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 .
Election/Restrictions
Applicant’s election without traverse of Group II, claims 7-12 and 15-18, in the reply filed on 06/17/2026 is acknowledged.
Claim(s) 1-6 and 13-14 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 06/17/2026.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
The use of the terms “X-Trans”, “Chatgpt”, and “GPT-4”, which are trade names or marks used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Interpretation
Under MPEP 2113(I), "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process."…The structure implied by the process steps should be considered when assessing the patentability of product-by-process claims over the prior art, especially where the product can only be defined by the process steps by which the product is made, or where the manufacturing process steps would be expected to impart distinctive structural characteristics to the final product.”.
Claim 7 recites the claim limitations: “A trained model obtained by optimizing the model by performing the machine learning on the model using the training data according to claim 1”. Thus, claim 7 discloses a product by process claim consisting of a trained model defined by a process of optimizing the model by performing machine learning.
For the purposes of examination, claim 7 is interpreted as “A trained model obtained by optimizing the model by performing machine learning on the model using training data”.
Applicant' s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
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(s) 7 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter for reciting a “trained model”. The broadest reasonable interpretation of the claimed “trained model” encompasses computer programs per se, or software per se, and thus are products that do not have a physical or tangible form. Thus, rendering the claims as a whole non-statutory for failing to be limited to one of the four statutory categories of invention.
Claims 8-11 and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process of processing an image for inference) without significantly more.
Claim(s) 8 recite(s):
“acquire the captured image and a first image output… by inputting an image for inference”; Which can be reasonably interpreted as a human observer viewing and mentally acquiring the images based on the captured imaged and an image for inference.
“generate a second image by blending the first image and the captured image”; Which can be reasonably interpreted as a human observer viewing and mentally blending/combining the images together.
This judicial exception is not integrated into a practical application because of additional elements:
“An image processing device comprising: a first processor”; is/are generically recited computer element(s) that does/do not add a meaningful limitation to the abstract idea because it/they amount to simply implementing the abstract idea on a computer and pertain to a generically recited image processing device comprising a generically recited processor.
“…acquire…from the trained model according to claim 7 by inputting an image for inference into the trained model”; is/are generically recited computer method(s) that does/do not add a meaningful limitation to the abstract idea because it/they amount to simply implementing the abstract idea on a computer and pertain to a generically recited inferencing by a generically recited trained model.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of additional elements:
“An image processing device comprising: a first processor”; is/are well-understood, routine, and conventional computer element(s) that does/do not add a meaningful limitation to the abstract idea because it/they amount to simply implementing the abstract idea on a computer and pertain to a well-understood, routine, and conventional image processing device comprising a well-understood, routine, and conventional processor.
“…acquire…from the trained model according to claim 7 by inputting an image for inference into the trained model”; is/are well-understood, routine, and conventional computer method(s) that does/do not add a meaningful limitation to the abstract idea because it/they amount to simply implementing the abstract idea on a computer and pertain to a well-understood, routine, and conventional inferencing by a well-understood, routine, and conventional trained model.
Depending claims 9-10 do not remedy these deficiencies.
Claim(s) 9 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of generating the second image by blending the first and captured images in units of standard regions, without significantly more. A person can mentally select, or assign, “standard” regions within the images to blend. The claim(s) is/are not patent eligible.
Claim(s) 10 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of generating the second image by blending the first and captured images based on a blending ratio determined in units of standard regions, without significantly more. A person can mentally determine a blending ratio, by determining whether to blend the images, based on colors present in the images. The claim(s) is/are not patent eligible.
As per claim(s) 11, arguments made in rejecting claim(s) 8 are analogous. Note that these claims recite additional elements: “imaging apparatus comprising: a second processor; and an image sensor” and “the captured image is obtained by imaging the subject via the image sensor”, which are generically recited and well-understood, routine, and conventional.
As per claim(s) 15-16, arguments made in rejecting claim(s) 8 are analogous.
