DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in response to the amendment filed on ----9/12/2025 for application 18/474,320. Claim 1 – 12 are pending and have been examined.
Claim 1, 2, 4 – 8 are amended.
Claim 9 – 12 are new.
Claim rejection under 35 U.S.C. 101 is withdrawn in light of applicant’s argument and amendment.
Claim rejection under 35 U.S.C. 112(b) is withdrawn in light of applicant’s amendment.
Respond to Amendment
Applicant’s amendment filed on 9/12/2025 has been entered..
Respond to Argument
Applicant’s arguments with respect to claim rejection under 35 U.S.C. 102 and 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 3 – 4 and 7 – 8 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al., (hereinafter Yu), CN107180530 in view of Yamada et a., (hereinafter Yamada), JP5316806.
Regarding Claim 1, Yu discloses: A congestion prediction device comprising: an execution circuit; and a memory (translation page 5, “The invention relates to the technical field of public traffic information processing, in particular to a road network state prediction method based on a deep space-time convolution cyclic network”; the information processing are carried out by device with execution circuit and memory),
wherein the memory stores mapping data that defines a mapping trained in advance by machine learning using time-series data (translation page 2, “extracting the spatial characteristics of congestion condition … time sequence of the road network state to realize the prediction”, “road network state prediction model”; the model is a mapping of input data to the output data, the model is stored in the memory for the information processing), and the execution circuit is configured to
acquire traffic data from one or more vehicles, the traffic data including a plurality of congestion variables, each congestion variable including at least an average vehicle speed in a specific area at a corresponding regular time interval within a defined time period (translation page 2 & eq. 1, “n represents the number of vehicles passing through the road section in the certain time section a represents the a-th road section (specific area) in the road network”; eq. 1 is the average vehicle speed in a specific area at a corresponding time; fig. 6 & translation page 8, “in fig. 6, the neural network comprises two layers of recurrent neural networks, the road network state of a certain time period in the future is predicted based on the characteristics of the first 15 time periods (defined time period)”; each input variable in the model represents a congestion variables at a corresponding regular time interval):
generate input data based on the plurality of congestion variables associated with the specific area and the defined time period; input the generated input data to the mapping; and output an output variable indicating a predicted congestion variable in the specific area (translation page 9, “(as shown in fig. 3A) as an example, assuming that the speeds of two road sections are respectively 20km/h and 30km/h, the value of the grid through which all the road sections pass is the speed value of the road section, when a certain grid has multiple road sections passing through, the value of the grid is the speed average value of the road sections (as shown in fig. 3C), then normalizing all the grids, and the obtained result is shown in fig. 3C, so that the road network state of each time period can be expressed by one picture, and is marked as pj”; fig. 6, pj are the input of the model which are indicating the speed of vehicles) after a lapse of the regular time interval from an end time of the defined time period (refer to the mapping above & translation page 6, “the value of the grid is the speed average value of the road sections; therefore, the road network state of each time period is expressed by one picture and is marked as pj The road network state to be predicted can be represented by a state vector, which is marked as Vj+m，Vj+m＝[v1,j+m,v2,j+m,…,vk,j+m] Where m represents the mth time period in the future”; i.e., the vector includes speed average values (degree of congestion) of each area and each time period is a time intervals in the prediction period the ),
wherein the mapping comprises a recurrent neural network configured to learn transitions in congestion variables for the specific area along a time axis (refer to the mapping above, translation page 3, “a recurrent neural network for extracting (learn) the time sequence rule of the evolution of the road network (transitions in congestion variables)”; the system learns to predict the congestion of future time, thus the transitions in congestion variables), and
the output variable is generated based at least on time-series congestion data from the specific area, including average vehicle speed and other traffic variables (refer to the mapping above, the output is a time-series data corresponding to the current input time-series data).
