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 .
Claim Objections
Claim 10 is objected to because of the following informalities: ”various processings” in line 2 should read “the processings”. Appropriate correction is required.
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: “a controller, an inputter, a display and an imager” in claim 10.
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 § 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 2-4 “at least one of a sensitivity or a specificity---“ and “the at least one of the sensitivity or the specificity”. The BRI of this recitals means only one is required to be met. Claim 1 further recites at lines 5-6 “a combination value of the sensitivity and a value of the specificity”. The recital at lines 5-6 renders the claim indefinite as to how it is possible to determine both the values of the sensitivity and the specificity when only one (due to the recital of or at lines 2-4 “one of a sensitivity or a specificity”) is specified in the specifying operation?. Examiner also notes that the specification figs 2-3, 6, 9, 11, 13 and the disclosure shows “determining specificity and the sensitivity. Amendment/clarification is required. Claims 2-10 depending from claim 1 are also 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 at lines 1-4
wherein the machine learning model is a trained model trained so as to determine, based on inputted information, a positive/negative for a predetermined disease, the cut off value is a reference for identifying the positive/negative,”. The broadest reasonable interpretation of the recital of “positive/negative” is “positive or negative”. I.e only one is required to be met. Claim 2 also recites at lines 5-8 “the sensitivity is a rate at which the positive is correctly determined as positive, the specificity is a rate at which the negative is correctly determined as negative, and the machine learning model is trained such that the sensitivity and the specificity differ for every of the cut off value.” The recital at lines 5-8 renders the claim indefinite as how both the positive and the negative are determined if only “positive/negative i.e positive or negative” is determined and inputted in lines 1-4?. Amendments/clarification are required. Claims 3 depends from claim 2, therefore it is rejected.
Claim 11 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 11 recites at lines 2-4 “at least one of a sensitivity or a specificity---“ and “the at least one of the sensitivity or the specificity”. The BRI of this recitals means only one is required to be met. Claim 11 further recites at lines 5-6 “a combination value of the sensitivity and a value of the specificity”. The recital at lines 5-6 renders the claim indefinite as to how it is possible to determine both the values of the sensitivity and the specificity when only one (due to the recital of or at lines 2-4 “one of a sensitivity or a specificity”) is specified in the specifying operation?. Examiner also notes that the specification figs 2-3, 6, 9, 11, 13 and the disclosure shows “determining specificity and the sensitivity. Amendment/clarification is required.
Claim 12 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 12 recites at lines 3-5 “at least one of a sensitivity or a specificity---“ and “the at least one of the sensitivity or the specificity”. The BRI of this recitals means only one is required to be met. Claim 12 further recites at lines 6-7 “a combination value of the sensitivity and a value of the specificity”. The recital at lines 6-7 renders the claim indefinite as to how it is possible to determine both the values of the sensitivity and the specificity when only one (due to the recital of or at lines 3-5 “one of a sensitivity or a specificity”) is specified in the specifying operation?. Examiner also notes that the specification figs 2-3, 6, 9, 11, 13 and the disclosure shows “determining specificity and the sensitivity. Amendment/clarification is required.
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 1 is rejected under 35 U.S.C 101. 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 because the broadest reasonable interpretation of “a system” absent any hardware/structural components performing the steps as recited in claim 1, encompasses a signal/program and signal/program are not statutory. Claims 2-9 depends directly or indirectly on claim 1 and missing the structural/hardware in the claims, therefore they are rejected.
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 1-5 and 7-12 are rejected under 35 U.S.C. 103 as being unpatentable over Cohen et al., (US20180068083) hereafter Cohen in view of NPL1 (Measurements, composite scores and the art of ‘cutting-off’, Pedro M Machado, eular, 2016, Pages 787-790) hereafter NPL1.
