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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/19/2025 has been entered.
Status of Claims
This action is in response to the RCE filed 12/19/2025.
Claims 1, 5-7 were amended 12/19/2025.
Claims 1 and 5-7 are currently pending and have been examined.
Claim Objections
Claim 1 is objected to because of the following informalities:
“input the fundus image to a plurality of inference models” is grammatically incorrect.
“create a second image that visualize the areas” is grammatically incorrect.
“corresponding to the specific disease to a convolutional layer desired to be visualized” is grammatically incorrect.
“calculating contributions of feature maps to output of the specific disease” is grammatically incorrect.
“and summing the weighted feature maps, for each of the selected inference models” is grammatically incorrect.
Appropriate correction is required.
Claim 5 is objected to because of the following informalities: “a value representing contribution to the level of certainty” is grammatically incorrect. Appropriate correction is required.
Claim 6 is objected to because of the following informalities:
“a corresponding disease- as ground truth” is grammatically incorrect.
“a second image that visualize the areas contributing to the level of certainty” is grammatically incorrect.
Appropriate correction is required.
Claim 7 is objected to because of the following informalities:
“a corresponding disease- as” is grammatically incorrect.
“a second image that visualize the areas” is grammatically incorrect.
“to the specific disease to a convolutional layer desired to be visualized” is grammatically incorrect.
“calculating contributions of feature maps to output of the specific disease” is grammatically incorrect.
“for each of the selected inference models-; and” is grammatically incorrect.
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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.
Claims 1 and 5-7 are 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.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
Claim 1 recites the limitation "input the fundus image to a plurality of inference models each corresponding to one of a plurality of diseases" and it is unclear whether the each corresponds to the fundus image or the inference models.
Claim 1 recites the limitation "corresponding to the specific disease" in line 19. There is insufficient antecedent basis for this limitation in the claim as it is unclear if it is referring to “a corresponding disease” or “a corresponding specific disease”.
Claim 1 recites the limitation " the specific disease" in line 20. There is insufficient antecedent basis for this limitation in the claim as it is unclear if it is referring to “a corresponding disease” or “a corresponding specific disease”.
Claim 1 recites the limitation “the forward calculations” in line 20. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation " each of the selected inference models" in lines 21-22. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites “the selected inference models” in line 24. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites “the one or more second images” in lines 24-25. There is insufficient antecedent basis for this limitation in the claim.
Claim 5 recites “the one or more second images” in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites “inputting the fundus image to a plurality of inference models each corresponding to one of a plurality of diseases" and it is unclear whether the each corresponds to the fundus image or the inference models.
Claim 6 recites “the one or more second images” in line 18. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation " each of the selected inference models" in line 15. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites “the selected inference models” in line 16-17. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites “inputting the fundus image to a plurality of inference models each corresponding to one of a plurality of diseases" and it is unclear whether the each corresponds to the fundus image or the inference models.
Claim 7 recites “the specific disease” in lines 17 and 18. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites “the forward calculations” in line 18. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites “for each of the selected inference models” in lines 19-20. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites “the selected inference models” in lines 21-22. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites “the one or more second images” in lines 22 and 23. There is insufficient antecedent basis for this limitation in the claim.
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.
Claims 1 and 5-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1 and 5-7 are drawn to an apparatus, method, and computer-readable recording medium (which is non-transitory in the specification paragraph 35) which are statutory categories of invention (Step 1: YES).
Independent claim 1 recites: presenting an area highly likely to be an abnormal area in a fundus image concerning an eye of an examinee, acquires the fundus image; inputs the fundus image to a plurality of inference models, each corresponding to one of a plurality of disease, to acquire levels of certainty that the eye in the fundus image is healthy and/pr levels of certainty that the eye in the fundus image has a corresponding specific disease, wherein each inference model is a model obtained by using a plurality of fundus images labeled with either healthiness or a corresponding disease as ground truth data on an image by image basis; select one or more inference models with the level of certainty that the eye has the corresponding specific disease is equal to or higher than a certain threshold based on levels of certainty acquired from the plurality of inference models and create a second image that visualize the areas contributing to the level of certainty that the eye has the corresponding disease by performing error back-propagation from an output layer corresponding to the specific disease to a convolutional layer desired to be visualized, calculating contributions of feature maps to output of the specific diseases, weighting feature maps obtained by the forward calculations with contributions, and summing the weighted feature maps, for each of the selected inference models and output inference results based on the labels of certainty acquired by the selected inference models and the one or more second images.
