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 .
Election/Restrictions
Claims 9 and 19 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on July 21st, 2026. Claims 1-8, 10-17 and 19 are pending and examined herein.
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:
“an image acquisition unit that acquires…”
“a type acquisition unit that acquires…”
“a model acquisition unit that acquires…”
“a detection unit that detects…”
in claims 1 and 10 and subsequent dependent claims 5-6 and 14-15.
And
“a rotation processing unit that rotates…” in claims 7 and 16
“a distortion correction unit that corrects…” in claims 8 and 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 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.
Claims 1-6, 10-15 and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by USPN 2022/0230021 to Muehlenstaedt et al.
With regard to claim 1, Muehlenstaedt discloses a detection device that detects a moving object included in a captured image (paragraphs [0024] and [0043] and Fig. 2, A system for detecting vehicles is disclosed), comprising:
an image acquisition unit that acquires the captured image (paragraph [0024], and Fig. 2, Cameras 262);
a type acquisition unit that acquires type information indicating one body type of the moving object included in the captured image, the one body type being classified according to an exterior shape of the moving object (paragraphs [0032] and [0043], a CNN is used to detect and label one or more objects of different types. Computing device 212 determines state information for the detected objects including shape, size, footprint, type, e.g. vehicle, pedestrian, bicycle, etc.);
a model acquisition unit (Fig. 2, onboarding computing device 212 receives a machine learning models such as an example object detection model) that acquires, from among a plurality of first detection models each of which is a machine learning model prepared for each of the one body type, the first detection model selected according to the one body type identified by the type information acquired by the type acquisition unit (paragraph [0043]: “At 304, the on-board computing device may receive a machine learning model for making predictions relating to the received data logs. For example, the machine learning model may be an example object detection model (e.g., a convolutional neural network (CNN), a mask R-CNN, etc.) that may be used to detect and label one or more objects and/or events in each received raw data log, output bounding boxes, or the like. In some embodiments, the on-board computing device may receive the machine learning model from a remote server See also paragraph [0054]: “∥.∥ is a norm (or a loss/cost function) that may be custom tailored for a specific use case (for example, for a type of machine learning model, a type of object class, a of algorithm of the machine learning model, etc.).” Specific models that are trained for specific object types may be acquired from a remote server to identify the vehicle types); and
a detection unit that detects the moving object included in the captured image by inputting the captured image to the first detection model acquired by the model acquisition unit and identifying a target region indicating the moving object in the captured image (paragraph [0031]: “In an embodiment, the on-board computing device 212 may include an object detector that detects and labels objects. An example object detector is a convolutional neural network (CNN). The object detector may output an image or point cloud that includes bounding boxes surrounding the detected objects, labels for the objects and/or bounding box scores that indicate a degree of confidence that the objects detected were correctly labeled.” The moving objects or vehicles are detected and identified as well as categorized).
With regard to claim 2, Muehlenstaedt discloses the detection device according to claim 1, wherein each of the plurality of first detection models is trained in advance to identify the target region by inputting a plurality of training images including M (M is an integer of 2 or more) first training images each containing the moving object classified into the one body type and N (N is an integer of 0 or more and less than M) second training images each containing the moving object classified into another type of the body type different from the one body type (paragraphs [0044], Training data images are used for different object types. See also paragraph [0054]: “∥.∥ is a norm (or a loss/cost function) that may be custom tailored for a specific use case (for example, for a type of machine learning model, a type of object class, a of algorithm of the machine learning model, etc.).” Specific models that are trained for specific object types may be acquired from a remote server to identify the vehicle types).
With regard to claim 3, Muehlenstaedt discloses the detection device according to claim 2, wherein a region correct answer label indicating either the target region or an out-of-target region indicating other than the moving object is associated with each region in the training images (paragraphs [0009], [0031], [0043] and [0050]-[0051], Images are labeled and confidence values associated with the labels are generated as well as a bounding box indicating the region where the object is located in the image).
With regard to claim 4, Muehlenstaedt discloses the detection device according to claim 1, wherein the body type indicates a type of the moving object (paragraphs [0032] and [0043], a CNN is used to detect and label one or more objects of different types. Computing device 212 determines state information for the detected objects including shape, size, footprint, type, e.g. vehicle, pedestrian, bicycle, etc.).
