DETAILED ACTION
This is the first office action on the merits of the instant application, which was filed February 16, 2024 as a national stage entry of PCT/JP2022/001832, filed January 19, 2022, which claims priority to JP2021-140626, filed August 31, 2021. After a preliminary amendment wherein claims 1-14 and 19 are amended and claim 21 is cancelled, claims 1-20 remain in the application.
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 .
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.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 5, 8-10, 12, 15-17 and 19 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Yamazaki et al. (US 2017/0116487 A1).
Yamazaki et al. teaches, according to claim 1, an information processing apparatus comprising:
at least one memory storing instructions, and at least one processor (Yamazaki et al., at least para. [0029], “The occupancy grid map generating apparatus 1 of the first embodiment is, for example, a dedicated or general-purpose computer. The occupancy grid map generating apparatus 1 includes…a processing circuit 5, a memory circuit 6…”) configured to execute the instructions to:
receive observation data from at least one sensor, the observation data being obtained by observing an obstacle in a plurality of areas (Yamazaki et al., at least para. [0045], “The distance data acquisition unit 11 acquires left and right images 7L and 7R obtained from the stereo camera 3 and data of three-dimensional distance to an object lying in the vicinity of the automobile 2, which are obtained by the laser sensor 4.”);
determine presence/absence of an obstacle in the plurality of areas by using the observation data (Yamazaki et al., at least para. [0042], “…That is, as illustrated in FIGS. 6 to 12, the occupancy grid map generating apparatus 1 always presents the automobile 2 on the center of the occupancy grid map M divided into pixels and generates the occupancy grid map M expressing the presence or absence of an obstacle in the unit of pixel for each time T.”); and
specify, when the presence/absence of an obstacle in a first area included in the plurality of areas is unknown, a probability of presence of an obstacle in the first area by using a determination result indicating presence/absence of an obstacle in an area near the first area, wherein the probability of the presence of an obstacle in the first area changes depending on presence/absence of an obstacle in the area near the first area (Yamazaki et al., at least para. [0126], “Next, the generation unit 17 calculates a probability of existence of an obstacle (a probability value between 0 and 1, hereinafter referred to as an “obstacle existence probability”) at each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A in time series (for each time T) from the distance data acquired by the distance data acquisition unit 11.”; and para. [0127], “As illustrated in FIG. 15, the generation unit 17 classifies each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A into three states, i.e., a movable area (an area where an obstacle is not present), an immovable area (an area where an obstacle is present), and an unknown area (an area where it is known whether or not an obstacle is present). It is here assumed that the obstacle existence probabilities of the movable area, the unknown area, and the unmovable area are about 1.0, about 0.5, and about 0.0. Hereinafter, the classifying method will be described.”).
Regarding claim 2, the at least one processor is further configured to execute the instructions to set a label indicating whether or not there is an obstacle in the plurality of areas by using the determination result for each of the plurality of areas and the probability of the presence of an obstacle in the first area (Yamazaki et al., at least para. [0132], “The generation unit 17 calculates and updates the obstacle existence probability of each pixel of the coordinate-transformed occupancy grid map M at time T2 and the obstacle existence probability of each pixel of a high resolution of the coordinate-transformed occupancy grid map M at time T2 of the coordinate transformation according to Bayes' theorem. For this update, the obstacle existence probability observed at time T1 is propagated to the occupancy grid map M and the range of interest A at time T2. An event observed that an obstacle is present is represented as O. An event O occurs in pixels labeled as “unmovable area.” A complementary set of the event O occurs in pixels labeled as “movable area.” Hone of the event O and complementary set of the event O occurs in pixels labeled as “unknown area”, because no information is obtained from the observation at time T1. Specifically, the occupancy grid map M and the range of interest A are updated as follows.”).
Regarding claim 3, the plurality of areas are a plurality of cells of a grid generated by dividing a travelling area of a mobile body, and the at least one processor is further configured to execute the instructions to calculate a probability of presence of an obstacle in a first cell of the grid where the observation data indicates that presence/absence of an obstacle is unknown (Yamazaki et al., at least para. [0126], “Next, the generation unit 17 calculates a probability of existence of an obstacle (a probability value between 0 and 1, hereinafter referred to as an “obstacle existence probability”) at each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A in time series (for each time T) from the distance data acquired by the distance data acquisition unit 11.”; and para. [0127], “As illustrated in FIG. 15, the generation unit 17 classifies each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A into three states, i.e., a movable area (an area where an obstacle is not present), an immovable area (an area where an obstacle is present), and an unknown area (an area where it is known whether or not an obstacle is present). It is here assumed that the obstacle existence probabilities of the movable area, the unknown area, and the unmovable area are about 1.0, about 0.5, and about 0.0. Hereinafter, the classifying method will be described.”).