Yu does not explicitly teach:
the traffic data being obtained from one or more vehicle-mounted sensors, including a vehicle speed sensor and a positioning sensor
Yamada, in the same field of endeavor, explicitly teach:
the traffic data being obtained from one or more vehicle-mounted sensors, including a vehicle speed sensor and a positioning sensor (Yamada, Fig. 2 – 3 & translation page 3, “the position information of the vehicle 10 is acquired from the position information acquisition unit 811”, “the average vehicle speed of the traveling section is acquired based on information from the vehicle speed sensor 210”; both speed sensors and position sensor are on the vehicle)
Yu and Yamada both teach crowed sourced traffic detection by information from plurality of vehicles and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the details of speed sensor and position sensor as taught by Yamada in the system of Yu to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this combination in order to acquire the required input data for the model/system.
Regarding Claim 3, Yu and Yamada combination teaches all the limitation of Claim 1. The combination further teach: the congestion variable is a variable indicating a speed of a vehicle (translation page 9, “(as shown in fig. 3A) as an example, assuming that the speeds of two road sections are respectively 20km/h and 30km/h, the value of the grid through which all the road sections pass is the speed value of the road section, when a certain grid has multiple road sections passing through, the value of the grid is the speed average value of the road sections (as shown in fig. 3C), then normalizing all the grids, and the obtained result is shown in fig. 3C, so that the road network state of each time period can be expressed by one picture, and is marked as pj”; fig. 6, pj are the input of the model which are indicating the speed of vehicles), and the output variable is a variable indicating the speed of the vehicle (translation page 7, “The road network state to be predicted can be represented by a state vector, Vj+m，Vj+m＝[v1,j+m,v2,j+m,…,vk,j+m”; the predicted output are the average speed of vehicle).
Regarding Claim 4, Yu and Yamada combination teaches all the limitation of Claim 1. Yu further teach: the recurrent neural network outputs the output variable when the congestion variables within a specific period are input to the recurrent neural network (translation page 3, “a recurrent neural network for extracting the time sequence rule of the evolution of the road network”; the output of the recurrent network is generated when inputting input variables of a time step).
Regarding Claim 7, Claim 7 is the corresponding method claim of Claim 1. Claim 7 is rejected with same reason.
Regarding Claim 8, Claim 8 is the non-transitory computer readable medium claim of Claim 1. The information processing device of Yu inherently process the steps based on predetermined/preprogrammed logic. Claim 8 is rejected with the same reason.
Claim(s) 2 and 9 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al., (hereinafter Yu), CN107180530 in view of Yamada et a., (hereinafter Yamada), JP5316806 as applied to claim 1 above, and further in view of Pant, “A Guide For Time Series Prediction Using Recurrent Neural Networks(LSTMs)”.
Regarding Claim 2, Yu and Yamada combination teaches all the limitation of Claim 1, Yu further teach: when N, which is a number of congestion variables, is an integer greater than 1 (refer to the mapping in Claim 1 & translation page 2, “numbering each road section as (1,2,3, …, k)”; K is number of congestion variable), L is any positive integer (refer to the mapping in Claim 1 translation page 6, “m (L) represents the mth time period in the future”; m is positive integer), and the output variable is output multiple times (translation page 6, “The road network state to be predicted can be represented by a state vector, which is marked as Vj+m，Vj+m＝[v1,j+m,v2,j+m,…,vk,j+m]Where m represents the mth time period in the future”; i.e., the prediction output is output for each time period (multiple times)),
a first specific period is the specific period corresponding to an L-th output variable, a second specific period is the specific period corresponding to the output variable of an (L+1)th output variable (refer to the mapping above & translation page 7, “for example, every 2 minutes is used as a time period”; the recurrent model output prediction for every 2 minutes. The (L+1)th output and the Lth output are 2 minutes in between),
a first start time is a start time of the first specific period, a first end time is an end time of the first specific period, a second start time is a start time of the second specific period, the second start time occurs one regular time interval after the first start time, a second end time is an end time of the second specific period, the second end time occurs one regular time interval after the first end time (refer to the mapping above, the start time and end time of each 2 minutes intervals has 2 minutes difference),
the execution circuit is configured to acquire a first set of input variables including the N congestion variables from the first start time to the first end time within the first specific period (refer to the mapping above, & translation page 3, “the input variables are the state p of a certain network”; the network has k (N) sections, the Input also include the N variables; eq. 1 & translation page 7, “average speed is calculated by the following method: the average of the average speeds of all vehicles passing through a certain road section in a certain time section”; the average speed (input variables) is collected during a time period. Each time period has a start time and an end time),
output the L-th output variable by inputting the acquired first set of input variables to the mapping (refer to the mapping in Claim 1, the output is generated by inputting the input to the model),
acquire a second set of input variables including the N congestion variables from the second start time to the second end time within the second specific period (refer to the mapping above, the average speed is collected during a time period, each time period has a start and end time. In this case the second time period)
output the (L + 1)th output variable by inputting the acquired second set of input variables to the mapping (refer to the mapping above, the output is generated by inputting the input to the model. Examiner notes that the entire claim is under a conditional clause “when”. Within BRI, none of the recited limitations are required by the claim.).
