DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The drawing objections have been withdrawn in light of the amended drawings submitted by the Applicant.
The interpretation under 35 U.S.C. 112(f) has been withdrawn in light of the amended claims.
The rejections under 35 U.S.C. 112(b) and 112(d) have been withdrawn in light of the amended claims. However, in light of the amended claim 12, a restriction has been introduced as further explained below.
Applicant’s arguments with respect to the rejection under 35 U.S.C. 102 on page 13-14 of Remarks for claim 11 states “Claim 11 was rejected under § 102 with the Examiner mapping Park paragraph 0046 (display of a 3D anatomical model) and paragraph 0049 (clinical information on a screen) to limitations such as "cell fate being dependent on the condition," "cell type distribution being dependent on the condition," and "entity distribution of a non-numerable entity being dependent on the condition" (Office Action pp 12-13). Applicant respectfully submits that the Examiner's mapping is not consistent with the broadest reasonable interpretation of these terms. Park does not disclose any "cell fate," "cell type distribution," or "entity distribution of a non-numerable entity." Rather, the primary reference concerns macroscopic anatomical structures (e.g., organs) imaged by CT/MRI for disease prediction (Park 0023, 0046). Equating a person's anatomy displayed on a screen to "cell fate" or to a "distribution of a non-numerable entity" is unreasonable. Beyond this, claim 11 depends from claim 9 (which itself depends from claim 1) and therefore inherits the end-to-end training distinction set forth above. Accordingly, withdrawal of the § 102(a)(2) rejection of claim 11 is respectfully requested”. The arguments have been fully considered and persuasive. The rejection of claim 11 has been withdrawn.
Applicant has amended claim 1 to state “wherein the first and second machine-learning models are trained together in an end-to-end manner by backpropagating the loss function through the second machine-learning model into the first machine-learning model”. Applicant’s arguments with respect to claim(s) 1-3, 5-10, 13-14, 17, 19-20 under 35 U.S.C. 102 and 103 have been considered but are moot view of the new grounds of rejection (detailed in the rejections below) necessitated by Applicant’s amendment to the claim(s).
The Applicant on Page 13 of Remarks states “Moreover, the secondary references do not cure Park's deficiencies. Pardasani, cited against claim 5, merely describes the use of pre-trained AI models in a different context (fetal ultrasound). Likewise, Lee, cited against claim 10, is directed to a virtual-secretary voice-input architecture for application control on a smartphone and does not relate to image analysis or hypothesis-driven model training. Nothing in either reference remedies Park's failure to disclose end-to-end joint training”.
The examiner would like to clarify that the modification for Pardasani is simply extracting the concept of a pre-trained model and modifying Park’s model to be pre-trained. The argument against Lee is moot in light of the amended claim and updated rejection as introduced below.
Restriction/Election
Newly submitted claim 12 is directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: The claims to the different species recite the mutually exclusive characteristics of such species. A few examples, which are illustrative and not an exhaustive list, are as follows:
Inputting an output of the image analysis workflow into a second model in claim 1, and processing the set of images using the image analysis workflow in claim 12,
Outputting a prediction of a hypothesis generated from the second model in claim 1, and outputting the image analysis workflow in claim 12, highlighting a different process entirely in both claims.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 12, 15-16, and 18 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
*Examiner’s note: The examiner initially introduced a 35 U.S.C. 112(d) rejection for claim 12 (prior to the amendments) because the claim was essentially restating what was already claimed in claim 1 (i.e. using a model to generate parameters for parameterizing an image analysis workflow, something that was already claimed in claim 1). The examiner recommends (but does not require), in order to make claim 12 a proper dependent claim, to claim something that would further expand on the details of claim 1, or an extra step in addition to the 2 models disclosed in claim 1 (similar to claim 5, where this dependent claim is an expansion of claim 1).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-3, 6-9, 13, 17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Park (US 20240127950 A1) in view of Cai (US 20230005165 A1).
