DETAILED ACTION
Status of Claims
This action is in reply to the application filed on 11/05/2025.
Claims 1-10 and 19-27 are currently pending and have been examined.
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 1-10 and 19-27 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-9 are directed to a system (i.e., a machine), claim 10 is directed to a method (i.e., a process), and claims 19-27 are directed to non-transitory computer readable medium (i.e., a manufacture). Accordingly, claims 1-10 and 19-27 are all within at least one of the four statutory categories.
Step 2A - Prong One:
An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Representative independent claim 1 includes limitations that recite an abstract idea. Independent claim 1 is the system claim and claim 19 covers the matching computer readable medium.
Specifically, independent claim 1 recites:
A system for generation of artificial intelligence (Al) based automated healthcare applications, comprising:
a data repository system, wherein the data repository system stores at least one of the following: formatted data associated with a plurality of medical procedures for generation of one or more machine learning models, data associated with a regulatory guidance and approval process, or any combination thereof;
an Al medical device platform that comprises at least one processor in communication with the data repository system, the at least one processor configured to:
receive the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models;
receive the data associated with one or more regulatory guidance and approval processes;
generate one or more machine learning models configured to predict one or more aspects associated with a medical procedure based on the formatted data associated with the plurality of medical procedures and the data associated with the regulatory guidance and approval process;
deploy the one or more machine learning models to a production environment based on generating the one or more machine learning models.
The Examiner submits that the foregoing underlined limitations constitute: (a) “certain methods of organizing human activity” because formatting data associated with medical procedures, predicting aspects associated with the medical procedures based on the formatted data associated with the medical procedures and the data associated with a regulatory guidance and approval process are providing healthcare services and are a part of radiology/radiation medical workflow, which are managing human behavior/interactions between people. Furthermore, these limitations constitute (b) “a mental process” because predicting aspects associated with the medical procedures based on the formatted data is an observation/evaluation/analysis that can be performed in the human mind or with a pen and paper. The foregoing underlined limitations also relate to claim 1 (similarly to claims 10 and 19).
Accordingly, the claim describes at least one abstract idea.
In relation to claims 9, 20 and 27, these claims merely recite specific kinds of machine learning models, such as at least a production environment of a third-party platform that is not associated with the Al medical device platform, a production environment of the Al medical device platform, a platform that includes a standardized container or any combination thereof.
In relation to claims 2-8, 10 and 21-26, these claims merely recite determining steps such as: claim 2 - transform the data of the plurality of data sources based on extract, transform, load (etl) pipelines to provide transformed data, claims 3 & 21 - perform one or more data curation procedures on the initial data associated with one or more data records to provide the formatted data associated with a plurality of medical procedures, claims 4 & 22 - validate an output of the one or more trained machine learning models based on the data associated with the regulatory guidance and approval process, claims 5 & 23 - determine whether the request for access complies with one or more criteria associated with access to the particular machine learning model and provide access to the particular machine learning model based on determining that the request for access complies with one or more criteria associated with access to the particular machine learning model, claims 6 & 24 -determine whether the request for access complies with one or more criteria associated with access to the particular dataset and provide access to the particular dataset based on determining that the request for access complies with one or more criteria associated with access to the particular dataset, claim 7 & 25 - perform an application programming interface (API) call associated with the particular dataset to the data repository system to provide access to the particular dataset, and claims 8 & 26 - assign a unique identifier to the first dataset based on training the one or more machine learning models; assign the unique identifier to the one or more trained machine learning models; and validate an output of the one or more trained machine learning models based on the data associated with the regulatory guidance and approval process, wherein when validating the output of the one or more trained machine learning models, the at least one processor is configured to: validate the first dataset and the one or more trained machine learning models based on the unique identifier assigned to the first dataset and the one or more trained machine learning models.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1, 10 and 19, as drafted is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a system, a data repository system, an AI medical device platform, a data ingestion subsystem, a data storage subsystem, a data processing subsystem, at least one processor, an application programming interface (API) and at least one non-transitory, computer-readable medium including program instructions to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation within a health care environment in the mind but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” and “Mental Process” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the system, data repository system, AI medical device platform, data ingestion subsystem, data storage subsystem, data processing subsystem, processor, application programming interface (API) and non-transitory, computer-readable medium including program instructions are recited at high levels of generality (i.e., as generic computer components performing generic computer functions of receiving data/inputs, determining and providing data) such that it amounts no more than mere instructions to apply the exception using the generic computer components.
