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 Office Action is in response to the amendments filed on 19 September 2025. Claims 1-15 are amended. Claims 1-15 are presently pending and examined.
Response to Arguments
Objection to Drawings
Applicant’s amendments, filed 19 September 2025, with respect to typographical error in Fig. 1, 10 and 11, have been fully considered and accepted. The Objection to drawings has been withdrawn.
112 Rejection
Applicant’s amendments and accompanying arguments, see remarks, filed 19 September 2025, with respect to 112 rejections have been fully considered and are persuasive. The 112 rejection of Claims 3, 10 and 11 have been withdrawn.
Prior Art Rejection
Applicant’s amendments and accompanying arguments, see remarks, filed 19 September 2025, with respect to the rejection(s) of claim(s) 1-15 under 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view L Karlsson et. al. US20040167669 (“Karlsson”) and David Ben Ezra et. al. US20230281851 (“Ezra”).
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.
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.
Claims 1-5, 7 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Michinori Yoshida (“Yoshida”) US20200370915A1 in view of Alex Masuo Kaneko et. al. (“Kaneko”) US11538241 and L Karlsson et. al. US20040167669 (“Karlsson”).
As per Claim 1,
Yoshida discloses,
A map generation device, comprising:
an external sensor that measures an environment around an own vehicle to generate sensor data (see at least Fig 1 and [0039] The sensor interface 930 is connected to a Global Positioning System (GPS) 931 and various types of sensors 932. Practical examples of the sensors 932 are a camera, a laser, an millimeter-wave radar, and a sonar.
generating a generation map based on the map generation data; (see at least [0011] a surrounding area map generation unit to generate a surrounding area map of the vehicle, during travel of the vehicle, as surrounding area map information, using the positional information, and [0064] In step S107, the surrounding area map generation unit 130 estimates an own position and generates the surrounding area map 182 simultaneously by the SLAM technique, using the sensor information such as a camera image and a point cloud from the laser sensor)
estimating a travel position of the own vehicle on the generation map based on the self-position estimation data to generate a self-position estimation result (see at least [0013] an alignment unit to align the simple map and the surrounding area map with each other based on the road characteristic, and to calculate a position of the vehicle as a vehicle position; and [0066] As illustrated in FIG. 4, with SLAM in the vehicle 200, a shape of a surrounding atmosphere is perceived by the sensors 932, and the own position is estimated from shape data. Also, with SLAM in the vehicle 200, the own position is estimated, the surrounding area map 182 is generated while correction of the own position is performed, and the vehicle 200 is moved. The surrounding area map 182 employs xyz coordinate representation, and latitude-longitude information is stored in a portion of the surrounding area map 182. The surrounding area map 182 is a map generated from the sensor information online in a real-time manner).
calculating a true value of a relative position between the map generation data and the self-position estimation data based on a data allocation method; (see at least [0066] Also, with SLAM in the vehicle 200, the own position is estimated, the surrounding area map 182 is generated while correction of the own position is performed, Fig. 6, [0093] as a result of alignment of the simple map 181 and the surrounding area map 182, the alignment unit 150 calculates a maximum likelihood position, which is a position of the own vehicle, as the vehicle position 151, [0094] Step S208 corresponds to step S111 of FIG. 2., and [0095] In step S209, the correction amount calculation unit 160 calculates a difference between the vehicle position 151 and the positional information 111 which is obtained by the GPS, as the position correction amount 161 to be used for correcting the positional information 111).
determining an error in self-position estimation based on comparing the true value of the relative position and the self-position estimation result (see at least [0079] In step S112, the correction amount calculation unit 160 calculates a position correction amount 161 for correcting the positional information 111, based on the vehicle position 151 calculated by the alignment unit 150. The position correction amount 161 is used for correction of the positional information 111 by the GPS 931, and [0095] In step S209, the correction amount calculation unit 160 calculates a difference between the vehicle position 151 and the positional information 111 which is obtained by the GPS, as the position correction amount 161 to be used for correcting the positional information 111. Using the position correction amount 161, the correction amount calculation unit 160 updates the position correction amount 184 stored in the storage unit 180.