1. Regarding claim 1 as best understood by the examiner, Cohen discloses a diagnosis support system (figs 1A-1B, 15A-15D, paras 0027, 0060-0062, 0215-0229, 0261-0262, 0389-0394 shows and discloses a diagnosis support system) that executes:
processing for detecting an input operation of specifying at least one of a sensitivity or a specificity of a machine learning model for medical diagnosis (figs 1A-1B, paras 0018-0019, 0215-0221 discloses processing for detecting an input operation of specifying at least one of a sensitivity or a specificity of a machine learning model for medical diagnosis); processing for determining, based on the at least one of the sensitivity or the specificity specified by the detected input operation, a combination of a value of the sensitivity and a value of the specificity (para 0389-0394, 0434-0436, 0478 and figs 15A-15D,, 17 and 19 and tables 6, 9 shows calculating the AUC (i.e the combination of a value as a percentage in the area under the curve as seen in the graphs) based on the value of the sensitivity or the specificity, examiner also notes that due to the recital of “or” only one is required to be met). As seen in the and processing for setting a cut off value of the machine learning model corresponding to the specificity (Figs 1A-1B, figs 15A-15D, 16-17, 19 and tables 6, 9 and paras 0110, 0389-0391, 0394, 0430-0436, 0478 discloses the cut-off of the model (i.e the machine learning model/Deep neural network) corresponding to the specificity of (0.43)). Cohen is however silent and fails to disclose and processing for setting a cut off value corresponding to the determined combination.
NPL1 discloses processing for setting a cut off value corresponding to the determined combination (pages 788-789 and fig 1 shows the cut-point (red dot and green dot, fig 1. Simulation of a receiver operating characteristic (ROC) curve and classical ‘optimal’ cut-points (adapted from ref 10).The vertical lines and reference arcs identify the Youden index (solid lines) and the closest point to(0,1) (dashed lines) and their corresponding cut-points (red dot and green dot, respectively); the cut-point for the Youden index corresponds to the point in the ROC curve with the maximum value of (sensitivity —(1—specificity)); the cut-point for the closest point to (0,1) corresponds to the point in the ROC curve with the minimum value of ((1—sensitivity)2 +(1—specificity)2).) based on the combination of the sensitivity and the specificity value as seen in the graph meeting the above claim limitations). Before the effective filing date of the invention was made, Cohen and NPL1 are combinable because they are from the same filed of endeavor and are analogous art of imaging and diagnosis. The suggestion/motivation would be an increased interpretability and effective early diagnosis method/system on page 787 middle col. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages NPL1 in the system/method of Cohen to obtain the invention as specified in claim 1.
2. Regarding claim 2 as best understood by the examiner, Cohen and NPL1 discloses the diagnosis support system according to claim 1. Cohen discloses further wherein the machine learning model is a trained model trained so as to determine, based on inputted information, a positive/negative for a predetermined disease, the cut off value is a reference for identifying the positive/negative, the sensitivity is a rate at which the positive is correctly determined as positive, the specificity is a rate at which the negative is correctly determined as negative, and the machine learning model is trained such that the sensitivity and the specificity differ for every of the cut off value (figs 1A-1B, 15A-15D, 17 and 19, paras 0103, 0109-0110, 0434-0436 and 0455 discloses wherein the machine learning model is a trained model trained so as to determine, based on inputted information, a positive/negative for a predetermined disease, the cut off value is a reference for identifying the positive/negative, the sensitivity is a rate at which the positive is correctly determined as positive, the specificity is a rate at which the negative is correctly determined as negative, and the machine learning model is trained such that the sensitivity and the specificity differ (i.e differentiate between cancer and non-cancer) for every of the cut off value).
3. Regarding claim 4 as best understood by the examiner, Cohen and NPL1 discloses the diagnosis support system according to claim 2. Cohen discloses further executing processing for: storing model information including information that expresses a performance of the machine learning model, the information expressing the sensitivity and the specificity for a cut off (paras 0103, 0109-0110, 0434-0436, 0455, 0464, 0478 discloses wherein the machine learning model is a trained model trained so as to determine, based on inputted information, a positive/negative for a predetermined disease, the cut off value is a reference for identifying the positive/negative, the sensitivity is a rate at which the positive is correctly determined as positive, the specificity is a rate at which the negative is correctly determined as negative, and the machine learning model is trained such that the sensitivity and the specificity differ (i.e differentiate between cancer and non-cancer) for every of the cut off value) and NPL1 discloses each of a plurality of the cut off value that differ from each other, and determining, the cut off value for the combination of a value of the sensitivity and a value of the specificity (pages 788-789 and fig 1 shows the cut-point (red dot and green dot, fig 1. Simulation of a receiver operating characteristic (ROC) curve and classical ‘optimal’ cut-points (adapted from ref 10).The vertical lines and reference arcs identify the Youden index (solid lines) and the closest point to(0,1) (dashed lines) and their corresponding cut-points (red dot and green dot, respectively); the cut-point for the Youden index corresponds to the point in the ROC curve with the maximum value of (sensitivity —(1—specificity)); the cut-point for the closest point to (0,1) corresponds to the point in the ROC curve with the minimum value of ((1—sensitivity)2 +(1—specificity)2).) based on the combination of the sensitivity and the specificity value as seen in the graph meeting the above claim limitations). Cohen and NPL1 together would therefore meet the limitations as claimed in claim 3.
