ETAILED 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
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 29, 2023 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Status of Claims
Claims 1-11 and 13-15 are pending in this application.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
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 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), 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):
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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f), 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), 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), 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), 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), because the claim limitations use 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 limitations are: “a learning unit” configured to “cause a machine learning model to learn by inputting medical images provides with annotation information of a blood circulation anomalous area to the machine learning model, the blood circulation anomalous area including a non-perfusion area and an area where a neovascularization occurred” in claim 1; “the learning unit” being configured to “cause the machine learning model to learn based on at least an order relation among a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred” in claim 1; “a model output unit” configured to “output a learned model having learned at the learning unit” in claim 1; “a learning data acquisition unit” configured to “acquire the-medical images provided with annotation information of a blood circulation anomalous area so that a ratio of the number of medical images including a blood circulation anomalous area and the number of medical images including no blood circulation anomalous area is equal to a certain ratio” in claim 2; “the learning unit” causes “the machine learning model to learn by using a loss function that dynamically attenuates a weight of a cross entropy loss of an area that is easy to infer” in claim 3; “a first acquisition unit” configured to “acquire a first image including a medical image” in claim 5; “an inference unit” configured to “infer a blood circulation anomalous area in the first image by inputting the first image to a second learned model, the blood circulation anomalous area including a non-perfusion area and an area where a neovascularization occurred, the second learned model being a learned model having learned to estimate, based on a medical image, a blood circulation anomalous area in the medical image based on at least an order relation among a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred” in claim 5; “an output unit” configured to “output a result of the inference by the inference unit” in claim 5; “a second acquisition unit” configured to “acquire a second image indicating a blood vessel area in the first image by inputting the first image to a first learned model, the first learned model being a learned model having learned to estimate a blood vessel area based on a medical image” in claim 7; “the inference unit” “infers the blood circulation anomalous area in the first image by inputting the first image and the second image into the second learned model” in claim 7; “a classification unit” configured to “classify the first image into an image that can include a blood circulation anomalous area and other images, wherein only the image that can include a blood circulation anomalous area is processed by the inference unit” in claim 9; “the learning unit” causes “the machine learning model to learn by inputting blood vessel images together with the medical images to the machine learning model, the blood vessel images being obtained by estimating a blood vessel area in the medical images based on the medical images” in claim 13; “the learning unit” causes “the machine learning model to learn by using a loss function that takes account of an error in classification between areas with the order of a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred” in claim 14; and “the learning unit” causes “the machine learning model to learn by using, as the annotation information, learning data provided with probability distribution including probabilities allocated to other areas related to ground truth data” in claim 15.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they 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 these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
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.
Claim 11 is rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the broadest reasonable interpretation of the claimed “a computer-readable recording medium”, consistent with a conclusion reached by one skilled in the art based on both the specification disclosure and the state-of-the-art, is that the full scope covers transitory “signal” embodiments. The state-of-the-art at the time the invention was made included signals, carrier waves and other wireless communication modalities (e.g. RF, infrared, etc.) as media on which executable code was recorded and from which computers acquired such code. Thus, the full scope of the claim covers “signals” and their equivalents, which are non-statutory per se (In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007)).
The examiner suggests clarifying the claims to exclude such non-statutory signal embodiments, such as (but not limited to) by reciting a “non-transitory computer-readable recording medium”, or equivalents.
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 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, 4, 5 and 8-11 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Kurihara et al. (U.S. Patent Application Publication No. US 2021/0000343 A1) (hereafter referred to as “Kurihara”).
The examiner would like to point out that the various “units” identified in section 7 hereinabove are being interpreted under 35 U.S.C. 112(f) as described in FIG. 3.
FIG. 3 is a schematic diagram showing the hardware configuration of the learning device 10. The above-mentioned configuration of the learning device 10 is a functional configuration achieved by cooperation of the hardware configuration shown in FIG. 3 and a program. As shown in FIG. 3, the learning device 10 includes a CPU 121, a memory 122, a storage 130, and a communication unit 140 as a hardware configuration. These are connected to each other by a bus. The CPU (Central Processing Unit) 121 controls another configuration in accordance with a program stored in the memory 122, performs data processing in accordance with the program, and stores the processing result in the memory 122. The CPU 121 can be a microprocessor. The memory 122 stores a program executed by the CPU 121 and data. The memory 122 can be a RAM (Random Access Memory).
