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 .
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.
the claimed invention is directed to an abstract idea without significantly more. The claims 6 and 7 recite mathematical concepts, because the claims provide image data that is used to train a machine learning model, in which images are mathematical data and training the model are mathematical calculations. This judicial exception is not integrated into a practical application because the claims do not provide any application to the trained image conversion unit, only providing that the conversion unit is trained. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not provide any specific machine elements that amount to more than a generic computer elements.
Claims 1- 5 are considered eligible subject matter because the claims provide specific machine elements, including the image acquisition units, that are interpreted under 112(f), as explained below, and therefore is not generic computer implementation.
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.
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: “first image acquisition unit”, “second image acquisition unit”, “image conversion unit”, “extraction unit” and “conversion training unit” in claim 1, “processing unit” in claim 4
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 4 and 5 are objected to because of the following informalities: The claims reefer to “the image” in lines 2 and 5 (claim 4), and line 2 (claim 5). It is not completely clear as to which image the applicant is referring to, because the applicant previously claims many images. The examiner believes the applicant intends to refer to “the second image” in line 2 of claim 4 and line 2 of claim 5, and “the first image” in line 5 of claim 4. Appropriate correction is required.
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 1-7 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by U.S. Patent Application Publication No. 20200372282 (Chang et al) .
Regarding claim 6, Chang et al discloses an information processing method comprising steps of: acquiring a first image (fig. 5, item 405 in which a subject is captured under a first image capturing condition, with certain imaging parameters (page 2, paragraph 20); acquiring a second image (fig. 5, item 403, fig. 6, A220) in which the subject is captured under a second image capturing condition, other image capturing conditions (page 2, paragraph 20); extracting a first feature value indicative of a feature of the first image, i.e. the color features of the images (page 4, paragraph 41) and a second feature value, i.e. the color features of the synthesized image of fig. 5, item 407, indicative of a feature of the second image (fig. 5, item 403) converted by an image conversion unit (fig. 5, item 401) that is a machine learning model, a generative network (fig. 5, item 401) to which the acquired second image is inputted (fig. 5, item 403 input into item 401) and which outputs a converted second image, the synthesized images (fig. 5, item 407); and training the image conversion unit in reference to feedback (fig. 5, feedback from item 411 to item 401), which is a difference in characteristics between the first feature value and the second feature value, because the statistics that are generated are for matching the image features which must evaluates differences to match (page 5, paragraph 52) in regard to a plurality of subjects from many images (Page 3, paragraph 29, fig. 6, A210, A220).
Claims 1 and 7 are rejected for the same reasons as claim 6. Thus, the arguments analogous to that presented above for claim6 are equally applicable to claims 1 and 7. Claims 1 and 7 distinguish from claim 6 only in that claim 6 is a method claim, claim 1 is a system with units that carry out the method, and claim 7 is a computer readable medium that carries out the method. Chang et al teaches further this feature, i.e. a system (fig. 7) with first and second image acquisition units (fig. 7, item 515, 525), image conversion unit, extraction units and training units (fig. 5, item 401, 407,405,411) and a non transitory computer readable medium that contains a program which when executed by a computer (fig. 7, item 530) executes the method (fig. 6).
Regarding claim 2, Chang et al discloses the first image capturing condition is image capturing using a first camera (fig. 7, item 515), and the second image capturing condition is image capturing using a second camera (Fig. 7, item 525) having a characteristic different from that of the first camera (Page 2, paragraph 20).
Regarding claim 3, Chang et al discloses the conversion training unit (fig. 5, item 411) trains the image conversion unit (fig. 5, feedback to item 411) in reference to a difference between a correct answer and a value outputted, the classification label and the ground truth (page 5, paragraph 47-48), when the first feature value or the second feature value is inputted to an identifier (fig. 5, item 409), from the identifier that is trained with learning data (page 5, paragraph 49) including a plurality of pieces of input data each including one of the first feature value and the second feature value and correct answer data indicative of which one of the first feature value and the second feature value each piece of the input data is, because the training occurs at the same time and thus utilizes the feature values and correct answer data of processed by the discriminative network (page 5, paragraph 49).
Regarding claim 5, Chang et al discloses a processing unit (fig. 5, input to item 409) that inputs a feature value extracted by the extraction unit from the image captured under the second image capturing condition and converted by the image conversion unit (fig. 5, item 407 converted from item 403 by item 401) to a machine learning model (fig. 5, item 411) trained in reference to a feature value extracted by the extraction unit from the image captured under the first capturing condition (fig. 5, item 405) and executes a process in reference to an output of the machine learning model in regard to the inputted feature value, i.e. the process of output of “real” or “synthesized” or the output to train the generative network (fig. 5).
Regarding claim 5, Chang et al discloses wherein the machine learning model outputs information indicative of whether or not the image captured under the second image capturing condition and converted by the image conversion unit includes a predetermined object, a real object (fig. 5, output of item 411) or recognize objects as disclosed in page 5, paragraph 54.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kathleen Yuan Dulaney whose telephone number is (571)272-2902. The examiner can normally be reached M-F: 9AM-5PM.
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/KATHLEEN Y DULANEY/Primary Examiner, Art Unit 2666 10/2/2026
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