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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because claim 20 recites “A program”. A computer program as recited is not patent eligible subject matter because it is “software/data per se”. Furthermore, it is not a remedy when such “software/data” is claimed as a product without any structural recitations. “Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a ‘means plus function’ limitation) has no physical or tangible form, and thus does not fall within any statutory category.” MPEP 2106.03(I). A recommended remedy for claiming a computer program is to have it embodied within a “non-transitory” computer readable medium. See also USPTO Published 2019 Patent Eligibility Guidance, and MPEP 2106 and 2106.03 for additional guidance.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “section” in claim group 1-17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Objections
Claims 1, 19 and 20 are objected to because of the following informalities:
Claims 1, 19 and 20 as recited are ambiguous as it is not clear what “the first task” and “the second task” refer to. For the record, the examiner recommends claims 1, 19 and 20 to be rewritten to incorporate the definition of “the first task” and “the second task” in the dependent claims into claims 1, 19 and 20.
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 1-2, 4-8, 13-14 and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Masayuki (Japan Patent Pub. No.: JP2017142760A).
Regarding claim 1, Masayuki teaches an information processing apparatus, comprising: a processing section capable of processing a plurality of tasks (In addition, a part or all of the respective configurations related to the computer 3, the functions of the respective configurations, the execution processing, and the like may be realized by hardware. [0095]) for a recognition target (The right camera imaging unit 110 and the left camera imaging unit 100 constitute a stereo camera, and each acquire a moving image by capturing a plurality of images in front of the own vehicle in time series. [0015]), including first (a three dimensional object detection unit configured to detect a three dimensional object based on a distance from the device, a vector detection unit configured to detect a motion vector of a feature point by tracking the feature point inside a predetermined region including the three dimensional object in the plurality of images. [0008]) and second tasks (Next, the protrusion detection unit 600 performs pattern matching of the protrusion solid object candidate. In FIG. 8, the protrusion detection unit 600 includes a candidate selection unit 610, a partial pattern matching unit 620, and a position / speed estimation unit 630. [0050]) that share a feature extraction (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion estimated to be the head portion or the leg portion is visible in the appearance region. [0049].
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), wherein the processing section decides whether or not to perform the second task processing (Here, a ratio of an image appearance portion (referred to as an "appearance region") to the entire image of the three dimensional object related to each mountain is referred to as an "image appearance ratio", and a ratio of a portion in which a motion vector is detected in the appearance region of the three dimensional object related to each mountain is referred to as a "vector appearance ratio". It is preferable to determine whether or not to execute the subsequent processing for each mountain appearing in the histogram based on the thresholds R2 of the vector appearance ratios. [0042]) using a recognition result of the recognition target from the first task processing (a moving three dimensional object detection unit configured to detect a moving three dimensional object present inside the region based on a detection result of the vector detection unit. [0008]).
Regarding claim 2, Masayuki teaches the information processing apparatus according to claim 1, wherein the processing section generates a parameter of the second task using the recognition result of the recognition target from the first task processing (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion (which reads on “a parameter of the second task”) estimated to be the head portion or the leg portion is visible in the appearance region (which reads on “the recognition result of the recognition target from the first task processing”). [0049].
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).
Regarding claim 4, Masayuki teaches the information processing apparatus according to claim 2, wherein the processing section extracts a plurality of features from the recognition target (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion estimated to be the head portion or the leg portion is visible in the appearance region. [0049]) and decides whether or not to perform the second task processing (Here, a ratio of an image appearance portion (referred to as an "appearance region") to the entire image of the three dimensional object related to each mountain is referred to as an "image appearance ratio", and a ratio of a portion in which a motion vector is detected in the appearance region of the three dimensional object related to each mountain is referred to as a "vector appearance ratio". It is preferable to determine whether or not to execute the subsequent processing for each mountain appearing in the histogram based on the thresholds R2 of the vector appearance ratios. [0042]) and generates the parameter using the recognition result of the recognition target from the first task processing by using the plurality of features (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion (which reads on “the parameter”) estimated to be the head portion or the leg portion (which reads on “the plurality of features”) is visible in the appearance region (which reads on “the recognition result of the recognition target from the first task processing”). [0049]).
Regarding claim 5, Masayuki teaches the information processing apparatus according to claim 4, wherein the parameter includes an area to be processed for the second task (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion (which reads on “the parameter”) estimated to be the head portion or the leg portion (which reads on “an area to be processed for the second task”) is visible in the appearance region. [0049]) and one or more features selected from the plurality of features (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion estimated to be the head portion or the leg portion is visible in the appearance region. [0049]).