Yoshida does not disclose
that evaluates an error in self-position estimation from the true value of the relative position and a self-position estimation result calculated by the self-position estimation unit.
Kaneko teaches system,
that evaluates an error in self-position estimation from the true value of the relative position and a self-position estimation result calculated by the self-position estimation unit. (see at least [Col. 8, line 13-24] The current position error estimation means 210d of the weighting means 210 estimates an error in the position estimated by the current position estimation means 201. For example, when the current position estimation means 201 estimates the current position of the moving object 100 by the GPS, an error output by the GPS is used as the current position error. In addition, when the current position of the moving object 100 is estimated by the current position estimation means 201 using a method of estimating a relative position from a certain reference, an error proportional to a traveling distance may be used as the current position error).
Thus, Yoshida discloses a map generation/self-positioning estimation device and Kaneko teaches calculation of the true value of the relative position using the relative movement amount.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the calculation of true value of the relative position as taught by Kaneko, with a reasonable expectation of success, to provide a position estimation device that can perform highly accurate position estimation (Col. 2, Line 14-16).
Yoshida does not disclose,
one or more processors configured to perform operations comprising: allocating the sensor data acquired by the external sensor as map generation data or self-position estimation data based on an attribute of the sensor data.
Karlsson teaches,
one or more processors configured to perform operations comprising: allocating the sensor data acquired by the external sensor as map generation data or self-position estimation data based on an attribute of the sensor data (see at least [0064] The control 108 can include hardware, such as microprocessors, memory, etc., can include firmware, can include software, can include network communication equipment, and the like, and [0008] It will be understood that while SLAM relates to the building of a map (mapping) and the use of the map (localizing), a process associated with localization and a process associated with mapping need not actually be performed simultaneously for a system to perform SLAM. For example, procedures can be performed in a multiplexed fashion).
Thus, Yoshida discloses a map generation/self-positioning estimation device and Karlsson teaches that localization and mapping need not actually be performed simultaneously; the procedure can be performed in a multiplexed fashion.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the multiplexing of sensor data as taught by Karlsson, with a reasonable expectation of success, thereby permitting the SLAM processes to be performed in software using relatively inexpensive microprocessor-based computer systems (0055).
As per Claim 2,
Yoshida discloses,
The map generation device according to claim 1, wherein allocating the sensor data further comprise:
allocating data of a measurement cycle to either the map generation data or the self-position estimation data for each measurement cycle of the external sensor (See at least [0009] a positional information acquisition unit to acquire positional information of the vehicle, [0010] a route generation unit to generate a travel route of the vehicle on a simple map used for course guidance, based on the positional information and simple map information which represents the simple map; [0011] a surrounding area map generation unit to generate a surrounding area map of the vehicle, during travel of the vehicle, as surrounding area map information, using the positional information);
calculating the true value of the relative position using a relative movement amount of the own vehicle acquired by a sensor (see at least [0013] an alignment unit to align the simple map and the surrounding area map with each other based on the road characteristic, and to calculate a position of the vehicle as a vehicle position, and [0147] At this time, the characteristic extraction unit 140 acquires the own position on the high-precision map 185 using sensor information acquired by a sensors 932).
Yoshida does not disclose,
wherein the attribute of the sensor data comprises an associated measurement cycle,
Karlsson teaches,
wherein the attribute of the sensor data comprises an associated measurement cycle (see at least [0008] It will be understood that while SLAM relates to the building of a map (mapping) and the use of the map (localizing), a process associated with localization and a process associated with mapping need not actually be performed simultaneously for a system to perform SLAM. For example, procedures can be performed in a multiplexed fashion).
Thus, Yoshida discloses a map generation/self-positioning estimation device and Karlsson teaches that localization and mapping need not actually be performed simultaneously; the procedure can be performed in a multiplexed fashion.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the multiplexing of sensor data as taught by Karlsson, with a reasonable expectation of success, thereby permitting the SLAM processes to be performed in software using relatively inexpensive microprocessor-based computer systems (0055).
