 402 in FIG. 4 when general vehicles and road public traffic are mixed in the same lane at a predetermined ratio. It is a characteristic showing the relationship with density.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of WO745 to Wang as modified by Miyagawa and WO745 such that the processor is further configured to determine that the future traffic speed will decrease as the number of vehicles scheduled to arrive for each arrival time point for each road section increases. Doing so would allow the system to predict an arrival time (WO745 Description).
Regarding claim 6, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 3. Wang further discloses that the processor is further configured for estimating a demand for each road section according to the number of vehicles scheduled to arrive for each arrival time point for each road section (Wang [0021] “The data received via vehicle-to-infrastructure communication 28 may include traffic volume (i.e., the quantity of vehicles operating in a geographical area, which may be estimated by observing the rate at which vehicles enter and/or a leave a geographical area)”) and to estimate the future traffic speed according to the demand (Wang [0028] – [0029] “determine the travel times through each road segment 42 and intersection 44 on the travel route 34 may include any type of the real-time data, historical data, or any combination thereof… estimated travel time to the destination 38 or to reach each stop along the travel route 34 if there are multiple stops) on the travel route 34 may be based on a statistical distribution of the data, which may be any of the real-time data, historical data, or combination thereof… The statistical distribution may be used to calculate probable traffic speeds through each road segment 42” and Claim 4 “the predicted vehicle speed through the first of the road segments is based on the historical traffic speed data and the real-time speed data.”).
Regarding claim 11, Wang as modified by Miyagawa teaches all of the elements of the current invention in claim 10. Wang further discloses past speed data, path navigation data, and axillary data (Wang [0028] “type of the real-time data, historical data”). Wang as modified by Miyagawa does not teach that the processor is further configured: in response to a case where the data is normally collected, to configure a path navigation demand table based on the path navigation data. However, WO745 teaches that the processor is further configured: in response to a case where the data is normally collected, to configure a path navigation demand table based on the path navigation data (WO745 Fig. 8A-8C & 12 and Description “The predicted arrival time display 803 is a display output example of the predicted arrival time at each point calculated by the arrival time prediction unit 105 based on the evaluation condition of the evaluation condition display 802. … display output of the excess traffic volume of each road section constituting the predetermined route.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the apparatus of Wang as modified by Miyagawa to incorporate the teachings of WO745 such that the processor is further configured: in response to a case where the data is normally collected, to configure a path navigation demand table based on the path navigation data. Doing so would allow users to view results of the processing and more visually recognize the estimated arrival times (WO745 Description).
Regarding claim 13, Wang teaches a traffic speed prediction method comprising (Wang claims 1-5):
collecting, by a processor (Wang [0012] “The navigation system 12 may include a microprocessor or central processing unit (CPU) in communication with various types of computer readable storage devices or media”), past speed data (Wang Claim 4 “navigation system is further programmed to receive historical traffic speed data”) and path navigation data from a probe vehicle from a current time point for road sections (Wang [0019] “The data received from other vehicles may include real-time data from locations on the travel route other than the current location of vehicle 10.”);
estimating, by the processor, a path navigation demand based on the path navigation data at the current time point for the road sections (Wang [0021] “The data received via vehicle-to-infrastructure communication 28 may include traffic volume (i.e., the quantity of vehicles operating in a geographical area, which may be estimated by observing the rate at which vehicles enter and/or a leave a geographical area), … roadway incidents, traffic flow rates (vehicles/minute), … and vehicle density (vehicles/mile). The type of vehicle flow may be characterized (e.g., in a 3-phase system that includes jammed, synchronous flow, or free flowing). The real-time data from vehicle-to-infrastructure communication 28 may be utilized by the traffic modeling module 14 alone or in conjunction with any other type of data mentioned”);
predicting, by the processor, a future traffic speed using the path navigation demand and the past speed data for the road sections (Wang [0028] – [0029] “determine the travel times through each road segment 42 and intersection 44 on the travel route 34 may include any type of the real-time data, historical data, or any combination thereof… estimated travel time to the destination 38 or to reach each stop along the travel route 34 if there are multiple stops) on the travel route 34 may be based on a statistical distribution of the data, which may be any of the real-time data, historical data, or combination thereof… The statistical distribution may be used to calculate probable traffic speeds through each road segment 42” and Claim 4 “the predicted vehicle speed through the first of the road segments is based on the historical traffic speed data and the real-time speed data.”); and
providing, by the processor, the predicted future traffic speed to a vehicle (Wang claim 4 “the navigation system is further programmed to receive historical traffic speed data of the first of the road segments and the predicted vehicle speed through the first of the road segments is based on the historical traffic speed data and the real-time speed data.”);
wherein the path navigation demand is determined based on a number of times a destination and path was entered by a user for the road sections (Wang [0021] “The data received via vehicle-to-infrastructure communication 28 may include traffic volume (i.e., the quantity of vehicles operating in a geographical area, which may be estimated by observing the rate at which vehicles enter and/or a leave a geographical area)”).
