DETAILED ACTION
Priority
Acknowledgment is made of applicant’s claim for priority. The certified copy has been filed in parent Application No. 63/602,004, filed on November 22, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 24, 2025 is being considered by the examiner.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1
The claims recite subject matter within a statutory category as a process, machine, and/or article of manufacture. However, it will be shown in the following steps, that claims 1-20 are nonetheless unpatentable under 35 U.S.C. 101.
Step 2A Prong One
Claim 11 states:
A computer-implemented method comprising:
generating, by a device comprising at least one processor, a multimodal model, wherein the multimodal model is generated based on at least one of a medical condition of a patient, historical data pertaining to the medical condition, imaging data pertaining to the medical condition, or medical knowledge pertaining to the medical condition;
determining, by the device, a potential treatment to address the medical condition of the patient, wherein the treatment is an output of the multimodal model;
determining, by the device, a probability of the potential treatment successfully addressing the medical condition of the patient, wherein the probability is determined based on application of the potential treatment to a probability model, wherein the probability model comprises a collection of treatments in conjunction with success of application of a respective treatment in the collection of treatments to a respective medical condition, wherein the probability is assigned to a risk score for the potential treatment of the patient’s medical condition; and
presenting, by the device, a recommendation for treatment of the patient’s medical condition, wherein the recommendation comprises the potential treatment in conjunction with the risk score.
The broadest reasonable interpretation of these steps includes “organizing human activity” and/or “mental processes” because each bolded component can practically be performed by the human mind or with pen and paper. Other than reciting generic computer terms like “computer”, “device”, “processor”, or “multimodal model”, nothing in the claims precludes the bold-font portions from practically being performed in the mind. For example, but for the “device” language, “presenting … a recommendation for treatment of the patient’s medical condition, wherein the recommendation comprises the potential treatment in conjunction with the risk score” in the context of this claim encompasses a mental process of a healthcare professional conveying a treatment plan to a prospective patient. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” or “Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Determining… a potential treatment to address the medical condition of the patient
determining, … a probability of the potential treatment successfully addressing the medical condition of the patient, wherein the probability is determined based on application of the potential treatment to a probability model,
Presenting … a recommendation for treatment of the patient’s medical condition, wherein the recommendation comprises the potential treatment in conjunction with the risk score
as drafted, could lay out the management of human behavior by a physician performing a differential diagnosis on a patient. Under the broadest reasonable interpretation, these steps include multiple abstract ideas that will be identified as a single abstract idea moving forward.
Independent claims 1 and 17 cover similar steps of generating a multimodal model, determining a potential patient treatment protocol, determine a probability of this protocol being successful, and presenting a recommendation. These claims fall under the same category of an abstract idea and follows the same rationale as claim 11.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 3, reciting particular aspects of how “the operations further comprising utilizing natural language programming to facilitate interaction with the presented recommended treatment and risk score” may be performed in the mind but for recitation of generic computer components).
Dependent claims 2, 3, 4, 8, 15, and 20 add additional elements to their parent claims which will be further inspected in the following steps for a practical application to their abstract idea.
Step 2A Prong Two
This judicial exception of “Mental Processes” or “Organizing Human Activity” is not integrated into a practical application. Independent claim 11’s method recites additional elements such as “computer”, “device”, “processor”, or “multimodal model”. In addition to the generic components and additional elements listed above, independent claims 1 and 20 ’s system and computer program product also includes a “memory”, “processor”, and “non-transitory computer readable medium”. The multimodal model will be analyzed further for conventionality in the following steps. The other additional elements will be treated as generic computer components. In particular, these additional elements do not integrate the abstract idea into a practical application because the additional elements:
amount to mere instructions to apply an exception (such as recitation of “A computer-implemented method comprising: generating, by a device comprising at least one processor, a multimodal model, wherein the multimodal model is generated based on at least one of a medical condition of a patient, historical data pertaining to the medical condition, imaging data pertaining to the medical condition, or medical knowledge pertaining to the medical condition”, “wherein the treatment is an output of the multimodal model” and “wherein the probability is determined based on application of the potential treatment to a probability model” amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [00164], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of “the probability model comprises a collection of treatments in conjunction with success of application of a respective treatment in the collection of treatments to a respective medical condition, wherein the probability is assigned to a risk score for the potential treatment of the patient’s medical condition” amounts to insignificant application, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For instance, dependent claim 3 reference additional elements of an interactive display alongside a mouse and cursor to their parent claims. Additionally, claim 2 “receiving a confirmation to implement the recommended treatment” and claim 2 “and updating the patient data in accordance with the recommended treatment being applied to treat the patient’s medical condition”, add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, claim 8 “wherein