As per Claim 3,
Yoshida discloses,
map generation device according to claim 2, wherein allocating the sensor data further comprises:
setting a relative position attitude between newest map generation data and the self-position estimation data as the true value of the relative position (see at least Fig.2, [0052] the positional information acquisition unit 110 acquires, via the sensor interface 930, the positional information 111 acquired by the GPS 931., and [0053] In step S102, the positional information acquisition unit 110 corrects the positional information 111 based on the position correction amount 184)
or
selecting the map generation data, of a plurality of sets of map generation data associated with a plurality of measurement cycles, having a smallest difference, relative to the plurality of sets of map generation data, in relative position attitude with respect to each of the self-position estimation data, and setting the relative position attitude between the self-position estimation data and the map generation data associated with the selected map generation data as the true value of the relative position.
As per Claim 4,
Yoshida discloses,
The map generation device according to claim 1, wherein the external sensor includes a plurality of sensors, and allocating the sensor data further comprises (see at least Fig 1. 931 and 932 and [0039] The sensor interface 930 is connected to a Global Positioning System (GPS) 931 and various types of sensors 932. Practical examples of the sensors 932 are a camera, a laser, an millimeter-wave radar, and a sonar.)
allocating data measured by each of the plurality of sensors to either the map generation data ( Fig. 2, Step S106) or the self-position estimation data (Fig. 2, Step S101),
calculates the true value of the relative position using an installation position attitude between each of the external sensors calculated by a prior calibration (see at least Fig. 2. Step S108-S113).
Yoshida does not disclose,
wherein the attribute of the sensor data comprises an associated sensor
Karlsson teaches,
wherein the attribute of the sensor data comprises an associated sensor (see at least [0020] a circuit configured to use a prior pose estimate and dead reckoning sensor data to compute a new pose estimate for particles in a selected group based on the estimated change in pose; and a circuit configured to use the landmark pose and the visually-measured relative pose estimate to compute the new pose estimate for particles not in the selected group, [0102] It will be understood that the nature of the raw pose data 610 can vary according to the type of dead reckoning sensor 614 and the type of output specified by the dead reckoning interface 618. Examples of the raw pose data 610 can include distance measurements, velocity measurements, and acceleration measurements, and [0103] Other inputs to the SLAM module 604 include visual localization data from the Visual Front End 602 and/or the optional Pre-Filter module 622)
Thus, Yoshida discloses a map generation/self-positioning estimation device and Karlsson teaches that localization and mapping need not actually be performed simultaneously; the procedure can be performed in a multiplexed fashion.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the multiplexing of sensor data as taught by Karlsson, with a reasonable expectation of success, thereby permitting the SLAM processes to be performed in software using relatively inexpensive microprocessor-based computer systems (0055).
As per Claim 5,
Yoshida discloses,
The map generation device according to claim 4, wherein allocating the map generation data and the self-position estimation data further comprises:
fixedly allocating data of each of the plurality of sensors to either the map generation data or the self-position estimation data regardless of a measurement cycle of each of the plurality of sensors (see at least Fig. 1, sensor interface (903) allocates sensor data to surrounding area map generation unit (130) or the positional information acquisition unit (110) and Fig. 2, step S101 and S106)
variably allocating data of each of the plurality of sensors to either the map generation data or the self-position estimation data for each measurement cycle of each of the plurality of sensors, (see at least [0052] In step S101, the positional information acquisition unit 110 acquires positional information 111 of the vehicle 200. Specifically, the positional information acquisition unit 110 acquires, via the sensor interface 930, the positional information 111 acquired by the GPS 931, and [0064] In step S107, the surrounding area map generation unit 130 estimates an own position and generates the surrounding area map 182 simultaneously by the SLAM technique, using the sensor information such as a camera image and a point cloud from the laser sensor).
or
the data allocation unit calculates the true value of the relative position using only data acquired by each of the external sensors while an own vehicle is stopped.
