DETAILED ACTION
This action is in response to the response to restriction/restriction filed on May 22, 2026. Group I (claims 1-21 and 23) was elected. Group II (claim 22) was not elected. Claims 1-23 have been examined and are currently pending.
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 .
Inventorship
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Information Disclosure Statement
The Information Disclosure Statement filed on January 14, 2025 has been considered. An initialed copy of the Form 1449 is enclosed herewith.
Response to Restriction
Applicant's election with traverse of Group I in the reply filed on May 22, 2026 is acknowledged. The traversal is found persuasive, therefore, the examiner has withdrawn the restriction.
Claim Objections
Claim 2 is objected to because of the following informalities: Dependent claim 2 recites the term, “if”, which implies the claim step is optional. Replace the “if” with “when”. Appropriate correction is required.
Claim 4 is objected to because of the following informalities: Dependent claim 4 recites “the or” which the examiner believes is a typographical error. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: Dependent claim 5 recites “the one or more convolutional neural networks” in lines 1-2 lacks antecedent basis. Appropriate correction is required.
Claim 7 is objected to because of the following informalities: Independent claim 7 recites the term, “the sending” in line 3 lacks antecedent basis. Appropriate correction is required.
Claim 8 is objected to because of the following informalities: Dependent claim 8 recites “the or” which the examiner believes is a typographical error. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: Dependent claim 9 recites “the or” which the examiner believes is a typographical error. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Dependent claim 12 recites the term, “the threshold” in line 3 lacks antecedent basis. Appropriate correction is required.
Claim 13 is objected to because of the following informalities: Dependent claim 13 recites the term, “the reference image set” in line 3 lacks antecedent basis. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: Dependent claim 17 recites the term, “the threshold” in line 3 lacks antecedent basis. Appropriate correction is required.
Claim 17 is objected to because of the following informalities: Dependent claim 17 recites the term, “the highest” in line 6 lacks antecedent basis. Appropriate correction is required.
Claim 23 is objected to because of the following informalities: Independent claim 23 recites the term, “the medical data sample” in line 4 lacks antecedent basis. Appropriate correction is required.
Claim 23 is objected to because of the following informalities: Independent claim 23 recites the term, “the disease” in line 8 lacks antecedent basis. Appropriate correction 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 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
ALICE/ MAYO: TWO-PART ANALYSIS
2A. First, a determination whether the claim is directed to a judicial exception (i.e., abstract idea).
Prong 1: A determination whether the claim recites a judicial exception (i.e., abstract idea).
Groupings of abstract ideas enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Mathematical concepts- mathematical relationships, mathematical formulas or equations, mathematical calculations.
Certain methods of organizing human activity- fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Mental processes- concepts performed in the human mind (including an observation, evaluation, judgement, opinion).
Prong 2: A determination whether the judicial exception (i.e., abstract idea) is integrated into a practical application.
Considerations indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception
Considerations that are not indicative of integration into a practical application enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
2B. Second, a determination whether the claim provides an inventive concept (i.e., Whether the claim(s) include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
Considerations indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Improvement to the functioning of a computer, or an improvement to any other technology or technical field
Applying the judicial exception with, or by use of a particular machine.
Effecting a transformation or reduction of a particular article to a different state or thing
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception NOTE: The only consideration that does not overlap with the considerations indicative of integration into a practical application associated with step 2A: Prong 2.
Considerations that are not indicative of an inventive concept (aka “significantly more”) enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance.
Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea.
Adding insignificant extra-solution activity to the judicial exception.
Generally linking the use of the judicial exception to a particular technological environment or field of use.
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. NOTE: The only consideration that does not overlap with the considerations that are not indicative of integration into a practical application associated with step 2A: Prong 2.
See also, 2019 Revised Patent Subject Matter Eligibility Guidance; Federal Register; Vol. 84, No. 4; Monday, January 7, 2019
Claim 22 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
1: Statutory Category
Applicant’s claimed invention, as described in independent claim 22 is directed to a method.
2(A): The claim(s) are directed to a judicial exception (i.e., an abstract idea).
PRONG 1: The claim(s) recite a judicial exception (i.e., an abstract idea).
Mental Processes
Independent claim 22 recites the limitations, “and performing pairwise comparison of each medical data example within the reference data set against every medical data example within the reference data set; and ranking the plurality of medical data examples according to a degree of severity of disease based on the pairwise comparisons.” is directed to the abstract idea of mental processes. Specifically, the claim is directed to concepts performed in the human mind (e.g., observation, evaluation, and judgment). In the currently pending claims, a person (through visual observation) can evaluate and compare images (medical data sample and reference data set). Additionally, through observation and judgment a person can rank medical data to assess and determine disease levels of a patient.
