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 .
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.
Allowable Subject Matter
Claims 8 and 9 are objected to as being dependent upon a rejected base claim, but would be allowable if the claims are included in the independent claim including all of the limitations of the base claim and any intervening claims. The following is a statement of reasons for the indication of allowable subject matter:
In regards to dependent claim 8, none of the cited prior art alone or in combination provides motivation to teach “wherein the adjustment processing uses a filter, and the filter is defined by a function of a statistical value of the pixel value of the outer periphery of at least one basic image and a statistical value of the pixel value of the inside of at least one basic image” as the references only teach providing image processing techniques for filtering a base image, however the references fail to explicitly teach the filter being defined as a statistical value in relation to an outer periphery of one basic image and inside the basic image, in conjunction with the limitations of claim 6 with which it depends, which defines the target image having a target object, the feature, and an adjustment process that emphasizes the outer periphery of the object.
In addition, there is no teaching, suggestion, or motivation found in the current references and none that can be inferred from the examiner’s own knowledge with respect to the current limitation.
In regards to dependent claim 9, this claim depends from an objected to base claim, and thus is objected to based on the same rationale as provided above.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5 and 12 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Li (US 2023/0134337 A1, hereinafter referenced “Li”).
In regards to claim 1. Li discloses a data processing method for generation of a test image, the data processing method being to be performed by a computer and applied to a machine learning model subjected to machine learning processing using a basic image (Li, Abstract), the data processing method comprising:
-obtaining, by circuitry of the computer, a target image in a domain different from a domain where the basic image is obtained (Li, para [0035]; Reference discloses the system 100 may be configured to modify the source image to have a new image configuration different from the original image configuration, which is referred to as a target image configuration hereinafter (i.e. different configurations interpreted as the different domains as the system 100 is interpreted as the computer having circuitry));
-and generating, by the circuitry of the computer, the test image by performing on the target image, adjustment processing based on a feature corresponding to the domain where the basic image is obtained (Li, para [0040]; Reference discloses once the candidate regions 400 are selected from the source image 200, the system 100 may modify the candidate regions to generate a plurality of regional proposal images (RPIs). The RPIs may be generated by, for example, resizing, cropping or warping at least some of the candidate regions 400 or adding a new region to at least some of the candidate regions 400 such that the corresponding RPIs 700 may have the target image configuration, for example, 600 pixels by 600 pixels as shown in FIG. 3B (i.e. regional proposal image interpreted as the test image based on adjustment processing regarding the resizing, cropping, or warping of a size feature in relation to source image or basic image)).
In regards to claim 2. Li discloses the data processing method according to claim 1.
Li further discloses
-wherein the target image includes a target object, the feature is associated with a position of the target object in the basic image, and the adjustment processing includes adjusting the position of the target object in the test image (Li, para [0038] and [0041]; Reference at [0038] discloses for example, when the source image 200 prominently shows an object (e.g., a person, logo, etc.), the set of rules may indicate that each candidate region 400 should show at least a portion of the source image 200 showing the object (i.e. wherein the target image includes a target object). Para [0041] discloses the system 100 may then determine an aesthetical value of each RPI 700. In an implementation, such aesthetical value may be determined by the aesthetical evaluation ML model 140B (hereinafter “evaluation model 140B”)….The sample images may have different visual features and image configuration. Some of the sample images may show similar features but may have different image configurations. Some of the sample images may have similar image configuration and show a similar feature positioned at different portions of on the sample images (i.e. the adjustment processing includes adjusting the position of the target object in the test image)).
In regards to claim 3. Li discloses the data processing method according to claim 1.
Li further discloses
-wherein the target image includes a target object, the feature is associated with a shape of the target object in the basic image, and the adjustment processing includes adjusting the shape of the target object in the test image (Li, para [0038] and [0041]; Reference at [0038] discloses for example, when the source image 200 prominently shows an object (e.g., a person, logo, etc.), the set of rules may indicate that each candidate region 400 should show at least a portion of the source image 200 showing the object (i.e. wherein the target image includes a target object). Para [0041] discloses the system 100 may then determine an aesthetical value of each RPI 700. In an implementation, such aesthetical value may be determined by the aesthetical evaluation ML model 140B (hereinafter “evaluation model 140B”)….The sample images may have different visual features and image configuration. Some of the sample images may show similar features but may have different image configurations. Some of the sample images may have similar image configuration and show a similar feature positioned at different portions of on the sample images (i.e. the adjustment processing includes adjusting the position of the target object in the test image)).