the recommended treatment is a first recommended treatment and the risk score is a first risk score, wherein the operations further comprise: generating, based on application of the patient data to the multimodal model, a second recommended treatment, wherein the second recommended treatment is based on at least one node in the sequence of nodes having sufficient similarity to the patient data;” and claim 8 “and presenting the ranking of the first recommended treatment and first risk score, and the second recommended treatment and the second risk score”, amounts to necessary data outputting, see MPEP 2106.05(g)), recitation of claim 3 “utilizing natural language programming to facilitate interaction with the presented recommended treatment and risk score” and claim 4 “wherein the interaction is via at least one of a mouse and cursor, interactive display, or speech-based interaction” and claim 8 “determining a second risk score for the second recommended treatment, wherein the second risk score presents a measure of a successful treatment outcome for the second recommended treatment regarding the patient’s medical condition, wherein the second risk score is determined based on applying the second recommended treatment to a probability model;” and claim 8 “ranking the first recommended treatment and the second recommended treatment based on the first risk score and the second risk score;” and claim 15 “the multimodal model and the probability model combine to form a prediction model, the prediction model comprises at least one of a visual language model, a large language model, graph neural network, or a Bayesian network.” and claim 20 “wherein the multimodal model and the probability model combine to form a prediction model, the prediction model comprises at least one of a visual language model, a large language model, graph neural network, or a Bayesian network.” amounts to insignificant application). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The remaining dependent claims 5-7, 9-14, 16, and 18-19 do not recite additional elements or activity but further narrow or define the abstract idea embodied in the claims and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As previously noted, the claim recites an additional element of a multimodal model. Tetsuro et al. (JP2000250677) demonstrates “the processing of the conventional multimodal interface will be described below” that multimodal modeling was conventional before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
To elaborate:
“wherein the probability model comprises a collection of treatments in conjunction with success of application of a respective treatment in the collection of treatments to a respective medical condition, wherein the probability is assigned to a risk score for the potential treatment of the patient’s medical condition”, is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv)
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. These additional limitations amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As previously noted, the claim recites an additional element of a mouse and cursor. Mei et al. (US20020118167) demonstrates in paragraph [0007] “Conventional mice for cursor moving/placing, object picking, editing, drawing, painting, menu selecting, window opening and closing, etc. are not used in notebook computer systems” that a mouse and cursor were conventional long before the priority data of the claimed invention. As such, this additional element, individually and in combination with the prior additional element, does not amount to significantly more.
To elaborate:
claim 2 “receiving a confirmation to implement the recommended treatment”, is equivalently, receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i);
claim 2 “and updating the patient data in accordance with the recommended treatment being applied to treat the patient’s medical condition”, is equivalently, electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii)
claim 8 “wherein the recommended treatment is a first recommended treatment and the risk score is a first risk score, wherein the operations further comprise: generating, based on application of the patient data to the multimodal model, a second recommended treatment, wherein the second recommended treatment is based on at least one node in the sequence of nodes having sufficient similarity to the patient data;” , is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv)
claim 8 “and presenting the ranking of the first recommended treatment and first risk score, and the second recommended treatment and the second risk score”, is equivalently, Presenting offers and gathering statistics, OIP Techs., MPEP 2106.05(d)(II)(iv)
claim 3 “utilizing natural language programming to facilitate interaction with the presented recommended treatment and risk score”, is equivalently, a web browser’s back and forward button functionality, Internet Patent Corp., MPEP 2106.05(d)(II)(vi))
claim 4 “wherein the interaction is via at least one of a mouse and cursor, interactive display, or speech-based interaction” , is equivalently, a web browser’s back and forward button functionality, Internet Patent Corp., MPEP 2106.05(d)(II)(vi))
claim 8 “determining a second risk score for the second recommended treatment, wherein the second risk score presents a measure of a successful treatment outcome for the second recommended treatment regarding the patient’s medical condition, wherein the second risk score is determined based on applying the second recommended treatment to a probability model;” , is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii)
claim 8 “ranking the first recommended treatment and the second recommended treatment based on the first risk score and the second risk score;” is equivalently, Arranging a hierarchy of groups, sorting information, Versata Dev. Group, Inc. v. SAP Am., Inc., MPEP 2106.05(d)(II)(ii)
claim 15 “the multimodal model and the probability model combine to form a prediction model, the prediction model comprises at least one of a visual language model, a large language model, graph neural network, or a Bayesian network.” , is equivalently, performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii);
claim 20 “wherein the multimodal model and the probability model combine to form a prediction model, the prediction model comprises at least one of a visual language model, a large language model, graph neural network, or a Bayesian network.” , is equivalently, performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii);
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 103
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 nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bui et al. (20210065859) in view Thierry et al. (EP4307315).
