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 statements (IDSs) submitted on November 26, 2024, February 11, 2025, September 12, 2025 and October 7, 2025 comply with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
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 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), 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):
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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), 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), 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), 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), because the claim limitations use 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 limitations are: “a memory section” configured to “store a trained model trained by machine learning with a data set including a training image group and a true image” in claim 1; “a processing section” configured to “use the trained model to correct a blur in a processing target image which is an image captured by a first imaging system, the blur being caused by defocus of the first imaging system, wherein the training image group includes a plurality of training images generated by performing defocus simulation processing that simulates, for a predetermined subject image in which a given imaging system is focused on a predetermined subject of which image is captured by the given imaging system, an effect of the blur caused by defocus of the first imaging system, based on a transfer function or a point spread function of the first imaging system at a plurality of object distances, the defocus simulation processing is performed for a region on an optical axis of the first imaging system and a region other than on the optical axis in each training image of the plurality of training images, based on the transfer function or the point spread function on the optical axis, the true image is an image generated by performing best focus simulation processing that simulates, for the predetermined subject image, a state in which the first imaging system is focused, based on the transfer function or the point spread function at an object distance at which the first imaging system is focused, or the predetermined subject image itself, and the trained model is trained by machine learning so that each of the training images is the true image” in claim 1; “optical wavefront modulation element” configured to “change the transfer function or the point spread function” in claim 4; “the processing section” “estimates an image in which a depth of field of the first imaging system is extended to a target extended depth of field wider than the depth of field, by using the trained model to correct the blur caused by defocus of the first imaging system for the processing target image, and the predetermined spatial frequency is a spatial frequency lower than a lowest spatial frequency at which a value of the MTF at a near point of the target extended depth of field is zero” in claim 5; “a processor unit” comprising “the information processing system according to claim 1” in claim 19.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they 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 these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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 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.
Claims 1, 2, 12, 14, 17 and 20-21 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Hiasa (U.S. Patent Application Publication No. US 2020/0388014 A1) (hereafter referred to as “Hiasa (‘014)”).
The examiner would like to point out that the various “sections” identified in section 6 above are being interpreted under 35 U.S.C. 112(f) as described in FIG. 1.
FIG. 1 is a schematic diagram showing the hardware configuration of the information processing system 100. The above-mentioned configuration of the information processing system 100 is a functional configuration achieved by cooperation of the hardware configuration shown in FIG. 1 and a program. As shown in FIG. 1, the information processing system 100, as described in paragraph [0056] of the specification includes a CPU, a memory, a storage, and an input/output IF as a hardware configuration. These are connected to each other by a bus. The CPU (Central Processing Unit) controls another configuration in accordance with a program stored in the memory, performs data processing in accordance with the program, and stores the processing result in the memory. The CPU can be a microprocessor. The memory stores a program executed by the CPU and data. The memory can be a SRAM (Static Random Access Memory).
With regard to claim 1, Hiasa (‘014) describes a memory section configured to store a trained model trained by machine learning with a data set including a training image group and a true image (see Figure 2, element 111 and refer for example to paragraph [0042]); and a processing section configured to use the trained model to correct a blur in a processing target image which is an image captured by a first imaging system (see Figure 2, element 123 and refer for example to paragraphs [0044], [0063] and [0108]), the blur being caused by defocus of the first imaging system (refer for example to paragraph [0035]), wherein the training image group includes a plurality of training images generated by performing defocus simulation processing that simulates, for a predetermined subject image in which a given imaging system is focused on a predetermined subject of which image is captured by the given imaging system, an effect of the blur caused by defocus of the first imaging system, based on a transfer function or a point spread function of the first imaging system at a plurality of object distances (refer for example to paragraph [0047] which discusses the performing of defocus simulation, and to paragraph [0048] which discusses an effect of the blur caused the defocus of the imaging system based on a point spread function), the defocus simulation processing is performed for a region on an optical axis of the first imaging system and a region other than on the optical axis in each training image of the plurality of training images, based on the transfer function or the point spread function on the optical axis (refer for example to paragraphs [0071] through [0074]), the true image is an image generated by performing best focus simulation processing that simulates, for the predetermined subject image, a state in which the first imaging system is focused, based on the transfer function or the point spread function at an object distance at which the first imaging system is focused, or the predetermined subject image itself (refer for example to paragraphs [0048], [0055], [0056], [0061], [0070] and [0085]), and the trained model is trained by machine learning so that each of the training images is the true image (refer to paragraphs [0071] and [0075]).
As to claim 2, Hiasa (‘014) describes wherein each of the training images is an image generated by performing the defocus simulation processing for the predetermined subject image, based on the transfer function or the point spread function at any one of the plurality of object distances (refer for example to paragraphs [0070] through [0075]).
In regard to claim 12, Hiasa (‘014) describes wherein the defocus simulation processing is processing of performing, for the predetermined subject image, convolution computation of a point spread function (PSF) at each of the object distances of the first imaging system (refer for example to paragraph [0048]).