Regarding claim 6, Masayuki teaches the information processing apparatus according to claim 2, wherein the processing section decides whether or not to perform the second task processing (Here, a ratio of an image appearance portion (referred to as an "appearance region") to the entire image of the three dimensional object related to each mountain is referred to as an "image appearance ratio", and a ratio of a portion in which a motion vector is detected in the appearance region of the three dimensional object related to each mountain is referred to as a "vector appearance ratio". It is preferable to determine whether or not to execute the subsequent processing for each mountain appearing in the histogram based on the thresholds R2 of the vector appearance ratios. [0042]) and generates the parameter using a scene feature obtained from the recognition result of the recognition target from the first task processing (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion (which reads on “the parameter”) estimated to be the head portion or the leg portion (which reads on “a scene feature”) is visible in the appearance region (which reads on “the recognition result of the recognition target from the first task processing”). [0049]).
Regarding claim 7, Masayuki teaches the information processing apparatus according to claim 6, wherein the recognition target is an image acquired by an image capturing section mounted on a moving object that captures surroundings of the moving object (The in-vehicle environment recognition device includes a right camera imaging unit 110 built in a right camera (first camera) mounted on the right side facing the front of the vehicle, a left camera imaging unit 100 built in a left camera (second camera) mounted on the left side facing the front of the vehicle. [0013]), and the scene feature (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion estimated to be the head portion or the leg portion is visible in the appearance region. [0049]) is a moving scene feature of the moving object (In this embodiment, as shown in FIG. 19, the rush-out pedestrian candidate is separated into the appearance region and the occlusion region by a labeling method of enlarging the region in the XY direction from the distribution of the motion vectors. [0048].
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), which means whether or not there is an object of interest in the image (This is because, due to the nature of the pattern matching, even when the image appearance ratio of the approaching three dimensional object exceeds the threshold value (30%), the type of the approaching three dimensional object cannot be determined even by the pattern matching and the accuracy of the pattern matching may decrease in a case where a portion (a leg portion or a head portion in the case of a pedestrian) in which the feature of the shape of the object is likely to appear is not included. [0049]) and whether or not the object of interest is a movable object (FIG. 12 shows the analysis result of the motion vectors in the three dimensional region 115 in FIG. 11. On the moving image of the right camera, since the parked vehicle apparently moves to the left side as the own vehicle approaches, a motion vector toward the left direction is generated in the feature point of the parked vehicle as illustrated in FIG. 12. On the other hand, a pedestrian who is about to jump out from behind the parked vehicle moves toward the center of the image (in the traveling direction of the vehicle) on the moving image of the right camera, and thus the feature points of the pedestrian are detected as a vector group in the right direction. [0021].
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Regarding claim 8, Masayuki teaches the information processing apparatus according to claim 7, wherein the object of interest is an object that is an obstacle to a movement of the moving object ([FIG. 11] An example of an image of a pedestrian rushing out scene captured by the own vehicle camera. [0010].
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).
Regarding claim 13, Masayuki teaches the information processing apparatus according to claim 7, wherein the plurality of image capturing sections is mounted on the moving object (The in-vehicle environment recognition device includes a right camera imaging unit 110 built in a right camera (first camera) mounted on the right side facing the front of the vehicle, a left camera imaging unit 100 built in a left camera (second camera) mounted on the left side facing the front of the vehicle. [0013]), and the processing section decides whether or not to perform the second task processing (Here, a ratio of an image appearance portion (referred to as an "appearance region") to the entire image of the three dimensional object related to each mountain is referred to as an "image appearance ratio", and a ratio of a portion in which a motion vector is detected in the appearance region of the three dimensional object related to each mountain is referred to as a "vector appearance ratio". It is preferable to determine whether or not to execute the subsequent processing for each mountain appearing in the histogram based on the thresholds R2 of the vector appearance ratios. [0042]) and generates the parameter using the image recognition result from the first task processing for each image (For example, as shown in the lower part of FIG. 18, the pedestrian candidate frame may be divided into a head portion, a trunk portion, and a leg portion in the vertical direction, and an additional condition for executing pattern matching may be that 50% or more of the portion (which reads on “the parameter”) estimated to be the head portion or the leg portion is visible in the appearance region (which reads on “the image recognition result from the first task processing for each image”). [0049]) acquired by each of the plurality of image capturing sections mounted on the moving object (The in-vehicle environment recognition device includes a right camera imaging unit 110 built in a right camera (first camera) mounted on the right side facing the front of the vehicle, a left camera imaging unit 100 built in a left camera (second camera) mounted on the left side facing the front of the vehicle. [0013]).