Regarding the additional limitations “generation of artificial intelligence (Al)”, “generation of one or more machine learning models”, “deploy the one or more machine learning models to a production environment based on generating the one or more machine learning models” and “a machine learning model” the Examiner submits that this additional limitation amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation “receive the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models” and “receive the data associated with one or more regulatory guidance and approval processes” the Examiner submits that this additional limitation merely adds insignificant pre-solution activity (data gathering; selecting data to be manipulated) to the at least one abstract idea (see MPEP § 2106.05(g)).
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvements in the functioning of a computer or an improvement to another technology or technical field, apply or us the above-noted implement/use to above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP §2106.05). Their collective functions merely provide conventional computer implementation.
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 the integration of the abstract idea into practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer component provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, in representative independent claim 1, regarding the additional limitations of the system, data repository system, AI medical device platform, data ingestion subsystem, data storage subsystem, data processing subsystem, processor, application programming interface (API) and non-transitory, computer-readable medium including program instructions, the Examiner submits that these limitations amount to merely using a computer to perform the at least one abstract idea (see MPEP § 2106.05(f)).
Thus, representative independent claim 1 and analogous independent claims 10 and 19 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The dependent claims no not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reason discussed above with respect to determining that the dependent claims do not integrate the at least abstract idea into a practical application.
Therefore, claims 1-10 and 19-27 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-5, 8-10, 19-23 and 26-27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hyvőnen (US 2024/0331833 A1).
Claim 1:
Hyvőnen discloses A system for generation of artificial intelligence (Al) based automated healthcare applications (See AI using various machine learning models in P0054.), comprising:
a data repository system, wherein the data repository system stores at least one of the following: formatted data associated with a plurality of medical procedures for generation of one or more machine learning models, data associated with a regulatory guidance and approval process, or any combination (See Fig. 1, system database 110 in P0048, clinic database 120 in P0050, radiation therapy and treatment as medical procedures in [P0053-P0054] The models 111 and/or 112 may represent any collection of algorithmic logic and/or artificial intelligence models (e.g., using various machine learning techniques). For instance, the treatable sector model 111 may include various algorithms to identify attributes and characteristics of a treatable sector based on patient data and/or treatment attributes. Also, see machine learning model in P0014-P0017, P0060 and P0071-P0072 using structure set such as planning target volume, organ at risk (OAR) and planning target volume (PTV) serves as formatted data associated with medical procedures. See P0020-P0025 exemplary medical images within the training dataset may be converted from a three-dimensional representation to a two-dimensional representation.) thereof;
an Al medical device platform that comprises at least one processor in communication with the data repository system, the at least one processor configured (See Fig. 1, communication standards in P0038-P0039 network for communications, an administrator computing device 150 and P0043 computer 142 as processors control medical device 140.) to:
receive the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models (Besides protocols segmenting medical images (P0060), analytical protocols querying medical records with natural language processing (P0065), rules and protocols regarding radiation therapy treatment (P0068), see customizing treatable models with Volumetric Modulated Arc Therapy (VMAT) protocol in P0078-P0079 and cleaned and pro-processed dataset trained in neural network in P0105-P0110.);
receive the data associated with one or more regulatory guidance and approval processes (See [P0068] the analytics server may receive/retrieve, using a clinic identifier, a file comprising clinic-specific logic indicating treatment attributes of a radiation therapy machine in accordance with at least one of radiation therapy treatment planning data or patient anatomy. Each clinic implementing treatment may have its own rules and protocols regarding radiation therapy treatment. The rules may be expressed as a list of logical equations/sentences (e.g., computer-readable logic/code) referring to how the clinic usually treats patients.);
generate one or more machine learning models configured to predict one or more aspects associated with a medical procedure based on the formatted data associated with the plurality of medical procedures and the data associated with the regulatory guidance and approval process (See [P0008-P0009] a machine learning model may be generated with which treatable sectors can be estimated/predicted that are suitable and customized for different patients. Also, see rules and protocols regarding radiation therapy treatment in P0068, Fig. 7, trained with cleaned and pro-processed dataset in P0105-P0106.);
deploy the one or more machine learning models to a production environment based on generating the one or more machine learning models (See Fig. 10, P0113. Also, see P0105-P0106 neural network serve as production environment including ingested treatment attributes inputted by a physician or a clinician.).