Regarding claim 5, the at least one processor is further configured to execute the instructions to receive, after receiving observation data of the plurality of cells of the grid at a first time, observation data of the plurality of cells of the grid at a second time, and determine whether or not there is a moving obstacle based on a transition in the determination result of each of the cells of the grid between the first and second times (Yamazaki et al., at least para. [0131], “Fourth, the generation unit 17 coordinate-transforms the occupancy grid map M and range of interest A centered at the automobile 2 at time T1 to an occupancy grid map M and range of interest A centered at the automobile 2 at next time T2, based on the attitude and the status of movement in real scale of the automobile 2 calculated by the motion calculation unit 12 (see, e.g., Step S8 in FIG. 2). This coordinate transformation is performed in the pixel orthogonal coordinate system.”; and para. [0132], “The generation unit 17 calculates and updates the obstacle existence probability of each pixel of the coordinate-transformed occupancy grid map M at time T2 and the obstacle existence probability of each pixel of a high resolution of the coordinate-transformed occupancy grid map M at time T2 of the coordinate transformation according to Bayes' theorem. For this update, the obstacle existence probability observed at time T1 is propagated to the occupancy grid map M and the range of interest A at time T2. An event observed that an obstacle is present is represented as O. An event O occurs in pixels labeled as “unmovable area.” A complementary set of the event O occurs in pixels labeled as “movable area.” Hone of the event O and complementary set of the event O occurs in pixels labeled as “unknown area”, because no information is obtained from the observation at time T1. Specifically, the occupancy grid map M and the range of interest A are updated as follows.”).
Yamazaki et al. teaches, according to claim 8, a communication system comprising:
at least one memory storing instructions, and at least one processor (Yamazaki et al., at least para. [0029], “The occupancy grid map generating apparatus 1 of the first embodiment is, for example, a dedicated or general-purpose computer. The occupancy grid map generating apparatus 1 includes…a processing circuit 5, a memory circuit 6…”) configured to execute the instructions to:
observe an obstacle in a plurality of areas and transmit observation data indicating a result of the observation (Yamazaki et al., at least para. [0045], “The distance data acquisition unit 11 acquires left and right images 7L and 7R obtained from the stereo camera 3 and data of three-dimensional distance to an object lying in the vicinity of the automobile 2, which are obtained by the laser sensor 4.”);
determine presence/absence of an obstacle in the plurality of areas by using the observation data (Yamazaki et al., at least para. [0042], “…That is, as illustrated in FIGS. 6 to 12, the occupancy grid map generating apparatus 1 always presents the automobile 2 on the center of the occupancy grid map M divided into pixels and generates the occupancy grid map M expressing the presence or absence of an obstacle in the unit of pixel for each time T.”); and
specify, when the presence/absence of an obstacle in a first area included in the plurality of areas is unknown, a probability of presence of an obstacle in the first area by using a determination result indicating presence/absence of an obstacle in an area near the first area, wherein the probability of the presence of an obstacle in the first area changes depending on presence/absence of an obstacle in the area near the first area (Yamazaki et al., at least para. [0126], “Next, the generation unit 17 calculates a probability of existence of an obstacle (a probability value between 0 and 1, hereinafter referred to as an “obstacle existence probability”) at each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A in time series (for each time T) from the distance data acquired by the distance data acquisition unit 11.”; and para. [0127], “As illustrated in FIG. 15, the generation unit 17 classifies each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A into three states, i.e., a movable area (an area where an obstacle is not present), an immovable area (an area where an obstacle is present), and an unknown area (an area where it is known whether or not an obstacle is present). It is here assumed that the obstacle existence probabilities of the movable area, the unknown area, and the unmovable area are about 1.0, about 0.5, and about 0.0. Hereinafter, the classifying method will be described.”).
