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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/08/2025 is/are compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim(s) 4 is/are objected to because of the following informalities:
In claim 4, line 8, “ should read “.
Appropriate correction is required.
Office Action Summary
Claim(s) 6 is/are interpreted under 35 USC 112(f).
Claim(s) 1-2 and 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuo et al (JP 2013/114596 A; See translation provided by Examiner) in view of Uzawa et al (US 2024/0062506 A1).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuo et al (JP 2013/114596 A; See translation provided by Examiner) in view of Uzawa et al (US 2024/0062506 A1), further in view of Amato et al (US 2021/0097354 A1).
Claim(s) 3-4 and 6 is/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.
Claim Interpretation
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: “the thinning judgement unit” in claim(s) 6.
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 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.
Claim(s) 1-2 and 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuo et al (JP 2013/114596 A; See translation provided by Examiner) in view of Uzawa et al (US 2024/0062506 A1).
Regarding claim(s) 1, 7, and 8, Matsuo teaches an object detection device comprising:
a memory (Figure 15; and Paragraph [0046]: “[…] data set storage unit 83 […]”); and
at least one processor coupled to the memory, the at least one processor (Figure 15; and Paragraph [0046]: “The image recognition unit 12 […] processing of the image size normalization unit 81 and the image feature amount conversion unit 82 […] comparison unit 84 from within the data set storage unit 83, and the vote determination unit 85 […]”) being configured to:
configured to extract a plurality of rectangles to be candidates to which object detection is applied from an input image (Figure 1; Paragraph [0044]: “The partial region extraction unit 11 extracts a partial region from the input image […] A large number of regions are extracted […] as candidate regions which may contain an object”; Paragraph [0046]: “The image recognition unit 12 receives the partial regions extracted by the partial region extraction unit 11 one by one as target images to be input, and estimates what objects are included for each”; and Paragraph [0056] – Paragraph [0059]: “The candidate selection unit 21 selects a candidate of an area which may be occupied by an object included in the input image […] that the shape of the partial region to be extracted is a rectangle […] the rectangle extracting unit 22 cuts out a partial region as a corresponding rectangle from each of the rectangular parameters output from the candidate selecting unit 21 from the input image […] which a target may exist, and each is passed to the image recognition unit 12 as a target image”);
configured to select a fixed number of rectangles to which the object detection is applied from among the rectangle candidates extracted (Paragraph [0091] – Paragraph [0092]: “Step 1. The rectangles which become candidate areas are arranged in descending order of the reliability SS (w) […] Step 2. If the i-th rectangle and the rectangles adopted so far overlap less than a certain percentage RO, it is adopted as the target area […] Step 3. If the number of adoption reaches the number of the final candidate number NF designated by the parameter, it ends […] the final candidate number NF of the end determination in step 3 is set smaller than the predetermined number selected by the candidate selection unit 21”).
Matsuo fails to teach to configured to perform the object detection on the rectangle selected to output metadata including at least a class, reliability, and a bounding box of the object included in the input image as an object detection result. However, Uzawa teaches to configured to perform the object detection on the rectangle selected to output metadata including at least a class, reliability, and a bounding box of the object included in the input image as an object detection result (Figure 1; Paragraph [0038]: “The object detection device […] executes object detection processing for an image input. Then, the object detection device 10 outputs an object detection result by the object detection processing by metadata […] which includes at least the attributes, confidence level, center coordinates, and frame surrounding the object in the input image”; Paragraph [0084]: “The object detection processing unit 134 inputs the divided image reduced by the image scaling processing unit 133 as object detection processing, and computes a learned object detection model based on predetermined deep learning […] generates a set of pieces of attribute information including the attribute value of the object […] and a frame surrounding the object as the metadata of the divided image (second metadata) […] The frame surrounding the object included in the metadata is referred to as ‘quadrangular frame BB2’. The quadrangular frame BB2 includes at least center coordinates (X, Y), a frame height (H), and a frame width (W)”; and Paragraph [0088]: “The confidence level filter processing unit 136 performs confidence level filter processing for selecting an object whose confidence level is equal to or more than a preset threshold Th with respect to the detection object group after the metadata adjustment processing unit 135 adjusts the metadata”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine before the effective filing date of the claimed invention to modify the object detection device of Matsuo to include the object detection processing of Uzawa, such that object detection is performed on the selected rectangles to output metadata including at least a class, reliability, and a bounding box of an object included in the input image as an object detection result, in order to provide detailed information regarding an object detected within the selected image regions. The motivation for this combination of references would have been to provide detailed information regarding an object detected within selected image regions while efficiently performing object detection on regions likely to contain an object. This motivation for the combination of Matsuo and Uzawa is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 2, Matsuo as modified by Uzawa teaches the object detection device according to claim 1, where Uzawa teaches wherein a means of selecting the rectangle is any of a means of selecting the rectangle on the basis of an overlap degree between a distribution estimation result obtained and a rectangle selected in the past input image, a means of selecting the rectangle on the basis of an object detection result obtained from the past input image, a means of selecting the rectangle on the basis of a image difference from the past input image, and a means of selecting the rectangle included in the section while the input image is divided into a plurality of sections and the section is cyclically selected, or a combination of a plurality of means to select the rectangle (Paragraph [0078]: “The estimated interpolation object number calculation processing unit 121 uses an object detection result of the entire processing unit 110 in the immediately preceding frame for calculation of the estimated value”; Paragraph [0079]: “The target divided image determination processing unit 122 performs target divided image determination processing for determining a divided image to be processed in the division processing unit 130 on the basis of the estimated interpolation object number calculated by the estimated interpolation object number calculation processing unit 121”; Paragraph [0082]: “The divided image selection processing unit 132 performs divided image selection processing for selecting […] pieces of divided image determined by the target divided image determination processing unit 122 from the divided image output by the division processing unit 130”; and Paragraph [0134]: “the object detection result from the entire processing unit 110 inputted to the divided image narrowing unit 120 is not the current frame but the result of the immediately preceding frame, and the divided image narrowing unit 120 may narrow the next frame. By using the result of the immediately preceding frame, the processing of the division processing unit 130 can be executed in parallel without waiting for the processing of the entire processing unit 110”).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Matsuo et al (JP 2013/114596 A; See translation provided by Examiner) in view of Uzawa et al (US 2024/0062506 A1), further in view of Amato et al (US 2021/0097354 A1).
