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 .
Specification
The marked up copy along with the clean copy of the specification filed on 01/24/2025 has been accepted and made of record.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 1 recites at lines 1-2 “training an artificial neural network”, and at line 7 “implement an artificial neural network”. Claim 1 also recites at lines 8-9 “to train the artificial neural network”. It is unclear as to which of the above recitals of “an artificial neural network” recited in lines 1-2 and 7, the recital of “the artificial neural network” at lines 8-9 refers to? Claim 1 also recites at lines 21-22 “train an artificial neural network”. Examiner notes that there are three instances of “an artificial neural network”. The published specification (US20260030874) paras 0071, 0076 discloses “an untrained artificial neural network” and at paras 0071 and 0094 “the trained artificial neural network”. It is also unclear as to which of the recitals of “an artificial neural network” is untrained and which one is trained artificial neural network? Amendments/clarifications are required. Claims 2-14 and 16-17 depending on claim 1 are rejected.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 2 recites in line 2 “the artificial neural network”. Claim 2 depends on claim 1. Claim 1 recites at lines 1-2 “training an artificial neural network”, and at line 7 “implement an artificial neural network”. Claim 1 also recites at lines 8-9 “to train the artificial neural network”. It is unclear as to which of the above recitals of “an artificial neural network” recited in lines 1-2 and 7, the recital of “the artificial neural network” at lines 8-9 refers to? Claim 1 also recites at lines 21-22 “train an artificial neural network”. It is unclear as to which of the above recitals “an artificial neural network” at lines 12-, 7 and 21-22, the recital of “the artificial neural network” at line 2 in claim 2 refers to? Amendments/clarifications are required.
Claim 8 recites the limitation "the first selection, the second selection, the third selection or the fourth selection" in lines 4-5. There is insufficient antecedent basis for these limitations in the claim. Claim 9 depending on claim 8 is also rejected.
Claim 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 15 recites at lines 1-2 “training an artificial neural network”, and at line 11 “initializing an artificial neural network”. Claim 15 also recites at line 12 “training the artificial neural network”. It is unclear as to which of the above recitals of “an artificial neural network” recited in lines 1-2 and 11, the recital of “the artificial neural network” at line 12 refers to? Claim 15 recites “training an artificial neural network” and “initializing an artificial neural network” in lines 1-2 and 11. Examiner notes that there are two instances of “an artificial neural network”. The published specification (US20260030874) paras 0071, 0076 discloses “an untrained artificial neural network” also at paras 0071 and 0094 “the trained artificial neural network”. It is also unclear as to which of the recitals of “an artificial neural network” is untrained and which one is trained artificial neural network? Amendments/clarifications are required.
Claim 3 recites the limitation "the color" in line 5. There is insufficient antecedent basis for this limitation in the claim.
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.
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 limitation(s) is/are: “an image processing device, an artificial intelligence module and a controller device” in claim 1, “a color-removal module in claim 3, “an edge-detection module” in claim 4, “a segmentation module” in claim 5, “a filtering module” in claim 6, “a transformation module” in claim 7, “a selector module” in claim 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 1-2, 4-5, 7-8, 10, 14-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al. US20160259963) hereafter Cohen in view of Wang et al. (US20190333198) hereafter Wang.
1. Regarding claim 1 as best understood by the examiner, Cohen discloses a training system (fig 1 shows a system) for training an artificial neural network, for medical applications, comprising:
an image processing device, which is configured to receive a set of acquired images (para 0017 and fig 1 element 100 is an image processing device configured to receive a set of acquired images 104 meeting the claim limitations);
an artificial intelligence module, configured to implement an artificial neural network (fig 1 and para 0033 shows and discloses an artificial intelligence module, configured to implement an artificial neural network); and
a controller device, configured to train the artificial neural network implemented by the artificial intelligence module (fig 1, para 0028 shows and discloses the user computer 108 (i.e a processor or controller configured to train the artificial neural network implemented by the artificial intelligence module); wherein the image processing device is further configured to generate modified images based on a modification of the acquired images (fig 1 image processing device 100 with element 110-112 and paras 0022-0023 shows filtering module 110 outputting the filtered images 112 of the acquired images 104 meeting the claim limitations, examiner notes that the specifics of generate modified images are not required by the current claim);
wherein the controller device is configured to receive a set of training images comprising at least part of the modified images and at least part of the acquired images and train an artificial neural network which, based on a classification scheme, is configured to output a probability distribution for each training image according to the classification scheme (fig 1 shows the controller 108, paras 0021, 0024-0030 discloses receiving a set of training images (training datastore 109), a set of filtered images 112 (i.e modified images) and the acquired images 104, classify the pixels (i.e probability distribution from the image according to classification scheme), user makes the corrections and iteratively refine the model meeting the limitations of wherein the controller device is configured to receive a set of training images comprising at least part of the modified images and at least part of the acquired images and train an artificial neural network which, based on a classification scheme, is configured to output a probability distribution for each training image according to the classification scheme). As seen above Cohen discloses the acquired images and the modified images. Examiner also notes that since the filtered images are modified version of the acquired images, the filtered images would have different texture/appearance/visual looks than the original acquired images. Cohen is however silent and fails to disclose wherein each of the modified images generated by the image processing device contains less texture information than the respective acquired image from which it stems.