PRONG 2: The judicial exception (i.e., an abstract idea) is not integrated into a practical application.
The applicant has not shown or demonstrated any of the requirements described above under "integration into a practical application" under step 2A. The applicant’s claimed limitations do not demonstrate an improvement to another technology or technical field, an improvement to the functioning of the computer itself, effecting a transformation or reduction of particular article to a different state or thing, applying or using the judicial exception in some meaningful way. The current application does not amount to 'significantly more' than the abstract idea as described above. The claim does not include additional elements or limitations individually or in combination that are sufficient to amount to significantly more than the judicial exception. The claim does not recite a machine, computer, or technical elements to perform the steps recited. The additional elements taken in combination add nothing more than what is present when the elements are considered individually. Therefore, based on the two-part Alice Corp. analysis, there are no meaningful limitations in the claims that transform the exception (i.e., abstract idea) into a patent eligible application.
The receiving steps is a data gathering directed to insignificant extra solution activity.
Since the claim(s) recite a judicial exception and fails to integrate the judicial exception into a practical application, the claim(s) is/are “directed to” the judicial exception. Thus, the claim(s) must be reviewed under the second step of the Alice/ Mayo analysis to determine whether the abstract idea has been applied in an eligible manner.
2(B): The claims do not provide an inventive concept (i.e., The claim(s) do not include additional elements, or combinations of elements, that are sufficient to amount to significantly more than the judicial exception (i.e., abstract idea)).
As discussed with respect to Step 2A Prong Two, the same analysis applies here in 2B, i.e., claim does not include additional elements or limitations individually or in combination that are sufficient to amount to significantly more than the judicial exception.
For these reasons, there is no invention concept in the claim, and thus the claim is ineligible.
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.
Claim(s) 22 is rejected under 35 U.S.C. 102(a)(1) as being anticipated by Nozaki et al. US Patent 11024031 B1.
Claim 22:
As per claim 22, Nozaki teaches a computer implemented method comprising:
receiving the reference data set (column 8, lines 14-18 and column 8, line 56 to column 9, line 8 “Reference image data is acquired in step 50. Reference image data 50 may include standard image data of healthy, normal subjects, and reference image data of subjects with various gastric conditions and severity levels thereof.”);
and performing pairwise comparison of each medical data example within the reference data set against every medical data example within the reference data set (column 5, lines 21-49 and column 9, lines 27-31 “The standard image is an image of a stomach in a healthy state. The standard image may be an earlier image of the subject in a healthy state or may be an image of a stomach of a different person in a healthy state. As discussed in more detail below, the different person may be selected based on one or more shared characteristics with the subject, such as age, race, and sex. Further with respect to FIG. 2, reference images 36 of stomachs exhibiting different severity levels of various gastric conditions are also compared to the standard image to generate reference abnormality images 38, which are indicative of differences between the reference images and the standard image. Reference images 36 may be standardized images acquired from a database of reference images for each diagnosed condition or disorder collected from a particular group of people diagnosed with such conditions, as discussed above...”);
and ranking the plurality of medical data examples according to a degree of severity of disease based on the pairwise comparisons (column 5, lines 28-49 “Further with respect to FIG. 2, reference images 36 of stomachs exhibiting different severity levels of various gastric conditions are also compared to the standard image to generate reference abnormality images 38, which are indicative of differences between the reference images and the standard image. Reference images 36 may be standardized images acquired from a database of reference images for each diagnosed condition or disorder collected from a particular group of people diagnosed with such conditions, as discussed above. The reference images 36 may be the actual images, optionally processed to enhance the structural feature of interest, collected from the people of a particular group or characteristic category. Alternatively, the reference images 36 may be average images created based on the data collected from the people of a particular population diagnosed with such conditions. For example, a representative average stomach image for each gastric condition may be generated. Additionally, representative average stomach images corresponding to various severity levels within a particular gastric condition may also be generated. Thus, multiple representative or average images may be created for each gastric condition and severity level.”).
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(s) 1-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Nozaki et al. US Patent 11024031 B1 in view of Reicher et al. US Publication 20160364862 A1.
