DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of Application No. JP2022-193284, filed in Japan on 12/02/2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/13/2023 was considered by the examiner.
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.
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 limitations 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.
Claim 3 recites the limitation “storage unit”. This limitation has been interpreted under 112(f) as a means plus function because of the combination of the non-structural, generic placeholder “storage unit”, as well as its respective functional languages “that stores sizes of detection areas” and is being interpreted as “the RAM 102” that corresponds to the structure found in the disclosure (Par. [0044]).
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
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 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 1-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Muta et al. (U.S. Patent App. Pub No. 2022/0180529 A1, hereafter referred as Muta).
Regarding Claim 1:
Muta teaches a non-transitory computer-readable recording medium storing a program for causing a computer to execute a process (Muta: Par. [0005]; a non-transitory computer-readable medium stores a program which causes a computer to perform), the process comprising: determining a size (Muta: Par. [0116] and Fig. 16B; the size of the margin region can be set according to the size of a person to be a target of flow amount measurement; in Fig. 16B, the margin region 1602 of the extraction region 1600 is set to a size that can include a part required to detect a person 1603) of an overlapping area such that adjacent divided areas among a plurality of divided areas obtained by dividing a captured image have the overlapping area with each other across a boundary between the adjacent divided areas (Muta: Par. [0117] and Fig 16C; in the case of setting the margin region, the extraction regions for extracting the partial images can be set so that adjacent extraction regions overlap with each other; in Fig. 16C, a margin region 1610 of the extraction region 1605 overlaps with a margin region 1611 of the extraction region 1606); determining a plurality of partial areas that respectively correspond to the plurality of divided areas based on the determined size of the overlapping area (Muta: Par. [0116-0117]; setting overlapping margin regions based on determined size of detected person); and cutting out a plurality of partial images that respectively correspond to the plurality of partial areas from the captured image (Muta: Par. [0118]; used for extraction of partial images including such margin regions, partial images can be extracted from each of the extraction regions and the peripheral regions, the images extracted from the peripheral regions of the extraction regions correspond to the image in the margin region of the extraction region).
In regards to Claim 2, Muta further teaches the non-transitory computer-readable recording medium according to claim 1, wherein one or more instances of a predetermined object appear in the captured image, and the process further comprises: detecting any instance of the predetermined object from each of the plurality of partial images (Muta: Par. [0116-0117]; the margin region 1602 of the extraction region 1600 is set to a size that can include a part required to detect a person 1603 or a person 1604 to be a target of flow amount measurement, when a person 1609 to be a detection target is in the margin region 1610 of the extraction region 1605, the person 1609 can be detected).
In regards to Claim 3, Muta further teaches the non-transitory computer-readable recording medium according to claim 2, the process further comprising: referring to a storage unit that stores sizes of detection areas in which any instances of the predetermined object are detected from one or more input images (Muta: Par. [0027] and [0069]; storage unit 12 holds programs and data necessary for operation; the size of a person at a specific position can be determined using the size of a person in the vicinity of the position detected by, for example, a person recognition process on the input image); and determining the size of the overlapping area based on the sizes of the detection areas (Muta: Par. [0116]; the size of the margin region can be set according to the size of a person to be a target of flow amount measurement, the margin region can be set to a size that can include a portion necessary for detecting a person).
In regards to Claim 4, Muta further teaches the non-transitory computer-readable recording medium according to claim 3, the process further comprising: determining the size of the overlapping area based on an average value of the sizes of the detection areas stored in the storage unit (Muta: Par. [0069]; the size of a person at a specific position can be determined using the size of a person in the vicinity of the position detected by, for example, a person recognition process on the input image; obvious to one skilled in the art that the person recognition process would include the averaging of sizes of different people in the vicinity to get to the most accurate size of the person at the specific position).
In regards to Claim 5, Muta further teaches the non-transitory computer-readable recording medium according to claim 3, the process further comprising: determining a first width of the overlapping area between adjacent partial areas among the plurality of partial areas (Muta: Par. [0116]; the size of the margin region can be set according to the size of a person to be a target of flow amount measurement) based on an average value and a standard deviation of second widths in a direction of the first widths in the sizes of detection areas stored in the storage unit (Muta: Par. [0069]; the size of a person at a specific position can be determined using the size of a person in the vicinity of the position detected by, for example, a person recognition process on the input image; obvious to one skilled in the art that the person recognition process would include the averaging and standard deviation of sizes of different people in the vicinity to get to the most accurate size of the person at the specific position).
