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 .
The IDS filed 30 December 2024 has been received and considered.
Claims 1 – 18 are pending
Claims 1 – 18, all the claims pending in this application, have been rejected.
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses 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:
“an inference unit” in claim 1.
“a processing unit” in claim 1.
“the inference unit” in claim 1.
“the processing unit” in claim 2.
“the inference unit” in claim 2.
“the processing unit” in claim 3.
“an image quality detection unit” in claim 4.
“the inference unit” in claim 4.
“the processing unit” in claim 5.
“the inference unit” in claim 5.
“the processing unit” in claim 6.
“an imaging unit” in claim 7.
“the processing unit” in claim 7.
“the imaging unit” in claim 7.
“the processing unit” in claim 8.
“a second imaging unit” in claim 8.
“the processing unit” in claim 9.
“the inference unit” in claim 9.
“the processing unit” in claim 10.
“the inference unit” in claim 10.
“the processing unit” in claim 11.
“the inference unit” in claim 12.
“the inference unit” in claim 13.
“an imaging unit” in claim 13.
“a supply unit” in claim 14.
“an image quality detection unit” in claim 15.
“a learning unit” in claim 16.
“an inference unit” in claim 17.
“a processing unit” in claim 17.
“the inference unit” in claim 17.
“the processing unit” in claim 17.
“an inference unit” in claim 18.
“a processing unit” in claim 18.
“the inference unit” in claim 18.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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 them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 14 is rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of 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.
Claim 18 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 claim is directed to towards a "a program", which broadly encompasses a computer program per se. Such computer programs, per se, are not, in and of themselves, methods or machines, nor are they physical products of manufacture or compositions of matter. Therefore, such programs do not fall into any of the categories of eligible subject matter defined in 35 U.S.C. § 101 and are not, by themselves, eligible for patent protection. Such programs can be eligible for patent protection if claimed as embodied on or in a computer readable storage device or medium, but only if the claim clearly and unambiguously excludes transitory, propagating signals from the full scope of the claimed subject matter, as such signals are also not eligible under 35 U.S.C. § 101. It is suggested that amending the claim language to define the computer program product as having the code instructions embodied on a "non-transitory computer-readable medium" would satisfy these requirements and would limit the claimed invention to eligible subject matter.
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 (i.e., changing from AIA to pre-AIA ) 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.
(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 – 7, 9, 12, and 14 – 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US Publication 2012/0020550 A1 to ZHOU et al. (hereinafter ZHOU).
Claim 1
Regarding Claim 1, an independent device claim, ZHOU teaches an information processing device (Abstract) comprising: an inference unit that performs inference processing on an input inference image ("The image processing section 34 applies a predetermined image process to the input image supplied thereto from the image inputting section 33 and supplies a resulting image to the recognition section 35.", Paragraph [0038]; "Then, the learning section 31 carries out a statistic learning process, which uses, for example, the AdaBoost algorithm or the Genetic Algorithm (GA), based on the input learning image and the recognition characteristic amounts to generate recognizers for recognition of presence or absence of the predetermined object on the input image in the recognition section 35.", [0034]);
and a processing unit that corrects an image quality of the inference image based on an image quality of a teacher image used for learning in the inference unit ("Further, the parameter setting section 37 sets an image processing parameter as a projection function for causing an average color of the object image to match with an average color of the average learning image, and the image processing section 34 uses the set projection function to carry out an image process for the input image of the second frame. Here, white balance correction may additionally be carried out for the input image in response to the image processing parameter set by the parameter setting section 37. It is to be noted that the processing for the color information is omitted in the case where the input image is a gray scale image.", Paragraph [0061]).
Claim 2
Regarding Claim 2, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU further teaches wherein the processing unit corrects the image quality of the inference image so that the inference image to be input to the inference unit has an image quality that is substantially the same as the image quality of the teacher image ("Further, the parameter setting section 37 sets an image processing parameter as a projection function for causing an average color of the object image to match with an average color of the average learning image, and the image processing section 34 uses the set projection function to carry out an image process for the input image of the second frame. Here, white balance correction may additionally be carried out for the input image in response to the image processing parameter set by the parameter setting section 37. It is to be noted that the processing for the color information is omitted in the case where the input image is a gray scale image.", Paragraph [0061]).