With regard to claim 5, Muehlenstaedt discloses the detection device according to claim 1, wherein the moving object moves by receiving a movement condition that defines a movement operation of the moving object from outside the moving object, and the type acquisition unit acquires the type information by identifying the one body type of the moving object included in the captured image using schedule information, the schedule information being associated with each imaging range of the plurality of imaging devices that acquire the captured image and indicating a scheduled time at which the moving object classified into the one body type moves in the imaging range (paragraphs [0030] and [0035]-[0036], The vehicle uses the detected objects such as other vehicles to effect the navigation of the vehicle by controlling movement of the vehicle in relation other detected moving objects).
With regard to claim 6, Muehlenstaedt discloses the detection device according to claim 1, wherein the type acquisition unit acquires the type information by inputting the captured image to an identification model being a machine learning model that has been trained to output the type information when the captured image is input (paragraphs [0032] and [0043], a CNN is used to detect and label one or more objects of different types. Computing device 212 determines state information for the detected objects including shape, size, footprint, type, e.g. vehicle, pedestrian, bicycle, etc. See also (paragraphs [0044], Training data images are used for different object types. See also paragraph [0054]: “∥.∥ is a norm (or a loss/cost function) that may be custom tailored for a specific use case (for example, for a type of machine learning model, a type of object class, a of algorithm of the machine learning model, etc.).” Specific models that are trained for specific object types may be acquired from a remote server to identify the vehicle types).
With regard to claim 10, Muehlenstaedt discloses a detection device that detects a moving object included in a captured image (paragraphs [0024] and [0043] and Fig. 2, A system for detecting vehicles is disclosed), comprising:
an image acquisition unit that acquires the captured image (paragraph [0024], and Fig. 2, Cameras 262);
a type acquisition unit that acquires type information indicating one body type of the moving object included in the captured image, the one body type being classified according to an exterior shape of the moving object (paragraphs [0032] and [0043], a CNN is used to detect and label one or more objects of different types. Computing device 212 determines state information for the detected objects including shape, size, footprint, type, e.g. vehicle, pedestrian, bicycle, etc.); and
a detection unit that detects the moving object included in the captured image by inputting the captured image and the one body type identified by the type information acquired by the type acquisition unit to a second detection model being a machine learning model and identifying a target region indicating the moving object in the captured image (paragraph [0043]: “At 304, the on-board computing device may receive a machine learning model for making predictions relating to the received data logs. For example, the machine learning model may be an example object detection model (e.g., a convolutional neural network (CNN), a mask R-CNN, etc.) that may be used to detect and label one or more objects and/or events in each received raw data log, output bounding boxes, or the like. In some embodiments, the on-board computing device may receive the machine learning model from a remote server See also paragraph [0054]: “∥.∥ is a norm (or a loss/cost function) that may be custom tailored for a specific use case (for example, for a type of machine learning model, a type of object class, a of algorithm of the machine learning model, etc.).” Specific models that are trained for specific object types may be acquired from a remote server to identify the vehicle types).
With regard to claim 11, Muehlenstaedt discloses the detection device according to claim 10, wherein the second detection model is trained in advance to identify the target region by inputting a plurality of training images containing the moving object and a type correct answer label, the type correct answer label being associated with each of the plurality of training images and indicating the body type of the moving object included in the training images (paragraph [0043]: “At 304, the on-board computing device may receive a machine learning model for making predictions relating to the received data logs. For example, the machine learning model may be an example object detection model (e.g., a convolutional neural network (CNN), a mask R-CNN, etc.) that may be used to detect and label one or more objects and/or events in each received raw data log, output bounding boxes, or the like. In some embodiments, the on-board computing device may receive the machine learning model from a remote server See also paragraph [0054]: “∥.∥ is a norm (or a loss/cost function) that may be custom tailored for a specific use case (for example, for a type of machine learning model, a type of object class, a of algorithm of the machine learning model, etc.).” Specific models that are trained for specific object types may be acquired from a remote server to identify the vehicle types).
