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 .
Response to Amendment
The Amendment filed August 20 2026 has been entered and considered. Claims 1-4, 6-7, 9, and 10 have been amended. Claim 5 has been canceled. The amendment does not overcome the rejection under 35 U.S.C. 103 previously set forth. The rejection is maintained and is restated below with additional discussion of the amended limitations; accordingly, this action is made final.
Specification Objections –
In view of the amendment to the specification, the objections are withdrawn as moot.
Claim Objections –
In view of the amendment to claim 6, the objection is withdrawn as moot.
112(b) Rejections –
In view of the amendments to claims 2 and 10, the rejection is withdrawn as moot.
Response to Arguments
Applicant's arguments filed 8/20/2026, see Remarks Pgs. 3-4, have been fully considered but they are not persuasive.
Applicant argues that the prior art does not disclose the amended limitations.
Examiner respectfully disagrees.
Applicant argues (Remarks Pgs. 3-4):
For example, Applicant submits that Green fails to teach or suggest deducing a serial code composed of successive numbers corresponding to successive geometric form objects and extracting “3D geographical coordinates” of at least one of those successive geometric form objects from the database using the deduced serial code.
Examiner responds:
Green teaches that geometric representations can be used as identifying indicia (Para. 39) and that the identification symbol is used to identify a location of the marker (Para. 100). Green further teaches to deduce a serial code composed of successive numbers corresponding to a set of symbols (Paras. 180-182) and extracting 3D geographical coordinates of the symbols using the deduced serial code (Para. 184). Green also discloses that the position of the machine is determined using the marker position relative to the guideway and distance (Para. 118). One of ordinary skill in the art would have understood that deducing a serial code corresponding to successive geometric form objects and extracting “3D geographical coordinates” of those successive geometric form objects from the database using the deduced serial code would have been an obvious variation of the techniques disclosed by Green.
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) 1-2, 4, 6, and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Ono et al. (Previously cited) in view of Green et al. (Previously cited).
Regarding claim 1, Ono teaches a data processing method implemented by an electronic data processing device (10) onboard a machine (1) adapted to move on at least one predetermined path (21, 22) (Fig. 1, 8b), the data processing device including:a database (11) storing definition data of landmark objects along the predetermined path, definition data of each landmark object being associated to its 3D geographical coordinates (Fig. 5, Para. 115, “The surrounding environment observation data 151 is transformed from the vehicle coordinate system Σ.sub.T fixed to the transport vehicle 102 into the external coordinate system Σ.sub.O in which the positions of the detection target object and the trajectory recorded in the detection target information database 110 and a trajectory information database 111 are defined.”); a capture block (18) providing images of the scene in front of the machine (1), said capture block including at least a teledetection block (13) adapted for emitting waves towards the path in front of the machine, for receiving echoes of emitted waves from at least one echoing object, to create an image from the received echos and to calculate from said waves and echoes, direction and distance, relative to the machine, of said echoing object (Para. 45, “In the conventional obstacle detection system, the detectable distance of the camera, the millimeter wave radar, or the LIDAR”; Para. 51, “The surrounding environment observation unit 107 is a device that is installed in front of the transport vehicle 102 and acquires the position, the shape, the color, the reflectance, and the like of an object around the transport vehicle 102. The surrounding environment observation unit 107 includes a camera, a laser radar, or a millimeter wave radar.”);
said method comprising the iterated following steps:
a) capturing by the capture block (18) a set of images of the current scene comprising at least one image from the teledetection block (13) (Para. 113, “In Step 401, the surrounding environment observation data 151 observed by the surrounding environment observation unit 107 is acquired.”);
b) identifying in said image a landmark object (50) as a function of landmark object definition data in the database (11) (Para. 114, “In Step 402, the surrounding environment observation data acquired in Step 401 is sorted into observation data of a known object and observation data of other unknown objects.”);
c) using the image from the teledetection block (13), determining direction and distance, relative to the machine (1), of the identified landmark object (Para. 115, “ In Step 403, the observation data of the known object is transformed from the vehicle coordinate system to the external coordinate system.”; Para. 116, “In Step 404, the surrounding environment observation data in the external coordinate system Σ.sub.O calculated in Step 403 is matched with the surrounding environment map 152 recorded as the detection target data in the detection target information database 110,”);
d) determining the current 3D position of the machine (1) as a function of said determined direction and distance relative to the machine and of the 3D geographical coordinates associated with the identified landmark object in the database (11) (Para. 118, “On the other hand, here, the transport vehicle travels on the trajectory. Thus, the surrounding environment observation data 168 and the surrounding environment map 152 only need to be correlated with each other while moving on the trajectory LI in FIG. 20, and the position (FIG. 21) having the highest correlation value only needs to be obtained as the self-position.”),