Regarding claim 1, Park discloses a method for adjusting a first and a second machine-learning model (Park, paragraph [0053], "Referring to FIG. 9, the disease prediction apparatus 100 may include a receiving unit 900, a quantitative analyzing unit 910, a predicting unit 920, a first artificial intelligence model 930, and a second artificial intelligence model 940"), the method comprising:
inputting a set of images representing a biological process into the first machine-learning model, the first machine-learning model being trained to perform an image analysis workflow or to generate parameters for parametrizing an image analysis workflow (Park, paragraph [0033], "For example, the disease prediction apparatus 100 may input the medical image 310 of the learning data to the first artificial intelligence model 300 to obtain the anatomical structure 320 and train the first artificial intelligence model 300 to reduce a value of a loss function indicating a difference between the anatomical structure 320 and the separation result of the learning data"),
inputting an output of the image analysis workflow into the second machine-learning model, the second machine-learning model being trained to output a prediction of a hypothesis being evaluated using the biological process (Park, paragraph [0037], "Referring to FIG. 4, a second artificial intelligence model 400 may be an artificial neural network model to output a disease prediction result 430 in response to an input of quantitative data 410 of an anatomical structure and clinical information 420"),
calculating a loss function based on a difference between the prediction of the hypothesis being evaluated using the biological process and an actual hypothesis being evaluated using the biological process (Park, paragraph [0038], "For example, the disease prediction apparatus 100 may train the second artificial intelligence model 400 based on a loss information indicating a difference between information, of the learning data, indicating whether the disease breaks out and a disease prediction result 430 obtained by inputting the quantitative data 410 and the clinical information 420 of the learning data to the second artificial intelligence model 400").
While Park discloses adjusting the first (Park, paragraph [0033], “For example, the disease prediction apparatus 100 may input the medical image 310 of the learning data to the first artificial intelligence model 300 to obtain the anatomical structure 320 and train the first artificial intelligence model 300 to reduce a value of a loss function indicating a difference between the anatomical structure 320 and the separation result of the learning data “) and second machine-learning model based on the loss function (Park, paragraph [0038], “For example, the disease prediction apparatus 100 may train the second artificial intelligence model 400 based on a loss information indicating a difference between information, of the learning data, indicating whether the disease breaks out and a disease prediction result 430 obtained by inputting the quantitative data 410 and the clinical information 420 of the learning data to the second artificial intelligence model 400”), they do not do so “wherein the first and second machine-learning models are trained together in an end-to-end manner by backpropagating the loss function through the second machine-learning model into the first machine-learning model”.
However, Cai teaches wherein the first and second machine-learning models are trained together in an end-to-end manner by backpropagating the loss function through the second machine-learning model into the first machine-learning model (Cai, paragraph [0094], “The system may also backpropagate a segmentation loss through the depth model, via the depth-to-segmentation model”, using the concept of backpropagating a loss function from one model to another).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to backpropagate Park’s loss function to their first model, as taught by Cai.
The suggestion/motivation for doing so would have been to allow for more detailed training and further segmentation of the region of interest.
Further, one skilled in the art could have combined the elements as described above by known methods 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 Park in view of Cai to obtain the invention as specified in claim 1.
Regarding claim 2, Park in view of Cai discloses the method according to claim 1, wherein the first and/or second machine-learning model are adjusted until the prediction of the hypothesis matches the actual hypothesis according to a matching criterion (Park, paragraph [0038], "For example, the disease prediction apparatus 100 may train the second artificial intelligence model 400 based on a loss information indicating a difference between information, of the learning data, indicating whether the disease breaks out and a disease prediction result 430 obtained by inputting the quantitative data 410 and the clinical information 420 of the learning data to the second artificial intelligence model 400"*).
*as additionally supported by Wikipedia, neural networks are trained to minimize the difference between their predicted and actual output. Thus, Park’s neural network is adjusted until it matches its disease information with its quantitative/clinical information.
