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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 02/06/2025 and 09/24/2025 are being considered by the examiner.
Specification
The abstract of the disclosure is objected to because the abstract exceeds 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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, and 8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhao et al. (US 2023/0290120 A1).
Regarding Claim 1, Zhao teaches “A diagnosis assistance apparatus, comprising at least one processor, the at least one processor carrying out:
a first acquisition process of acquiring a first caption from a first learned model” (Zhao, [0028] discloses “According to the solutions provided in the embodiments of this application, a classification model may be trained based on the artificial intelligent machine learning technology, and the trained classification model is used to realize the classification of a pathological image”; where a classification model is a first learned mode; see Fig. 7. Zhao, [0137] discloses “The computer device classifies the updated spliced image feature to obtain a category to which the pathological image belongs”; where a category is a first caption), “the first learned model being constructed by machine learning so as to generate, in a case where a pathological image is inputted, a caption describing content of the pathological image in a predetermined format, the first caption describing, in the predetermined format, content of a first pathological image to be subjected to diagnosis” (Zhao, [0139] discloses “In a possible implementation, the category to which the pathological image belongs indicates a state of a specific region in the pathological image”; where a state of a specific region is a predetermined format. Zhao, [0184] discloses “The sample pathological image is a pathological image used for presenting the pathological morphology of a body organ, tissue or cell, and a category indicated by the sample label is the category to which the sample pathological image belongs”; where pathological morphology is content of a first pathological image);
“a first evaluation process of evaluating a first similarity which is a similarity between (i) content of at least a part of a plurality of findings which are accumulated in a database and which represent, in writing in the predetermined format, a respective plurality of pathological subtypes and (ii) content of the first caption” (Zhao, [0141] discloses “The updated spliced image feature is classified to obtain the classified feature vector indicating probabilities that the pathological image belongs to multiple categories, and the category corresponding to the maximum probability in the classified feature vector is determined as the category to which the pathological image belongs, so that the accuracy of the determined category is ensured”; where a probability that the pathological image belongs to multiple categories is a similarity between a plurality of findings in a database which represent pathological subtypes and a content of a first caption); “and
an output process of outputting information pertaining to a subtype whose first similarity is evaluated to be the highest among those of the plurality of pathological subtypes” (Zhao, [0141] discloses “The updated spliced image feature is classified to obtain the classified feature vector indicating probabilities that the pathological image belongs to multiple categories, and the category corresponding to the maximum probability in the classified feature vector is determined as the category to which the pathological image belongs, so that the accuracy of the determined category is ensured”; where obtaining a the category corresponding to the maximum probability is outputting a subtype whose first similarity is evaluated to be the highest).
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Fig. 7 of Zhao
Regarding Claim 7, Claim 7 recites a system with elements corresponding to the elements recited in Claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim. Zhao discloses “A non-transitory recording medium having recorded thereon a diagnosis assistance program” (Zhao, [0027] discloses “The embodiments of this application further provide a computer device, which includes a process and a memory. The memory stores at least one segment of computer program that, when loaded and executed by the processor, implements operations performed in the image classification method according to the foregoing embodiments.”)
Regarding Claim 8, Claim 8 recites a method with steps corresponding to the elements of the system recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding system claim.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al. (US 2023/0290120 A1) in view of Ren et al. (US 2024/0282131 A1).
Regarding Claim 4, Zhao does not explicitly teach the apparatus of Claim 4.
However, in an analogous field of endeavor, Ren teaches “The diagnosis assistance apparatus according to claim 1, wherein:
the at least one processor further carries out an input process of inputting a prompt to the first learned model together with the first pathological image, the prompt being related to output of the caption” (Ren, [0126] discloses “The plurality of prompts can be associated with a phrase to provide with a classification output. In some implementations, the plurality of prompts can include a plurality of caption templates”); “and
in the first acquisition process, the at least one processor acquires, from the first learned model, the first caption that is based on the prompt” (Ren, [0126] discloses “The plurality of caption templates can be configured to be augmented to include a classification label and be descriptive of an example caption for an input image. The plurality of prompts may be obtained from a database and/or may be generated with a machine-learned model.”)