In regards to claim 4. Li discloses the data processing method according to claim 1.
Li further discloses
-wherein the target image includes a target object, the feature is associated with a posture of the target object in the basic image, and the adjustment processing includes adjusting the posture of the target object in the test image (Li, para [0038] and [0056]; Reference at [0038] discloses for example, when the source image 200 prominently shows an object (e.g., a person, logo, etc.), the set of rules may indicate that each candidate region 400 should show at least a portion of the source image 200 showing the object (i.e. wherein the target image includes a target object). Para [0056] discloses Item 3. The system of Item 1, wherein, for generating the plurality of regional proposal images, the AI engine is further trained to perform modifying a size or shape of at least some of the identified candidate regions. Item 4. The system of Item 3, wherein, for modifying the size or shape of at least some of the identified candidate regions, the AI engine is further trained to perform resizing, cropping or warping at least some of the candidate regions; or adding a new region to at least some of the candidate regions. (i.e. the adjustment processing includes adjusting the posture of the target object in the test image)).
In regards to claim 5. Li discloses the data processing method according to claim 1.
Li further discloses
-wherein the feature is associated with contrast in the basic image, and the adjustment processing includes adjusting the contrast in the test image (Li, para [0040]; Reference discloses For example, the system 100 may adjust a brightness, contrast, color, sharpness, etc. to visually enhance the candidate region 400A. Other RPIs 700 having an image configuration different from the target image configuration may be modified by, for example, resizing, cropping, warping, and/or the like).
In regards to claim 12. Li discloses an image generation method using a machine learning model, the image generation method being to be performed by a computer, the machine learning model having been subjected to machine learning processing using basic data (Li, Abstract), the image generation method comprising:
-obtaining, by circuitry of the computer, a target image obtained in a domain different from a domain where the basic data is obtained (Li, para [0035]; Reference discloses the system 100 may be configured to modify the source image to have a new image configuration different from the original image configuration, which is referred to as a target image configuration hereinafter (i.e. different configurations interpreted as the different domains as the system 100 is interpreted as the computer having circuitry));
-generating, by the circuitry of the computer, a test image by performing on the target image, adjustment processing based on a feature corresponding to the domain where the basic data is obtained (Li, para [0040]; Reference discloses once the candidate regions 400 are selected from the source image 200, the system 100 may modify the candidate regions to generate a plurality of regional proposal images (RPIs). The RPIs may be generated by, for example, resizing, cropping or warping at least some of the candidate regions 400 or adding a new region to at least some of the candidate regions 400 such that the corresponding RPIs 700 may have the target image configuration, for example, 600 pixels by 600 pixels as shown in FIG. 3B (i.e. regional proposal image interpreted as the test image based on adjustment processing regarding the resizing, cropping, or warping of a size feature in relation to source image or basic image));
-and obtaining, by the circuitry of the computer, an output image by applying the test image to the machine learning model (Li, para [0049]; Reference discloses at step 1370, the system 100 may select, based on the determined aesthetical value of each RPI 700, a first regional proposal image, which is one of the plurality of RPI 700, as the target image 300. At step 1380, the system 100 may extract, from the AI engine 130, the first RPI selected as the target image 300. At step 1380, the system 100 may then cause the first regional proposal image to be displayed via the display 114 of the local user device 110 (i.e. output image by applying the test image to the machine learning model)).
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 6, 7, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Li (US 2023/0134337 A1) in view of Kobayashi (US 2023/0281889 A1, hereinafter referenced “Koba”).
In regards to claim 6. Li discloses the data processing method according to claim 1.
Li does not explicitly disclose but Koba teaches
-wherein the target image includes a target object, the feature is associated with a ratio of a pixel value between an outer periphery and an inside of the target object, and the adjustment processing includes emphasis of the outer periphery in the target object (Koba, Fig. 5 and para [0071]; Reference illustrates the tissue composition ratio image 71 is an image of a human head and includes four channels of images, i.e., a background image channel, a cavity image channel, a soft tissue image channel, and a bone image channel as the image illustrates the distinguishing of the outer periphery of the soft tissue regarding the bone image)
Li and Koba are combinable because they are in the same field of endeavor regarding image manipulation through machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the AI based image modification system of Li to include the attenuation coefficient image generation features of Koba in order to provide the user with a system that allows for use of a system for modifying an image through use of an AI engine where a final proposal image is generated through identification of visual features and subsequent modifications of an initial input image as taught by Li, while incorporating the attenuation coefficient image generation features of Koba for use of techniques for generating an intermediate image from an input image and generating an attenuation coefficient image via a trained model generation process to improve image processing accuracy for applications such as medical image data, applicable to image processing/modification systems such as those taught in Li.