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Regarding claim 3, Park in view of Cai discloses the method according to claim 1, wherein the method is performed over a plurality of iterations using a plurality of sets of images as training input images (Park, paragraph [0033], "The disease prediction apparatus 100 may train the first artificial intelligence model 300 with supervised learning by using learning data including a dataset of the medical image 310 and a separation result (i.e., ground truth)") and a plurality of corresponding actual hypotheses for comparison with the hypotheses predicted by the second machine-learning model to train the first and/or second machine-learning model (Park, paragraph [0038], "The disease prediction apparatus 100 may train the second artificial intelligence model 400 by using learning data including a dataset of the quantitative data 410 of the anatomical structure, the clinical information 420, and information indicating whether a disease breaks out. For example, the disease prediction apparatus 100 may train the second artificial intelligence model 400 based on a loss information indicating a difference between information, of the learning data, indicating whether the disease breaks out and a disease prediction result 430 obtained by inputting the quantitative data 410 and the clinical information 420 of the learning data to the second artificial intelligence model 400").
Regarding claim 6, Park in view of Cai discloses the method according to claim 1, wherein the first machine-learning model is trained to generate parameters for parametrizing the image analysis workflow, the method comprising processing the set of images using the image analysis workflow, the image analysis workflow being parametrized based on an output of the first machine-learning model (Park, paragraph [0034], "In an embodiment, the first artificial intelligence model 300 may a model that separates predefined at least one anatomical structure. For example, when a liver cancer occurrence possibility is predicted, the first artificial intelligence model 300 may be a model that separates a 3D liver region from a chest CT image. In another example, the first artificial intelligence model 310 may be a model that separates a plurality of anatomical structures such as a liver region, a spleen region, a muscle region, etc., from a chest CT image.", model separates regions based on predicted cancers, thus generating parameters for a segmentation based on an output).
Regarding claim 7, Park in view of Cai discloses the method according to claim 6, wherein the first machine-learning model is trained to select at least one of a use of one or more image processing steps, one or more numerical parameters of one or more image processing steps, and one or more categorical parameters of one or more image processing steps for the image analysis workflow (Park, paragraph [0034], “In an embodiment, the first artificial intelligence model 300 may a model that separates predefined at least one anatomical structure. For example, when a liver cancer occurrence possibility is predicted, the first artificial intelligence model 300 may be a model that separates a 3D liver region from a chest CT image. In another example, the first artificial intelligence model 310 may be a model that separates a plurality of anatomical structures such as a liver region, a spleen region, a muscle region, etc., from a chest CT image.”, image processing step is the segmentation step, numerical parameter is separating at least one structure, and categorical is separating based on the regions).
Regarding claim 8, Park in view of Cai discloses the method according to claim 1, wherein the set of images or a processed version of the set of images is used as further input to the second machine-learning model (Park, paragraph [0037], "Referring to FIG. 4, a second artificial intelligence model 400 may be an artificial neural network model to output a disease prediction result 430 in response to an input of quantitative data 410 of an anatomical structure and clinical information 420.", quantitative data is a processed version of the inputted medical images).
Regarding claim 9, Park in view of Cai discloses the method according to claim 1, wherein the second machine-learning model is trained to output a formal representation of the prediction of the hypothesis, with the loss function being calculated based on a comparison between the formal representation of the prediction of the hypothesis and a formal representation of the actual hypothesis (Park, paragraph [0038], "For example, the disease prediction apparatus 100 may train the second artificial intelligence model 400 based on a loss information indicating a difference between information, of the learning data, indicating whether the disease breaks out and a disease prediction result 430 obtained by inputting the quantitative data 410 and the clinical information 420 of the learning data to the second artificial intelligence model 400").
Regarding claim 13, Park in view of Cai discloses a system comprising one or more processors and one or more storage devices (Park, paragraph [0057], "Examples of the computer-readable recording medium may include read-only memory (ROM), random access memory (RAM), compact-disc ROM (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc."), wherein the system is configured to perform the method according to claim 1 (Park, paragraph [0057], "The computer-readable recording medium may be distributed over computer systems connected through a network to store and execute a computer-readable code in a distributed manner").