Regarding claim 1, Bui teaches.
A system, comprising: at least one processor; and ([0131] The user computing system 102 includes one or more processors 112 and a memory 114”)
a memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, comprising: ([0131] “memory”)
receiving pathomic information, radiomic information, and transcriptomic information; ([0048] “the systems and methods disclosed herein can allow a computing system to receive a plurality of images of a patient's skin.” comprises receiving pathomic information)
generating a multimodal model, wherein the multimodal model is generated based on combining the received pathomic information, radiomic information, and transcriptomic information; ([0089] “The medical conditions classification model may be trained and/or configured for multimodal processing. In some implementations, the medical conditions classification model can process a multimodal query and/or context data to generate the one or more predicted condition classifications… The one or more candidate medical conditions can include skin conditions, hair conditions, infections, cardiovascular conditions, dental conditions, oral conditions, podiatry conditions, genetic conditions, rheumatology conditions, diabetes side effects, blood conditions, cancer classifications, gastrointestinal conditions, trauma conditions, benign cosmetic conditions, eye conditions, and/or other medical condition” comprises receiving information to generate a multimodal model)
applying patient data to the multimodal model, wherein the patient data pertains to a medical condition of a patient; ([0048] “metadata associated with the patient can also be additionally input into the model… the additional patient metadata can include patient demographic information, medical history, and/or other information concerning the patient (e.g., the user).”)
generating, based on application of the patient data to the multimodal model, a recommended treatment, ([0048] “the machine-learned skin condition classification model can be configured to jointly process such additional patient metadata alongside the input imagery to produce the output skin condition classification”) wherein the recommended treatment is based on at least one node, in a sequence of nodes included in the multimodal model, having sufficient similarity to the patient data; ([0211] “In general, prediction layers can leverage various kinds of artificial neural networks that can understand or generate sequences of information”)
Regarding claim 1, Bui does not explicitly teach, as taught by Thierry:
determining a risk score for the recommended treatment, wherein the risk score presents a measure of a successful treatment outcome for the recommended treatment regarding the patient’s medical condition, wherein the risk score is determined based on applying the recommended treatment to a probability model; and ([0015] “a trained prediction machine learning model trained to predict patient's treatment response or treatment efficacy” where the treatment efficacy comprises presenting a measure of a successful treatment outcome for the recommended treatment in the form of a risk score)
presenting the recommended treatment and risk score for review.([0015] “and output prediction of the patient's response to the treatment or prediction of the patient's treatment efficacy defined as length of time to an event.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 2, Bui- Thierry as a combination teaches all of the limitations of claim 1. Thierry also teaches:
receiving a confirmation to implement the recommended treatment; and ([0017] “Figure 1 shows selection of features for a patient for prediction of treatment or treatment efficacy at second evaluation time (t2) (from selected features at t0 and t1) or further evaluation time (t3) (from selected features at t0, t1 and t2)”; see also [0020] "second/further evaluation (time) clinical, biological, genomic and/or radiological feature" refer to all data or features collected after treatment continuation.”
updating the patient data in accordance with the recommended treatment being applied to treat the patient’s medical condition. ([0017] “Figure 1 shows selection of features for a patient for prediction of treatment or treatment efficacy at second evaluation time (t2) (from selected features at t0 and t1) or further evaluation time (t3) (from selected features at t0, t1 and t2).”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 3, Bui- Thierry as a combination teaches all of the limitations of claim 1. Thierry also teaches
utilizing natural language programming to facilitate interaction with the presented recommended treatment and risk score ([0037] “The machine learning model may be trained within a supervised, semi-supervised or unsupervised learning framework. Within a supervised learning framework, a model learns a function to map an output result from an input data set, based on example pairs of inputs and matching outputs.” See also [0047] “the multimodal data may be obtained or generated from one or more sources… Radiological data may be obtained from Picture Archiving and Communication System (PACS)” comprises natural language programming)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 5, Bui- Thierry as a combination teaches all of the limitations of claim 1. Bui also teaches:
wherein the multimodal model comprises at least one of a visual language model, a large language model, a Bayesian network, a convolutional neural network, graph neural network, or a knowledge graph. ([0137] “Example machine-learned models can include decision tree based models, support vector machines, hidden Markov models, Bayesian networks, linear regression models, k-means clustering models, etc.”)