Claim 10:
Hyvőnen discloses for generation of artificial intelligence (AI) based automated healthcare applications (See AI using various machine learning models in P0054.), comprising:
receiving, with at least one processor of an Al medical device platform, formatted data associated with a plurality of medical procedures for generation of one or more machine learning models from a data repository system (Besides protocols segmenting medical images (P0060), analytical protocols querying medical records with natural language processing (P0065), rules and protocols regarding radiation therapy treatment (P0068), see customizing treatable models with Volumetric Modulated Arc Therapy (VMAT) protocol in P0078-P0079 and cleaned and pro-processed dataset trained in neural network in P0105-P0110.);
receiving, with the at least one processor, data associated with one or more regulatory guidance and approval processes (See [P0068] the analytics server may receive/retrieve, using a clinic identifier, a file comprising clinic-specific logic indicating treatment attributes of a radiation therapy machine in accordance with at least one of radiation therapy treatment planning data or patient anatomy. Each clinic implementing treatment may have its own rules and protocols regarding radiation therapy treatment. The rules may be expressed as a list of logical equations/sentences (e.g., computer-readable logic/code) referring to how the clinic usually treats patients.);
generating, with the at least one processor, one or more machine learning models configured to predict one or more aspects associated with a medical procedure based on the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models and the data associated with the regulatory guidance and approval process (See [P0008-P0009] a machine learning model may be generated with which treatable sectors can be estimated/predicted that are suitable and customized for different patients. Also, see rules and protocols regarding radiation therapy treatment in P0068, Fig. 7, trained with cleaned and pro-processed dataset in P0105-P0106. Also, see machine learning model in P0014-P0017, P0060 and P0071-P0072 using structure set such as planning target volume, organ at risk (OAR) and planning target volume (PTV) serves as formatted data associated with medical procedures. See P0020-P0025 exemplary medical images within the training dataset may be converted from a three-dimensional representation to a two-dimensional representation.);
deploying, with the at least one processor, the one or more machine learning models to a production environment based on generating the one or more machine learning models (See Fig. 10, P0113. Also, see P0105-P0106 neural network serve as production environment including ingested treatment attributes inputted by a physician or a clinician.).