wherein landmark objects (50) includes objects with respective geometrical form each associated wherein the database stores a correspondence between the geometrical form and a respective number (Para. 104, “In any case, the position and the reflectance of the detection target are recorded in advance in the detection target information database 110 as the detection target data 157”, the existence of a database indicates the usage of a respective number for identification),
comprising the following steps : identifying geometric form objects along the path (21, 22) (Para. 53, “When the obstacle detection system 103 detects an obstacle that hinders traveling of the transport vehicle 102”);
determination of the current 3D position of the machine (1) being as a function of the determined direction and distance of the at least one geometric form object relative to the machine and of the extracted 3D geographical coordinates (Para. 118, “On the other hand, here, the transport vehicle travels on the trajectory. Thus, the surrounding environment observation data 168 and the surrounding environment map 152 only need to be correlated with each other while moving on the trajectory LI in FIG. 20, and the position (FIG. 21) having the highest correlation value only needs to be obtained as the self-position.”).
Ono does not explicitly disclose deducing a serial code composed of the successive numbers corresponding to the successive geometric form objects identified; the database (11) storing serial codes associated with the 3D geographical coordinates of at least one of the successive geometric form objects giving rise to the serial code, extracting from the database (11), from said deduced serial code, 3D geographical coordinates of at least one of the successive geometric form objects associated with the deduced serial code. However, they do have a database storing relevant geometrical forms.
Green discloses wherein landmark objects (50) includes objects with respective geometrical form each associated wherein the database stores a correspondence between the geometrical form and a respective number, comprising the following steps: identifying geometric form objects along the path (21, 22) (Para. 39, “The optical sensor is capable of identifying the presence of objects as well as unique identification codes associated with detected objects. In some embodiments, the unique identification codes include barcodes, quick response (QR) codes, alphanumeric sequences, pulsed light sequences, color combinations, images, geometric representations or other suitable identifying indicia.”); deducing a serial code composed of the successive numbers corresponding to the successive geometric form objects identified (Para. 39 above; Para. 181, “For example, if vehicle 102 is moving in the GD1 direction, and 5 elements are sequentially detected with the following logical values: E1=1, E2=2, E3=0, E4=2, E5=0. In this example, controller 108 calculates numerical value 844…”); the database (11) storing serial codes associated with the 3D geographical coordinates of at least one of the successive geometric form objects giving rise to the serial code, extracting from the database (11), from said deduced serial code, 3D geographical coordinates of at least one of the successive geometric form objects associated with the deduced serial code (Para. 184, “Plate position 850 is associated with numerical value 844. In some embodiments, controller 108 is configured to determine plate position 850 based on numerical value 844 stored in the database (e.g., memory 904).”); determination of the current 3D position of the machine (1) being as a function of the determined direction and distance of the at least one geometric form object relative to the machine and of the extracted 3D geographical coordinates (Para. 65, “In some embodiments, to determine the position of the vehicle 102, the controller 108 is configured to query the memory 109 for information describing a detected marker 120. For example, the memory 109 includes location information describing the geographic location of the detected marker 120.”; Para. 118, “Vehicle 502 or first sensor 510a has a position P.sub.vehicle relative to the metasurface plate 620 that is calculated by equation 1 based on the position P.sub.plate of metasurface plate 620 relative to the guideway 514 and distance D. Vehicle 502 or first sensor 510a has a position P.sub.vehicle relative to the metasurface plate 620, as calculated by equation 1”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ono to incorporate the teachings of Green to include deducing a serial code composed of the successive numbers corresponding to the successive geometric form objects identified; the database (11) storing serial codes associated with the 3D geographical coordinates of at least one of the successive geometric form objects giving rise to the serial code, extracting from the database (11), from said deduced serial code, 3D geographical coordinates of at least one of the successive geometric form objects associated with the deduced serial code. Ono discloses a system for detecting obstacles and storing those detected obstacles into a database for efficient identification and retrieval later, however they do not explicitly disclose to deduce a serial code composed of number corresponding to successive geometric forms and comparing those codes to a database to extract 3D geographical coordinates. Green teaches to identify unique identification codes associated with detected geometric representations to identify a location of the object. One of ordinary skill in the art would have understood that implementing the identification code method of Green into the system of Ono would improve the reliability of identification and differentiation of successive landmarks along the railway path, allowing for more accurate association of detected objects with stored database entries and their corresponding geographic coordinates.
