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 .
Notice to Applicants
This communication is in response to the action filed on 07/01/2024.
Claims 1-19 are currently pending.
Information Disclosure Statement
The information disclosure statements (IDS’s) filed on 07/01/2024, and 06/03/2026 have been considered.
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 1-6, 8-9, 11-12, 14-19 are rejected under 35 § U.S.C. 102(a)(1) as being anticipated by US 11,861,833 B2 to MIN et al. (hereinafter “MIN”).
As per claim 1, MIN discloses a training apparatus (a teaching/training model to train a machine learning model to identify plaque in cardiovascular images and determine heart disease severity; title; abstract; figs 1-4), comprising processing circuitry configured to: acquire a plurality of items of subject data and a plurality of items of incidental data corresponding to the plurality of items of subject data (the computing system comprises a computing processor component to execute data, programs and instructions stored in the computing systems memory component which of the stored information would include a plurality of items of subject medical data such as medical image scans and incidental patient data; title; abstract; figs 1-4, 16; column 5, lines 20-62; column 6, lines 1-46); calculate an importance of each of the plurality of items of subject data based on a distribution of the plurality of items of incidental data (the computing system is adapted to calculate severity of plaque buildup in the heart in relation to heart diseases characteristics in order to track progression of the disease as good or bad related to observed characteristic feature changes/progression over time in the subject by observing a plurality of medical images captured over time and to use them as training inputs in a historical database/databank and learn characteristics that may signal high rate of heart disease; column 24, lines 7-12; column 33, lines 5-36; column 38, line 22-column 39, line 8; column 192, line 22-column 193, line 19); determine, for each of the plurality of items of subject data, a number of items of training data according to the importance, and generate a plurality of items of training data corresponding to the determined number of items of training data (determine data to be used as training data related to the heart diseases being tracked in the patient/subject and to use a plurality of data point collected over time from the subject and stored in a database; column 191, lines 8-64; column 192, lines 2-51; column 240, lines 54-67); and iteratively train a learning model on the plurality of items of training data for each of the plurality of items of subject data by unsupervised learning (the disease progression tracking model is trained iteratively as new patient cases are added to the database keeping the model up to date with each case observed; column 24, lines 7-12; column 83, lines 14-34; column 92, lines 4-63; column 247, lines 7-33).
As per claim 2, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to calculate the importance to be high for an item of incidental data corresponding to an item of subject data that appears infrequently (global ischemia index (used to account for importance of a factor in determining ischemia heart disease and used in the machine learning algorithm to determine importance of observed factors) accounts numerically for the direct contributors to ischemia, the early consequences of ischemia, the late consequences of ischemia, the associated factors with ischemia and other test findings in relation to ischemia the index using these factors can be identified and/or derived automatically, using one or more algorithms, such as a machine learning algorithms trained to identify index values of a certain level and since the disease occurs infrequently the index of importance relates to infrequently occurring data points; column 191, lines 8-64; column 192, lines 2-51; column 240, lines 54-67).
As per claim 3, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to calculate the importance to be inversely proportional to a frequency of classification of the plurality of items of incidental data (the computing system calculates a variety of values related to importance an additional value is the coronary artery calcium score CACS and a Agatston score related to the CACS score and includes a Hounsfield unit related to density of plaque buildup and is inversely proportional to the frequently occurring data and would be occurs/appears as a high value in the infrequent data; column 203, lines 5-41; column 206, lines 42-67).
As per claim 4, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to calculate the importance to be higher for an item of incidental data that is farther from a mean or a median of the distribution (the system can be configured to analyze and/or utilize as input plaque heterogeneity, the system can be configured to analyze and utilize as input calcified plaque volume versus non-calcified plaque volume the system can be configured to analyze the plaque volume of buildup and utilize as input standard deviation of one or more of the 3 different components of plaque and will have a higher index rating or CASC score; column 192, line 50- column 193, line 19; column 197, lines 47-54).