Yu and Yamada combination does not explicitly teach:
while setting the congestion variable of the second end time to the L-th output variable
Pant, in the same field of endeavor, explicitly teach:
while setting the congestion variable of the second end time to the L-th output variable (Pant, page 3 & figure in page 3 “retain state from one iteration to the next by using their own output as input for the next step”; i.e., the Lth output can be the input of the next time period. The output of Yu represents congestion, thus congestion variable. The next time period is the second time period which has a start and an end time. The input is for the second time period thus for both the start time and end time of the second time period)
Yu (in view of Yamada) and Pant both teach using recurrent neural network for time series prediction and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the connection of the prior time step as teach by Pant in the model of Yu (in view of Yamada) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification in order to “retain state from one iteration to the next” (Pant, page 3).
Regarding Claim 9, Yu and Yamada combination teaches all the limitation of Claim 1. The combination further teach:
perform iterative prediction control; shift the time window of the defined time period forward by the regular time interval for each iteration; and repeatedly input the updated input data set to the mapping to predict average vehicle speeds at a plurality of future time points in the specific area (refer to the mapping in Claim 1 & Yu, translation page 12, “predicting the state of the road network in a certain time period in the future And inputting the previous road network state data for prediction into the trained model in the second step as an input variable to obtain an output vector, wherein the output vector is the predicted road network state of the next time period”; Yamada translation page 2, “immediately provide traffic information reflecting the current traffic situation”; i.e., Yu teaches using a window of 15 time segments for the future traffic prediction. Yamada teaches to calculate/provide updated traffic information in every time segment of the collected data from the vehicle. The combination renders obviousness of the claimed limitation.)
The combination does not explicitly teach:
the output variable generated in a previous prediction cycle is included as a congestion variable in a subsequent input data set;
Pant, in the same field of endeavor, explicitly teach:
the output variable generated in a previous prediction cycle is included as a congestion variable in a subsequent input data set (Pant, page 3 & figure in page 3 “retain state from one iteration to the next by using their own output as input for the next step”; i.e., the Lth output can be the input of the next time period. The output of Yu represents congestion, thus congestion variable. The next time period is the second time period which has a start and an end time. The input is for the second time period thus for both the start time and end time of the second time period)
The reason for combination is same as Claim 2.
Regarding Claim 10 and 11, they are the corresponding method and non-transitory computer readable medium claim of Claim 9. These are rejected with same reason.
Claim(s) 5 – 6 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al., (hereinafter Yu), CN107180530 in view of Yamada et al., (hereinafter Yamada), JP5316806 as applied to claim 1 above, and further in view of Choi et al., (hereinafter Choi), KR20230057558.
Regarding Claim 5, Yu and Yamada combination teaches all the limitation of Claim 1. The combination does not explicitly teach: the input variables include a variable indicating a date and time in the specific area within the specific period as a variable different from the congestion variable.