Regarding claim 17, Park in view of Cai discloses a non-transitory, computer-readable medium comprising a program code that, when the program code is executed on a processor, a computer, or a programmable hardware component, causes the processor, computer, or programmable hardware component to perform the method of claim 1 (Park, paragraph [0057], "The computer-readable recording medium may include all types of recording devices in which data that is readable by a computer system is stored. Examples of the computer-readable recording medium may include read-only memory (ROM), random access memory (RAM), compact-disc ROM (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc. The computer-readable recording medium may be distributed over computer systems connected through a network to store and execute a computer-readable code in a distributed manner").
Regarding claim 19, Park discloses a method for adjusting a first and a second machine-learning model (Park, paragraph [0053], "Referring to FIG. 9, the disease prediction apparatus 100 may include a receiving unit 900, a quantitative analyzing unit 910, a predicting unit 920, a first artificial intelligence model 930, and a second artificial intelligence model 940"), the method comprising:
inputting a set of images representing a biological process into the first machine-
learning model, the first machine-learning model being trained to generate parameters
for parametrizing an image analysis workflow (Park, paragraph [0034], “In an embodiment, the first artificial intelligence model 300 may a model that separates predefined at least one anatomical structure. For example, when a liver cancer occurrence possibility is predicted, the first artificial intelligence model 300 may be a model that separates a 3D liver region from a chest CT image. In another example, the first artificial intelligence model 310 may be a model that separates a plurality of anatomical structures such as a liver region, a spleen region, a muscle region, etc., from a chest CT image”, parameters by definition are boundaries/limits of a system. Segmentation in this case is a parameter)
processing the set of images using the image analysis workflow, the image analysis workflow being parametrized based on an output of the first machine-learning model (Park, paragraph [0033], "For example, the disease prediction apparatus 100 may input the medical image 310 of the learning data to the first artificial intelligence model 300 to obtain the anatomical structure 320 and train the first artificial intelligence model 300 to reduce a value of a loss function indicating a difference between the anatomical structure 320 and the separation result of the learning data"),
inputting an output of the image analysis workflow into the second machine-learning
model, the second machine-learning model being trained to output a prediction of a
hypothesis being evaluated using the biological process (Park, paragraph [0037], "Referring to FIG. 4, a second artificial intelligence model 400 may be an artificial neural network model to output a disease prediction result 430 in response to an input of quantitative data 410 of an anatomical structure and clinical information 420"),
calculating a loss function based on a difference between the prediction of the
hypothesis being evaluated using the biological process and an actual hypothesis
being evaluated using the biological process (Park, paragraph [0038], "For example, the disease prediction apparatus 100 may train the second artificial intelligence model 400 based on a loss information indicating a difference between information, of the learning data, indicating whether the disease breaks out and a disease prediction result 430 obtained by inputting the quantitative data 410 and the clinical information 420 of the learning data to the second artificial intelligence model 400").
Although Park teaches adjusting the second machine-learning model based on the loss function (Park, paragraph [0038], "For example, the disease prediction apparatus 100 may train the second artificial intelligence model 400 based on a loss information indicating a difference between information, of the learning data, indicating whether the disease breaks out and a disease prediction result 430 obtained by inputting the quantitative data 410 and the clinical information 420 of the learning data to the second artificial intelligence model 400"), Park does not teach adjusting the first machine-learning model based on the loss function.
However, Cai teaches and adjusting the first and second machine-learning model based on the loss function (Cai, paragraph [0094], “The system may also backpropagate a segmentation loss through the depth model, via the depth-to-segmentation model”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to backpropagate Park’s loss function to the first model, as taught by Cai.
The suggestion/motivation for doing so would have been to allow for more detailed training and further segmentation of the region of interest.
Further, one skilled in the art could have combined the elements as described above by known methods 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 Park in view of Cai to obtain the invention as specified in claim 19.