Regarding claim 2, Ono as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the following steps are performed in step b: determining, among the landmark objects (50) defined in the database (11), a subset of the landmark objects distant at most of a threshold distance from an estimated position of the machine (1) (Para. 103, “Only the position of an existing object (rail, sign, or the like) having a detection rate of a certain value or more in the detection region is set as a detection target. (Measure 2) An object having a detection rate of a certain value or more is installed in the detection region as a detection target.”); and identifying the landmark object (50) in said image by comparing the landmark object in said image with the determined subset as comprising the landmark object to be identified and excluding the landmark objects out of the subset (Para. 104, “In addition, only when the position of the detection target is included in the obstacle monitoring area at the current position of the transportation vehicle, the detection target is used to determine the occurrence of the obstacle.”).
Regarding claim 4, Ono as modified above teaches all of the elements of claim 1, as stated above, as well as wherein the machine (1) is a railway machine (Fig. 1, Para. 55, “An ATO device (automatic train operation device) is exemplified.”) and said landmark objects (50) includes objects among: QR codes, station platforms, marker boards, flood gates, geometry figures, signals (Figs. 18-19, Para. 103, “Only the position of an existing object (rail, sign, or the like)”).
Regarding claim 6, Ono as modified above teaches all of the elements of claim 1, as stated above, as well as calculating an estimated current position of the machine (1) based upon a 3D position determined in step d at a previous position and estimation of machine movement between the previous position and the current position (Green; Para. 48, “The controller 108 is configured to determine a first position of vehicle 102 on guideway”; Para. 49, “The controller 108 is configured to determine a second position of vehicle 102 on guideway 114”); comparing the current 3D position determined by step d with the estimated current position calculated; evaluating if the teledetection block (13) is reliable based upon said comparison to determine if the teledetection sensor is working properly (Green; Para. 51, “In some embodiments, controller 108 is configured to perform consistency checks between the first position and second position by comparing the first position with the second position. In some embodiments, controller 108 determines that first sensor 110a and third sensor 112a are not faulty, if the first position does not differ by more than a predefined tolerance from the second position.”).
Claim 8 corresponds to claim 1 and is rejected under the same analysis.
Claim 9 corresponds to claim 1 and is rejected under the same analysis.
Claim 10 corresponds to claim 2 and is rejected under the same analysis.
Claim(s) 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ono as modified in view of Green further in view of Mashima et al. (Previously cited).
Regarding claim 3, Ono as modified in view of Green teaches all of the elements of claim 1, as stated above, as well as wherein the capture block (18) further includes a camera (17) (Para. 51, “The surrounding environment observation unit 107 includes a camera, a laser radar, or a millimeter wave radar.”) and the set of images of the current scene includes an image from the camera (17) and an image from the teledetection block (13) (Para. 62, “The front obstacle monitoring unit 112 has a function of detecting an obstacle in the obstacle monitoring area by using a camera, a laser radar, or the like. Here, the front obstacle monitoring unit 112 may share a sensor with the surrounding environment observation unit 107.”, the two units may share a sensor, implying that they may also have two separate sensors), identifying of the landmark object (50) is performed in the image from the camera (17) (Para. 51 above);
Ono does not explicitly disclose to project the LiDAR image into the video image to match the representations or to make a determination on direction and distance using the matched representation. However, they disclose utilizing both LiDAR and video images concurrently.