With regard to claim 14, Hiasa (‘014) describes wherein the given imaging system is the first imaging system, and the defocus simulation processing further includes processing of removing an effect of the first imaging system from the predetermined subject image, based on the transfer function or the point spread function at an object distance at which the first imaging system is focused, and the transfer function or the point spread function at the plurality of object distances of the first imaging system (refer for example to paragraphs [0037], [0040], [0051] and [0073]).
As to claim 17, Hiasa (‘014) describes wherein the object distance that achieves focus is the object distance in a best focus condition (refer for example to paragraphs [0070] through [0075]).
In regard to claim 20, Hiasa (‘014) describes a non-transitory information storage medium that stores a trained model trained by machine learning with a data set including a training image group and a true image, wherein the trained model is used by an information processing system including a memory section configured to store the trained model, an input section, a processing section, and an output section (see Figure 2, elements 111 and 123, and refer for example to paragraphs [0042], [0044], [0063] and [0108]), the training image group includes a plurality of training images generated by performing defocus simulation processing that simulates, for a predetermined subject image in which a given imaging system is focused on a predetermined subject of which image is captured by the given imaging system, an effect of a blur caused by defocus of a first imaging system, based on a transfer function or a point spread function of the first imaging system at a plurality of object distances (refer for example to paragraph [0047] which discusses the performing of defocus simulation, and to paragraph [0048] which discusses an effect of the blur caused the defocus of the imaging system based on a point spread function), the defocus simulation processing is performed for a region on an optical axis of the first imaging system and a region other than on the optical axis in each training image of the plurality of training images, based on the transfer function or the point spread function on the optical axis (refer for example to paragraphs [0074] through [0074]), the true image is an image generated by best focus simulation processing that simulates, for the predetermined subject image, a state in which the first imaging system is focused, based on the transfer function or the point spread function at an object distance at which the first imaging system is focused, or the predetermined subject image itself (refer for example to paragraphs [0048], [0055], [0056], [0061], [0070] and [0085]), the trained model is trained by machine learning so that each of the training images is the true image, the input section inputs, to the trained model, a processing target image which is an image captured by the first imaging system, the processing section uses the trained model to perform correction processing of correcting the blur caused by defocus of the first imaging system in the processing target image, and the output section outputs a corrected image produced by the correction processing (refer for example to paragraphs [0071] and [0075]).
With regard to claim 21, Hiasa (‘014) describes a blur in a processing target image is corrected with a trained model trained by machine learning with a data set including a training image group and a true image, the processing target image being an image captured by a first imaging system, the blur being caused by defocus of the first imaging system (refer for example to paragraph [0035]), wherein the training image group includes a plurality of training images generated by performing defocus simulation processing that simulates, for a predetermined subject image in which a given imaging system is focused on a predetermined subject of which image is captured by the given imaging system, an effect of the blur caused by defocus of the first imaging system, based on a transfer function or a point spread function of the first imaging system at a plurality of object distances (refer for example to paragraph [0047] which discusses the performing of defocus simulation, and to paragraph [0048] which discusses an effect of the blur caused the defocus of the imaging system based on a point spread function), the defocus simulation processing is performed for a region on an optical axis of the first imaging system and a region other than on the optical axis in each training image of the plurality of training images, based on the transfer function or the point spread function on the optical axis (refer for example to paragraphs [0074] through [0074]), the true image is an image generated by performing best focus simulation processing that simulates, for the predetermined subject image, a state in which the first imaging system is focused, based on the transfer function or the point spread function at an object distance at which the first imaging system is focused, or the predetermined subject image itself refer for example to paragraphs [0048], [0055], [0056], [0061], [0070] and [0085]), and the trained model is trained by machine learning so that each of the training images is the true image (refer to paragraphs [0071] and [0075]).
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 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(a) 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 19 is rejected under 35 U.S.C. §103(a) as being unpatentable over Ono (U.S. Patent Application Publication No. US 2009/0195672 A1) (hereafter referred to as “Ono”) in view of Hiasa (U.S. Patent Application Publication No. US 2020/0388014 A1).
Ono discloses an image capturing system which provides for correcting a captured image for blur caused by defocus (refer for example to the abstract) which describes endoscope system (refer for example to paragraph [0088]) comprising a processor unit which provides for correcting a captured image for blur caused by defocus and an endoscopic scope coupled to the processor unit and configured to capture the processing target image (refer to paragraphs [0088] through [0091]).
Hiasa discloses an image processing system which provides for using a trained model to correct a blur in a processing target image which is an image captured by a first imaging system, the blur being caused by defocus of the first imaging system (refer to the arguments advanced in section 9 hereinabove).
Given the teachings of the two references and the same environment of operation, namely that of systems for correcting a captured image for blur caused by defocus, 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 modify the Ono system in the manner described by Hiasa according to known methods to yield predictable results and would have been motivated to do so with a reasonable expectation of success in order to provide for increased processing efficiency and higher accuracy as suggested by Hiasa (refer for example to paragraph [0010]), which fails to patentably distinguish over the prior art absent some novel and unexpected result.
Allowable Subject Matter
Claims 3-11, 13, 15-16 and 18 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
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hiasa (‘942) and (‘883), Kobayashi, Ollilaand Siddiqui all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
July 16, 2026
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