Regarding claim 9, the at least one processor is further configured to execute the instructions to set a label indicating whether or not there is an obstacle in the plurality of areas by using the determination result for each of the plurality of areas and the probability of the presence of an obstacle in the first area (Yamazaki et al., at least para. [0132], “The generation unit 17 calculates and updates the obstacle existence probability of each pixel of the coordinate-transformed occupancy grid map M at time T2 and the obstacle existence probability of each pixel of a high resolution of the coordinate-transformed occupancy grid map M at time T2 of the coordinate transformation according to Bayes' theorem. For this update, the obstacle existence probability observed at time T1 is propagated to the occupancy grid map M and the range of interest A at time T2. An event observed that an obstacle is present is represented as O. An event O occurs in pixels labeled as “unmovable area.” A complementary set of the event O occurs in pixels labeled as “movable area.” Hone of the event O and complementary set of the event O occurs in pixels labeled as “unknown area”, because no information is obtained from the observation at time T1. Specifically, the occupancy grid map M and the range of interest A are updated as follows.”).
Regarding claim 10, the plurality of areas are a plurality of cells of a grid generated by dividing a travelling area of a mobile body, and the at least one processor is further configured to execute the instructions to calculate a probability of presence of an obstacle in a first cell of the grid where the observation data indicates that presence/absence of an obstacle is unknown (Yamazaki et al., at least para. [0126], “Next, the generation unit 17 calculates a probability of existence of an obstacle (a probability value between 0 and 1, hereinafter referred to as an “obstacle existence probability”) at each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A in time series (for each time T) from the distance data acquired by the distance data acquisition unit 11.”; and para. [0127], “As illustrated in FIG. 15, the generation unit 17 classifies each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A into three states, i.e., a movable area (an area where an obstacle is not present), an immovable area (an area where an obstacle is present), and an unknown area (an area where it is known whether or not an obstacle is present). It is here assumed that the obstacle existence probabilities of the movable area, the unknown area, and the unmovable area are about 1.0, about 0.5, and about 0.0. Hereinafter, the classifying method will be described.”).
Regarding claim 12, the at least one processor is further configured to execute the instructions to receive, after receiving observation data of the plurality of cells of the grid at a first time, observation data of the plurality of cells of the grid at a second time; and determine whether or not there is a moving obstacle based on a transition in the determination result of each of the cells of the grid between the first and second times (Yamazaki et al., at least para. [0131], “Fourth, the generation unit 17 coordinate-transforms the occupancy grid map M and range of interest A centered at the automobile 2 at time T1 to an occupancy grid map M and range of interest A centered at the automobile 2 at next time T2, based on the attitude and the status of movement in real scale of the automobile 2 calculated by the motion calculation unit 12 (see, e.g., Step S8 in FIG. 2). This coordinate transformation is performed in the pixel orthogonal coordinate system.”; and para. [0132], “The generation unit 17 calculates and updates the obstacle existence probability of each pixel of the coordinate-transformed occupancy grid map M at time T2 and the obstacle existence probability of each pixel of a high resolution of the coordinate-transformed occupancy grid map M at time T2 of the coordinate transformation according to Bayes' theorem. For this update, the obstacle existence probability observed at time T1 is propagated to the occupancy grid map M and the range of interest A at time T2. An event observed that an obstacle is present is represented as O. An event O occurs in pixels labeled as “unmovable area.” A complementary set of the event O occurs in pixels labeled as “movable area.” Hone of the event O and complementary set of the event O occurs in pixels labeled as “unknown area”, because no information is obtained from the observation at time T1. Specifically, the occupancy grid map M and the range of interest A are updated as follows.”).
Yamazaki et al. teaches, according to claim 15, a specifying method comprising:
receiving observation data from at least one sensor, the observation data being obtained by observing an obstacle in a plurality of areas (Yamazaki et al., at least para. [0045], “The distance data acquisition unit 11 acquires left and right images 7L and 7R obtained from the stereo camera 3 and data of three-dimensional distance to an object lying in the vicinity of the automobile 2, which are obtained by the laser sensor 4.”);
determining presence/absence of an obstacle in the plurality of areas by using the observation data (Yamazaki et al., at least para. [0042], “…That is, as illustrated in FIGS. 6 to 12, the occupancy grid map generating apparatus 1 always presents the automobile 2 on the center of the occupancy grid map M divided into pixels and generates the occupancy grid map M expressing the presence or absence of an obstacle in the unit of pixel for each time T.”); and
specifying, when the presence/absence of an obstacle in a first area included in the plurality of areas is unknown, a probability of presence of an obstacle in the first area by using a determination result indicating presence/absence of an obstacle in an area near the first area, wherein the probability of the presence of an obstacle in the first area changes depending on presence/absence of an obstacle in the area near the first area (Yamazaki et al., at least para. [0126], “Next, the generation unit 17 calculates a probability of existence of an obstacle (a probability value between 0 and 1, hereinafter referred to as an “obstacle existence probability”) at each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A in time series (for each time T) from the distance data acquired by the distance data acquisition unit 11.”; and para. [0127], “As illustrated in FIG. 15, the generation unit 17 classifies each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A into three states, i.e., a movable area (an area where an obstacle is not present), an immovable area (an area where an obstacle is present), and an unknown area (an area where it is known whether or not an obstacle is present). It is here assumed that the obstacle existence probabilities of the movable area, the unknown area, and the unmovable area are about 1.0, about 0.5, and about 0.0. Hereinafter, the classifying method will be described.”).