In regards to claim 7. Li in view of Koba teach the data processing method according to claim 6.
Li does not explicitly disclose but Koba teaches
-wherein the target object is a head of a human, the outer periphery corresponds to a skin area of the head, and the inside corresponds to a brain area of the head (Koba, Fig. 5 and para [0071]; Reference illustrates the tissue composition ratio image 71 is an image of a human head and includes four channels of images, i.e., a background image channel, a cavity image channel, a soft tissue image channel, and a bone image channel).
Li and Koba are combinable because they are in the same field of endeavor regarding image manipulation through machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the AI based image modification system of Li to include the attenuation coefficient image generation features of Koba in order to provide the user with a system that allows for use of a system for modifying an image through use of an AI engine where a final proposal image is generated through identification of visual features and subsequent modifications of an initial input image as taught by Li, while incorporating the attenuation coefficient image generation features of Koba for use of techniques for generating an intermediate image from an input image and generating an attenuation coefficient image via a trained model generation process to improve image processing accuracy for applications such as medical image data, applicable to image processing/modification systems such as those taught in Li.
In regards to claim 10. Li discloses the data processing method according to claim 1.
Li does not explicitly disclose but Koba teaches
-wherein the target image is a positron emission tomography (PET) image or a single photon emission computed tomography (SPECT) image (Koba, para [0161]; Reference discloses further, in the above-described embodiments, an example is shown in which the nuclear medicine diagnostic apparatus is a PET device, but the present invention is not limited thereto. For example, the nuclear medicine diagnostic apparatus may be a SPECT (Single Photon Emission Computed Tomography) device other than a PET device (i.e. interpreted as producing PET or SPECT images)).
Li and Koba are combinable because they are in the same field of endeavor regarding image manipulation through machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the AI based image modification system of Li to include the attenuation coefficient image generation features of Koba in order to provide the user with a system that allows for use of a system for modifying an image through use of an AI engine where a final proposal image is generated through identification of visual features and subsequent modifications of an initial input image as taught by Li, while incorporating the attenuation coefficient image generation features of Koba for use of techniques for generating an intermediate image from an input image and generating an attenuation coefficient image via a trained model generation process to improve image processing accuracy for applications such as medical image data, applicable to image processing/modification systems such as those taught in Li.
In regards to claim 11. Li in view of Koba teach an attenuation coefficient image generation method to be performed by a computer comprising.
Li does not explicitly disclose but Koba teaches
-generating, by circuitry of the computer, an intermediate image including an image relating to a tissue area based on a test image generated according to the data processing method according to claim 10; and generating, by the circuitry of the computer, an attenuation coefficient image based on the intermediate image and a known attenuation coefficient of the tissue area (Koba, para [0072]; Reference discloses then, as shown in FIG. 3 and FIG. 4 , in Step 104, an attenuation coefficient image 9 is generated based on the intermediate image 7 and known attenuation coefficients of tissue areas. In this embodiment, as shown in FIG. 6 , in Step 104, an attenuation coefficient is assigned to a tissue in the tissue composition ratio image 71 based on known attenuation coefficients to generate an attenuation coefficient image 9).
Li and Koba are combinable because they are in the same field of endeavor regarding image manipulation through machine learning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention for the AI based image modification system of Li to include the attenuation coefficient image generation features of Koba in order to provide the user with a system that allows for use of a system for modifying an image through use of an AI engine where a final proposal image is generated through identification of visual features and subsequent modifications of an initial input image as taught by Li, while incorporating the attenuation coefficient image generation features of Koba for use of techniques for generating an intermediate image from an input image and generating an attenuation coefficient image via a trained model generation process to improve image processing accuracy for applications such as medical image data, applicable to image processing/modification systems such as those taught in Li.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: See the Notice of References Cited (PTO-892)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TERRELL M ROBINSON whose telephone number is (571)270-3526. The examiner can normally be reached 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KENT CHANG can be reached at 571-272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TERRELL M ROBINSON/Primary Examiner, Art Unit 2614
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