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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. § 119 (a)-(d). The certified copy has been filed in parent Application No. JP2023-206304, filed on 12/06/2023.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 3 is rejected under 35 U.S.C. § 112(a) or 35 U.S.C. § 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Applicant cites “a weighted addition of the perturbation partial images,” and “a position corresponding to the partial image.”1 The claim limits the replacement step to “a position corresponding to the partial image after the addition of the input image.” However, the specification discloses replacing a portion in the original input image with the combined perturbation partial image. The claim language introduces a step—i.e., adding the input image—that there is no support in the written description, thereby failing to particularly point out and distinctly claim.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 3 is rejected under 35 U.S.C. § 112(b) or 35 U.S.C. § 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 recites the limitation "after the addition of the input image2 with the partial image after the addition.” There is insufficient antecedent basis for this limitation in the claim.
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 (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 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.
Claims 1, 6–7, 11 and 13–14 are rejected under 35 U.S.C. § 103 as being unpatentable over Inoshita et al. (U.S. 12,608,925 B2) in view of Wen et al. (U.S. 2023/0237771 A1).
Regarding claim 1, Inoshita discloses a pre-training apparatus comprising
processing circuitry that is configured to: (Fig. 5, a processor 504)
convert an input image to generate a conversion image. (Per Fig. 1, Inoshita discloses an image conversion model 100 where an input image 10 is converted to another image. Inoshita col. 7 lines 3–25. [t]he image conversion model 100 can be trained so that the processing of the image conversion model 100 of converting an image representing a scene of a road in the daytime into an image representing that scene in the nighttime…)
However, Inoshita fails to specifically disclose generate a first extended image and a second extended image from the conversion image based on a method different from a method for generating the conversion image;
input the first extended image to a first feature extractor to calculate a first feature amount;
input the second extended image to a second feature extractor to calculate a second feature amount; and
update a parameter of at least one of the first feature extractor and the second feature extractor based on the first feature amount and the second feature amount.
In related art, Wen discloses generate a first extended image (a first extended image construed as a first enhance image) and a second extended image (a second extended image construed as a second enhanced image) from the conversion image based on a method different from a method for generating the conversion image; (Per Fig. 1, Wen discloses a first enhance image 12 and a second enhanced image 13 from an original image 12 to conduct a feature extraction in his trained model. Wen ¶38. [a] computer device uses a data enhancement technique to obtain a first enhanced image 12 and a second enhanced image 13 being positive samples of each other based on an original medical image 11,)
input the first extended image to a first feature extractor to calculate a first feature amount; (Per Fig. 7, Wen discloses a first feature extraction branch where a first feature extraction network 704 is rendered to obtain a first intermediate image feature. Ibid. ¶76. [t]he computer device inputs a first enhanced image 702 into the first feature extraction branch from which a first feature extraction network 704 performs the feature extraction to obtain the first intermediate image feature.)
input the second extended image to a second feature extractor to calculate a second feature amount; and (Per Fig. 6 at step 603 and Fig. 7, Wen discloses a second feature extraction branch where a second feature extraction network 706 is rendered. Ibid. ¶84. Perform the feature extraction on the second enhanced image by a second feature extraction branch to obtain the second image feature, the second feature extraction branch including a second feature extraction network.)
update a parameter of at least one of the first feature extractor and the second feature extractor based on the first feature amount and the second feature amount. (Per Fig. 7, Wen discloses updating a parameter of his first feature extraction network 704 and the second feature extraction network 706. Ibid. ¶¶127–128. [t]he computer device updates the parameter of the first feature extraction network 704 based on the model loss 718; Update the network parameter of the second feature extraction network based on a network parameter of a trained first feature extraction network.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Wen into the teachings of Inoshita to ensure a quality of a feature extraction such that a model training efficiency is enhanced. Ibid. ¶38.