Regarding claim 6, Bui- Thierry as a combination teaches all of the limitations of claim 1. Thierry also teaches:
wherein the multimodal model comprises the network of nodes and a network of edges connected to a set of potential treatments, a respective node in the network of nodes represents content pertaining to the patient’s medical condition or medical knowledge regarding a medical condition, wherein the medical knowledge pertains or does not pertain to the patient’s medical condition. ([0037] “in the case of a deep learning classifier, the data input is further processed through a series of data processing layers to implicitly capture the hidden data structures, the data signatures and underlying patterns. Thanks to the use of multiple data processing layers, deep learning facilitates the generalization of automated data processing to a diversity of complex pattern detection and data analysis tasks.”; see also [0016] “calculation of at least one longitudinal feature performed after a step of imputing missing patient's features may be described as performed after a complete vector of features' values has been obtained and/or as performed before a step of features aggregation to a complete aggregated multimodal and longitudinal vector of features' values, as seen on fig. 5.” Where data processing layers and vector of features comprises a network of nodes and edges; see also [figure 5] where the system comprises clinical features used to diagnose a condition that are optionally connected to the treatment plan of a patient.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 7, Bui- Thierry as a combination teaches all of the limitations of claim 1. Thierry also teaches:
wherein the probability model comprises at least one of a visual language model, a large language model, a Bayesian network, a convolutional neural network, graph neural network, or a knowledge graph. ([0037] “Examples of machine learning models used for supervised learning include Support Vector Machines (SVM), regression analysis, linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithms, random forest, artificial neural networks (ANN) such as convolutional neural networks (CNN),”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 8, Bui- Thierry as a combination teaches all of the limitations of claim 1. Thierry also teaches:
wherein the recommended treatment is a first recommended treatment and the risk score is a first risk score, wherein the operations further comprise: ([0017] “Figure 1 shows selection of features for a patient for prediction of treatment or treatment efficacy at second evaluation time (t2) (from selected features at tO and t1) or further evaluation time (t3) (from selected features at tO, t1 and t2).”; see also [0020] “The terms "first evaluation (time) data", "first evaluation (time) feature", "first evaluation (time) clinical, biological, genomic and/or radiologicals data" or "first evaluation (time) clinical, biological, genomic and/or radiologicals feature" refer to all data or features collected after treatment initiation” where a first evaluation comprises a first recommendation treatment and a first risk score)
generating, based on application of the patient data to the multimodal model, a second recommended treatment, wherein the second recommended treatment is based on at least one node in the sequence of nodes having sufficient similarity to the patient data; ([0185] “2. b) receive separately the patient's multimodal features comprising at least two types of features selected from clinical, biological, genomic and radiological features, wherein the patient's multimodal features are not complete, wherein the patient's at least one multimodal feature is collected at least at two time points, c) aggregate the patient's multimodal features into a vector of features' values, wherein the vector of features' values is not complete,”)
determining a second risk score for the second recommended treatment, wherein the second risk score presents a measure of a successful treatment outcome for the second recommended treatment regarding the patient’s medical condition, wherein the second risk score is determined based on applying the second recommended treatment to a probability model; ([0185] “d) input the vector of features' values to the trained imputation machine learning model and output a complete vector of features' values,”)
ranking the first recommended treatment and the second recommended treatment based on the first risk score and the second risk score; and ([0185] “e) filter the features of the complete vector of features' values according to the list of informative features identifiers and obtain a predictive vector of features' values that is a subset of the complete vector of features' values consisting of filtered features' values,”)
presenting the ranking of the first recommended treatment and first risk score, and the second recommended treatment and the second risk score. ([0185] “f) input the predictive vector of features' values to the trained prediction machine learning model and output prediction of the patient's response to the treatment or prediction of the patient's treatment efficacy defined as length of time to an event.”)