Claim 19:
Hyvőnen discloses A computer program product for generation of artificial intelligence (AI) based automated healthcare applications comprising at least one non- transitory computer-readable medium including program instructions that, when executed by at least one processor, cause the at least one processor to (See AI using various machine learning models in P0054, a non-transitory computer-readable or processor-readable storage medium in P0017, P0121. Also, see Fig. 1, communication standards in P0038-P0039 network for communications, an administrator computing device 150 and P0043 computer 142 as processors control medical device 140.), comprising:
receive formatted data associated with a plurality of medical procedures for generation of one or more machine learning models from a data repository system (Besides protocols segmenting medical images (P0060), analytical protocols querying medical records with natural language processing (P0065), rules and protocols regarding radiation therapy treatment (P0068), see customizing treatable models with Volumetric Modulated Arc Therapy (VMAT) protocol in P0078-P0079 and cleaned and pro-processed dataset trained in neural network in P0105-P0110. Also, see P0020-P0025 exemplary medical images within the training dataset may be converted from a three-dimensional representation to a two-dimensional representation.);
receive data associated with one or more regulatory guidance and approval processes from the data repository system (See [P0068] the analytics server may receive/retrieve, using a clinic identifier, a file comprising clinic-specific logic indicating treatment attributes of a radiation therapy machine in accordance with at least one of radiation therapy treatment planning data or patient anatomy. Each clinic implementing treatment may have its own rules and protocols regarding radiation therapy treatment. The rules may be expressed as a list of logical equations/sentences (e.g., computer-readable logic/code) referring to how the clinic usually treats patients.);
generate one or more machine learning models configured to predict one or more aspects associated with a medical procedure based on the data associated with the plurality of medical procedures and the data associated with the regulatory guidance and approval process (See [P0008-P0009] a machine learning model may be generated with which treatable sectors can be estimated/predicted that are suitable and customized for different patients. Also, see rules and protocols regarding radiation therapy treatment in P0068, Fig. 7, trained with cleaned and pro-processed dataset in P0105-P0106.);
deploy the one or more machine learning models to a production environment based on generating the one or more machine learning models (See Fig. 10, P0113. Also, see P0105-P0106 neural network serve as production environment including ingested treatment attributes inputted by a physician or a clinician.).
Regarding claim 2, Hyvőnen discloses the system of claim 1, wherein the data repository system comprises:
a data ingestion subsystem to receive data from a plurality of data sources (See ingesting patient data in P0009, P0017.);
a data storage subsystem to store data for the data repository system (See P0038, P0048, P0050 the system and clinic databases and P0053-P0054 collection of algorithmic logic and/or artificial intelligence models.);
a data processing subsystem to transform the data of the plurality of data sources based on extract, transform, load (etl) pipelines to provide transformed data to be used for generation of the one or more machine learning models (See collected patient data from various sources in P0055, P0057, for trained treatable sector model in P0063-P0064, extract needed patient data and the treatable sector model ingested by the plan optimizer in P0098-P0099, information forwarded and memory sharing in P0118-P0119.);
a data analytics subsystem to provide access to data visualization tools and database analytics tools to be used for generation of the one or more machine learning models (See P0049, displayed GUI in Fig. 10 and [P0112-P0113] The plan optimizer may then use the parameters predicted by the treatable sector computer model to generate a treatment plan for the patient.); and
a data cloud foundation subsystem to provide one or more rules associated with control operations for the data repository system (See Fig. 1, P0045 in cloud environment.).
Regarding claim 3, Hyvőnen discloses the system of claim 1, wherein the data repository system comprises: wherein the data repository system is configured to: receive initial data associated with one or more data records from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following:
an electronic medical record (EMR) system;
a medical imaging system;
a fluid injection system;
a pathology information system;
a laboratory information system; or
any combination thereof; and
perform one or more data curation procedures on the initial data associated with one or more data records to provide the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models (See Fig. 1, P0038 clinic with radiation therapy machine 140, Fig. 5, P0048, P0059-P0060 access patient's electronic health data records, medical images and treatment-specific data. Also, see P0066-P0067 as adding or revising data associated with particular patient attributes, treatment, tumor stages, anterior/posterior position, as information used to predict using a machine-learning model.).
Regarding claim 4, Hyvőnen discloses the system of claim 1, wherein, when generating one or more machine learning models configured to predict one or more aspects associated with a medical procedure, the at least one processor is configured to: train the one or more machine learning models based on the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models to provide one or more trained machine learning models (See machine learning model in P0014-P0017, P0060 and P0071-P0072 using structure set such as planning target volume, organ at risk (OAR) and planning target volume (PTV) serves as formatted data associated with medical procedures.); and validate an output of the one or more trained machine learning models based on the data associated with the regulatory guidance and approval process (See [P0060] a medical image may be segmented (using various protocols, including receiving input from a clinician and/or AI auto-segmentation methods).).