It would have been obvious to one of ordinary skill in the art before the effective filing
date of the claimed invention to have modified Zhao to incorporate the teachings of Ren by obtaining prompts including image caption templates. One of ordinary skill in the art would be motivated to combine the Zhao and Ren references in order to reduce computational costs of classification: Ren, [0066] discloses “Another example of technical effect and benefit relates to improved computational efficiency and improvements in the functioning of a computing system. For example, the systems and methods disclosed herein can leverage prompt ensembling with weighted outputs to reduce the computational costs of classification tasks as the systems and methods can be less computationally expensive when compared to classification model training and/or retraining for specific classes.” Accordingly, the combination of Zhao and Ren discloses the invention of Claim 4.
Allowable Subject Matter
Claims 2, 3, 5, and 6 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.
Regarding Claim 2, Zhao teaches “The diagnosis assistance apparatus according to claim 1, wherein the at least one processor further carries out:
a second acquisition process of acquiring first feature information from a second learned model, the second learned model being constructed by machine learning so as to generate, in a case where the pathological image is inputted, feature information indicating the pathological image, the first feature information indicating a feature of the first pathological image” (Zhao, [0174] discloses “Based on the feature extraction sub-model, according to steps 301 to 303, a pathological image is segmented into multiple sub-images, an image feature of each sub-image is extracted, and the image features of the multiple sub-images are spliced into an image feature of the pathological image”; where a feature extraction sub-model is a second learned model; where image feature of the pathological image is feature information indicating a feature of the first pathological image); “and
Zhao does not explicitly teach “a second evaluation process of evaluating a second similarity which is a similarity between (i) at least a part of a plurality of pieces of feature information which are accumulated in a database and indicate respective features of a plurality of pathological images respectively corresponding to the plurality of pathological subtypes and (ii) the first feature information, and in the output process, the at least one processor outputs information pertaining to a subtype whose statistic of a first evaluation value and a second evaluation value is the highest among those of the plurality of pathological subtypes, the first evaluation value being a result of evaluation of the first similarity, the second evaluation value being a result of evaluation of the second similarity.”
Although Zhao teaches a classification model that determines image features of the pathological image and uses the features to determine a category, Zhao does not explicitly teach “information pertaining to a subtype whose statistic of a first evaluation value and a second evaluation value is the highest among those of the plurality of pathological subtypes.” That is, the final limitation of Claim 2 requires the first and second learned models are distinct models with distinct outputs of a first and second similarity, and thus also requires distinct first and second evaluation values. Thus, Zhao does not explicitly teach the invention of Claim 2.
Qin et al. (What a Whole Slide Image Can Tell? Subtype-guided Masked Transformer for Pathological Image Captioning, published 2023) discloses a method of generating captions of pathological images using machine learning (see Fig. 1). However, Qin also does not teach a second learned model producing a second evaluation value as a result of evaluating a second similarity.
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Fig. 1 of Qin
That is, although the prior art teaches a classification model that determines a category of a pathological image by determine image features, and the prior art also teaches a transformer for generating captions of pathological images, there is no motivation to teach acquiring both a caption and feature information using distinct learned models, and outputting a highest of the pathological subtypes of first and second evaluation values determined by evaluating first and second similarities.
Thus, none of the cited prior art, alone or in combination, provides a motivation to teach the ordered combination of “The diagnosis assistance apparatus according to claim 1, wherein the at least one processor further carries out: a second acquisition process of acquiring first feature information from a second learned model, the second learned model being constructed by machine learning so as to generate, in a case where the pathological image is inputted, feature information indicating the pathological image, the first feature information indicating a feature of the first pathological image; and a second evaluation process of evaluating a second similarity which is a similarity between (i) at least a part of a plurality of pieces of feature information which are accumulated in a database and indicate respective features of a plurality of pathological images respectively corresponding to the plurality of pathological subtypes and (ii) the first feature information, and in the output process, the at least one processor outputs information pertaining to a subtype whose statistic of a first evaluation value and a second3 evaluation value is the highest among those of the plurality of pathological subtypes, the first evaluation value being a result of evaluation of the first similarity, the second evaluation value being a result of evaluation of the second similarity.”
Claims 3, 5, and 6 depend on Claim 2 and thus contain all allowable subject matter of Claim 2.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Tsuneki et al. (Inference of Captions from Histopathological Patches, published 2022) teaches a method of predicting captions for histopathological images using an attention-based model and measuring “word overlaps of the occurrence of the subtype class in the predicted captions” (Results).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAROLINE TABANCAY DUFFY whose telephone number is (703)756-1859. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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/CAROLINE TABANCAY DUFFY/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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