Mashima teaches the set of images of the current scene includes an image from the camera (17) and an image from the teledetection block (13), identifying of the landmark object (50) is performed in the image from the camera (17) (Pg. 5, “Examples of the sensor information acquired include a visible light image (RGB (Red, Green, Blue) image, grayscale image, or the like) and distance information. The visible light image is acquired from, for example, a monocular camera, a compound eye camera in which monocular cameras are combined, or a stereo camera. In addition, the distance information is acquired from a compound eye camera, a stereo camera, a laser sensor that measures a light receiving position and a time when light is returned by projecting a laser beam to acquire the position information of an object, light detection and ranging (LiDAR), or the like.”); and a projection of the image from the teledetection block (13) into the referential of the video image is performed in order to match the representation of the identified landmark object (50) in both images (Pg. 5, “In the first embodiment, the description will be given on the premise of an RGB image photographed and synthesized by the compound eye camera and a distance image having the same resolution as the RGB image acquired by the compound eye camera.”); determination of the direction and distance, relative to the machine (1), of the identified landmark object (50) is achieved based upon the matched representation of the identified object in the image from the teledetection block (13) (Pg. 5, “Next, in Step S2, the equipment detection module 20B attempts to detect the equipment, which is a detection target, from the sensor information acquired in Step S1”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ono and Green to incorporate the teachings of Mashima to include projecting the LiDAR image into the video image to match the representations and making a determination on direction and distance using the matched representation. Ono discloses an autonomous system of obstacle detection for a train utilizing both LiDAR and video cameras to detect the surrounding environment and perform processing, however they do not explicitly disclose projecting the LiDAR image into the video image and matching them. Mashima discloses a system for detecting equipment along the path of a train using both LiDAR and a video camera, as well as synthesizing the two images for matching and detection. One of ordinary skill in the art would have recognized that the image synthesizing technique of Mashima improves the detection of the surrounding environment by making full use of the sensors disclosed by Ono.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ono as modified in view of Green further in view of Roque (Previously cited).
Regarding claim 7, Ono as modified in view of Green teaches all of the elements of claim 1, as stated above, as well as wherein the landmark object (50)identified is a sign (Para. 103, “Only the position of an existing object (rail, sign, or the like)”), and at least one of the following steps is performed: identification of said sign triggers a command to an onboard signal reading block (15); the database (11) storing detection information (Fig. 5).
Ono as modified does not explicitly disclose identifying a circulation signal by comparing the aspect to stored alternative aspects. However, they do disclose an obstacle detection method for trains that detects signs as well as colors.
Roque teaches identifying a circulation signal, reading the current aspect of the signal, storing the alternative aspects of the circulation signal, and comparing the current aspect with alternative aspects (Pgs. 5-9).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ono and Green to incorporate the teachings of Roque to include identifying a circulation signal by comparing the aspect to stored alternative aspects. Ono discloses a system for automated obstacle and trajectory detection of a train; however they do not explicitly disclose any processing of railroad signals. Roque discloses how to identify the different kinds of railroad signals. One of ordinary skill in the art would have understood that implementing the railroad signal identification techniques of Roque into the train obstacle detection method of Ono predictably improves the robustness of the autonomous system. Detecting well-known railroad signals would have allowed for the train to perform the necessary control action without user intervention.
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 DAVID A WAMBST whose telephone number is (703)756-1750. The examiner can normally be reached M-F 9-6:30 EST.
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/DAVID ALEXANDER WAMBST/Examiner, Art Unit 2663
/SEAN M CONNER/Primary Examiner, Art Unit 2663
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