Independent claim 6 recites for presenting an area highly likely to be an abnormal area in a fundus image concerning an eye of an examinee, comprising: acquiring the fundus image; inputting the fundus image to a plurality of inference models, each corresponding to one of a plurality of disease, to acquire levels of certainty that the eye in the fundus image is healthy and/or levels of certainty that the eye in the fundus image has a corresponding specific disease, wherein each inference model is a model obtained by using a plurality of fundus images labeled with either healthiness or a corresponding disease as ground truth data on an image by image basis; selecting one or more inference models with the level of certainty that the eye has the corresponding specific disease is equal to or higher than a certain threshold based on levels of certainty acquired form the plurality of inference models, and creating a second image that visualize the areas contributing to the level of certainty that the eye has the corresponding disease for each of the selected inference models; and outputting inference results based on the levels of certainty acquired by the selected inference models and the one or more second images, including outputting the fundus image by superimposing the one or more second images.
Independent claim 7 recites: present an area highly likely to be an abnormal area in a fundus image concerning an eye of an examinee, acquiring the fundus image; inputting the fundus image to a plurality of inference models, each corresponding to one of a plurality of diseases, to acquire levels of certainty that the eye in the fundus image is healthy and/or levels of certainty that the eye in the fundus image has a corresponding specific disease, wherein each inference model is a model obtained by using a plurality of fundus images labeled with either healthiness or a corresponding disease as ground truth data on an image by image basis; selecting one or more inference models with the level of certainty that the eye has the corresponding specific disease is equal to or higher than a certain threshold based on levels of certainty acquired form the plurality of inference models, and creating a second image that visualize the areas contributing to the level of certainty that the eye has the corresponding disease by performing error back-propagation from an output layer corresponding to the specific disease to a convolutional layer desired to be visualized, calculating contributions of feature maps to output of the specific disease , weighting feature maps obtained by the forward calculations with contributions, and summing the weighted feature maps, for each of the selected inference models; and outputting inference results based on the levels of certainty acquired by the selected inference models and the one or more second images, including outputting the fundus image by superimposing the one or more second images.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity between an administrator and a patient (examinee), as reflected in the specification, which states that “Although in the present embodiment, abnormal areas in fundus images, which are an example of medical images, are presented, according to another embodiment, abnormal areas in medical images such as images of the kidneys or images of the liver acquired by photographing another examination region of a patient may be presented.” (see: specification paragraph 82). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address “According to the present embodiment, it is assumed that before the inference process described in Figure 7 is performed, a learned model acquired from the storage apparatus 30 is stored as the learned model 232 under the supervision of the administrator of the inference apparatus 20. It is also assumed that a fundus image to be used for inference is stored in the image storage location 231 of the inference apparatus 20. Note that the process shown in Figure 7 is performed, for example, when the administrator enters a command via the input unit 210 to perform the inference process.” (see: specification page 70). Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).”
The judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “information processing apparatus”, “a processor”, “display device”, “computer readable recording medium recording a program”, “one or more computers”, are recited at a high level of generality (e.g., that the creating and outputting is performed using generic computer components with instructions are executed to perform the claimed limitations and the calculation models are generic). Such that they amount to no more than mere instructions to apply the exception using generic computer components. See: MPEP 2106.05(f).
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic component cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, Figure 4, Figure 5 and
Paragraph 101, where “An information processing apparatus according to one aspect of the present disclosure may comprise a plurality of image classification units, and select one or more image classification units based on levels of certainty acquired from the plurality of image classification units, and the image creation unit may create one or more second images by visualizing areas contributing to the classification by the selected image classification unit(s)”
Paragraph 35, where “The control unit 120 includes a computational processing unit 121, such as a CPU or an MPU, which corresponds to a processor, and a memory 122 such as a RAM. Based on various types of input, the computational processing unit 121 (processor) executes a program stored in the storage unit 130 by loading the program into the memory 122 and thereby implements after-mentioned functions and processes of the computational processing unit 121. The program may be installed on a computer by being stored in a non-transitory computer-readable recording medium such as a CD-ROM or by being distributed through a network. The memory 122 functions as working memory needed by the computational processing unit 121 (processor) in order to execute the program.”
Paragraph 24, where “Here, the machine learning model has a certain model structure and process parameters that change with a learning process and has its identification accuracy improved when the process parameters are optimized based on experience obtained from learning data. That is, the machine learning model learns optimum process parameters through a learning process. Regarding algorithms for the machine learning model, for example, Support Vector Machine, Logistic Regression, and Neural Network are available for use, but the type of neural network is not specifically limited. Some of the machine learning models that undergo the learning have not undergone any learning yet, and the others have already undergone some learning using learning data.”
Paragraph 25, “Note that the learned model is a model that has done learning in advance using appropriate learning data in contrast to the machine learning model that does learning based on any machine learning algorithm. However, it is not that the learned model no longer does any more learning and the learned model can do additional learning.”