Regarding claim 5, Hyvőnen discloses the system of claim 1, wherein the at least one processor is further configured to: receive a request for access to a particular machine learning model of a plurality of machine learning models; determine whether the request for access complies with one or more criteria associated with access to the particular machine learning model; and provide access to the particular machine learning model based on determining that the request for access complies with one or more criteria associated with access to the particular machine learning model (Taught in P0047-P0048 as authenticating a user’s credentials and role by executing an access directory protocol.).
Regarding claim 8, Hyvőnen discloses the system of claim 1, wherein the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models comprises a plurality of datasets, wherein each dataset of the plurality of datasets is associated with a particular medical procedure of the plurality of medical procedures (See [P0064] the patient data may include each patient's physical attributes (e.g., height, weight, BMI, and the like). This information can be included within the training dataset, such that the trained treatable sector computer model can identify a correlation between the respective data points.), wherein the at least one processor is further configured to:
train the one or more machine learning models based on a first dataset of the plurality of datasets to provide one or more trained machine learning models (See P0020-P0025 exemplary medical images within the training dataset may be converted from a three-dimensional representation to a two-dimensional representation.);
assign a unique identifier to the first dataset based on training the one or more machine learning models (See P0060 exemplary dataset such as patient identifier, electronic health data records, medical images and radiation dose distribution within an anatomical region of the patient, P0062 tumor location and P0064 patient's physical attributes.);
assign the unique identifier to the one or more trained machine learning models (See the trained treatable sector computer model can identify a correlation between the respective data points in P0064. Also, see P0020-P0025 exemplary medical images within the training dataset may be converted from a three-dimensional representation to a two-dimensional representation and Fig. 2A, P0069-P0072.); and
validate an output of the one or more trained machine learning models based on the data associated with the regulatory guidance and approval process, wherein when validating the output of the one or more trained machine learning models (See Figs. 7-8, P0109-P0110 a validation dataset.), the at least one processor is configured to:
validate the first dataset and the one or more trained machine learning models based on the unique identifier assigned to the first dataset and the one or more trained machine learning models (See Figs. 7-8, P0109-P0112 where the neural network results are validated again the validation dataset.).
Regarding claim 9, Hyvőnen discloses the system of claim 1, wherein, when deploying the one or more machine learning models to the production environment, the at least one processor is configured to:
deploy the one or more machine learning models to at least one of the following:
a production environment of a third-party platform that is not associated with the Al medical device platform;
a production environment of the Al medical device platform;
a platform that includes a standardized container; or any combination thereof (See Fig. 1, system database 110 in P0048, clinic database 120 in P0050, radiation therapy and treatment as medical procedures in [P0053-P0054] The models 111 and/or 112 may represent any collection of algorithmic logic and/or artificial intelligence models (e.g., using various machine learning techniques). For instance, the treatable sector model 111 may include various algorithms to identify attributes and characteristics of a treatable sector based on patient data and/or treatment attributes.).
Regarding claim 20, Hyvőnen discloses the computer program product of claim 19, wherein the program instructions further cause the at least one processor to: receive initial data associated with one or more data records from a plurality of data sources, wherein the plurality of data sources comprises at least one of the following:
an electronic medical record (EMR) system;
a medical imaging system
;a fluid injection system;
a pathology information system;
a laboratory information system;
any combination thereof (See Fig. 1, P0038 clinic with radiation therapy machine 140, Fig. 5, P0048, P0059-P0060 access patient's electronic health data records, medical images and treatment-specific data.).
Regarding claim 21, Hyvőnen discloses the computer program product of claim 20, wherein the program instructions further cause the at least one processor to: perform one or more data curation procedures on the initial data associated with one or more data records to provide the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models (Taught in P0066-P0067 as adding or revising data associated with particular patient attributes, treatment, tumor stages, anterior/posterior position, as information used to predict using a machine-learning model.).
