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 (IDS) submitted on 4/11/2024 and 2/18/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Objections
Claims 8, 9, and 11 are objected to because of the following informalities:
In claim 8, line 8, “a” should be changed to --the--
In claim 9, line 4, “configure” should be changed to --configured--
In claim 11, line 8, “a” should be changed to --the--
Appropriate correction is required.
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: all of the “unit that…” in claims 1-9 and 11-12.
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.
Claims 1, 2, 8-12 are rejected under 35 U.S.C. 102a1 as being anticipated by JP2021089654, herein referred to as “JP’654.” Note that the citations to JP’654 below are in reference to the supplied English translation of JP’654.
As per claim 1, JP’654 discloses a learning device comprising: a first evaluation unit that performs quantitative evaluation on a plurality of pieces of multimedia data and thereby acquires a plurality of first evaluation results for each of the plurality of pieces of multimedia data (bottom pf page 2, image acquisition unit 301 acquires a correction target image (e.g. the image to be corrected) and a target image (e.g. a reference image), then evaluates both images via an evaluation value acquisition unit 302) (at the top of page 3, the evaluation value acquisition unit 302 analyzes each image and generates a plurality of evaluation values for each (e,g, color, noise and a luminance-based histogram, see steps 601, 602, and 801)); a generation unit that selects a second parameter from among a plurality of first parameters having values different from each other on the basis of the plurality of first evaluation results and generates a first set of teacher data including the selected second parameter (see all of page 6 and the learning data generation unit 303 which selects one of color, noise, or histogram in order to create teacher data); and a first learning unit that performs a first type of machine learning using first sets of teacher data and thereby generate a first learned model that outputs a third parameter used for processing of multimedia data of a processing target (see page 7, middle of the page where a learning model generation unit 304 takes the generated teacher data and creates a machine learning model (CNN), see steps 1102 and 1103).
With regard to claim 2, JP’654 at the bottom of page 5, step 801 creates a histogram based upon brightness (L), e.g. a distribution of luminance values of each pixel.
Claims 8-12 are rejected for reasoning, mutatis mutandis, as that of claim 1 above. In addition, JP’654 discloses at page 2, 3rd para, a CPU 101, a ROM 102, RAM 103, display 105, and an external storage device 110. The claimed generation of a third parameter from a learned model in order to process the multimedia data of the processing target is taught at step 1105, top of page 8 (selects the most appropriate learning model) and then outputs a corrected image.
Claims 1, 8-12 are rejected under 35 U.S.C. 102a1 as being anticipated by WO 2020/261503 A1, hereinafter “WO’503.” It is noted that all citations listed in reference to WO’503 below are in relation to the supplied English translation of WO’503.
As per claim 1, WO’503 discloses a learning device 10 (middle of page 2) comprising: a first evaluation unit that performs quantitative evaluation on a plurality of pieces of multimedia data and thereby acquires a plurality of first evaluation results for each of the plurality of pieces of multimedia data (evaluation unit 14 at the second half of page 3 et seq, the evaluation unit 14 takes multiple input images and evaluates each image based upon various quality values like brightness, contrast, saturation, and tone); a generation unit that selects a second parameter from among a plurality of first parameters having values different from each other on the basis of the plurality of first evaluation results and generates a first set of teacher data including the selected second parameter (teaching data generation unit 15, middle of page 4); and a first learning unit that performs a first type of machine learning using first sets of teacher data and thereby generate a first learned model that outputs a third parameter used for processing of multimedia data of a processing target (second machine learning unit 17 that carries out machine learning (via CNN) on the teacher data to generates a first trained model, top of page 5).
Claims 8-12 are rejected for reasoning, mutatis mutandis, as that of claim 1 above. In addition, WO’503 discloses at page 10, a processor system 1, and a recording medium, e.g. memory cards, USB memory, etc. The claimed generation of a third parameter from a learned model 17 in order to process the multimedia data is taught at the top of page 5.
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.
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 3 is rejected under 35 U.S.C. 103 as being unpatentable over JP’654.
With regard to claim 3, JP’654 discloses a learning device that involves the evaluation of various parameters such as a histogram related to luminance values of the image pixels as noted in claim 2 above.
JP’654 fails to explicitly disclose the parameter being an average luminance of the image. However, it would have been obvious before the effective filing date of the claimed invention to have used average luminance as the evaluation parameter as opposed to the distribution of luminance (histogram) as one of ordinary skill in the art of image analysis would be well aware of luminance based upon both distribution characteristics and average luminance characteristics of the image. The use of average luminance for the analyzed parameter as opposed to the luminance distribution would have been within the level of an artisan familiar with image analysis techniques. Average luminance would allow for the law of averages to smooth out the data being analyzed, and often results in simplified calculations of the image data.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over JP’654 in view of WO 2020/209337 A1, hereinafter referred to as “WO’337.” Note that the supplied English translation of WO’337 is relied upon in the below citations.
With regard to claim 4, JP’654 discloses a learning device that involves the evaluation of various parameters such as color, noise, and a histogram related to luminance values of the image pixels as noted in claim 2 above.
JP’654 fails to disclose the evaluation parameter being based upon a frequency characteristic of the multimedia sound data.
On the other hand, WO’337 discloses a learning device at page 6, Figure 7, that uses an acoustic frequency characteristic as an evaluation parameter to thereby generate teacher data for training machine learning model.
Therefore, it would have been obvious before the effective filing date of the claimed invention to have provided the learning device of JP’654 with the added quality parameter of acoustic frequency characteristics as taught in the training device of WO’337 since doing this would permit additional evaluations to be performed on the input data so that not only image data, but also sound data, could be analyzed and optimized by the learning device.
Allowable Subject Matter
Claims 5-7 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited prior art sets forth the general state of the art in image analysis that surrounds the evaluation of correction parameters for updating multimedia data, whereby a machine learning model is trained based upon generated teacher data.
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DAVID OMETZ
Primary Examiner
Art Unit 2672
/DAVID OMETZ/Primary Examiner, Art Unit 2672
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