Wang discloses wherein each of the modified images generated by the image processing device contains less texture information than the respective acquired image from which it stems (paras 0118-0120, fig 4B shows the input image(s)453 and the texture loss (less texture) image 458 (i.e modified image having less texture information) than the respective input image 453 (i.e from which it stems) meeting the above claim limitations). Before the effective filing date of the invention was made, Cohen and Wang are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be an easy, compact, fast and efficient system at paras 0046-0047. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of Wang in the system of Cohen to obtain the invention as specified in claim 1,
2. Regarding claim 2, Cohen and Wang disclose the training system according to claim 1. Cohen discloses further wherein the artificial neural network is configured to classify an image based on the probability distribution associated with the image (fig 1 shows the controller 108, paras 0021, 0024-0030 and 0034 discloses receiving a set of training images (training datastore 109), a set of filtered images 112 (i.e modified images) and the acquired images 104, classify the pixels (i.e probability distribution from the image according to classification scheme associated with the image) and classifying the images (para 0034), user makes the corrections and iteratively refine the model meeting the limitations of wherein the controller device is configured to receive a set of training images comprising at least part of the modified images and at least part of the acquired images and train an artificial neural network which, based on a classification scheme, is configured to output a probability distribution for each training image according to the classification scheme).
3. Regarding claim 4, Cohen and Wang disclose the training system according to claim 1. Cohen discloses further wherein the image-processing device further comprises an edge-detection module, which is configured to perform a modification of an image in a second selection by detecting the edges of the image (paras 0021-0026 discloses the edge selection by the user and the edge filtering (modification) of the detected edges meeting the above claim limitations).
4. Regarding claim 5, Cohen and Wang disclose the training system according to claim 1. Cohen discloses further wherein the image-processing device further comprises a segmentation module, which is configured to perform a modification of an image in a third selection by segmenting parts of the image (paras 0021, 0022, 0047-0048 discloses wherein the image-processing device further comprises a segmentation module, which is configured to perform a modification of an image in a third selection by segmenting parts of the image).
5. Regarding claim 7, Cohen and Wang disclose the training system according to claim 1. Cohen discloses further wherein the image-processing device further comprises a transformation module, which is configured to further modify a modified image by applying a set of transformations, comprising or translations (para 0023 discloses the filtering (i.e translations into a filtered image) module applying the subsampling filter and then apply the edge detection filter to the sub-sampled image (i.e set of transformations comprising translations), examiner notes that due to the recital of or only one is required to be met, examiner also notes that the specifics of translations are not required by the current claim) or
6. Regarding claim 8 as best understood by the examiner, Cohen and Wang disclose the training system according to claim 7. Cohen discloses further wherein the controller device further comprises a selector module, which is configured to select the modified images from any one or more of the first selection, (fig 1, para 0028 shows the training module getting the user input (i.e selection from computer 108 for refining the modified data) and also receiving the input filtered images (i.e modified and transformed images) meeting the above claim limitations).
7. Regarding claim 10, Cohen and Wang disclose the training system according to claim 1. Cohen discloses further wherein the artificial neural network comprises a convolutional neural network (para 0033 discloses a neural network meeting the claim limitations).
8. Regarding claim 14, Cohen and Wang disclose the training system according to claim 1. Cohen discloses further wherein the training images contain in paras 0026-0027 classifying the filtered versions of the images using the predetermined rule of 95 percent. Cohen also discloses “other predetermined percentages may be selected by the user or in accordance with the types of the images being classified in para 0026. Cohen and Wang however fail to disclose between 10% and 30%, preferably 20%, of modified images. Examiner notes that from the above teachings of Cohen in paras 0026-0027 classifying the filtered versions of the images using the predetermined rule of 95 percent. Cohen also discloses “other predetermined percentages may be selected by the user or in accordance with the types of the images being classified in para 0026,
between 10% and 30%, preferably 20%, of modified images would be obvious and within one of ordinary skill in the art before the effective filing date of the invention was made. The rationales supporting the rejections would be rationales B, E and F. See MPEP 2141 III.