Regarding claim(s) 5, Matsuo as modified by Uzawa teaches the object detection device according to claim 1, but do not specifically teach comprising: a pipelined processing mechanism configured to apply the rectangles obtained by the processing in rectangle extraction and rectangle selection in a frame inputted at time point t - 1 to a frame inputted at time point t and to perform processing in object detection.
However, Amato teaches a pipelined processing mechanism configured to apply the rectangles obtained by the processing in rectangle extraction and rectangle selection in a frame inputted at time point t - 1 to a frame inputted at time point t and to perform processing in object detection (Figure 2; Paragraph [0035]: “a pipeline 200 for an example object detection process based on object relation […] An anchor object detector 204 can detect for a first anchor object in a scene depicted by the digital image, and a search region determiner 206 can determine a region in the digital image relative to (e.g., surrounding) the first anchor object detected by the anchor object detector 204 […] a new anchor object determiner 208 can determine whether another (new) anchor object is to be detected for in the determined region (in search towards a target object). If not, the new anchor object determiner 208 can cause a target object detector 212 to detect for the target object in the determined region”; and Paragraph [0036] – Paragraph [0037]: “multiple iterations of anchor object detection (by the anchor object detector 204) and region determination (by the search region determiner 206) can be performed prior to a target object being searched (by the target object detector 212) in the final set of determined regions (by the search region determiner 206) […] coarse-to-fine approach/strategy for using detection of multiple levels of anchor objects to refine (e.g., constrain) an image search space for detecting the target object”), Examiner’s Note: Under a broadest reasonable interpretation, Amato’s pipeline teaches applying regions obtained from preceding region determination processing to subsequent object detection processing, such that previously determined regions are used as the regions on which subsequent object detection is performed.
Therefore, it would have been obvious to one of ordinary skill in the art to combine before the effective filing date of the claimed invention to modify the object detection device of Matsuo in view of Uzawa to implement the region selection and object detection processing in a pipelined manner as taught by Amato, such that regions obtained from preceding region extraction and selection processing are applied to subsequent image processing for object detection, in order to improve the efficiency of the object detection processing. The motivation for this combination of references would have been to efficiently perform object detection by pipelining region determination and subsequent object detection processing. This motivation for the combination of Matsuo, Uzawa, and Amato is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Allowable Subject Matter
Claim(s) 3-4 and 6 is/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.
Relevant Prior Art Directed to State of Art
Vajapey et al (US 2020/0151884 A1) are relevant prior art not applied in the rejection(s) above. Vajapey discloses a system for object-level tracking comprising: a processor and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: present a first frame in a series of pre-recorded frames; receive a first annotation of a first bounding region of an object in the first frame; assign an object identifier associated with the first bounding region; obtain a second bounding region of the object in a second frame in the series of pre-recorded frames based at least in part on a prediction of a location of the object in the second frame, wherein the second bounding region is associated with the object identifier; output the second bounding region; and maintain a prediction of the location of the object in subsequent frames using the object identifier.
Iio et al (US 2023/0196773 A1) are relevant prior art not applied in the rejection(s) above. Iio discloses an object detection device, comprising: an image acquisition unit configured to acquire an image at a predetermined time interval; a first image processing unit configured to extract an object from the acquired image; a second image processing unit configured to extract a plurality of candidate areas of the object in the image, based on a position of the object acquired in a previous frame of the image; a comparison unit configured to compare the object extracted by the first image processing unit and the candidate areas with an object extracted from an image in the previous frame of the image; and a specification unit configured to specify a candidate area of a current frame that matches the object extracted from the image in the previous frame, from the candidate areas, based on a comparison result of the comparison unit.
Conclusion
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/JONGBONG NAH/Examiner, Art Unit 2674
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