As per Claim 7,
Yoshida discloses,
The map generation device according to claim 1, wherein at least one processor of the one or more processors operate online, and the operations further comprise: (see at least [0107] some of the functions of the travel assist device 100 may be assigned to a center server. In this case, the travel assist device 100 is provided with a communication device to communicate with the center server. The communication device communicates with another device, specifically the center server, via a network. The communication device has a receiver and a transmitter)
estimating the travel position of the own vehicle on the generation map based on the map generated by the map generation unit and the self-position estimation data of past that is temporarily saved (see at least [0093] In step S208, the alignment unit 150 obtains a coincident point where the number of roads detected from the surrounding area map 182 and the angle between the roads respectively coincide with the number of roads detected from the simple map 181 and the angle between the roads. Based on the coincident point, the alignment unit 150 aligns the simple map 181 and the surrounding area map 182 with each other. Then, as a result of alignment of the simple map 181 and the surrounding area map 182, the alignment unit 150 calculates a maximum likelihood position, which is a position of the own vehicle, as the vehicle position 151).
or, the data allocation unit prepares a plurality of sets of the map generation data, the self-position estimation data, and the true value of the relative position in which a data allocation method is changed,
or, the data allocation unit gives noise to the map generation data and the self-position estimation data, and the accuracy evaluation unit calculates a relationship between a magnitude of the noise and the error in the self- position estimation.
As per Claim 13,
Yoshida discloses,
map generation device according to claim 1, wherein the operations further comprise:
determining an abnormality of the external sensor based on the error in the self-position estimation (see at least [0079] the correction amount calculation unit 160 calculates a position correction amount 161 for correcting the positional information 111, based on the vehicle position 151 calculated by the alignment unit 150. The position correction amount 161 is used for correction of the positional information 111 by the GPS 931).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yoshida in view of Kaneko and Karlsson as applied to Claim 1 above, and further in view of David Ben Ezra et. al. US20230281851 (“Ezra”).
As per Claim 6,
Yoshida does not disclose,
The map generation device according to claim 1, wherein allocating the sensor data further comprises:
allocating data of the external sensor based on a maximum data capacity that set in advance or a data capacity associated with the map generation device, wherein the attribute of the sensor data comprises a data amount associated with the sensor data.
Ezra teaches,
allocating data of the external sensor based on a maximum data capacity that set in advance or a data capacity associated with the map generation device, wherein the attribute of the sensor data comprises a data amount associated with the sensor data (see at least [0041] Any one or more of the functional components (e.g., modules) of the SLAM system 240 may be implemented using hardware (e.g., the processor 210 of the computing device 200) or a combination of hardware and software, [0019] the partial SLAM process only performs a localization portion of a SLAM process. In alternative embodiments, the partial SLAM process only performs a mapping portion of a SLAM process. By performing only a portion of a SLAM process, a partial SLAM process may perform using less computing resources than a full SLAM process, and may perform faster than a full SLAM process, [0021] In another instance, a new key image frame may be generated only after a certain amount of time or certain number of cycles (e.g., partial SLAM process cycles) has passed between identification (e.g., generation) of the last new key image frame, [0021] a new key image frame may be generated only after a certain amount of translation (e.g., caused by image sensor position change in the physical environment with respect to X, Y, or Z coordinates) is detected between the current captured image frame and a previous image frame).
Thus, Yoshida discloses a map generation/self-positioning estimation device and Ezra teaches that localization and mapping need not actually be performed simultaneously; partial SLAM may perform localization portion or mapping portion.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the partial SLAM approach taught by Ezra, with a reasonable expectation of success, achieve SLAM results (e.g., useful and accurate SLAM results) while limiting the computer resources needed to achieve those results (0019).
Claim 8-9, 12, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida in view of Kaneko and Karlsson as applied to Claim 1 above, and further in view of Philip Fong et. al. US9952053B2 (“Fong”).
As per Claim 8,
Yoshida discloses,
The map generation device according to claim 1, wherein the operations further comprise:
recording the generation map as a record map based on the error in the self-position estimation (see at least [0066] the surrounding area map 182 is generated while correction of the own position is performed, and the vehicle 200 is moved. The surrounding area map 182 employs xyz coordinate representation, and latitude-longitude information is stored in a portion of the surrounding area map 182. The surrounding area map 182 is a map generated from the sensor information online in a real-time manner)
Yoshida does not disclose,
wherein the generation map is recorded as the record map only when the error is smaller than a preset threshold.