9. Claim 15 is a corresponding method claim of claim 1. See the corresponding explanation of claim 1.
10. Regarding claim 17, explanation corresponding to claim 14 applies. See the corresponding explanation of claim 14.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Cohen in view of Wang and in further view of WATANABE, SHINJI (WO2019106946A1) hereafter WATANABE.
11. Regarding claim 3, Cohen and Wang disclose the training system according to claim 1. Cohen discloses the image filtering the images as seen in fig 1. Cohen and Wang are silent and however fails to disclose wherein the image-processing device further comprises a color-removal module, which is configured to perform a modification of an image in a first selection by removing the color of the image.
WATANABE discloses wherein the image-processing device further comprises a color-removal module, which is configured to perform a modification of an image in a first selection by removing the color of the image (page 3, fig 1 shows the user operation unit 212 for selecting the image for color removal and color removal filter 204 for removing the color in the image meeting the claim limitations). Before the effective filing date of the invention was made, Cohen, Wang and WATANABE are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be automatic and accurate system (page 2). Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of WATANABE in the system of Cohen and Wang to obtain the invention as specified in claim 3.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Cohen in view of Wang and in further view of Le Dinh et al., (US20060139376) hereafter Le.
12. Regarding claim 6, Cohen and Wang disclose the training system according to claim 1. Cohen in fig 1 discloses filtering module 11 and user selecting the images for filtering in paras 0018-0022 meeting the limitations of a filtering module, which is configured to perform a modification of an image in a fourth selection
Le discloses wherein the image-processing device a filtering module, which is configured to perform smearing and blurring the image (fig 1, para 0009 discloses non-linear sharpeners (i.e filtering) to correct (i.e filter) smeary and blurred images meeting the above claim limitations of wherein the image-processing device a filtering module, which is configured to perform smearing and blurring the image). Before the effective filing date of the invention was made, Cohen, Wang and Le are combinable because they are form the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be a scaled crisper image (para 0009). Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of Le in the system of Cohen and wang to obtain the invention as specified in claim 6.
Claims 11-13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen in view of Wang and in further view of NPL1 (Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network, Khaled Almezhghwi et a., Hindawi, 2020, Pages 1-12) hereafter NPL1.
13. Regarding claim 11, Cohen and Wang disclose the training system according to claim 1. Cohen discloses wherein the acquired images comprise images of cell
NPL1 discloses images of blood samples (figs 2 and 4-5 shows the acquired images of the blood samples). Before the effective filing date of the invention was made, Cohen, Wang and NPL1 are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be an automatic, fast, accurate and cost-effective system (page 2 col 1 2nd para). Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of NPL1 in the system of Cohen and Wang to obtain the invention as specified in claim 11.
14. Regarding claim 12, Cohen and Wang disclose the training system according to claim 1. Cohen discloses the classification scheme classifying pixels (para 0024, 0029). Cohen and Wang are silent and however fail to disclose further wherein classes in the classification scheme correspond at least to types of leucocytes.
NPL1 shows and discloses further wherein classes in the classification scheme correspond at least to types of leucocytes (figs 2, 4 and 5 shows and discloses wherein classes in the classification scheme correspond at least to types of white blood cells (leukocytes) meeting the claim limitations). Before the effective filing date of the invention was made, Cohen, Wang and NPL1 are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be an automatic, fast, accurate and cost-effective system (page 2 col 1 2nd para). Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of NPL1 in the system of Cohen and Wang to obtain the invention as specified in claim 12.
15. Regarding claim 13, Cohen, Wang and NPL1 disclose the training system according to claim 12. NPL1 shows and discloses further wherein the classes in the classification scheme correspond to types of leucocytes (fig 2 shows the types of the leucocytes (i.e white blood cells) and at least one additional class corresponding to anomalous leucocytes (fig 3 shows high percentage (i.e anomalous shown in RED, examiner notes that the specifics of anomalous at least one class are not required by the current claim) of neutrophils (i.e at least one additional class of leucocytes) meeting the above claim limitations).
16. Regarding claim 16, Cohen and Wang disclose the training system according to claim 1. Cohen discloses wherein the acquired images comprise images of cell. Cohen and Wang however fail to disclose images of blood samples wherein the acquired images consist of images of blood samples.
NPL1 discloses wherein the acquired images consist of images of blood samples (figs 2 and 4-5 show the acquired images of the blood samples). Before the effective filing date of the invention was made, Cohen, Wang and NPL1 are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be an automatic, fast, accurate and cost-effective system (page 2 col 1 2nd para). Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of NPL1 in the system of Cohen and Wang to obtain the invention as specified in claim 16.
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicants are advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAYESH PATEL whose telephone number is (571)270-1227. The examiner can normally be reached IFW Mon-FRI.
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/JAYESH A PATEL/Primary Examiner, Art Unit 2677
/JAYESH PATEL/
Primary Examiner
Art Unit 2677
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