4. Regarding claim 4 as best understood by the examiner, Cohen and NPL1 discloses the diagnosis support system according to claim 1. Cohen discloses further executing processing for: displaying, on a display device, a graph expressing a relationship between the sensitivity and the specificity of the machine learning model, wherein the input operation is an operation of specifying the sensitivity and the specificity for a region in which the graph is displayed (figs 15A-15D, 17 and 19, paras 0389-0390, 0027, 0080, 0113 shows displaying, on a display device, a graph expressing a relationship between the sensitivity and the specificity of the machine learning model, wherein the input operation is an operation of specifying the sensitivity and the specificity for a region in which the graph is displayed).
5. Regarding claim 5 as best understood by the examiner, Cohen and NPL1 disclose the diagnosis support system according to claim 4. Cohen discloses further wherein the graph is a ROC graph having the sensitivity and the specificity on two axes (paras 0389-0391 and figs 15A-15D, 17 and 19 shows and discloses wherein the graph is a ROC graph having the sensitivity and the specificity on two axes).
6. Regarding claim 7, Cohen and NPL1 discloses the diagnosis support system according to claim 1. NPL1 discloses and shows, further executing processing for: displaying, on a display device, the sensitivity and the specificity of the determined combination (fig 1 shows displaying, on a display device, the sensitivity and the specificity of the determined combination).
7. Regarding claim 8 as best understood by the examiner, Cohen and NPL1 disclose the diagnosis support system according to claim 1. Cohen discloses the displaying of the sensitivity and the specificity values on the graph using the model and setting the cutoff value of the model (figs 15A-15D, 17 and 19, paras 0389-0390, 0027, 0080, 0113 shows displaying, on a display device, a graph expressing a relationship between the sensitivity and the specificity of the machine learning model, wherein the input operation is an operation of specifying the sensitivity and the specificity for a region in which the graph is displayed) and NPL1 discloses, further executing processing for: associating and storing a display setting of a setting screen for setting the cut off value as seen in fig 1 meeting the claim limitations of claim 8. Cohen and NPL1 together would therefore meet the limitations of claim 8.
8. Regarding claim 9 as best understood by the examiner, Cohen and NPL1 discloses the diagnosis support system according to claim 1. Cohen discloses further wherein the machine learning model is a trained model trained so as to determine, based on an inputted image, a positive/negative of a disease related to a tumor (Figs 1A-1B, figs 15A-15D, 16-17, 19 and tables 6, 9 and paras 0103, 0109-0110, 0186, 0359, 0389-0391, 0394, 0430-0436, 0455, 0464, 0478 shows and discloses wherein the machine learning model is a trained model trained so as to determine, based on an inputted image, a positive/negative of a disease related to a tumor).
9. Regarding claim 10, Cohen and NPL1 discloses the diagnosis support system according to claim 1. Cohen discloses further comprising: a controller that executes various processings of the diagnosis support system; an inputter that performs an input operation of specifying at least one of the sensitivity or the specificity; a display that displays information including the sensitivity and the specificity; and an imager that captures an image to be input into the machine learning model (figs 1A-1B, 7, 8, 15A-15D, 17 and 19 para 0027, 0080, 0113 discloses a computer implemented method/system for further comprising: a controller that executes various processings of the diagnosis support system; an inputter that performs an input operation of specifying at least one of the sensitivity or the specificity; a display that displays information including the sensitivity and the specificity; and an imager that captures an image to be input into the machine learning model).
10. Claim 11 is a corresponding method claim of claim 1. See the corresponding explanation of claim 1.
11. Claim 12 is a corresponding non-transitory computer readable recording medium claim of claim 1. See the corresponding explanation of claim 1. Cohen discloses (figs 1A-1B, 7, 8, 15A-15D, 17 and 19 para 0027, 0080, 0113 discloses a computer implemented method/system and memory storing a program that causes a computer to execute the steps recited in claim 12.
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 the 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. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Conclusion
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/JAYESH PATEL/
Primary Examiner
Art Unit 2677
/JAYESH A PATEL/Primary Examiner, Art Unit 2677
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