Fong teaches,
wherein the generation map is recorded as the record map only when the error is smaller than a preset threshold (see at least [Col.2, line 23-26] Data is recorded to a pre-existing parameter grid if the uncertainty between the current robot pose estimate and the pose estimate associated with the pre-existing grid is below a predetermined threshold).
Thus, Yoshida discloses recording of the self-position generation map and Fong teaches recording the data when the error is below a predetermined threshold.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the recording only when error is below a threshold taught by Fong, with a reasonable expectation of success, to efficiently create an occupancy map using new information to improve accuracy of the map without the storage requirement growing linearly with time (Col. 1, line 59-61).
As per Claim 9,
Yoshida does not disclose,
The map generation device according to claim 8, wherein only a section of the generation map in which the error in the self-position estimation is smaller than a preset threshold is recorded as the record map.
Fong teaches,
The map generation device according to claim 8, wherein only a section of the generation map in which the error in the self-position estimation is smaller than a preset threshold is recorded as the record map (see at least [Col. 1 Line 65 – Col. 2 Line 1] features a system and method for mapping parameter data acquired by a robot or other mapping system that travels through an environment, and [Col.2 line 7-9] When the threshold is exceeded, the robot generates a new grid associated with a new anchor node to record parameter data).
Thus, Yoshida discloses recording of the self-position generation map and Fong teaches recording only section of the map where the error is smaller than a predetermined threshold.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the recording of limited segments only when error is below a threshold as taught by Fong, with a reasonable expectation of success, to efficiently creates an occupancy map using new information to improve accuracy of the map without the storage requirement growing linearly with time (Col. 1, line 59-61).
As per Claim 12,
Yoshida discloses,
The map generation device according to claim 8, wherein recording the generation map further comprises:
recording the record map into a server connected to the map generation device via a network (see at least [0107] some of the functions of the travel assist device 100 may be assigned to a center server. In this case, the travel assist device 100 is provided with a communication device to communicate with the center server. The communication device communicates with another device, specifically the center server, via a network. The communication device has a receiver and a transmitter).
or
the record unit transmits information on a travel section of the own vehicle to the server when recording the record map into the server connected to the map generation/self-position estimation device via the network, and not recording the generation map as the record map based on the error in the self-position estimation.
As per Claim 14,
Yoshida discloses,
The map generation device according to claim 8, wherein the operations further comprise:
including the error in the self-position estimation calculated by the accuracy evaluation unit into the record map,
when the own vehicle travels again in a section where the record map exists, estimating a position attitude of the own vehicle on the record map from data of the external sensor and the record map,(see at least Fig. 6 (Step S208 and S209), and Fig. 9 (Alignment Unit 150 and Correction Amount Calculation Unit 160))
calculating a current prediction error from a position attitude of the own vehicle on the record map and the error in the self-position estimation included in the record map, and selecting an operation mode of the map generation device from a map generation mode and a position estimation mode based on the current prediction error (see at least Fig 2, S112, Calculate Position correction amount and select between S102 Correct Positional information and S113 Project travel route onto surrounding area map, [0094] Step S208 corresponds to step S111 of FIG. 2, [0095] In step S209, the correction amount calculation unit 160 calculates a difference between the vehicle position 151 and the positional information 111 which is obtained by the GPS, as the position correction amount 161 to be used for correcting the positional information 111. Using the position correction amount 161, the correction amount calculation unit 160 updates the position correction amount 184 stored in the storage unit 180, and [0096] Step S209 corresponds to step S112 of FIG. 2)
As per Claim 15,
Yoshida discloses,
The map generation device according to claim 14, wherein selecting the operation mode of the map generation device further comprises:
selecting an operation mode allowed for driving assistance system connected to the map generation device based on the current prediction error (see at least [0007] to generate, even for a local road for which a high-precision map has not been created, a path to be employed in driving assist such as automatic driving by utilizing a simple map employed in course guidance and [0015] with the travel assist system according to the present invention, even for a local road for which a high-precision map has not been created, a path to be employed in driving assist such as automatic driving can be generated with using the simple map and the surrounding area map).