Claim 1:
As per claim 1, Nozaki teaches a method comprising:
performing a pairwise comparison of the medical data sample against each example of medical data in a reference data set using one or more machine learning algorithms to determine a difference in severity of the medical data compared to each example medical data in the reference data set (column 1, lines 50-59 “The method for determining a severity of gastric cancer in a subject may include obtaining various images of a stomach of the subject including wavelength images, and generating difference images from the wavelength images. The subject images may then be compared with reference images representative of different severity levels of gastric cancer, or input into a learned model trained using the reference images stored in the database to extract a feature pattern corresponding to a severity of gastric cancer to diagnose the subject as having a particular severity level of gastric cancer.”)
wherein the reference data set includes a plurality of medical data examples, the plurality of medical data examples ranked according to their degree of severity of disease (column 5, lines 28-49 “Further with respect to FIG. 2, reference images 36 of stomachs exhibiting different severity levels of various gastric conditions are also compared to the standard image to generate reference abnormality images 38, which are indicative of differences between the reference images and the standard image. Reference images 36 may be standardized images acquired from a database of reference images for each diagnosed condition or disorder collected from a particular group of people diagnosed with such conditions, as discussed above. The reference images 36 may be the actual images, optionally processed to enhance the structural feature of interest, collected from the people of a particular group or characteristic category. Alternatively, the reference images 36 may be average images created based on the data collected from the people of a particular population diagnosed with such conditions. For example, a representative average stomach image for each gastric condition may be generated. Additionally, representative average stomach images corresponding to various severity levels within a particular gastric condition may also be generated. Thus, multiple representative or average images may be created for each gastric condition and severity level.”)
Nozaki does not teach and flagging the medical data sample as requiring referral for investigation for the disease based on results of the pairwise comparisons. However, Reicher teaches Methods and Systems for Performing Image Analytics using Graphical Reporting Associated with Clinical Images and further teaches, “The learning engine 110 may also categorize images to flag images needing manual review. For example, the learning engine 110 may label images stored in a PACS system as “normal,” “abnormal,” or “indeterminate,” and a diagnosing physician or other healthcare professional may use this information to triage images (e.g., prioritize images categorized as “abnormal” or “indeterminate”). For example, when an image study including a plurality of images is submitted to the learning engine 110 for analysis, the learning engine 101 may flag at least one of the plurality of images included in the image study for manual review based on the categorization of each image of the plurality of images. Similarly, the learning engine 110 may automatically identify images or exams that need to be sent to an external reading service. In addition, in some embodiments, the learning engine 110 automatically identifies images as reference images that may be stored in various repositories and used for research, teaching, marketing, patient education, public health, or other purposes.” (paragraph 0075). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Nozaki to include and flagging the medical data sample as requiring referral for investigation for the disease based on results of the pairwise comparisons as taught by Reicher in order to allow a human to further investigate a patient’s medical issue/image.
Claim 23:
As per claim 23, Nozaki teaches a non-transitory computer readable medium comprising:
perform a pairwise comparison of the medical data sample against each example of medical data in a reference data set using one or more machine learning algorithms to determine a difference in severity of the medical data compared to each example medical data in the reference data set (column 1, lines 50-59 “The method for determining a severity of gastric cancer in a subject may include obtaining various images of a stomach of the subject including wavelength images, and generating difference images from the wavelength images. The subject images may then be compared with reference images representative of different severity levels of gastric cancer, or input into a learned model trained using the reference images stored in the database to extract a feature pattern corresponding to a severity of gastric cancer to diagnose the subject as having a particular severity level of gastric cancer.”);
wherein the reference data set includes a plurality of medical data examples, the plurality of medical data examples ranked according to their degree of severity of disease (column 5, lines 28-49 “Further with respect to FIG. 2, reference images 36 of stomachs exhibiting different severity levels of various gastric conditions are also compared to the standard image to generate reference abnormality images 38, which are indicative of differences between the reference images and the standard image. Reference images 36 may be standardized images acquired from a database of reference images for each diagnosed condition or disorder collected from a particular group of people diagnosed with such conditions, as discussed above. The reference images 36 may be the actual images, optionally processed to enhance the structural feature of interest, collected from the people of a particular group or characteristic category. Alternatively, the reference images 36 may be average images created based on the data collected from the people of a particular population diagnosed with such conditions. For example, a representative average stomach image for each gastric condition may be generated. Additionally, representative average stomach images corresponding to various severity levels within a particular gastric condition may also be generated. Thus, multiple representative or average images may be created for each gastric condition and severity level.”).