Regarding claim 16, the method further comprises setting a label indicating whether or not there is an obstacle in the plurality of areas by using the determination result for each of the plurality of areas and the probability of the presence of an obstacle in the first area (Yamazaki et al., at least para. [0126], “Next, the generation unit 17 calculates a probability of existence of an obstacle (a probability value between 0 and 1, hereinafter referred to as an “obstacle existence probability”) at each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A in time series (for each time T) from the distance data acquired by the distance data acquisition unit 11.”; and para. [0127], “As illustrated in FIG. 15, the generation unit 17 classifies each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A into three states, i.e., a movable area (an area where an obstacle is not present), an immovable area (an area where an obstacle is present), and an unknown area (an area where it is known whether or not an obstacle is present). It is here assumed that the obstacle existence probabilities of the movable area, the unknown area, and the unmovable area are about 1.0, about 0.5, and about 0.0. Hereinafter, the classifying method will be described.”).
Regarding claim 17, the plurality of areas are a plurality of cells of a grid generated by dividing a travelling area of a mobile body, and when a probability of presence of an obstacle is specified, a probability of presence of an obstacle in a first cell of the grid where the observation data indicates that presence/absence of an obstacle is unknown is calculated (Yamazaki et al., at least para. [0126], “Next, the generation unit 17 calculates a probability of existence of an obstacle (a probability value between 0 and 1, hereinafter referred to as an “obstacle existence probability”) at each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A in time series (for each time T) from the distance data acquired by the distance data acquisition unit 11.”; and para. [0127], “As illustrated in FIG. 15, the generation unit 17 classifies each pixel of the occupancy grid map M and each pixel of a high resolution of the range of interest A into three states, i.e., a movable area (an area where an obstacle is not present), an immovable area (an area where an obstacle is present), and an unknown area (an area where it is known whether or not an obstacle is present). It is here assumed that the obstacle existence probabilities of the movable area, the unknown area, and the unmovable area are about 1.0, about 0.5, and about 0.0. Hereinafter, the classifying method will be described.”).
Regarding claim 19, when the observation data is received, after observation data of the plurality of cells of the grid at a first time is received, observation data of the plurality of cells of the grid at a second time is received, and whether or not there is a moving obstacle is determined based on a transition in the determination result of each of the cells of the grid between the first and second times (Yamazaki et al., at least para. [0131], “Fourth, the generation unit 17 coordinate-transforms the occupancy grid map M and range of interest A centered at the automobile 2 at time T1 to an occupancy grid map M and range of interest A centered at the automobile 2 at next time T2, based on the attitude and the status of movement in real scale of the automobile 2 calculated by the motion calculation unit 12 (see, e.g., Step S8 in FIG. 2). This coordinate transformation is performed in the pixel orthogonal coordinate system.”; and para. [0132], “The generation unit 17 calculates and updates the obstacle existence probability of each pixel of the coordinate-transformed occupancy grid map M at time T2 and the obstacle existence probability of each pixel of a high resolution of the coordinate-transformed occupancy grid map M at time T2 of the coordinate transformation according to Bayes' theorem. For this update, the obstacle existence probability observed at time T1 is propagated to the occupancy grid map M and the range of interest A at time T2. An event observed that an obstacle is present is represented as O. An event O occurs in pixels labeled as “unmovable area.” A complementary set of the event O occurs in pixels labeled as “movable area.” Hone of the event O and complementary set of the event O occurs in pixels labeled as “unknown area”, because no information is obtained from the observation at time T1. Specifically, the occupancy grid map M and the range of interest A are updated as follows.”).
Allowable Subject Matter
Claims 4, 6-7, 11, 13-14, 18 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONALD J. WALLACE whose telephone number is
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/DONALD J WALLACE/Primary Examiner, Art Unit 3665