Claim 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yoshida in view of Kaneko, Karlsson and Fong as applied to Claim 8 above, and further in view of Ichiro Yamaguchi et. al. US 9797981 B2 (“Yamaguchi”).
As per Claim 10,
Yoshida does not disclose,
map generation device according to claim 8, wherein the operations further comprise:
preparing a plurality of sets of the map generation data, the self-position estimation data, and a true value of the relative position in which a data allocation method is varied,
generating the generation map for each of a plurality of pieces of the map generation data
estimating a travel position of the own vehicle on each of the generation maps by using the plurality of generation maps and the self-position estimation data corresponding to the plurality of generation maps,
recording, as the record map, the generation map generated in a set having a smallest mean, median, or maximum value of the error in the self-position estimation relative to the plurality of generation maps.
Fong teaches,
wherein the operations further comprise:
preparing a plurality of sets of the map generation data, the self-position estimation data, and a true value of the relative position in which a data allocation method is varied,
generating the generation map for each of a plurality of pieces of the map generation data (see at least [Col. 4, line 11-14] The parameter mapping module 136 is configured to generate a plurality of sub-maps or grids comprising local parameters and build global parameter maps based on those grids, and [Col. 5, line 38-42] robotic system generates a map of one or more parameters of interest in parallel with the location determination. In particular, the parameter mapping module senses properties of the environment and generates a parameter map depicting those properties.)
estimating a travel position of the own vehicle on each of the generation maps by using the plurality of generation maps and the self-position estimation data corresponding to the plurality of generation maps, ( see at least [Col. 5, line 33-37]The landmark information, in combination with the odometry information, enables the robotic system to make accurate estimates of the robot's location in the environment).
recording, as the record map, the generation map generated in a set having a smallest mean, median, or maximum value of the error in the self-position estimation relative to the plurality of generation maps (see at least [Col. 6, line 38-43] If the uncertainty associated with the relative pose between the anchor nodes is below a threshold, the decision block is answered in the affirmative and the summaries (comprised of sensor data) for the anchor nodes are combined into a single summary associated with a single anchor node, [Col 6, Line 55-65] Other criteria may also be used when determining whether to combine grids. These criteria may include, but are not limited to: … (c) whether the map quality improves, i.e., whether merging or eliminating relatively “old” and outdated maps while retaining relatively “newer” maps improves the accuracy of the parameter map, and [Col.2 line 7-9] When the threshold is exceeded, the robot generates a new grid associated with a new anchor node to record parameter data.
Thus, Yoshida discloses a map generation/ self-positioning device and Fong teaches a system and method for combining sensor data into a plurality of sub-maps based upon the location of the sensor when the data was acquired and the certainty with its location was known.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the approach of creating a plurality of sub-maps taught by Fong, with a reasonable expectation of success, to efficiently creates an occupancy map to properly make decisions and navigate the environment (Col. 1, line 40-41).
Yoshida does not disclose,
calculating the error in the self-position estimation for data of each set,
Yamaguchi teaches,
calculating the error in the self-position estimation for data of each set, (see at least Fig 1, [Col. 5, line 65-Col. 5 line 3] The candidate sets of virtual positions and virtual attitude angles are randomly generated using a random number table or the like within the respective ranges between upper and lower error limits set for position and attitude angle parameters of six degrees of freedom., and [Col. 7, line 38-45] for each of the plural candidate sets of virtual positions and virtual attitude angles set in step S3, the ECU 1 (likelihood setting unit 12) compares the edge image created in step S1 with the virtual image created in step S4. Based on the result of comparison, the likelihood setting unit 12 sets a position likelihood and an attitude angle likelihood for each candidate set of the virtual position and the virtual attitude angle.)