With regard to claim 1, Kurihara describes a learning unit (see Figure 2, element 200 and refer for example to paragraph [0253]) configured to cause a machine learning model to learn by inputting medical images provides with annotation information of a blood circulation anomalous area to the machine learning model, the blood circulation anomalous area including a non-perfusion area and an area where a neovascularization occurred, the learning unit being configured to cause the machine learning model to learn based on at least an order relation among a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred (refer for example to paragraphs [0043], [0048], [0052], [0053] and [0056]); and a model output unit configured to output a learned model having learned at the learning unit (refer for example to paragraphs [0172] and [0173]).
With regard to claim 4, Kurihara describes acquiring medical images provided with annotation information of a blood circulation anomalous area, the blood circulation anomalous area including a non-perfusion area and an area where a neovascularization occurred (refer for example to paragraphs [0043], [0048], [0052], [0053], [0056] and [0077]); inputting the medical images to a machine learning model (see Figure 2, element 200 and refer for example to paragraph [0253]) and the blood vessel images to causing the machine learning model to learn based on at least an order relation among a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred (refer for example to paragraphs [0043], [0048], [0052], [0053] and [0056]); and outputting a learned model obtained through the learning (refer for example to paragraphs [0172] and [0173]).
As to claim 5, Kurihara describes a first acquisition unit configured to acquire a first image including a medical image (refer for example to paragraph [0077]); an inference unit configured to infer a blood circulation anomalous area in the first image by inputting the first image to a second learned model (see Figure 2, element 200 and refer for example to paragraph [0253]), the blood circulation anomalous area including a non-perfusion area and an area where a neovascularization occurred, the second learned model being a learned model having learned to estimate, based on a medical image, a blood circulation anomalous area in the medical image based on at least an order relation among a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred (refer for example to paragraphs [0043], [0048], [0052], [0053] and [0056]); and an output unit configured to output a result of the inference by the inference unit (refer for example to paragraphs [0172] and [0173]).
As to claim 8, Kurihara describes wherein the second learned model is a neural network and includes a convolutional layer with a large stride (see Figure 15 and refer for example to paragraph [0172] and paragraphs [0205] through [0210]).
In regard to claim 9, Kurihara describes classification unit configured to classify the first image into an image that can include a blood circulation anomalous area and other images, wherein only the image that can include a blood circulation anomalous area is processed by the inference unit (refer for example to paragraph [0165] and to paragraphs [0171] through [0175]).
With regard to claim 10, Kurihara describes acquiring a first image including a medical image (refer for example to paragraph [0077]); inferring a blood circulation anomalous area in the first image by inputting the first image to a second learned model (see Figure 2, element 200 and refer for example to paragraph [0253]), the blood circulation anomalous area including a non-perfusion area and an area where a neovascularization occurred, the second learned model being a learned model having learned to estimate, based on a medical image, a blood circulation anomalous area in the medical image based on at least an order relation among a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred (refer for example to paragraphs [0043], [0048], [0052], [0053] and [0056]); and outputting a result of the inference (refer for example to paragraphs [0172] and [0173]).
As to claim 11, Kurihara describes a computer-readable medium that records a computer program configured to cause one or a plurality of computers (refer for example to paragraphs [0062] and [0069]) to execute acquiring a first image including a medical image (refer for example to paragraph [0077]); inferring a blood circulation anomalous area in the first image inputting the first image to a second learned model (see Figure 2, element 200 and refer for example to paragraph [0253]), the blood circulation anomalous area including a non-perfusion area and an area where a neovascularization occurred, the second learned model being a learned model having learned to estimate, based on a medical image, a blood circulation anomalous area in the medical image based on at least an order relation among a non-anomalous area, a non-perfusion area, and an area where a neovascularization occurred (refer for example to paragraphs [0043], [0048], [0052], [0053] and [0056]); and outputting a result of the inference (refer for example to paragraphs [0172] and [0173]).
Allowable Subject Matter
Claims 2, 3, 6, 7 and 13-15 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.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shiba, Iwase, Imamura and Hidaki all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
February 12, 2026