Choi, in the same field of endeavor, explicitly teach:
the input variables include a variable indicating a date and time in the specific area within the specific period as a variable different from the congestion variable (Choi, translation page 5, “The data set includes speed information according to a plurality of routes, time, and day classification, wherein the day classification is classified into at least four different categories according to holidays and working days … and the time conditions may be working hours and commuting hours.”).
Yu (in view of Yamada) and Choi both teach congestion prediction using recurrent neural network and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the time and date consideration in the traffic congestion prediction taught by Choi in the model of Yu (in view of Yamada) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification in order to further refine the traffic prediction based on the time and date.
Regarding Claim 6, Yu and Yamada combination teaches all the limitation of Claim 1. The combination does not explicitly teach: the input variables include a variable indicating weather in the specific area within the specific period as a variable different from the congestion variable.
Choi, in the same field of endeavor, explicitly teach:
the input variables include a variable indicating weather in the specific area within the specific period as a variable different from the congestion variable (Choi, translation page 5, “The data set includes speed information according to a plurality of routes, weather conditions … the weather conditions is rain, sunny, cloudy, and fog conditions”).
Yu (in view of Yamada) and Choi both teach congestion prediction using recurrent neural network and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the weather consideration in the traffic congestion prediction taught by Choi in the model of Yu (in view of Yamada) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification in order to further refine the traffic prediction based on the weather.
Claim(s) 12 is rejected under 35 U.S.C. 103 as being unpatentable over Yu et al., (hereinafter Yu), CN107180530 in view of Yamada et al., (hereinafter Yamada), JP5316806 as applied to claim 1 above, and further in view of Kumar, “Google Maps Show Traffic Updates”, TechWelkin.com.
Regarding Claim 12, Yu and Yamada combination teaches all the limitation of Claim 1. The combination does not explicitly teach:
a personal terminal having a touch panel display and a communication unit, wherein the personal terminal is configured to receive a user selection of a specific area and a request period via the touch panel display;
in response to the user selection, the execution circuit transmits, to the personal terminal, the output variable indicating the predicted congestion variable for the specific area and surrounding areas corresponding to the request period; and
the personal terminal is configured to cause the touch panel display to present a map including the selected specific area and surrounding areas, and to display congestion information corresponding to the predicted congestion variable, including graphical indicators showing traffic jam conditions when an average vehicle speed is below a predetermined threshold.
Kumar, in the same field of endeavor, explicitly teach:
a personal terminal having a touch panel display and a communication unit, wherein the personal terminal is configured to receive a user selection of a specific area and a request period via the touch panel display; in response to the user selection, the execution circuit transmits, to the personal terminal, the output variable indicating the predicted congestion variable for the specific area and surrounding areas corresponding to the request period; and the personal terminal is configured to cause the touch panel display to present a map including the selected specific area and surrounding areas, and to display congestion information corresponding to the predicted congestion variable, including graphical indicators showing traffic jam conditions when an average vehicle speed is below a predetermined threshold (Kumar page 1, “download Google Maps in your mobile phone and use it to get Google Maps traffic updates”, “opened Google Maps and checked the traffic situation on the road”; page 2, “Google calculates the speed at which you’re traveling and … figures out whether the traffic is smooth or snarling.”; Google Maps user can use mobile phone to choose/move to an map area of interest (specific area), in response, the Google server returns the current (request period) calculated/predicted traffic situation of on the map with colors indicating if the average speed is lower than usual (predetermined threshold)).
Yu (in view of Yamada) and Kumar both teach crowed sourced congestion prediction application and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the mobile application taught by Kumar in the model of Yu (in view of Yamada) to achieve the claimed teaching. One of the ordinary skill in the art would have motivated to make this modification to help user plan the route (Kumar page 1).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIEN MING CHOU whose telephone number is (571)272-9354. The examiner can normally be reached Monday- Friday 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HELAL ALGAHAIM can be reached on (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHIEN MING CHOU/Examiner, Art Unit 3666
/HELAL A ALGAHAIM/SPE , Art Unit 3666