Thus, Yoshida discloses a map generation/ self-positioning device and Yamaguchi teaches a method of error calculation and recording the map below a certain threshold.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the approach of recording as taught by Yamaguchi, with a reasonable expectation of success, to accurately estimate the position and the attitude angle of a moving object (Col. 1, line 53-54).
As per Claim 11,
Yoshida does not disclose,
wherein the operations further comprise:
generating a plurality of the generation maps by varying a map generation parameter,
calculating a plurality of the self-position estimation results by varying a self-position estimation parameter for each of the plurality of generation maps,
calculating a plurality of the self-position estimation errors,
Yamaguchi teaches,
generating a plurality of the generation maps by varying a map generation parameter (see at least [Col. 5, line 58-65] The virtual image acquisition unit 11 generates plural candidate sets of virtual positions and virtual attitude angles which have values possible for the true values of the position and the attitude angle of the vehicle, taking account of odometry errors induced by measurement errors and communication delays of the vehicle sensor group 4 and dynamic characteristics of the vehicle which cannot be taken into account in odometry.)
calculating a plurality of the self-position estimation results by varying a self-position estimation parameter for each of the plurality of generation maps (see at least [Col. 7, line 3-10] In this embodiment, in the case of a first loop, 500 particles are created. Moreover, the upper and lower error limits of the parameters of six degrees of freedom for each particle are set to ±0.05 [m], ±0.05 [m], ±0.05 [m], ±0.5 [deg], ±0.5 [deg], and ±0.5 [deg] in the order of forward/backward position, left/right position, vertical position, roll, pitch, and yaw, and [Col. 7, line 10-18] for each of the plural candidate sets of virtual positions and virtual attitude angles created in step S3, the virtual image acquisition unit 11 creates a virtual image (projected image). At this time, the virtual image acquisition unit 11 creates the virtual image by converting three-dimensional position information such as edges or the like stored in the three-dimensional map database 3 to a camera image captured from the virtual position at the virtual attitude angle.)
calculating a plurality of the self-position estimation errors (see at least [Col. 6, line 19-23] it is desirable to increase the upper and lower error limits of these three parameters and to increase the number of candidate sets of virtual positions and virtual attitude angles to be created)
Thus, Yoshida discloses a map generation/ self-positioning device and Yamaguchi teaches a method of error calculation and recording the map below a certain threshold.
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the approach of recording as taught by Yamaguchi, with a reasonable expectation of success, to accurately estimate the position and the attitude angle of a moving object (Col. 1, line 53-54).
Yoshida does not disclose,
recording, as the record map, the generation map generated in a set having a smallest mean, median, or maximum value of errors in the self-position estimation relative to the plurality of generation maps, the map generation parameter used for generation of the generation map, and the self-position estimation parameter used for calculation of the self- position estimation result
Fong teaches,
recording, as the record map, the generation map generated in a set having a smallest mean, median, or maximum value of errors in the self-position estimation relative to the plurality of generation maps, the map generation parameter used for generation of the generation map, and the self-position estimation parameter used for calculation of the self- position estimation result (see at least [Col. 6, line 38-43] If the uncertainty associated with the relative pose between the anchor nodes is below a threshold, the decision block is answered in the affirmative and the summaries (comprised of sensor data) for the anchor nodes are combined into a single summary associated with a single anchor node, [Col 6, Line 55-65] Other criteria may also be used when determining whether to combine grids. These criteria may include, but are not limited to: … (c) whether the map quality improves, i.e., whether merging or eliminating relatively “old” and outdated maps while retaining relatively “newer” maps improves the accuracy of the parameter map, and [Col.2 line 7-9] When the threshold is exceeded, the robot generates a new grid associated with a new anchor node to record parameter data)
Thus, Yoshida discloses a map generation/ self-positioning device and Fong teaches a system and method for combining sensor data into a plurality of sub-maps based upon the certainty with its location (self-position location error).
As a result, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide the inventions as disclosed by Yoshida with the approach of recording a plurality of sub-maps taught by Fong, with a reasonable expectation of success, to efficiently creates an occupancy map to properly make decisions and navigate the environment (Col. 1, line 40-41).
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.
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/A.P./Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
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