Regarding claim 22, Hyvőnen discloses the computer program product of claim 19, wherein the program instructions that cause the at least one processor to generate one or more machine learning models configured to predict one or more aspects associated with a medical procedure cause the at least one processor to: train the one or more machine learning models based on the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models to provide one or more trained machine learning models (See machine learning model in P0014-P0017, P0060 and P0071-P0072 using structure set such as planning target volume, organ at risk (OAR) and planning target volume (PTV) serves as formatted data associated with medical procedures.); and validate an output of the one or more trained machine learning models based on the data associated with the regulatory guidance and approval process (See [P0060] a medical image may be segmented (using various protocols, including receiving input from a clinician and/or AI auto-segmentation methods).).
Regarding claim 23, Hyvőnen discloses the computer program product of claim 19, wherein the program instructions further cause the at least one processor to: receive a request for access to a particular machine learning model of a plurality of machine learning models; determine whether the request for access complies with one or more criteria associated with access to the particular machine learning model; and provide access to the particular machine learning model based on determining that the request for access complies with one or more criteria associated with access to the particular machine learning model (Taught in P0047-P0048 as authenticating a user’s credentials and role by executing an access directory protocol.).
Regarding claim 26, Hyvőnen discloses the computer program product of claim 19, wherein the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models comprises a plurality of datasets, wherein each dataset of the plurality of datasets is associated with a particular medical procedure of the plurality of medical procedures (See [P0064] the patient data may include each patient's physical attributes (e.g., height, weight, BMI, and the like). This information can be included within the training dataset, such that the trained treatable sector computer model can identify a correlation between the respective data points.), wherein the program instructions further cause the at least one processor to:
train the one or more machine learning models based on a first dataset of the plurality of datasets to provide one or more trained machine learning models (See P0020-P0025 exemplary medical images within the training dataset may be converted from a three-dimensional representation to a two-dimensional representation.);
assign a unique identifier to the first dataset based on training the one or more machine learning models (See P0060 exemplary dataset such as patient identifier, electronic health data records, medical images and radiation dose distribution within an anatomical region of the patient, P0062 tumor location and P0064 patient's physical attributes.);
assign the unique identifier to the one or more trained machine learning models (See the trained treatable sector computer model can identify a correlation between the respective data points in P0064. Also, see P0020-P0025 exemplary medical images within the training dataset may be converted from a three-dimensional representation to a two-dimensional representation and Fig. 2A, P0069-P0072.); and
validate an output of the one or more trained machine learning models based on the data associated with the regulatory guidance and approval process, wherein the program instructions that cause the at least one processor to validate the output of the one or more trained machine learning models (See Figs. 7-8, P0109-P0110 a validation dataset.), cause the at least one processor to:
validate the first dataset and the one or more trained machine learning models based on the unique identifier assigned to the first dataset and the one or more trained machine learning models (See Figs. 7-8, P0109-P0112 where the neural network results are validated again the validation dataset.).
Regarding claim 27, Hyvőnen discloses the computer program product of claim 19, wherein the program instructions that cause the at least one processor to deploy the one or more machine learning models to the production environment cause the at least one processor to:
deploy the one or more machine learning models to at least one of the following:
a production environment of a third-party platform that is not associated with the Al medical device platform;
a production environment of the Al medical device platform;
a platform that includes a standardized container; or any combination thereof (See Fig. 1, system database 110 in P0048, clinic database 120 in P0050, radiation therapy and treatment as medical procedures in [P0053-P0054] The models 111 and/or 112 may represent any collection of algorithmic logic and/or artificial intelligence models (e.g., using various machine learning techniques). For instance, the treatable sector model 111 may include various algorithms to identify attributes and characteristics of a treatable sector based on patient data and/or treatment attributes.).
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 6-7 and 24-25 are rejected under 35 U.S.C. 103 as being unpatentable over Hyvőnen (US 2024/0331833 A1) in view of Shrager (US 11,887,738 B2).