Regarding claim 9, Bui- Thierry as a combination teaches all of the limitations of claim 1. Thierry also teaches:
wherein multimodal model comprises transcriptomic data pertaining to the medical condition of the patient in combination with at least one of radiology data pertaining to the medical condition of the patient, or pathology data pertaining to the medical condition of the patient. ([0185] “the second machine learning model have been trained to predict patient's treatment response or treatment efficacy using a set of features comprising at least two types of features selected from clinical, biological, genomic and radiological features of cohort of patients having the same disease and receiving the same treatment as the patient for whom the prediction is performed, wherein for each patient in the cohort at least one of the multimodal feature was collected at least at two time points and optionally at least one longitudinal feature was obtained, predicting the patient's treatment response or treatment efficacy.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 10, Bui- Thierry as a combination teaches all of the limitations of claim 9. Thierry also teaches:
wherein the transcriptomic data pertaining to the medical condition of the patient further comprises at least one of proteomic information, single-cell RNA sequencing field (scRNAseq) information, an autofluorescence image, matrix-assisted laser desorption/ionization (MALDI) information, spatial transcriptomic information, multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial gene expression, or metaboliomic information. ([0047] “Genomic data may be obtained from any system that stores genetic sequences,”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 11, Bui teaches:
A computer-implemented method comprising:
generating, by a device comprising at least one processor, ([0131] The user computing system 102 includes one or more processors 112 and a memory 114”) a multimodal model, wherein the multimodal model is generated based on at least one of a medical condition of a patient, historical data pertaining to the medical condition, imaging data pertaining to the medical condition, or medical knowledge pertaining to the medical condition; ([0089] “At 358, the computing system can process the one or more images with the medical conditions classification model to generate one or more predicted condition classifications. The one or more predicted condition classifications can be descriptive of one or more candidate medical conditions determined to be potentially depicted in the one or more images. The medical conditions classification model may be trained and/or configured for multimodal processing. In some implementations, the medical conditions classification model can process a multimodal query and/or context data to generate the one or more predicted condition classifications… The one or more candidate medical conditions can include skin conditions, hair conditions, infections, cardiovascular conditions, dental conditions, oral conditions, podiatry conditions, genetic conditions, rheumatology conditions, diabetes side effects, blood conditions, cancer classifications, gastrointestinal conditions, trauma conditions, benign cosmetic conditions, eye conditions, and/or other medical condition” comprises receiving information to generate a multimodal model)
Regarding claim 11, Bui does not explicitly teach, as taught by Thierry:
determining, by the device, a potential treatment to address the medical condition of the patient, wherein the treatment is an output of the multimodal model; ([0016] “a computer implemented method of predicting treatment response or treatment efficacy of a patient,” [0185] “2. b) receive separately the patient's multimodal features comprising at least two types of features selected from clinical, biological, genomic and radiological features, wherein the patient's multimodal features are not complete, wherein the patient's at least one multimodal feature is collected at least at two time points, c) aggregate the patient's multimodal features into a vector of features' values, wherein the vector of features' values is not complete, d) input the vector of features' values to the trained imputation machine learning model and output a complete vector of features' values, where aggregating features from at least two time points comprises generating a second recommendation e) filter the features of the complete vector of features' values according to the list of informative features identifiers and obtain a predictive vector of features' values that is a subset of the complete vector of features' values consisting of filtered features' values,”)
determining, by the device, a probability of the potential treatment successfully addressing the medical condition of the patient, wherein the probability is determined based on application of the potential treatment to a probability model, wherein the probability model comprises a collection of treatments in conjunction with success of application of a respective treatment in the collection of treatments to a respective medical condition, wherein the probability is assigned to a risk score for the potential treatment of the patient’s medical condition; and ([0015] “prediction machine learning models were trained, and the list of informative features identifiers was obtained, using a set of multimodal features comprising at least two types of features selected from clinical, biological, genomic and radiological features of cohort of patients having the same disease and receiving the same treatment as the patient for whom the prediction is performed, wherein for each patient in the cohort at least one of the multimodal feature was collected at least at two time points.”; see also [0017] “Figure 2 shows a processing system for use in a method to predict treatment response or treatment efficacy according to embodiment 2 or 5. HER - Electronic Health Record; PACS - Picture Archiving and Communication System; LIMS - Laboratory Information Management System.”)
presenting, by the device, a recommendation for treatment of the patient’s medical condition, wherein the recommendation comprises the potential treatment in conjunction with the risk score. ([0185] “f) input the predictive vector of features' values to the trained prediction machine learning model and output prediction of the patient's response to the treatment or prediction of the patient's treatment efficacy defined as length of time to an event.” And [0023] “The "first-line treatment" or "primary/initial treatment" or "induction therapy" refers to the initial, or first treatment recommended for a given disease, such as cancer. For example, first-line treatment of stage IV NSCLC may be a pembrolizumab monotherapy, a chemotherapy and pembrolizumab combination therapy, a chemotherapy doublet and any other suitable treatment.” Comprises a recommendation for a treatment based on the patient’s medical condition)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 12, Bui- Thierry as a combination teaches all of the limitations of claim 11. Bui also teaches:
wherein the multimodal model is one of a visual language model, a large language model, graph neural network, or a Bayesian network. ([0137] “Example machine-learned models can include decision tree based models, support vector machines, hidden Markov models, Bayesian networks, linear regression models, k-means clustering models, etc.”)