Regarding claim 13, Inoshita discloses a method comprising:
converting an input image to generate a conversion image. (Per Fig. 1, Inoshita discloses an image conversion model 100 where an input image 10 is converted to another image. Inoshita col. 7 lines 3–25. [t]he image conversion model 100 can be trained so that the processing of the image conversion model 100 of converting an image representing a scene of a road in the daytime into an image representing that scene in the nighttime…)
However, Inoshita fails to specifically disclose generating a first extended image and a second extended image from the conversion image based on a method different from a method for generating the conversion image;
inputting the first extended image to a first feature extractor to calculate a first feature amount;
inputting the second extended image to a second feature extractor to calculate a second feature amount; and
updating a parameter of at least one of the first feature extractor and the second feature extractor based on the first feature amount and the second feature amount.
In related art, Wen discloses generating a first extended image and a second extended image from the conversion image based on a method different from a method for generating the conversion image; (Per Fig. 1, Wen discloses a first enhance image 12 and a second enhanced image 13 from an original image 12 to conduct a feature extraction in his trained model. Wen ¶38. [a] computer device uses a data enhancement technique to obtain a first enhanced image 12 and a second enhanced image 13 being positive samples of each other based on an original medical image 11,)
inputting the first extended image to a first feature extractor to calculate a first feature amount; (Per Fig. 7, Wen discloses a first feature extraction branch where a first feature extraction network 704 is rendered to obtain a first intermediate image feature. Ibid. ¶76. [t]he computer device inputs a first enhanced image 702 into the first feature extraction branch from which a first feature extraction network 704 performs the feature extraction to obtain the first intermediate image feature.)
inputting the second extended image to a second feature extractor to calculate a second feature amount; and (Per Fig. 6 at step 603 and Fig. 7, Wen discloses a second feature extraction branch where a second feature extraction network 706 is rendered. Ibid. ¶84. Perform the feature extraction on the second enhanced image by a second feature extraction branch to obtain the second image feature, the second feature extraction branch including a second feature extraction network.)
updating a parameter of at least one of the first feature extractor and the second feature extractor based on the first feature amount and the second feature amount. (Per Fig. 7, Wen discloses updating a parameter of his first feature extraction network 704 and the second feature extraction network 706. Ibid. ¶¶127–128. [t]he computer device updates the parameter of the first feature extraction network 704 based on the model loss 718; Update the network parameter of the second feature extraction network based on a network parameter of a trained first feature extraction network.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Wen into the teachings of Inoshita to ensure a quality of a feature extraction such that a model training efficiency is enhanced. Ibid. ¶38.
Regarding claim 14, Inoshita discloses a non-transitory computer-readable storage medium storing a program for causing a computer to execute:
converting an input image to generate a conversion image. (Per Fig. 1, Inoshita discloses an image conversion model 100 where an input image 10 is converted to another image. Inoshita col. 7 lines 3–25. [t]he image conversion model 100 can be trained so that the processing of the image conversion model 100 of converting an image representing a scene of a road in the daytime into an image representing that scene in the nighttime…)
However, Inoshita fails to specifically disclose generating a first extended image and a second extended image from the conversion image based on a method different from a method for generating the conversion image;
inputting the first extended image to a first feature extractor to calculate a first feature amount;
inputting the second extended image to a second feature extractor to calculate a second feature amount; and
updating a parameter of at least one of the first feature extractor and the second feature extractor based on the first feature amount and the second feature amount.
In related art, Wen discloses generating a first extended image and a second extended image from the conversion image based on a method different from a method for generating the conversion image; (Per Fig. 1, Wen discloses a first enhance image 12 and a second enhanced image 13 from an original image 12 to conduct a feature extraction in his trained model. Wen ¶38. [a] computer device uses a data enhancement technique to obtain a first enhanced image 12 and a second enhanced image 13 being positive samples of each other based on an original medical image 11,)
inputting the first extended image to a first feature extractor to calculate a first feature amount; (Per Fig. 7, Wen discloses a first feature extraction branch where a first feature extraction network 704 is rendered to obtain a first intermediate image feature. Ibid. ¶76. [t]he computer device inputs a first enhanced image 702 into the first feature extraction branch from which a first feature extraction network 704 performs the feature extraction to obtain the first intermediate image feature.)