Wang does not teach that the path navigation demand is determined based on a number of times a destination and path was searched by a user. However, Miyagawa teaches that the path navigation demand is determined based on a number of times a destination and path was searched by a user (Miyagawa [0242] – [0243] “the management apparatus 1 may predict a congestion state of predetermined public transportation by using OD information included in search requests from a large number of user IDs. … ”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the apparatus of Wang to incorporate the teachings of Miyagawa such that the path navigation demand is determined based on a number of times a destination and path was searched by a user. Doing so would allow users to avoid crowded locations (Miyagawa [0243]).
Wang as modified by Miyagawa does not teach estimating, by the processor, a path navigation demand based on the path navigation data from a probe vehicle. However, WO745 teaches estimating, by the processor, a path navigation demand based on the path navigation data from a probe vehicle (WO745 Abstract “The travel speed (probe speed) of each road section is acquired from the travel data (probe data) collected from a plurality of general vehicles, and the traffic volume of the predetermined road section is estimated from the probe speed.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the apparatus of Wang as modified by Miyagawa to incorporate the teachings of WO745 such that method comprises estimating, by the processor, a path navigation demand based on the path navigation data from a probe vehicle. Doing so would allow the system to predict an arrival time (WO745 Description).
With respect to claim 14, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 13. Additionally, the limitations recited in claim 14 mirror the limitations recited in claim 2, which were rejected above. See the rejection of claim 2 above.
With respect to claim 15, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 13. Additionally, the limitations recited in claim 15 mirror the limitations recited in claim 3, which were rejected above. See the rejection of claim 3 above.
With respect to claim 19, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 13. Additionally, the limitations recited in claim 19 mirror the limitations recited in claim 8, which were rejected above. See the rejection of claim 8 above.
Regarding claim 20, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 13. Wang further discloses that the predicting of the future traffic speed includes:
selecting, by the processor, at least one road section for data collection (Wang [0028] “The type of data that is utilized to determine the travel times through each road segment 42 and intersection 44 on the travel route 34”);
determining, by the processor, whether the past speed data, the path navigation data, and an auxiliary data are normally collected from the probe vehicle in the at least one selected road section (Wang [0028] “For Example, the real-time traffic speed data transmitted from other vehicles 40, when available, on a particular road segment 42 may be weighted heavier than historical data, or the real-time data may be the only data considered, when estimating the travel time through the particular segment 42. Another example may include estimating the travel time through the particular segment 42 using historical data alone, if real-time traffic speed data transmitted from other vehicles 40 is not available.”); and
reselecting, by the processor, another road section in response to a case where any of the past speed data, the path navigation data, and the auxiliary data is not collected normally (Wang [0015] “However, the accuracy of estimating the traffic speed may decrease when real-time data from vehicle sensors is utilized to estimate traffic speed at locations on the travel route other than the current location. Therefore, the real-time data from the vehicle sensors 24 may be weighted so that it has an increased affect in estimating travel times through portions or segments of the travel route that are closer to the current location and a decreased affect in estimating travel times through portions or segments of the travel route that are further away from the current location. The real-time data from the vehicle sensors 24 may be weighted based on the distance data relative to other vehicles in the front of and/or behind the vehicle 10, and the speed limit of the road.”).
Claim(s) 4, and 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Miyagawa, further in view of WO745 and CN-113159374-B to Li et. al. (“Li”).
Regarding claim 4, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 3. Wang as modified by Miyagawa and WO745 does not teach that the processor is further configured:
to select the road section using a link section of a road,
to divide 24 hours into predetermined first time units to assign an index for each path navigation request time point for each road section,
to divide 24 hours into second time units different from the first time units to assign an index for each arrival time point for each road section, and
to generate the path navigation demand table by mapping the index for each path navigation request time point for each road section and the number of vehicles scheduled to arrive for each index for each arrival time point for each road section.
However, Li teaches that the processor is further configured:
to select the road section using a link section of a road (Li Description “traffic flow rate real-time data of the road section”),
to divide 24 hours into predetermined first-time units to assign an index for each path navigation request time point for each road section (Li Fig. 6),
to divide 24 hours into second time units different from the first time units to assign an index for each arrival time point for each road section (Li Fig. 6), and
to generate the path navigation demand table by mapping the index for each path navigation request time point for each road section and the number of vehicles scheduled to arrive for each index for each arrival time point for each road section (Li Fig. 6 and Description “the traffic flow rate of the road section can be predicted in a short term in real time. real time prediction diagram as shown in FIG. 6, assuming that the current needs to predict the traffic flow rate of Beijing expressway with 60561100057 time section number of September 21, 50-10: 20, 21, January 21, 2020, using the prediction method of the invention can realize fast prediction.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of Li to Wang as modified by Miyagawa and WO745 such that the processor is further configured: to select the road section using a link section of a road, to divide 24 hours into predetermined first time units to assign an index for each path navigation request time point for each road section, to divide 24 hours into second time units different from the first time units to assign an index for each arrival time point for each road section, and to generate the path navigation demand table by mapping the index for each path navigation request time point for each road section and the number of vehicles scheduled to arrive for each index for each arrival time point for each road section. Doing so would allow traffic management to consider historical evolution and real-time prediction to provide effective information for real-time control of road traffic (Li Background).