Nozaki does not teach and flag the medical data sample as requiring referral for investigation for the disease based on results of the pairwise comparisons. However, Reicher teaches Methods and Systems for Performing Image Analytics using Graphical Reporting Associated with Clinical Images and further teaches, “The learning engine 110 may also categorize images to flag images needing manual review. For example, the learning engine 110 may label images stored in a PACS system as “normal,” “abnormal,” or “indeterminate,” and a diagnosing physician or other healthcare professional may use this information to triage images (e.g., prioritize images categorized as “abnormal” or “indeterminate”). For example, when an image study including a plurality of images is submitted to the learning engine 110 for analysis, the learning engine 101 may flag at least one of the plurality of images included in the image study for manual review based on the categorization of each image of the plurality of images. Similarly, the learning engine 110 may automatically identify images or exams that need to be sent to an external reading service. In addition, in some embodiments, the learning engine 110 automatically identifies images as reference images that may be stored in various repositories and used for research, teaching, marketing, patient education, public health, or other purposes.” (paragraph 0075). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Nozaki to include and flagging the medical data sample as requiring referral for investigation for the disease based on results of the pairwise comparisons as taught by Reicher in order to allow a human to further investigate a patient’s medical issue/image.
Claim 2:
As per claim 2, Nozaki and Reicher teach the method of claim 1 as described above and Nozaki further teaches further comprising:
determining a position of the medical data sample against the medical data examples within the reference data set (column 9, lines 27-31);
Reicher further teaches and comparing the position of the medical data sample with a threshold for referral, wherein the flagging the medical data sample as requiring referral comprises flagging the medical data sample as requiring referral if the position of the medical data sample is above the threshold for referral (paragraph 0101). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Nozaki to include comparing the position of the medical data sample with a threshold for referral, wherein the flagging the medical data sample as requiring referral comprises flagging the medical data sample as requiring referral if the position of the medical data sample is above the threshold for referral as taught by Reicher in order to determine the medical sample requires manual review.
Claim 3:
As per claim 3, Nozaki and Reicher teach the method of claim 1 as described above and Nozaki further teaches wherein the one or more machine learning algorithms comprises one or more neural networks (column 41, lines 27-29).
Claim 4:
As per claim 4, Nozaki and Reicher teach the method of claim 3 as described above and Nozaki further teaches wherein the or each neural network is a convolutional neural network (column 41, lines 27-29).
Claim 5:
As per claim 5, Nozaki and Reicher teach the method of claim 4 as described above and Nozaki further teaches wherein the one or more convolutional neural networks is a plurality of convolutional neural networks (column 41, lines 27-29).
Claim 6:
As per claim 6, Nozaki and Reicher teach the method of claim 5 as described above and Nozaki further teaches wherein each of the plurality of convolutional neural networks is trained on a different data set (column 41, lines 27-29).
Claim 7:
As per claim 7, Nozaki and Reicher teach the method of claim 5 as described above and Nozaki further teaches further comprising amalgamating the results of the pairwise comparisons from each convolutional neural network, and wherein the sending the medical data sample for referral for investigation for the disease is based on the amalgamated pairwise comparisons (column 41, lines 21-49).
Claim 8:
As per claim 8, Nozaki and Reicher teach the method of claim 7 as described above and Nozaki further teaches wherein the amalgamating of the results of the pairwise comparisons comprises supplying the results of the pairwise comparison as inputs to one or more lasso regression models, the or each lasso regression model having a decision boundary associated with a threshold of disease severity (column 41, lines 21-49).
Claim 9:
As per claim 9, Nozaki and Reicher teach the method of claim 8 as described above and Nozaki further teaches wherein the or each lasso regression model is a plurality of lasso regression models and wherein the threshold of disease severity for each model is different (column 41, lines 21-49).
Claim 10:
As per claim 10, Nozaki and Reicher teach the method of claim 7 as described above and Reicher further teaches further comprising estimating a confidence interval of a probability of needing referral (paragraphs 0101 and 0037). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Nozaki to include comprising estimating a confidence interval of a probability of needing referral as taught by Reicher in order to determine manual review.
Claim 11:
As per claim 11, Nozaki and Reicher teach the method of claim 10 as described above and Reicher further teaches wherein the estimating comprises performing bootstrapping (paragraphs 0101 and 0037). Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Nozaki to include wherein the estimating comprises performing bootstrapping as taught by Reicher in order to generate distinct sets of data or calculations.