As per claim 5, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to determine the number of items of training data to be larger for an item of subject data with a higher importance (during training by utilizing machine, the system allows for data driven exploration of the contribution of multiple variables, even if they share a specific feature, the system may take into account certain temporal considerations when training and/or applying an algorithm for generating the global ischemia index, for example, the system can be configured to give greater weight to consequences/sequelae rather than causes/contributors, as the consequences/sequelae have already occurred; column 192, line 50- column 193, line 19; column 197, lines 47-54; column 242, lines 47-67).
As per claim 6, MIN discloses the training apparatus according to claim 1, wherein the distribution is a normal distribution, and the processing circuitry is further configured to calculate the importance based on a probability density function of the normal distribution (one or more threshold values of one or more quantitative phenotyping measures or variables can be tied to a percentage such as normal distribution of risk of CAD among a wider population, such as for example the average, 75th percentile, 90th percentile, and, the percentage and normal distribution of CAD risk can be for asymptomatic and symptomatic population at large and for an age or gender group of the subject and any other feature group determined by another clinical factor such as the prior discussed scores and index’s; column 283, line 55 – column 284, line 5).
As per claim 8, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to cause a correlation chart expressing feature vectors by different components to be displayed (as seen in figures 28a and 28b these are charts showing medical images and comprising data such as feature vectors of the image features being observed and displayed; fig 27a-28C; column 60, lines 13-67).
As per claim 9, MIN discloses the training apparatus according to claim 8, wherein the processing circuitry is further configured to cause the correlation chart and an item of training data corresponding to a coordinate point selected on the correlation chart to be displayed (as seen in the plurality of example figures for figure 7 the computing system is adapted to on the displayed chart of the imaged anatomy point out specific points of interest on the chart relating to important image features for tracking disease progression; figs 7AD, 7AE, 7AF, and 7AG; column 11, lines 18-28; column 73, line 32-column 74, line 49; column 76, lines 4-45; column 328, line 63-coumn 329, line 24).
As per claim 11, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to: acquire calculation resource information (the computing system is adapted to collect information relating to score/index calculation this includes measurable data features and values within a multi variable equation; column 183, line 8-column 184, line 60; column 294, lines 36-67); and adjust the number of items of training data based on the calculation resource information (based on the resulting index score results the training items are adjusted and changed based on observed measurable image features; column 183, line 8-column 184, line 60; column 294, lines 36-67).
As per claim 12, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured to change a method of calculating the importance according to a progression in training of the learning model (the computing system provides a plurality of importance values/scores/ indexes including the ischemia index and the CASC score which have different calculation methods and both factor into characteristic importance; column 183, line 8-column 184, line 60; column 203, lines 5-41; column 206, lines 42-67; column 305, lines 8-19).
As per claim 14, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured, if each of the plurality of items of incidental data is a quantitative variable, to calculate the importance based on a statistical value of each of the plurality of items of incidental data (the computing system is adapted to compute multiple variable equations and includes data which is a quantitative variable which includes the measured values such as distance; figs 12A, 12j; column 34, lines 29-61; column 183, line 8-column 184, line 60; column 294, lines 36-67).
As per claim 15, MIN discloses the training apparatus according to claim 1, wherein the processing circuitry is further configured, if each of the plurality of items of incidental data is a qualitative variable, to calculate the importance based on a percentage made up by a category to which each of the plurality of items of incidental data belongs, of a total number of categories (the computing system is adapted to compute multiple variable equations and includes data which is a qualitative variable which includes the unknown variables such as importance or CASC or the ischemia index; figs 12A, 12j; column 34, lines 29-61; column 183, line 8-column 184, line 60; column 294, lines 36-67).
As per claim 16, MIN discloses the training apparatus according to claim 1, wherein the unsupervised learning is contrastive learning, and the processing circuitry is further configured to generate the plurality of items of training data in such a manner that a plurality of items of partial data forming each of the plurality of items of subject data do not overlap one another (the training of the heart disease tracking model is done in a way that the optimized feature and tracked features do not overlap; column 75, lines 1-67; column 92, lines 26-64).