As per claim(s) 17-18, arguments made in rejecting claim(s) 8 are analogous. Note that these claims recite additional elements: “A non-transitory computer-readable storage medium storing a program executable by a computer”, which are generically recited and well-understood, routine, and conventional.
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.
Claim(s) 7-12 & 15-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Iwase et al. (JP2020166814A) hereinafter referenced as Iwase.
Regarding claim 7, Iwase discloses: A trained model obtained by optimizing the model by performing the machine learning on the model using the training data according to claim 1 (Claim limitation is interpreted according to the Claim Interpretation of claim 7 disclosed above.) (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine”; Wherein the machine learning engine is trained.).
Regarding claim 8, Iwase discloses: An image processing device comprising: a first processor, wherein the first processor is configured to: acquire the captured image and a first image output from the trained model according to claim 7 by inputting an image for inference into the trained model (Claim limitation is interpreted according to the Claim Interpretation of claim 7 disclosed above.); and generate a second image by blending the first image and the captured image (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine; and a display control unit that displays a composite image obtained by combining the first image and the second image according to a ratio obtained using information about at least a part of the region of the first image on a display unit.”;
0448: “the high-resolution image generated by the high-resolution engine and the input image may be combined and output.”).
Regarding claim 9, Iwase discloses: The image processing device according to claim 8, wherein the first processor is configured to generate the second image by blending the first image and the captured image in units of standard regions (Iwase: 0448: “In this case, the ratio of the two images to be combined may be determined by using the pixel values (brightness of at least a portion of the region) of at least a portion of the input image as the above information…The statistical values calculated from the input image can be obtained for the entire image, or they can be obtained by dividing the image into several regions and calculating local statistical values.”).
Regarding claim 10, Iwase discloses: The image processing device according to claim 9,
wherein the first processor is configured to generate the second image by blending the first image and the captured image in accordance with a blending ratio determined in units of the standard regions (Iwase: 0448: “In this case, the ratio of the two images to be combined may be determined by using the pixel values (brightness of at least a portion of the region) of at least a portion of the input image as the above information…The statistical values calculated from the input image can be obtained for the entire image, or they can be obtained by dividing the image into several regions and calculating local statistical values.”), the blending ratio is a value based on at least one of a first blending ratio, a second blending ratio, or a third blending ratio, the first blending ratio is determined in accordance with a classification result obtained by performing object classification processing on the captured image or the first image in units of the standard regions using an Al, the second blending ratio is determined in accordance with a highest signal value among a plurality of fourth signal values indicating the three primary colors in units of the standard regions for the second image, and the third blending ratio is determined in accordance with a lowest signal value among the plurality of fourth signal values (Iwase: 0019: “Furthermore, the medical images to be processed are images of a predetermined area of the subject (test subject)…medical images may be still images or moving images, and may be black and white images or color images.”;
0408: “composite color image is generated by using a color image as the input image for alignment, and setting the RG component of the RGB components to the En-Face image of the surface OCTA, and the B component to the En-Face image of the OCTA to be aligned.”;
0448: “the lower the pixel value (darker) in the input image, the higher the proportion of the input image that is combined with a high-resolution image. Furthermore, for example, the higher the pixel value (brighter) in the input image, the lower the proportion of the input image used to combine with a high-resolution image.”).
Regarding claim 11, Iwase discloses: An imaging apparatus comprising: a second processor; and
an image sensor (Iwase: 0017-0018: “each component of the medical image processing device may consist of software modules executed by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit)…The medical images to be processed may include medical images acquired by any imaging device, etc., or images created by a medical image processing device or medical image processing method according to the embodiments described below.”), wherein the second processor is configured to: input an image for inference into the trained model according to claim 7 (Claim limitation is interpreted according to the Claim Interpretation of claim 7 disclosed above.); and acquire an inference result output from the trained model in accordance with input of the image for inference (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine”), and the captured image is obtained by imaging the subject via the image sensor (Iwase: 0021: “The imaging device includes, for example, a device that obtains an image of a predetermined area of a subject by irradiating that area with light…the imaging apparatus according to the following embodiment includes at least an X-ray imaging apparatus, a CT scanner, an MRI scanner, a PET scanner, a SPECT scanner, an SLO scanner, an OCT scanner, an OCTA scanner, a fundus camera, and an endoscope, etc.”).