Claim 12:
As per claim 12, Nozaki and Reicher teach the method of claim 7 as described above and Nozaki further teaches wherein the amalgamating the results of the pairwise comparison comprises accumulating results from each convolutional neural network, and comparing the accumulated results to the threshold for referral (column 41, lines 21-49).
Claim 13:
As per claim 13, Nozaki and Reicher teach the method of claim 7 as described above and Nozaki further teaches wherein the amalgamating the results comprises setting a plurality of thresholds of disease severity within the reference image set, and determining a frequency of occurrence of the medical data above each of the thresholds of disease severity (column 41, lines 21-49).
Claim 14:
As per claim 14, Nozaki and Reicher teach the method of claim 13 as described above and Nozaki further teaches wherein the threshold for referral corresponds to one of the plurality of thresholds of disease severity (column 5, lines 28-49).
Claim 15:
As per claim 15, Nozaki and Reicher teach the method of claim 7 as described above and Nozaki further teaches wherein the amalgamating the results of the pairwise comparison comprises fitting an S-curve to the results of each convolutional neural network (column 41, lines 21-49);
determining a probability of requiring referral based each fitted S-curve (column 41, lines 21-49);
and performing linear discriminant analysis on the determined probabilities (column 41, lines 21-49).
Claim 16:
As per claim 16, Nozaki and Reicher teach the method of claim 6 as described above and Nozaki further teaches further comprising selecting a subset of the plurality of convolutional neural networks using a selecting algorithm (column 41, lines 21-49).
Claim 17:
As per claim 17, Nozaki and Reicher teach the method of claim 16 as described above and Nozaki further teaches wherein the selecting algorithm comprises a lasso regression model, the lasso regression model having a decision boundary associated with the threshold for referral, the selecting comprising applying the results from each convolutional neural network into the lasso regression model, ordering the convolutional neural networks in terms of accuracy at predicting referral, and selecting a predetermined number of the highest ranked convolutional neural networks as the subset (column 41, lines 21-49).
Claim 18:
As per claim 18, Nozaki and Reicher teach the method of claim 1 as described above and Nozaki further teaches further comprising:
receiving the reference data set (column 8, lines 14-18 and column 8, line 56 to column 9, line 8);
and performing pairwise comparison of each medical data example of the plurality of examples of medical data within the reference data set against every other medical data example of the plurality of medical data example within the reference data set (column 5, lines 21-49 and column 9, lines 27-31);
and ranking the plurality of medical data examples according to a degree of severity of disease based on the pairwise comparisons (column 5, lines 28-49).
Claim 19:
As per claim 19, Nozaki and Reicher teach the method of claim 1 as described above and Reicher further teaches wherein the disease is selected from a list of diseases including: breast cancer (paragraph 0069), pneumonia, lung cancer (paragraph 0084), skin cancer, and cardiovascular disease. Therefore, it would have been obvious to one of ordinary skilled in the art at the time of filing to modify Nozaki to include wherein the disease is selected from a list of diseases including: breast cancer, pneumonia, lung cancer, skin cancer, and cardiovascular disease as taught by Reicher in order to determine a disease associated with a patient.
Claim 20:
As per claim 20, Nozaki and Reicher teach the method of claim 1 as described above and Nozaki further teaches wherein the medical data is selected from a list of medical data including:
a two-dimensional image, a three-dimensional image (column 24, lines 32-37), and trace data.
Claim 21:
As per claim 21, Nozaki and Reicher teach the method of claim 20 as described above and Nozaki further teaches wherein the two-dimensional image comprises any of X-ray and mammography X-ray, and wherein the three-dimensional image comprises a three-dimensional image selected from a list of three- dimensional images including: a magnetic resonance image, a computerised tomography images, and an Ultrasound image, and wherein the trace data comprises an eco-cardiogram (column 24, lines 32-37 and column 52, lines 46-55).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al. US Publication 20210042916 A1 Deep Learning-Based Diagnosis and Referral of Diseases and Disorders
Zhang discloses disclosed herein are systems, methods, devices, and media for carrying out medical diagnosis of diseases and conditions using artificial intelligence or machine learning approaches. Deep learning algorithms enable the automated analysis of medical images such as X-rays to generate predictions of comparable accuracy to clinical experts for various diseases and conditions including those afflicting the lung such as pneumonia.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW L HAMILTON whose telephone number is (571)270-1837. The examiner can normally be reached Monday-Thursday 9:30-5:30 pm EST.
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, Fonya Long can be reached at (571)270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW L HAMILTON/Primary Examiner, Art Unit 3682
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