With respect to claim 16, Wang as modified by Miyagawa and WO745 teaches all of the elements of the current invention in claim 15. Additionally, the limitations recited in claim 16 mirror the limitations recited in claim 4, which were rejected above. See the rejection of claim 4 above.
Regarding claim 17, Wang as modified by Miyagawa, WO745, and Li teaches all of the elements of the current invention in claim 16. WO745 further discloses that the predicting of the future traffic speed includes: determining, by the processor, that the future traffic speed will decrease as the number of vehicles scheduled to arrive for each arrival time point for each road section increases (WO745 Fig. 4 and Description “the speed and traffic of the mixed traffic represented by the second curve 402 in FIG. 4 when general vehicles and road public traffic are mixed in the same lane at a predetermined ratio. It is a characteristic showing the relationship with density.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of WO745 to Wang as modified by Miyagawa, WO745, and Li such that the predicting of the future traffic speed includes: determining, by the processor, that the future traffic speed will decrease as the number of vehicles scheduled to arrive for each arrival time point for each road section increases. Doing so would allow the system to predict an arrival time (WO745 Description).
Regarding claim 18, Wang as modified by Miyagawa, WO745, and Li teaches all of the elements of the current invention in claim 16. Wang further discloses that the predicting of the future traffic speed includes: estimating, by the processor, a demand for each road section according to the number of vehicles scheduled to arrive for each arrival time point for each road section (Wang [0021] “The data received via vehicle-to-infrastructure communication 28 may include traffic volume (i.e., the quantity of vehicles operating in a geographical area, which may be estimated by observing the rate at which vehicles enter and/or a leave a geographical area)”) and to estimate the future traffic speed according to the demand (Wang [0028] – [0029] “determine the travel times through each road segment 42 and intersection 44 on the travel route 34 may include any type of the real-time data, historical data, or any combination thereof… estimated travel time to the destination 38 or to reach each stop along the travel route 34 if there are multiple stops) on the travel route 34 may be based on a statistical distribution of the data, which may be any of the real-time data, historical data, or combination thereof… The statistical distribution may be used to calculate probable traffic speeds through each road segment 42” and Claim 4 “the predicted vehicle speed through the first of the road segments is based on the historical traffic speed data and the real-time speed data.”).
Response to Arguments/Remarks
With respect to Applicant’s remarks filed on 05/11/2026; Applicant's “Amendments and Remarks” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
Office Note: Claim XX has been cancelled, therefore any rejection or objection pertaining thereupon is now considered moot.
With respect to the claim objections, applicants “Amendment and Remarks” have been fully considered.
Office Note: Due to applicant’s amendments, new claim objections appear on the record as stated in the above Office Action.
With respect to the claim rejections under 35 U.S.C. § 112 (b), applicants “Amendment and Remarks” have been fully considered.
With respect to the claim rejections under 35 U.S.C. § 101, applicants “Amendment and Remarks” have been fully considered.
Applicant remarks:
In particular, the amended claims 1 and 13 include the claim limitation of a processor configured for estimating a path navigation demand based on current path navigation data for each of road sections, and to predict a future traffic speed using the path navigation demand and past speed data for the road sections; and provide the predicted future traffic speed to a vehicle", which amount to significantly more than the abstract idea or a human activity but instead is a practical application of the presently claimed invention.
Office Response:
As stated in the above office action, providing the predicted future traffic speed to a vehicle is not a practical application, but rather simply data transferring, which is an insignificant extra solution activity.
Therefore, the Office's respectfully disagrees with applicant’s arguments.
With respect to the claim rejections under 35 U.S.C. § 102 and 103, applicants “Amendment and Remarks” have been fully considered. Applicant has amended the independent claim and these amendments have changed the scope of the original application and the Office has supplied new grounds for rejection attached below in the FINAL office action and therefore the prior arguments are considered moot. Applicant further argues that the other independent claims which recite similar features are allowable and the dependent claims are also allowable since they depend on allowable subject and the Office respectfully disagrees. It is the Office's stance that all of the claimed subject matter has been properly rejected; therefore, the Office's respectfully disagrees with applicant’s arguments.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.N./Examiner, Art Unit 3666
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
8/6/26
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