Regarding claim 13, Bui- Thierry as a combination teaches all of the limitations of claim 12. Thierry also teaches:
wherein the visual language model comprises transcriptomic data pertaining to the medical condition of the patient in combination with at least one of radiology data pertaining to the medical condition of the patient, or pathology data pertaining to the medical condition of the patient. ([0141] “Example data types for input(s) or output(s) include natural language text data, … genetic data or other chemical or biochemical data, image data, audio data, audiovisual data, haptic data, biometric data, medical data, financial data, statistical data, geographical data, astronomical data, historical data, sensor data generally (e.g., digital or analog values”; see also [0082] “the systems and methods disclosed herein can be leveraged for other medical condition visual searches outside of just skin condition visual search. For example, the systems and methods can be utilized for the fields of hair loss, nail conditions, infections (e.g., abscess, cellulitis, and/or HSV), cardiovascular (e.g., arterial insufficiency, vasculitis, and/or venous ulcers), dental/oral/ENT issues (e.g., aphthous ulcers and/or angular cheilitis), podiatry (e.g., corns and/or foot lesions), genetic issues (e.g., neurofibromas and/or xeroderma), rheumatology (e.g., erythema nodosum and/or dermatomyositis), diabetes risk (e.g., acanthosis nigricans), hematology, oncology (e.g., T-cell lymphoma and/or Kaposi's sarcoma), GI (e.g., pyoderma gangrenosum), trauma (e.g., burns, abrasions, and/or wounds), cosmetic (e.g., freckles and/or benign lesions), and/or eye conditions.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 14, Bui- Thierry as a combination teaches all of the limitations of claim 11. Thierry also teaches:
wherein the probability model is one of a visual language model, a large language model, or a Bayesian network. ([0037] “Examples of machine learning models used for supervised learning include Support Vector Machines (SVM), regression analysis, linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithms, random forest, artificial neural networks (ANN) such as convolutional neural networks (CNN),”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 15, Bui- Thierry as a combination teaches all of the limitations of claim 11. Thierry also teaches:
wherein the multimodal model and the probability model combine to form a prediction model, the prediction model comprises at least one of a visual language model, a large language model, graph neural network, or a Bayesian network. ([0037-0038] “The machine learning model may be trained within a supervised, semi-supervised or unsupervised learning framework. Within a supervised learning framework, a model learns a function to map an output result from an input data set, based on example pairs of inputs and matching outputs. Examples of machine learning models used for supervised learning include Support Vector Machines (SVM), regression analysis, linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithms, random forest, artificial neural networks (ANN) such as convolutional neural networks (CNN), recurrent neural networks (RNN), fully-connected neural networks, long short-term memory (LSTM) models, and others; and/or a combination thereof … Convolutional neural networks may also be combined with recurrent neural networks to produce a deep learning classifier.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 16, Bui- Thierry as a combination teaches all of the limitations of claim 11. Thierry also teaches:
wherein the risk score indicates a risk stratification of at least one of the medical condition of the patient or the recommended treatment. ([0024] “Alternatively, the patient's response to a treatment may be provided as a probability of the patient's response to the treatment. In addition, confidence ranges may be provided” where confidence ranges comprise risk stratification)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 17, Bui teaches:
A computer program product stored on a non-transitory computer-readable medium and comprising machine-executable instructions, wherein, in response to being executed, the machine-executable instructions cause a system to perform operations, comprising: ([0195] “In one embodiment, is provided a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of predicting treatment response or treatment efficacy of a patient as described herein”)