Paragraph 26, where “For example, when a neural network is used as a machine learning model, acquiring a learned model means acquiring at least information about the number of layers in the neural network, the number of nodes in each layer, weight parameters of links interconnecting the nodes, bias parameters of the respective nodes, and function forms of activation functions of the respective nodes.”
Paragraph 46, where “Note that only necessary functional components are shown in Figure 5 by assuming a single inference apparatus 20, but the inference apparatus 20 may be configured as part of a multi-functional distributed system made up of multiple computer systems.”
Paragraph 49, where “The program may be installed on a computer by being stored in a non-transitory computer-readable recording medium such as a CD-ROM or by being distributed through a network. The memory 222 functions as working memory needed by the computational processing unit 221 (processor) in order to execute the program.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with route, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim 5 when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are directed to an abstract idea without significantly more. Claim 5 recites adjusting, calculating thresholds, and classifying patient image data on the generically recited computing device as shown in the parent claims above.
These claims fail to remedy the deficiencies of their parent claims above, and therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Allowable Subject Matter
Claims 1, 5-7 are allowable over the prior art. Specifically the claim limitations in:
claim 1 of “acquire levels of certainty that the eye in the fundus image is healthy and/or levels of certainty that the eye in the fundus image has a corresponding specific disease” and “the level of certainty that the eye has the corresponding disease by performing error back-propagation form an output layer corresponding to the specific disease to a convolutional layer desired to be visualized”
claim 6 of “wherein each inference model is a model obtained by using a plurality of fundus images labeled with either healthiness or a corresponding disease as ground truth data on an image by image basis” and “selecting one or more inference models with the level of certainty that the eye has the corresponding specific disease is equal to or higher than a certain threshold based on levels of certainty acquired from the plurality of inference models” and “outputting the fundus image by superimposing the one or more second images”
claim 7 of “present an area highly likely to be an abnormal area in a fundus image concerning an eye of an examinee”, “selecting one or more inference models with the level of certainty that the eye has the corresponding specific disease is equal to or higher than a certain threshold based on levels of certainty acquired from the plurality of inference models”, “performing error back-propagation from an output layer corresponding to the specific disease to convolutional layer desired to be visualized”
in combination of the other claim limitations in the respective independent claims is not taught by the prior art of record of Sawkey (US 2022/0414402 A1) , Zhang (US 2021/0042916 A1), and Kim (WO 2020242239 A1). A new search was conducted and found the prior art of Gargeya (US 20190191988 A1) that teaches fundus images using features to determine diagnosis of eyes, however it did not teach calculating the features based on convolutional layers, superimposition, or back-propagation calculations. The 103 rejection has been withdrawn.
Response to Arguments
The arguments filed 12/19/2025 have been fully considered.
Regarding the arguments pertaining to the 103 rejections, these arguments are persuasive as the amendments overcome the 103 rejection and it has been withdrawn.
Regarding the arguments pertaining to the 101 rejection, these arguments are not persuasive. Applicant argues that the amended claims provide significantly more because there is not a prior art rejection. Examiner respectfully disagrees, as the amendments do not provide significantly more to the abstract idea and reminds the Applicant that the indicia for a 101 rejection is not the same for the indicia when overcoming a 103 rejection.
Applicant further argues that the claimed invention is not directed towards an abstract idea because they recite specific technical improvement such as in Enfish. Examiner respectfully disagrees as the machine learning models running calculations on the generic computing devices are described generically in the specification as evidenced above and do not provide significantly more to the abstract idea. Using classification algorithms such as error back-propagation and superimposition does not create a technological improvement of a computing system. In Enfish, the computing system itself was improved and is dissimilar to the current claimed invention where the calculations are not improving the computing system but merely running on the generic computing system to provide input/output of data. The functions argued are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea.
Further, not every claim that recites concrete, tangible components escapes the reach of the abstract-idea inquiry. (See, e.g., Alice, 134). It is well-settled that mere recitation of concrete, tangible components that are generic is insufficient to confer patent eligibility to an otherwise abstract idea. In order to amount to an inventive concept, the components must involve more than performance of “’well-understood, routine, conventional activities’ previously known to the industry.” (Alice, 134 S. Ct. at 2359 (quoting Mayo, 132 S.Ct. at 1294)). The originally filed specification was investigated and found to support this conclusion.
The dependent claim relies on the arguments of the independent claims and is rejected for the reasons stated above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gargeya (US 20190191988 A1) that teaches fundus images using features to determine diagnosis of eyes, however it did not teach calculating the features based on convolutional layers, superimposition, or back-propagation calculations.
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/KIMBERLY A. SASS/Examiner, Art Unit 3686