inputting the second extended image to a second feature extractor to calculate a second feature amount; and (Per Fig. 6 at step 603 and Fig. 7, Wen discloses a second feature extraction branch where a second feature extraction network 706 is rendered. Ibid. ¶84. Perform the feature extraction on the second enhanced image by a second feature extraction branch to obtain the second image feature, the second feature extraction branch including a second feature extraction network.)
updating a parameter of at least one of the first feature extractor and the second feature extractor based on the first feature amount and the second feature amount. (Per Fig. 7, Wen discloses updating a parameter of his first feature extraction network 704 and the second feature extraction network 706. Ibid. ¶¶127–128. [t]he computer device updates the parameter of the first feature extraction network 704 based on the model loss 718; Update the network parameter of the second feature extraction network based on a network parameter of a trained first feature extraction network.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Wen into the teachings of Inoshita to ensure a quality of a feature extraction such that a model training efficiency is enhanced. Ibid. ¶38.
Regarding claim 6, Inoshita as modified by Wen, discloses the pre-training apparatus, wherein the processing circuitry generates the first extended image and the second extended image by executing one or more processes of color conversion, rigid transformation, filtering, image masking, and image clipping. (Per Fig. 10 at step 4002, Wen discloses filtering images given that the image information is processed. Wen ¶139. Filter the sliced images based on the image information content to obtain the original medical image.)
Regarding claim 7, Inoshita as modified by Wen, discloses the pre-training apparatus, wherein the processing circuitry determines whether to use the conversion image for training. (Per Fig. 1, Inoshita discloses an image conversion model 100 where an input image 10 is converted to another image. Inoshita col. 7 lines 3–25. [t]he image conversion model 100 can be trained so that the processing of the image conversion model 100 of converting an image representing a scene of a road in the daytime into an image representing that scene in the nighttime…)
Regarding claim 11, Inoshita as modified by Wen, discloses the pre-training apparatus, wherein the pre-training apparatus is a training device that trains a neural network including at least one of the first feature extractor and the second feature extractor. (Per Fig. 7, Wen discloses updating a parameter of his first feature extraction network 704 and the second feature extraction network 706. Wen ¶¶127–128. [t]he computer device updates the parameter of the first feature extraction network 704 based on the model loss 718; Update the network parameter of the second feature extraction network based on a network parameter of a trained first feature extraction network.)
Claims 8–10 are rejected under 35 U.S.C. § 103 as being unpatentable over Inoshita in view of Wen and further in view of Nishi et al. (U.S. 11,190,804 B2).
Regarding claim 8, Inoshita as modified by Wen, discloses the claimed invention, but fails to specifically disclose the pre-training apparatus, wherein the processing circuitry calculates an error between the input image and the conversion image, and determines whether to use the conversion image for training based on the error.
In related art, Nishi discloses the pre-training apparatus, wherein the processing circuitry calculates an error between the input image and the conversion image, and determines whether to use the conversion image for training based on the error. (Per Fig. 1, Nishi’s subtractor 104 discloses prediction of errors to process an image. Nishi col. 9 line 64 – col. 10 line 3. Subtractor 104 then outputs the calculated prediction errors to transformer 106.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Nishi into the teachings of Inoshita and Wen such that a prediction network generates a probability to match a reconstructed image based on the input of images. Ibid. col. 22 line 61 – col. 23 line 6.
Regarding claim 9, it has been rejected in the same manner as claim 8.
Regarding claim 10, it has been rejected in the same manner as claim 8.
Allowable Subject Matter
Claims 2–5 and 12 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.3
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ichino et al. (U.S. 12,142,079 B2) discloses a feature conversion.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENEDICT LEE whose telephone number is (571)270-0390. The examiner can normally be reached 10:00-16:00 (EST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen R. Koziol can be reached at (408) 918-7630. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BENEDICT E LEE/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
1 Refer to Applicant’s Spec. ¶28. Examiner found no association between the structure and the function can be found in the specification.
2 In the claim, the phrase “after the addition of the input image” structurally implies that the input image was added to something. The specification clarifies that the addition happens to the perturbation images, and the replacement happens inside the original input image. The claimed language suggests the opposite rationale and fails to clearly define the boundaries of the replacement step.
3 Applicant should clarify claim 3 in terms of the 112 rejections.
Prosecution Insights