As per claim 17, MIN discloses the training apparatus according to claim 1, wherein each of the plurality of items of subject data is an image obtained by photographing a cross section of a product, and each of the plurality of items of incidental data is a value representing a characteristic of the product (a cross section of the medical image is obtained and the image features are observed and the images features are assigned characteristic values to help associate characteristics to the calculations made relating to the indexes and the scores; column 126, lines 1-31).
As per claim 18, MIN discloses a training method (a computing system comprising a method of teaching/training model to train a machine learning model to identify plaque in cardiovascular images and determine heart disease severity; title; abstract; figs 1-4), comprising: acquiring a plurality of items of subject data and a plurality of items of incidental data corresponding to the plurality of items of subject data (the computing system comprises a computing processor component to execute data, programs and instructions stored in the computing systems memory component which of the stored information would include a plurality of items of subject medical data such as medical image scans and incidental patient data; title; abstract; figs 1-4, 16; column 5, lines 20-62; column 6, lines 1-46); calculating an importance of each of the plurality of items of subject data based on a distribution of the plurality of items of incidental data (the computing system is adapted to calculate severity of plaque buildup in the heart in relation to heart diseases characteristics in order to track progression of the disease as good or bad related to observed characteristic feature changes/progression over time in the subject by observing a plurality of medical images captured over time and to use them as training inputs in a historical database/databank and learn characteristics that may signal high rate of heart disease; column 24, lines 7-12; column 33, lines 5-36; column 38, line 22-column 39, line 8; column 192, line 22-column 193, line 19); determining, for each of the plurality of items of subject data, a number of items of training data according to the importance, and generating a plurality of items of training data corresponding to the determined number of items of training data (determine data to be used as training data related to the heart diseases being tracked in the patient/subject and to use a plurality of data point collected over time from the subject and stored in a database; column 191, lines 8-64; column 192, lines 2-51; column 240, lines 54-67); and iteratively training a learning model on a plurality of items of training data for each of the plurality of items of subject data by unsupervised learning (the disease progression tracking model is trained iteratively as new patient cases are added to the database keeping the model up to date with each case observed; column 24, lines 7-12; column 83, lines 14-34; column 92, lines 4-63; column 247, lines 7-33).
As per claim 19, MIN discloses a non-transitory computer-readable storage medium storing a program for causing a computer to execute processing comprising (a computing system comprising a method of teaching/training model to train a machine learning model to identify plaque in cardiovascular images and determine heart disease severity the computing system comprises a computing processor component to execute data, programs and instructions stored in the computing systems memory component which of the stored information would include a plurality of items of subject medical data such as medical image scans and incidental patient data; title; abstract; figs 1-4, 16; column 5, lines 20-62; column 6, lines 1-46)): acquiring a plurality of items of subject data and a plurality of items of incidental data corresponding to the plurality of items of subject data (the computing system comprises a computing processor component to execute data, programs and instructions stored in the computing systems memory component which of the stored information would include a plurality of items of subject medical data such as medical image scans and incidental patient data; title; abstract; figs 1-4, 16; column 5, lines 20-62; column 6, lines 1-46); calculating an importance of each of the plurality of items of subject data based on a distribution of the plurality of items of incidental data (the computing system is adapted to calculate severity of plaque buildup in the heart in relation to heart diseases characteristics in order to track progression of the disease as good or bad related to observed characteristic feature changes/progression over time in the subject by observing a plurality of medical images captured over time and to use them as training inputs in a historical database/databank and learn characteristics that may signal high rate of heart disease; column 24, lines 7-12; column 33, lines 5-36; column 38, line 22-column 39, line 8; column 192, line 22-column 193, line 19); determining, for each of the plurality of items of subject data, a number of items of training data according to the importance, and generating a plurality of items of training data corresponding to the determined number of items of training data (determine data to be used as training data related to the heart diseases being tracked in the patient/subject and to use a plurality of data point collected over time from the subject and stored in a database; column 191, lines 8-64; column 192, lines 2-51; column 240, lines 54-67); and iteratively training a learning model on a plurality of items of training data for each of the plurality of items of subject data by unsupervised learning (the disease progression tracking model is trained iteratively as new patient cases are added to the database keeping the model up to date with each case observed; column 24, lines 7-12; column 83, lines 14-34; column 92, lines 4-63; column 247, lines 7-33).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claim 7 is rejected under 35 § U.S.C. 103 as being obvious over US 11,861,833 B2 to MIN et al. (hereinafter “MIN”) in view of US 2025/01311563 A1 to BA et al. (hereinafter “BA”).