In regards to Claim 6, Muta further teaches the non-transitory computer-readable recording medium according to claim 2, the process further comprising: in a case where a first instance of the predetermined object is detected from a first partial image among the plurality of partial images, enlarging a first detection area where the first instance is detected in the first partial image by a ratio of a size of a first divided area that corresponds to the first partial image among the plurality of divided areas to a size of the first partial image (Muta: Par. [0116] and Fig. 16B; the margin region can be set to a size that can include a portion necessary for detecting a person, in Fig. 16B, the margin region 1602 of the extraction region 1600 is set to a size that can include a part required to detect a person 1603 or a person 1604 to be a target of flow amount measurement); and storing a size of the enlarged first detection area in the storage unit (Muta: Par. [0068]; the size of each extraction region can be determined so that the ratio between the size of the extraction region and the size of a person (measurement target) in the image in the extraction region is substantially constant; obvious to one skilled in the art to store this size in the storage unit for operation).
Regarding Claim 7:
Muta further teaches an image processing method (Muta: Par. [0004]; an image processing method), comprising: determining, by a computer (Muta: Par. [0005]; a computer), a size (Muta: Par. [0116] and Fig. 16B; the size of the margin region can be set according to the size of a person to be a target of flow amount measurement; in Fig. 16B, the margin region 1602 of the extraction region 1600 is set to a size that can include a part required to detect a person 1603) of an overlapping area such that adjacent divided areas among a plurality of divided areas obtained by dividing a captured image have the overlapping area with each other across a boundary between the adjacent divided areas (Muta: Par. [0117] and Fig 16C; in the case of setting the margin region, the extraction regions for extracting the partial images can be set so that adjacent extraction regions overlap with each other; in Fig. 16C, a margin region 1610 of the extraction region 1605 overlaps with a margin region 1611 of the extraction region 1606); determining a plurality of partial areas that respectively correspond to the plurality of divided areas based on the determined size of the overlapping area (Muta: Par. [0116-0117]; setting overlapping margin regions based on determined size of detected person); and cutting out a plurality of partial images that respectively correspond to the plurality of partial areas from the captured image (Muta: Par. [0118]; used for extraction of partial images including such margin regions, partial images can be extracted from each of the extraction regions and the peripheral regions, the images extracted from the peripheral regions of the extraction regions correspond to the image in the margin region of the extraction region).
Regarding Claim 8:
Muta further teaches an information processing device, comprising: a memory; and a processor coupled to the memory and the processor configured to (Muta: Par. [0003]; an image processing apparatus comprises a processor and a memory): determine a size (Muta: Par. [0116] and Fig. 16B; the size of the margin region can be set according to the size of a person to be a target of flow amount measurement; in Fig. 16B, the margin region 1602 of the extraction region 1600 is set to a size that can include a part required to detect a person 1603) of an overlapping area such that adjacent divided areas among a plurality of divided areas obtained by dividing a captured image have the overlapping area with each other across a boundary between the adjacent divided areas (Muta: Par. [0117] and Fig 16C; in the case of setting the margin region, the extraction regions for extracting the partial images can be set so that adjacent extraction regions overlap with each other; in Fig. 16C, a margin region 1610 of the extraction region 1605 overlaps with a margin region 1611 of the extraction region 1606); determine a plurality of partial areas that respectively correspond to the plurality of divided areas based on the determined size of the overlapping area (Muta: Par. [0116-0117]; setting overlapping margin regions based on determined size of detected person); and cut out a plurality of partial images that respectively correspond to the plurality of partial areas from the captured image (Muta: Par. [0118]; used for extraction of partial images including such margin regions, partial images can be extracted from each of the extraction regions and the peripheral regions, the images extracted from the peripheral regions of the extraction regions correspond to the image in the margin region of the extraction region).
Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jaramillo-Avila et al. (NPL: Foveated image processing for faster object detection and recognition in embedded systems using deep convolutional neural networks) teaches how detection, recognition and processing speed in a CNN are affected by reducing image size using a foveated transformation.
Conclusion
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/RENAE A BITOR/Examiner, Art Unit 2663
/GREGORY A MORSE/Supervisory Patent Examiner, Art Unit 2698