Claim 3
Regarding Claim 3, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU further teaches wherein the processing unit corrects the image quality of the inference image by comparing the image quality of the inference image with the image quality of the teacher image (Fig. 3; "Here, it is assumed that the object image, that is, the image in the region of the hand, of the input image P(1) shown on the upper side in FIG. 3 is sufficiently dark in comparison with the average learning image. Consequently, if it is decided at step S16 that the difference in image information is greater than the predetermined threshold value, or in other words, if it is decided that both of the difference in luminance information and the difference in color information between the object image and the average learning image are greater than the respective threshold values, then the processing advances to step S17…At step S17, the parameter setting section 37 sets or updates an image processing parameter to be used in the image process by the image processing section 34 in response to the differences in image information and supplies the image processing parameter to the image processing section 34. In particular, the parameter setting section 37 sets an image processing parameter with which the image processing section 34 carries out such an image process as to cause the luminance information and the color information of the object image to approach the luminance information and the color information of the average learning image. Then, the parameter setting section 37 supplies the set image processing parameter to the image processing section 34", Paragraphs [0053 - 0054], Paragraph [0061]).
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Claim 4
Regarding Claim 4, dependent on claim 3, ZHOU teaches the invention as claimed in claim 3.
ZHOU further teaches comprising an image quality detection unit that detects the image quality of the inference image to be input to the inference unit (Paragraphs [0051 - 0055], specifically [0053] where in Fig. 3 it is determined that the input image is "sufficiently" dark).
Claim 5
Regarding Claim 5, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU further teaches wherein the processing unit corrects the image quality of the inference image by changing, based on the image quality of the teacher image, an operation of pre-processing to be performed on the inference image before being input to the inference unit (Rejected as applied to claim 4).
Claim 6
Regarding Claim 6, dependent on claim 5, ZHOU teaches the invention as claimed in claim 5.
ZHOU further teaches wherein the processing unit acquires processing contents of pre-processing performed on the teacher image as information on the image quality of the teacher image, and corrects the image quality of the inference image based on processing contents of the pre-processing (Rejected as applied to claim 4), wherein the "luminance" information and the "color" information are acquired for both the learning (teacher) image and the input (inference) image.
Claim 7
Regarding Claim 7, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU further teaches comprising an imaging unit (Figure 6, #231) that captures the inference image, wherein the processing unit corrects the image quality of the inference image by changing an operation of the imaging unit based on the image quality of the teacher image (Rejected as applied to claims 3 and 4; see also [0101]: “wherein an image pickup parameter of an image pickup section is set in response to a difference in image information between an object image on an image acquired by an image pickup section and an average learning image”).
Claim 9
Regarding Claim 9, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU further teaches wherein the processing unit corrects the image quality of the inference image based on an inference result of the inference unit ( Figure 1; "The image processing section 34 applies a predetermined image process to the input image supplied thereto from the image inputting section 33 and supplies a resulting image to the recognition section 35.", Paragraph [0038]; "The parameter setting section 37 reads out an average learning image from the storage section 32 in response to recognition of the predetermined object by the recognition section 35 and compares the average learning image with a region of an object on the input image recognized by the recognition section 35. The parameter setting section 37 sets a parameter to be used in an image process by the image processing section 34 in response to a result of the comparison.", Paragraph [0041]).
Claim 12
Regarding Claim 12, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU further teaches wherein the inference unit performs inference processing using an inference model learned by a machine learning technology (Rejected as applied to claim 1), wherein the AdaBoost algorithm is a machine learning model.