As per claim 7, MIN discloses the training apparatus according to claim 1. MIN fails to disclose wherein the processing circuitry is further configured to calculate a loss using a technique which yields a smaller loss as an error between a first feature vector and a second feature vector obtained from different items of subject data included in the plurality of items of subject data increases.
BA discloses wherein the processing circuitry is further configured to calculate a loss using a technique which yields a smaller loss as an error between a first feature vector and a second feature vector obtained from different items of subject data included in the plurality of items of subject data increases (the computing system is adapted to use a loss/cost function in order to minimize performance metrics such as error between the tracked parameters and includes feature vectors X and Y obtained from the training data; paragraphs [0010-0011], [0155-0156]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify MIN to have a loss using a technique which yields a smaller loss as an error of BA reference. The Suggestion/motivation for doing so would have been to provide the ability to rank correlation coefficients and characteristics using a Bland-Altman method that gives reliable predictable ranking results related to which characteristics/features are the most influential/important as suggested by paragraph [0156] of BA. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BA with MIN to obtain the invention as specified in claim 7.
Claims 10 and 13 are rejected under 35 § U.S.C. 103 as being obvious over US 11,861,833 B2 to MIN et al. (hereinafter “MIN”) in view of US 2021/0166381 A1 to YIP et al. (hereinafter “YIP”).
As per claim 10, MIN discloses the training apparatus according to claim 8. MIN fails to disclose wherein the processing circuitry is further configured to cause the correlation chart and a plurality of items of training data corresponding to a cluster including a coordinate point selected on the correlation chart to be displayed.
YIP discloses wherein the processing circuitry is further configured to cause the correlation chart and a plurality of items of training data corresponding to a cluster including a coordinate point selected on the correlation chart to be displayed (a digital reconstructed image of the subjects medical image is used as a chart to annotate medical information onto it related to disease progression and treatment and includes a centroid used to determine a region of interest of key feature points and can further find coordinate point values of key features within the centroid acting as the point cluster; paragraphs [0043]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify MIN to have a cluster including a coordinate point selected on the correlation chart to be displayed of YIP reference. The Suggestion/motivation for doing so would have been to provide a centroid shape to act as the clustering region for the feature points as suggested in paragraph [0043] of YIP. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine YIP with MIN to obtain the invention as specified in claim 10.
As per claim 13, MIN discloses the training apparatus according to claim 12. MIN fails to disclose wherein the processing circuitry is further configured to calculate the importance in such a manner that a variation in the importance among the plurality of items of subject data decreases as the progression in training advances.
YIP discloses wherein the processing circuitry is further configured to calculate the importance in such a manner that a variation in the importance among the plurality of items of subject data decreases as the progression in training advances (the training set images are converted into grayscale masks for annotation where different values 0-255 in the mask image represent different classes, further each histopathology image can exhibit large degrees of variation in visual features, including tumor appearance, so a training set may include digital slide images that are highly dissimilar to better train the model for the variety of slides that it may analyze and as training advances may be further augmented as the variation will decrease as the model sees more variety of training/example inputs; paragraphs [0166], [0225], [0338-0343]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify MIN to have a variation in the importance among the plurality of items of subject data decreases as the progression in training advances of YIP reference. The Suggestion/motivation for doing so would have been to provide a trained model which can identify an unlimited number of tissue classes in relation to biological tissues as suggested by paragraph [0337] of YIP. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine YIP with MIN to obtain the invention as specified in claim 13.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
US 11,551,353 B2
US 10,646,156 B1
US 2021/0313043 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/Devin Dhooge/
USPTO Patent Examiner
Art Unit 2677
/Jonathan S Lee/Primary Examiner, Art Unit 2677
Prosecution Insights