With regard to claim 12, Muehlenstaedt discloses the detection device according to claim 11, wherein a region correct answer label indicating either the target region or an out-of-target region indicating other than the moving object is associated with each region in the training images (paragraph [0050]: “…The object detection model may output an image or point cloud that includes bounding boxes surrounding the detected objects and/or labels for the objects. A bounding box indicates a region of the unlabeled data log that is believed to contain an identified object. Thus, the bounding box may be assigned a label indicating the identified object that is believed to be contained within the bounding box. Each detected object may be represented by its location (centroid or center point), boundary, label class, or even size. In an embodiment, the object detector can output an image or point cloud that includes bounding boxes surrounding the detected objects and/or labels for the detected objects. An example of an image 400 including bounding boxes 401a-n is shown in FIG. 4. As shown in FIG. 4, the bounding boxes include various detected objects such as vehicles, traffic signs, pedestrians, or the like.” See also paragraph [0031]: “An example object detector is a convolutional neural network (CNN). The object detector may output an image or point cloud that includes bounding boxes surrounding the detected objects, labels for the objects and/or bounding box scores that indicate a degree of confidence that the objects detected were correctly labeled.”)
With regard to claim 13, Muehlenstaedt discloses the detection device according to claim 10, wherein the body type indicates a type of the moving object (paragraphs [0032] and [0043], a CNN is used to detect and label one or more objects of different types. Computing device 212 determines state information for the detected objects including shape, size, footprint, type, e.g. vehicle, pedestrian, bicycle, etc.).
With regard to claim 14, Muehlenstaedt discloses the detection device according to claim 10, wherein the moving object moves by receiving a movement condition that defines a movement operation of the moving object from outside the moving object, and the type acquisition unit acquires the type information by identifying the one body type of the moving object included in the captured image using schedule information, the schedule information being associated with each imaging range of the plurality of imaging devices that acquire the captured image and indicating a scheduled time at which the moving object classified into the one body type moves in the imaging range ((paragraphs [0030] and [0035]-[0036], The vehicle uses the detected objects such as other vehicles to effect the navigation of the vehicle by controlling movement of the vehicle in relation other detected moving objects).
With regard to claim 15, Muehlenstaedt discloses the detection device according to claim 10, wherein the type acquisition unit acquires the type information by inputting the captured image to an identification model being a machine learning model that has been trained to output the type information when the captured image is input (paragraphs [0032] and [0043], a CNN is used to detect and label one or more objects of different types. Computing device 212 determines state information for the detected objects including shape, size, footprint, type, e.g. vehicle, pedestrian, bicycle, etc. See also (paragraphs [0044], Training data images are used for different object types. See also paragraph [0054]: “∥.∥ is a norm (or a loss/cost function) that may be custom tailored for a specific use case (for example, for a type of machine learning model, a type of object class, a of algorithm of the machine learning model, etc.).” Specific models that are trained for specific object types may be acquired from a remote server to identify the vehicle types).
With regards to claim 19, the discussion of claim 1 applies. Muehlenstaedt discloses the method of claim 19 in the operation of the device discussed in claim 1.
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 7-8 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of USPNs 2022/0230021 to Muehlenstaedt et al. and 2023/0098787 to Matsui et al.
With regard to claim 7, Muehlenstaedt discloses the detection device according to claim 1, but does not explicitly disclose further comprising a rotation processing unit that rotates the captured image such that a movement direction of the moving object faces a predetermined direction. The act of rotating images to ensure proper orientation is commonplace in the art. Matsui discloses a similar vehicle system to that of Muehlenstaedt et al. and further discloses that image correction is performed in order to display images to the vehicle operator properly and teaches that rotational deviation is corrected (paragraphs [0031]-[0032], [0044] and [0058]-[0061]). Therefore it would have been obvious to one of ordinary skill in the art before time of filing to perform image correction in the form of rotational correction and distortion correction as taught by Matsui in combination with the vehicle imaging system of Muehlenstaedt in order to provide the driver with the best quality and accurate image.
With regard to claim 8, the discussion of claim 7 applies Matsui also discloses performing distortion correction in order to provide a proper image to be displayed to the driver (paragraphs [0031]-[0032], [0044] and [0058]-[0061]).
With regard to claim 16, the discussion of claim 7 applies.
With regard to claim 17, the discussion of claim 8 applies.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESLEY J TUCKER whose telephone number is (571)272-7427. The examiner can normally be reached 9AM-5PM Monday-Friday.
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/WESLEY J TUCKER/Primary Examiner, Art Unit 2661