Claim 14
Regarding Claim 14, an independent device claim, ZHOU teaches an information processing device comprising a supply unit that supplies, to an inference device that implements an inference model generated by a machine learning technology, information on an image quality of a teacher image used to learn the inference model ("Further, the learning section 31 generates, based on the recognition characteristic amounts, an average learning image which is an average image of the predetermined object on the learning image from an average value of the recognition characteristic amounts. The learning section 31 supplies the generated recognition characteristic amounts, recognizers and average learning image to the storage section 32 so as to be stored into the storage section 32.", Paragraph [0036]; "The recognition section 35 reads out recognition characteristic amounts and recognizers stored in the storage section 32 and recognizes or detects the predetermined object on the input image supplied thereto from the image processing section 34 based on the characteristic amounts and the recognizers corresponding to the recognition characteristic amounts from among the calculated characteristic amounts.", Paragraph [0039]).
Claim 15
Regarding Claim 15, dependent on claim 14, ZHOU teaches the invention as claimed in claim 14.
ZHOU further teaches comprising an image quality detection unit that detects the image quality of the teacher image (Rejected as applied to claims 2 and 3), wherein the luminosity and color data (image quality) from the learning image is detected.
Claim 16
Regarding Claim 16, dependent on claim 14, ZHOU teaches the invention as claimed in claim 14.
ZHOU further teaches comprising a learning unit that learns the inference model using the teacher image ("According to the AdaBoost algorithm, a learning image in which an object to be detected is included and a learning image in which the object is not included are used as samples to generate a weak recognizer also called weak leaner. A large number of such weak recognizers are combined to construct a strong recognizer. If the strong recognizer obtained in this manner is used, then an object can be detected from an arbitrary image.", Paragraph [0035]).
Claim 17, an independent method claim, is rejected for the same reasons as applied to claim 1.
Claim 18, an independent program claim, is rejected for the same reasons as applied to claim 1.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over US Publication 2012/0020550 A1 to ZHOU et al. (hereinafter ZHOU) in view of US Publication 2016/0217569 A1 to BHATIA et al. (hereinafter BHATIA).
Claim 8
Regarding Claim 8, dependent on claim 7, ZHOU teaches the invention as claimed in claim 7.
ZHOU does not teach wherein the processing unit acquires an operation of a second imaging unit that has captured the teacher image as information on the image quality of the teacher image, and corrects the image quality of the inference image based on the operation of the second.
However, BHATIA teaches wherein the processing unit acquires an operation of a second imaging unit that has captured the teacher image as information on the image quality of the teacher image, and corrects the image quality of the inference image based on the operation of the second imaging unit (Figures 2 and 3; "FIG. 2 is directed to a general embodiment for a method of training a model to learn associations between an imaging subject (e.g., a patient), acquisition parameters (including parameters used for imaging and/or an image acquisition instrument/device), operator characteristics, and preliminary scans (e.g., images of the imaging subject acquired using the acquisition parameters). FIG. 3 is directed to a general embodiment for a method of producing an output of image acquisition parameters suitable for a particular patient, based on the model of associations from the method in FIG. 2. In some embodiments, the parameters output of FIG. 3 may be taken as recommendations for image acquisition parameters. More specifically, the parameters output from the method and FIG. 3 may include parameters for producing quality images under designated conditions including imaging subjects and image acquisition instruments or devices. In other words, the method of FIG. 3 may provide image acquisition parameters to optimize image quality for capturing a specified image subject using a specified image acquisition device, based on image quality of images of the same (or similar) imaging subject(s), using the same (or similar) image acquisition instrument(s)/device(s), and for the same operator.", Paragraph [0026]; "As previously discussed, image acquisition (e.g., from step 513) may further serve as feedback on the predicative ability of the model developed by the training phase. Images acquired using output optimized image acquisition parameters may be expected to be of a similar quality to the image quality of the training data set associated with the output image acquisition parameters, at least with respect to the designated acquisition parameter. For the image or scan of step 513 to serve as feedback, step 513 may further include determining a data set associated with the image or scan. The data set may include one or more reconstructions, one or more preliminary scans and/or patient information, and one or more acquisition parameters, consistent with the previously described training data sets.", Paragraph [0055]).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of ZHOU to incorporate a system that utilized the parameters used on a model that outputs the training/learning image and apply those same parameters to acquisition device that captures the “input” image to increase the similarity quality between the images, as disclosed by BHATIA. The suggestion/motivation for doing so would have been to acquire images as similar to the training image to increase the accuracy of the output image that has been processed.