Regarding claim 20, Park in view of Cai discloses the method according to claim 19, wherein the first machine-learning model is trained to select at least one of: a use of one or more image processing steps, one or more numerical parameters of one or more image processing steps, and one or more categorical parameters of one or more image processing steps for the image analysis workflow (Park, paragraph [0034], “In an embodiment, the first artificial intelligence model 300 may a model that separates predefined at least one anatomical structure. For example, when a liver cancer occurrence possibility is predicted, the first artificial intelligence model 300 may be a model that separates a 3D liver region from a chest CT image. In another example, the first artificial intelligence model 310 may be a model that separates a plurality of anatomical structures such as a liver region, a spleen region, a muscle region, etc., from a chest CT image.”, image processing step is the segmentation step, numerical parameter is separating at least one structure, and categorical is separating based on the regions).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Park (US 20240127950 A1) in view of Cai (US 20230005165 A1) and in further view of Pardasani (US 20230087363 A1).
Regarding claim 5, Park in view of Cai discloses the method according to claim 1.
Park in view of Cai does not teach “wherein the first and second machine-learning models are pre-trained machine-learning models, which are adjusted in the field”.
However, Pardasani teaches wherein the first and second machine-learning models are pre-trained machine-learning models, which are adjusted in the field (Pardasani, paragraph [0088], "In an example embodiment, each of the first pre-trained model and the second pre-trained model comprises artificial intelligence (AI) based model.").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to pre-train Park’s (in view of Cai) AI models, as taught by Pardasani.
The suggestion/motivation for doing so would have been to save time on training and reduce computational costs.
Further, one skilled in the art could have combined the elements as described above by known methods 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 Park in view of Cai and in further view of Pardasani to obtain the invention as specified in claim 5.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Park (US 20200411002 A1) in view of Cai (US 20230005165 A1) and in further view of Lee (US 20200411002 A1).
Regarding claim 10, Park in view of Cai discloses the method of claim 9.
Park in view of Cai does not teach “wherein the method comprises processing user input to generate the formal representation of the actual hypothesis, wherein the user input comprises one of spoken text and unstructured written text, the method comprising processing the user input using natural language processing, or wherein the user input comprises structured input”.
However, Lee teaches wherein the method comprises processing user input to generate the formal representation of the actual hypothesis, wherein the user input spoken text or unstructured written text (Lee, paragraph [0058], "As an example, if the user's voice is input, a voice recognition model among the artificial intelligence models of the virtual secretary service may convert the user's voice into text"), the method comprising processing the user input using a natural language processing model, or wherein the user input comprises structured input (Lee, paragraph [0058], "In addition, if the user's voice is converted into the text, at least one domain related to the user's voice may be identified through the domain classifier model included in the natural language understanding model among the artificial intelligence models of the virtual secretary service").
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to implement a voice recognition model in addition to Park’s (in view of Cai) models, that takes in text as user input, as taught by Lee.
The suggestion/motivation for doing so would have been to enhance accessibility and reduce storage use when compared to images.
Further, one skilled in the art could have combined the elements as described above by known methods 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 Park in view of in view of Cai and in further view of Lee to obtain the invention as specified in claim 10.
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Park (US 20200411002 A1) in view of Cai (US 20230005165 A1) and in further view of Teich (US 20080158664 A1).
Regarding claim 14, Park in view of Cai discloses an imaging system comprising the system according to claim 13.
Park in view Cai does not teach “a scientific imaging device, with the scientific imaging device comprising a microscope being configured to generate the set of images”.
However, Teich teaches a scientific imaging device, with the scientific imaging device comprising a microscope being configured to generate the set of images (Teich, paragraph [0001], “The system is provided with an image capturing device, which is mounted above the viewing aperture and which comprises a microscope with an objective lens and an objective holder.”).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to capture Park’s (in view of Cai) images with a microscope, as taught by Teich.
The suggestion/motivation for doing so would have been to identify smaller diseases that may not be visible with a general camera.
Further, one skilled in the art could have combined the elements as described above by known methods 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 Park in view of Cai and in further view of Teich to obtain the invention as specified in claim 14.
Allowable Subject Matter
Claim 11 is objected to for being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WAYNE ZHANG whose telephone number is (571) 272-0245. The examiner can normally be reached Monday-Friday 10:00-6:00 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, Ms. Sumati Lefkowitz can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WAYNE ZHANG/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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