Regarding claim 14, Masayuki teaches the information processing apparatus according to claim 7, wherein the image capturing section is a stereo camera (The in-vehicle environment recognition device includes a right camera imaging unit 110 built in a right camera (first camera) mounted on the right side facing the front of the vehicle, a left camera imaging unit 100 built in a left camera (second camera) mounted on the left side facing the front of the vehicle. [0013]) or a monocular camera.
Regarding claim 18, Masayuki teaches the information processing apparatus according to claim 1, wherein the recognition target is an image (The right camera imaging unit 110 and the left camera imaging unit 100 constitute a stereo camera, and each acquire a moving image by capturing a plurality of images in front of the own vehicle in time series. [0015]), the first task is the semantic segmentation (Since the area occupied by the pixels related to the parked vehicle in the three dimensional region is large, the number of motion vectors of the parked vehicle is larger than that of the pedestrian, and the left side mountain related to the parked vehicle is higher than the right side mountain related to the pedestrian. [0041]. Next, the appearance / occlusion region separation unit 530 divides the inside of the assumed pedestrian frame 180 generated using the camera geometry as shown in FIG. 19 into an appearance region 191 and an occlusion region (non-appearance region) 192. [0047]), and the second task includes one or more selected from the object detection (Next, the protrusion detection unit 600 performs pattern matching of the protrusion solid object candidate. In FIG. 8, the protrusion detection unit 600 includes a candidate selection unit 610, a partial pattern matching unit 620, and a position / speed estimation unit 630. [0050]), the motion detection, the distance detection, normal estimation, attitude estimation, and trajectory estimation (Note: the claim language is interpreted as disjunctive).
Method claim 19 is drawn to the method of using the corresponding apparatus claimed in claim 1. Therefore method claim 19 corresponds to apparatus claim 1 and is rejected for the same reasons of anticipation as used above.
Claim 20 is drawn to a program that causes an information processing apparatus to perform the method of using the corresponding apparatus as claimed in claim 1. Therefore, claim 20 corresponds to apparatus claim 1, and is rejected for the same reasons of anticipation as used above.
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.
Claims 3 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Masayuki (Japan Patent Pub. No.: JP2017142760A) hereinafter Masayuki, in view of Liu (Chinese Patent Pub. No.: CN 111626198 A), hereinafter Liu.
Regarding claim 3, Masayuki teaches all of the elements of the claimed invention as stated in claim 2 except for the following limitations as further recited. However, Liu teaches wherein the processing section uses the parameter generated (at the heart of body pix is an algorithm that performs body segmentation, performing a binary decision on each pixel of the input image to estimate whether the pixel belongs to a person. The images were fed through a MobileNet network and the output was converted to a value between 0 and 1 (which reads on “the parameter generated”) using an S-type activation function. Page 7 11th paragraph) to configure a neural network (Sequence-to-sequence learning typically employs a recurrent neural network encoder-decoder architecture. The invention adopts a longtime memory network to realize a coder-decoder structure, and replaces a cyclic structure of a cyclic neural network with a long-term memory unit, thereby learning a logic relationship with a longer distance in a sequence. Page 8 8th paragraph) of the second task (2. Predicting the motion trail of the pedestrian. Page 8 5th paragraph).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Masayuki to incorporate the teachings of Liu to use the parameter generated to configure a neural network of the second task in order to effectively feedback an automatic driving decision system.
Regarding claim 15, Liu in the combination teaches the information processing apparatus according to claim 7, wherein the processing section performs the second task on the image (2. Predicting the motion trail of the pedestrian. Page 8 5th paragraph) using the neural network of the second task (Sequence-to-sequence learning typically employs a recurrent neural network encoder-decoder architecture. The invention adopts a longtime memory network to realize a coder-decoder structure, and replaces a cyclic structure of a cyclic neural network with a long-term memory unit, thereby learning a logic relationship with a longer distance in a sequence. Page 8 8th paragraph) configured by using the parameter generated (at the heart of body pix is an algorithm that performs body segmentation, performing a binary decision on each pixel of the input image to estimate whether the pixel belongs to a person. The images were fed through a MobileNet network and the output was converted to a value between 0 and 1 (which reads on “the parameter generated”) using an S-type activation function. Page 7 11th paragraph).