generating a multimodal model, wherein the multimodal model is generated based on at least one of a medical condition of a patient, historical data pertaining to the medical condition, imaging data pertaining to the medical condition, or medical knowledge pertaining to the medical condition; ([0089] “At 358, the computing system can process the one or more images with the medical conditions classification model to generate one or more predicted condition classifications. The one or more predicted condition classifications can be descriptive of one or more candidate medical conditions determined to be potentially depicted in the one or more images. The medical conditions classification model may be trained and/or configured for multimodal processing. In some implementations, the medical conditions classification model can process a multimodal query and/or context data to generate the one or more predicted condition classifications… The one or more candidate medical conditions can include skin conditions, hair conditions, infections, cardiovascular conditions, dental conditions, oral conditions, podiatry conditions, genetic conditions, rheumatology conditions, diabetes side effects, blood conditions, cancer classifications, gastrointestinal conditions, trauma conditions, benign cosmetic conditions, eye conditions, and/or other medical condition” comprises receiving information to generate a multimodal model)
Regarding claim 11, Bui does not explicitly teach, as taught by Thierry:
determining a potential treatment to address the medical condition of the patient, wherein the treatment is an output of the multimodal model; ([0016] “a computer implemented method of predicting treatment response or treatment efficacy of a patient,” [0185] “2. b) receive separately the patient's multimodal features comprising at least two types of features selected from clinical, biological, genomic and radiological features, wherein the patient's multimodal features are not complete, wherein the patient's at least one multimodal feature is collected at least at two time points, c) aggregate the patient's multimodal features into a vector of features' values, wherein the vector of features' values is not complete, d) input the vector of features' values to the trained imputation machine learning model and output a complete vector of features' values, where aggregating features from at least two time points comprises generating a second recommendation e) filter the features of the complete vector of features' values according to the list of informative features identifiers and obtain a predictive vector of features' values that is a subset of the complete vector of features' values consisting of filtered features' values,”)
determining a probability of the potential treatment successfully addressing the medical condition of the patient, wherein the probability is determined based on application of the potential treatment to a probability model, wherein the probability model comprises a collection of treatments in conjunction with success of application of a respective treatment in the collection of treatments to a respective medical condition, wherein the probability is assigned to a risk score for the potential treatment of the patient’s medical condition; and ([0015] “prediction machine learning models were trained, and the list of informative features identifiers was obtained, using a set of multimodal features comprising at least two types of features selected from clinical, biological, genomic and radiological features of cohort of patients having the same disease and receiving the same treatment as the patient for whom the prediction is performed, wherein for each patient in the cohort at least one of the multimodal feature was collected at least at two time points.”; see also [0017] “Figure 2 shows a processing system for use in a method to predict treatment response or treatment efficacy according to embodiment 2 or 5. HER - Electronic Health Record; PACS - Picture Archiving and Communication System; LIMS - Laboratory Information Management System.”)
presenting a recommendation for treatment of the patient’s medical condition, wherein the recommendation comprises the potential treatment in conjunction with the risk score. ([0185] “f) input the predictive vector of features' values to the trained prediction machine learning model and output prediction of the patient's response to the treatment or prediction of the patient's treatment efficacy defined as length of time to an event.” And [0023] “The "first-line treatment" or "primary/initial treatment" or "induction therapy" refers to the initial, or first treatment recommended for a given disease, such as cancer. For example, first-line treatment of stage IV NSCLC may be a pembrolizumab monotherapy, a chemotherapy and pembrolizumab combination therapy, a chemotherapy doublet and any other suitable treatment.” Comprises a recommendation for a treatment based on the patient’s medical condition)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes in page 1 “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols
Regarding claim 18, Bui- Thierry as a combination teaches all of the limitations of claim 17. Bui also teaches:
wherein the multimodal model is one of a visual language model, a large language model, graph neural network, or a Bayesian network, and ([0137] “Example machine-learned models can include decision tree based models, support vector machines, hidden Markov models, Bayesian networks, linear regression models, k-means clustering models, etc.”)