Regarding claim 12, Iwase discloses: A learning device comprising: a third processor (Iwase: 0017: “each component of the medical image processing device may consist of software modules executed by a processor such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit)”), wherein the third processor is configured to optimize the model by performing the machine learning on the model using the training data according to claim 1 (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine”;
0027: “A machine learning model is a model that has been trained (learned) in advance using appropriate training data (training data) for any machine learning algorithm. Training data consists of one or more pairs of input data and output data (correct answer data). Furthermore, the format and combination of input and output data for the pairs constituting the training data may be suitable for the desired configuration…Other examples of training data include training data (hereinafter referred to as "second training data") which consists of pairs of noisy, low-resolution images obtained by normal OCT imaging and high-resolution images obtained by imaging multiple times with OCT and processing to enhance image quality.”; Wherein the machine learning engine is trained.).
Regarding claim 15, Iwase discloses: An image processing method comprising: acquiring the captured image and a first image output from the trained model according to claim 7 by inputting an image for inference into the trained model (Claim limitation is interpreted according to the Claim Interpretation of claim 7 disclosed above.); and generating a second image by blending the first image and the captured image (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine; and a display control unit that displays a composite image obtained by combining the first image and the second image according to a ratio obtained using information about at least a part of the region of the first image on a display unit.”;
0448: “the high-resolution image generated by the high-resolution engine and the input image may be combined and output.”).
Regarding claim 16, Iwase discloses: An inference method comprising: inputting an image for inference into the trained model according to claim 7 (Claim limitation is interpreted according to the Claim Interpretation of claim 7 disclosed above.); and acquiring an inference result output from the trained model in accordance with input of the image for inference (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine”).
Regarding claim 17, Iwase discloses: A non-transitory computer-readable storage medium storing a program executable by a computer (Iwase: 0479: “The present invention can also be realized by supplying a program that implements one or more of the functions of the above embodiments to a system or device via a network or storage medium, and by having one or more processors in the computer of the system or device read and execute the program.”) to execute a process comprising: acquiring the captured image and a first image output from the trained model according to claim 7 by inputting an image for inference into the trained model (Claim limitation is interpreted according to the Claim Interpretation of claim 7 disclosed above.); and generating a second image by blending the first image and the captured image (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine; and a display control unit that displays a composite image obtained by combining the first image and the second image according to a ratio obtained using information about at least a part of the region of the first image on a display unit.”;
0448: “the high-resolution image generated by the high-resolution engine and the input image may be combined and output.”).
Regarding claim 18, Iwase discloses: A non-transitory computer-readable storage medium storing a program executable by a computer (Iwase: 0479: “The present invention can also be realized by supplying a program that implements one or more of the functions of the above embodiments to a system or device via a network or storage medium, and by having one or more processors in the computer of the system or device read and execute the program.”) to execute a process comprising:
inputting an image for inference into the trained model according to claim 7 (Claim limitation is interpreted according to the Claim Interpretation of claim 7 disclosed above.); and
acquiring an inference result output from the trained model in accordance with input of the image for inference (Iwase: 0010: “A medical image processing apparatus according to one embodiment of the present invention includes: an acquisition unit that acquires a first image which is a medical image of a predetermined part of a subject; an image enhancement unit that generates a second image which is of higher quality than the first image using an image enhancement engine including a machine learning engine”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY J RODRIGUEZ whose telephone number is (703)756-5821. The examiner can normally be reached Monday-Friday 10am-7pm.
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/ANTHONY J RODRIGUEZ/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
Prosecution Insights