Regarding claim 6, although Hyvőnen discloses the system of claim 1, wherein the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models comprises a plurality of datasets, wherein each dataset of the plurality of datasets is associated with a particular medical procedure of the plurality of medical procedures mentioned above, Hyvőnen does not explicitly teach requesting access to particular datasets, based on determining that the request for access complies with criteria associated with access to the particular dataset. Shrager teaches:
wherein the at least one processor is further configured to: receive a request for access to a particular dataset of a plurality of datasets; determine whether the request for access complies with one or more criteria associated with access to the particular dataset; and provide access to the particular dataset based on determining that the request for access complies with one or more criteria associated with access to the particular dataset (See request for additional information (column 53, lines 2-15) in the form of dataset in column 54, lines 40-54 using registry data where a cluster model can be produced and new registry data from patients collected under a protocol can be fitted to existing groupings.).
Therefore, it would have been obvious to one of ordinary skill in the art of virtual clinical trials before the effective filing date of the claimed invention to modify the system and software of Hyvőnen to include requesting access to particular datasets, based on determining that the request for access complies with criteria associated with access to the particular dataset as taught by Shrager when planning, searching and solving problems for successful cancer treatment mentioned in Shrager’s column 2, lines 11-23.
Regarding claim 7, Hyvőnen teach Shrager the system of claim 6 mentioned above and Shrager teaches:
wherein, when providing access to the particular dataset, the at least one processor is configured to: perform an application programming interface (API) call associated with the particular dataset to the data repository system to provide access to the particular dataset (See API in column 13, lines 40-44, column 31, lines 10-14.).
Therefore, it would have been obvious to one of ordinary skill in the art of virtual clinical trials before the effective filing date of the claimed invention to modify the system and software of Hyvőnen to include performing an application programming interface (API) call associated with the particular dataset as taught by Shrager when planning, searching and solving problems for successful cancer treatment mentioned in Shrager’s column 2, lines 11-23.
Regarding claim 24, although Hyvőnen discloses computer program product of claim 19, wherein the formatted data associated with a plurality of medical procedures for generation of one or more machine learning models comprises a plurality of datasets, wherein each dataset of the plurality of datasets is associated with a particular medical procedure of the plurality of medical procedures mentioned above, Hyvőnen does not explicitly teach requesting access to particular datasets, based on determining that the request for access complies with criteria associated with access to the particular dataset. Shrager teaches:
wherein the program instructions further cause the at least one processor to: receive a request for access to a particular dataset of a plurality of datasets; determine whether the request for access complies with one or more criteria associated with access to the particular dataset; and provide access to the particular dataset based on determining that the request for access complies with one or more criteria associated with access to the particular dataset (See request for additional information (column 53, lines 2-15) in the form of dataset in column 54, lines 40-54 using registry data where a cluster model can be produced and new registry data from patients collected under a protocol can be fitted to existing groupings.).
Therefore, it would have been obvious to one of ordinary skill in the art of virtual clinical trials before the effective filing date of the claimed invention to modify the system and software of Hyvőnen to include requesting access to particular datasets, based on determining that the request for access complies with criteria associated with access to the particular dataset as taught by Shrager when planning, searching and solving problems for successful cancer treatment mentioned in Shrager’s column 2, lines 11-23.
Regarding claim 25, Hyvőnen teach Shrager the computer program product of claim 24 mentioned above and Shrager teaches:
wherein the program instructions that cause the at least one processor to provide access to the particular dataset cause the at least one processor to: perform an application programming interface (API) call associated with the particular dataset to the data repository system to provide access to the particular dataset (See API in column 13, lines 40-44, column 31, lines 10-14.).
Therefore, it would have been obvious to one of ordinary skill in the art of virtual clinical trials before the effective filing date of the claimed invention to modify the system and software of Hyvőnen to include performing an application programming interface (API) call associated with the particular dataset as taught by Shrager when planning, searching and solving problems for successful cancer treatment mentioned in Shrager’s column 2, lines 11-23.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Hyvőnen (US 2024/0331833 A1), Kasthurirathne (US 2020/0312457 A1), & Goetz (US 11,587,678 B2).
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/T.S.W./Examiner, Art Unit 3687 09/01/2026
/Anita Y Coupe/Supervisory Patent Examiner, Art Unit 3619
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