Claims 10 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over US Publication 2012/0020550 A1 to ZHOU et al. (hereinafter ZHOU) in view of WO Publication 2010050333 A1 to KAJI.
Examiner Note: A translated version of WO Publication 2010050333 A1 is referenced for the following claim rejection (hereinafter referred to as Translated Document 1).
Claim 10
Regarding Claim 10, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU does not teach wherein the processing unit corrects the image quality of the inference image based on a certainty factor for an inference result of the inference unit.
However, KAJI teaches wherein the processing unit corrects the image quality of the inference image based on a certainty factor for an inference result of the inference unit ("When it is determined that the certainty factor for the determination result of the imaging region is low, the control unit 31 shades or adds a color to the character of the imaging region of the medical image determined that the certainty factor is low on the viewer screen D1.", Translated Document 1, lines 450 - 453).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of ZHOU to incorporate performing image correction if the certainty factor is considered low, as disclosed by KAJI. The suggestion/motivation for doing so would have been to allow the system to continuously correct an image until an object or subject in the image can be identified with high confidence.
Claim 11
Regarding Claim 11, dependent on claim 10, ZHOU, in view of KAJI, teaches the invention as claimed in claim 10.
ZHOU does not teach wherein the processing unit corrects the image quality of the inference image so as to increase the certainty factor.
However, KAJI further teaches wherein the processing unit corrects the image quality of the inference image so as to increase the certainty factor ("Further, the control unit 31 determines whether the calculated certainty factor is high or low, and when it is determined that the certainty factor is high, the image processing unit 36 selects an imaging region having the maximum estimated value for the medical image. When class-specific image processing is performed and it is determined that the certainty level is low, general-purpose image processing is performed on the medical image. As a result, general-purpose image processing can be selected when the possibility of erroneous determination is high, and inappropriate image processing can be prevented from being performed. In addition, since the control unit 31 displays warning information such as a message, a color, and shading that warns that the certainty factor is low on the display unit 33, the user can easily grasp that the certainty factor is low.", Translated Document 1, lines 475 - 485).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over US Publication 2012/0020550 A1 to ZHOU et al. (hereinafter ZHOU) in view of US Publication 2022/0130136 A1 to ANDO.
Claim 13
Regarding Claim 13, dependent on claim 1, ZHOU teaches the invention as claimed in claim 1.
ZHOU does not teach wherein the inference unit is mounted in a chip that is the same as where an imaging unit that captures the inference image is mounted.
However, ANDO teaches wherein the inference unit is mounted in a chip that is the same as where an imaging unit that captures the inference image is mounted (Figure 3; "The data augmentation section 31 acquire an input image from the acquisition section 20, and applies data augmentation to the input image. The data augmentation section 31 performs a process of generating the first augmented image by applying the first data augmentation to the input image, and a process of generating the second augmented image by applying the second data augmentation to the input image. The data augmentation section 31 outputs the first augmented image and the second augmented image to the neural network application section 33.", Paragraph [0080]).
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It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of ZHOU to incorporate a device that captures the input image as well as being able to perform the image processing, as disclosed by ANDO. The suggestion/motivation for doing so would have been to allow for the input and processing of an input image as well as the output image to be part of the same system for faster analysis and reliability.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ronde Miller whose telephone number is (703) 756-5686 The examiner can normally be reached Monday-Friday 8:00-4:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RONDE LEE MILLER/Examiner, Art Unit 2663
/SEAN M CONNER/Primary Examiner, Art Unit 2663
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