Regarding claim 18, Bui fails to explicitly teach, as taught by Thierry:
the probability model is one of a visual language model, a large language model, or a Bayesian network. ([0037] “Examples of machine learning models used for supervised learning include Support Vector Machines (SVM), regression analysis, linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithms, random forest, artificial neural networks (ANN) such as convolutional neural networks (CNN),”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Regarding claim 19, Bui- Thierry as a combination teaches all of the limitations of claim 17. Bui also teaches:
wherein the visual language model comprises transcriptomic data pertaining to the medical condition of the patient in combination with at least one of radiology data pertaining to the medical condition of the patient, or pathology data pertaining to the medical condition of the patient. ([0141] “Example data types for input(s) or output(s) include natural language text data, … genetic data or other chemical or biochemical data, image data, audio data, audiovisual data, haptic data, biometric data, medical data, financial data, statistical data, geographical data, astronomical data, historical data, sensor data generally (e.g., digital or analog values”; see also [0082] “the systems and methods disclosed herein can be leveraged for other medical condition visual searches outside of just skin condition visual search. For example, the systems and methods can be utilized for the fields of hair loss, nail conditions, infections (e.g., abscess, cellulitis, and/or HSV), cardiovascular (e.g., arterial insufficiency, vasculitis, and/or venous ulcers), dental/oral/ENT issues (e.g., aphthous ulcers and/or angular cheilitis), podiatry (e.g., corns and/or foot lesions), genetic issues (e.g., neurofibromas and/or xeroderma), rheumatology (e.g., erythema nodosum and/or dermatomyositis), diabetes risk (e.g., acanthosis nigricans), hematology, oncology (e.g., T-cell lymphoma and/or Kaposi's sarcoma), GI (e.g., pyoderma gangrenosum), trauma (e.g., burns, abrasions, and/or wounds), cosmetic (e.g., freckles and/or benign lesions), and/or eye conditions.”)
Regarding claim 20, Bui- Thierry as a combination teaches all of the limitations of claim 17. Thierry also teaches:
wherein the multimodal model and the probability model combine to form a prediction model, the prediction model comprises at least one of a visual language model, a large language model, graph neural network, or a Bayesian network. ([0037-0038] “The machine learning model may be trained within a supervised, semi-supervised or unsupervised learning framework. Within a supervised learning framework, a model learns a function to map an output result from an input data set, based on example pairs of inputs and matching outputs. Examples of machine learning models used for supervised learning include Support Vector Machines (SVM), regression analysis, linear regression, logistic regression, naive Bayes, linear discriminant analysis, decision trees, k-nearest neighbor algorithms, random forest, artificial neural networks (ANN) such as convolutional neural networks (CNN), recurrent neural networks (RNN), fully-connected neural networks, long short-term memory (LSTM) models, and others; and/or a combination thereof … Convolutional neural networks may also be combined with recurrent neural networks to produce a deep learning classifier.”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Thierry, with a reasonable expectation of success, by explicitly using a probabilistic model to calculate a risk score using biological, genomic and radiological information. This would have improved the accuracy of developing a patient’s successful treatment protocol and thus reduce risks on diagnostic protocols. Thierry is adaptable to Bui as both inventions utilize Bayesian machine learning models to process patient information so as to improve patient outcomes. Bui would have found Thierry’s teaching as Thierry emphasizes “Lung cancer constitutes a major public health burden” and thus would benefit from improved treatment protocols.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bui et al. (20210065859) in view Thierry et al. (EP4307315) and further in view of Walsh et al. (US20230207124).
Regarding claim 4, Bui- Thierry as a combination teaches all of the limitations of claim 3. Bui- Thierry as a combination does not explicitly teach, as taught by Walsh:
wherein the interaction is via at least one of a mouse and cursor, interactive display, or speech-based interaction. ([0027] “recommendation element 104 may output, e.g., to a classical computing device for display on a display of a computing device, the one or more treatment recommendations with their recommendation scores”; see also [0030] “Classical computing device 202 may include other components not shown in FIG. 2, such input devices, output devices, display screens, a power source, and so on. Communication channel(s) 214 may interconnect each of components 208, 210, and 212 for inter-component communications (physically, communicatively, and/or operatively)” where interactions for the )
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Bui with the teachings of Walsh, with a reasonable expectation of success, by explicitly including a display screen for inter-component communication channels. This would have allowed users to easily input information. Walsh is adaptable to Bui as both inventions use classical computing devices to intake patient information and properly diagnose patients. Bui would have found Walsh’s teaching after researching solutions to avoid any operations that “may increase the risk of misdiagnosis and/or mistreatment” [para 0005].
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Sellergren et al. (WO202503009) discloses receiving training data comprising medical images and associated text reports, training an image encoder using a vision-language model in an LLM.
Patwardhan et al. (US20240112752) discloses receiving unannotated genomic data, annotating this data, and determining a risk score for the subject. The method can function to: provide genomic data analysis to a user; predict disease risk; and/or provide recommendations for screenings, treatment, and/or lifestyle changes.
Lau et al. (US20240120096) discloses a therapeutic prediction model using multimodal data that includes a biomarker and subtype identification aspect, multimodality aspect, machine learning aspect, and training data.
Conclusion
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/R.A.S/Examiner, Art Unit 3685
/MARC Q JIMENEZ/Supervisory Patent Examiner, Art Unit 3681