Masayuki in the combination further teaches further includes a presentation control section that controls a presentation section that provides assistance to an operator of the moving object based on a recognition result of the second task (The warning / control unit 700 calculates the risk of collision with the own vehicle and the collision timing according to the position and speed of the detection result and the behavior of the own vehicle, and performs warning by the display device 5 and the warning device 6, control of emergency braking by the brake 8, acceleration control by the throttle valve and the injector 7, and the like according to the time until collision with the moving three dimensional object. [0024]).
Regarding claim 16, Masayuki in the combination teaches the information processing apparatus according to claim 15, wherein one or more selected from a display section (The warning / control unit 700 calculates the risk of collision with the own vehicle and the collision timing according to the position and speed of the detection result and the behavior of the own vehicle, and performs warning by the display device 5 and the warning device 6, control of emergency braking by the brake 8, acceleration control by the throttle valve and the injector 7, and the like according to the time until collision with the moving three dimensional object. [0024]), a light emission section (Note: the claim language is interpreted as disjunctive according to specification [0100]), and a sound output section(At the control level 4, it is considered that the own vehicle will certainly collide with the pedestrian, and in order to urgently stop the own vehicle, the brake 8 is operated, and the presence of the pedestrian is notified to the driver by the sound of the warning device 6. [0065]) as the presentation section is mounted on the moving object (The warning / control unit 700 calculates the risk of collision with the own vehicle and the collision timing according to the position and speed of the detection result and the behavior of the own vehicle, and performs warning by the display device 5 and the warning device 6, control of emergency braking by the brake 8, acceleration control by the throttle valve and the injector 7, and the like according to the time until collision with the moving three dimensional object. [0024]), and the presentation control section controls at least one of display control of the display section (The warning / control unit 700 calculates the risk of collision with the own vehicle and the collision timing according to the position and speed of the detection result and the behavior of the own vehicle, and performs warning by the display device 5 and the warning device 6, control of emergency braking by the brake 8, acceleration control by the throttle valve and the injector 7, and the like according to the time until collision with the moving three dimensional object. [0024]), lighting control of the light emission section (Note: the claim language is interpreted as disjunctive according to specification [0100]), and sound output control of the sound output section (At the control level 4, it is considered that the own vehicle will certainly collide with the pedestrian, and in order to urgently stop the own vehicle, the brake 8 is operated, and the presence of the pedestrian is notified to the driver by the sound of the warning device 6. [0065]).
Regarding claim 17, Liu in the combination teaches the information processing apparatus according to claim 7, wherein the moving object is a moving object capable of moving autonomously (The invention adopts the long-term and short-term memory network associated spatiotemporal context information to predict the pedestrian movement locus,
thereby effectively feeding back an automatic driving decision system. Abstract), and the processing section performs the second task on the image (2. Predicting the motion trail of the pedestrian. Page 8 5th paragraph) using the neural network of the second task (Sequence-to-sequence learning typically employs a recurrent neural network encoder-decoder architecture. The invention adopts a longtime memory network to realize a coder-decoder structure, and replaces a cyclic structure of a cyclic neural network with a long-term memory unit, thereby learning a logic relationship with a longer distance in a sequence. Page 8 8th paragraph) configured by using the parameter generated (at the heart of body pix is an algorithm that performs body segmentation, performing a binary decision on each pixel of the input image to estimate whether the pixel belongs to a person. The images were fed through a MobileNet network and the output was converted to a value between 0 and 1 (which reads on “the parameter generated”) using an S-type activation function. Page 7 11th paragraph).
Masayuki in the combination further teaches further includes a planning section that plans a travel and an action of the moving object based on the recognition result of the second task (The warning / control unit 700 calculates the risk of collision with the own vehicle and the collision timing according to the position and speed of the detection result and the behavior of the own vehicle, and performs warning by the display device 5 and the warning device 6, control of emergency braking by the brake 8, acceleration control by the throttle valve and the injector 7, and the like according to the time until collision with the moving three dimensional object. [0024]).
Allowable Subject Matter
Claims 9-12 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.
The following is a statement of reasons for the indication of allowable subject matter: the closest prior arts of record teach the information processing apparatus according to claim 7, wherein the first task is semantic segmentation. However, none of them alone or in any combination teaches the second task includes object detection and motion detection, and distance detection, and the processing section only performs the distance detection if there is no object of interest in the image, performs the object detection and the distance detection if there is the object of interest in the image and the object of interest is not the movable object, and performs the object detection, the motion detection, and the distance detection if there is the object of interest in the image and the object of interest is the movable object as specified in claim 9.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEI ZHAO whose telephone number is (703)756-1922. The examiner can normally be reached Monday - Friday 8:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VU LE can be reached at (571)272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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