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 .
Status of the Claims
Claims 1-20 are currently pending.
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 (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-12 are drawn to methods for evaluating and optimizing treatment plans, which is within the four statutory categories (i.e. process). Claims 13-20 are drawn to a system for evaluating and optimizing treatment plans, which is within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 9, which is representative of the inventive concept, recites: A computer-implemented method for optimizing healthcare outcomes using adversarial imitation deep learning, comprising:
receiving patient data from one or more medical sensors monitoring a patient;
processing the patient data to remove irrelevant data based on correlation to a healthcare key performance indicator (KPI);
generating, using a policy generator network with a transformer-based architecture, an optimal sequence of treatment actions based on the patient data;
employing a discriminator network to differentiate between the optimal sequence of treatment actions and real-world high-performance treatment sequences;
estimating final healthcare KPI results based on the optimal sequence of treatment actions using a performance prediction network; and
applying the optimal sequence of treatment actions to a patient's care plan to optimize healthcare KPI in real-time.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of receiving patient data, processing the patient data to remove irrelevant data, generating an optimal sequence of treatment actions based on the patient data, differentiating between the optimal sequence of treatment actions and real-world high-performance treatment sequences, estimating final healthcare KPI results based on the optimal sequence of treatment actions, and applying the optimal sequence of treatment actions to a patient’s care plan are reasonably interpreted as following rules or instructions to optimize behavior and/or actions in the form of the patient’s care plan), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 1 and 13 is identical as the abstract idea for Claim 9, because the only difference between Claims 1, 9, and 13 is that Claim 9 recites a method, whereas Claim 1 recites a method but does not recite that the sensors are medical sensors, that the sensor data is patient data, and/or that the sequence of actions are treatment actions, and Claim 13 recites a system that executes the same functions as those claimed in Claim 9.
Dependent Claims 2-8, 10-12, and 14-20 include other limitations, for example Claims 2, 12, and 14 recite a mechanism to capture temporal dependencies in the sensor data, Claims 3 and 15 recite minimizing discrepancies between action sequences and real-world high-performance sequences, Claims 4 and 16 recite a mechanism to estimate the final KPI results, Claims 5-6, 11, and 17-18 recite performing simulations on the data to determine future states of the process and consequences of potential actions, Claims 7, 10, and 19 recite utilizing historical data, Claim 8 recites that the sequence of actions comprise patient treatment actions, and Claim 20 recites types of medical interventions for the optimal sequence, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-8, 10-12, and 14-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 9, and 13.
Hence Claims 1-20 are directed towards the aforementioned abstract idea.
Prong 2 of Step 2A
Claims 1, 9, and 13 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the medical sensors, the policy generator network with a transformer-based architecture, the discriminator network, the performance prediction network, and hardware processor and memory) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the medical sensors, the hardware processor, the memory, and the various types of machine-learning architecture, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0033]-[0035], [0041]-[0044], [0048]-[0051], and [0054] of the as-filed Specification, and see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of the data being patient data, the sensors being medical sensors, and the sequence of actions being patient treatment actions, which amounts to limiting the abstract idea to the field of healthcare, e.g. see MPEP 2106.05(h).
Additionally, dependent Claims 2-8, 10-12, and 14-20 include other limitations, but these limitations also amount to no more than mere instructions to apply an exception (e.g. the various machine learning limitations recited of dependent Claims 2-4, 7, 10, 12, 14-16, and 19), and/or do not include any additional elements beyond those already recited in independent Claims 1, 9, and 13, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-20 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1, 9, and 13 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case ***), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0033]-[0035], [0041]-[0044], [0048]-[0051], and [0054] of the as-filed Specification discloses that the additional elements (i.e. the medical sensors, the hardware processor, the memory, and the various types of machine-learning architecture) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the additional elements recite receiving sensor data and real-world high performance sequence data, and utilizing (i.e. retrieving) the received data in order to ultimately estimate the final KPI results and apply the optimal sequence to a plan to optimize the KPI;
Dependent Claims 2-8, 10-12, and 14-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply an exception (e.g. the various machine learning limitations recited of dependent Claims 2-4, 7, 10, 12, 14-16, and 19), and/or do not include any additional elements beyond those already recited in independent Claims 1, 9, and 13, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-20 do not include any additional elements that amount to “significantly more” than the judicial exception(s).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-5, 8-10, 13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over White (US 2020/0342969) in view of Cheng (US 2018/0264258), further in view of Inam (US 2021/0338387).
Regarding Claim 1, White teaches the following: A computer-implemented method for optimizing key performance indicators (KPIs) using adversarial imitation deep learning, comprising:
generating an optimal sequence of actions based on the sensor data (The system includes data collection components to collect evidence based medicine (EBM) guidelines and medical billing data, wherein the data collection components include sensors (i.e. sensor data), that are used to generate an ideal treatment plan, e.g. see White [0045]-[0046] and [0091].);
differentiating between the optimal sequence of actions and real-world high performance sequences (The system analyzes an actual treatment plan (i.e. a real-world high-performance sequence) relative to the ideal treatment plan, e.g. see White [0050]-[0051].);
estimating final KPI results based on the optimal sequence of actions (The analysis of the actual treatment plan relative to the ideal treatment plan produces a compliance score (i.e. final KPI results) that serves as a proxy for quality of care, e.g. see White [0036] and [0051].); and
applying the optimal sequence of actions to a process to optimize KPI in real-time (When it is determined that the compliance score for a patient treatment does not meet a threshold, the system engages in remedial actions to improve quality of care, e.g. see White [0036], [0042], and [0055], wherein the system receives and processes data in real-time, e.g. see White [0037], [0105], and [0113].).
But White does not teach and Cheng teaches the following:
processing sensor data received from sensors to remove irrelevant data based on correlation to a final KPI (The system removes noise signal (i.e. irrelevant data) in order to evaluate the efficacy of the treatment (i.e. a KPI) without the possible corruption that the noise signal may cause, e.g. see Cheng [0144].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify White to incorporate receiving the patient data and removing the irrelevant portions of the patient data as taught by Cheng in order to evaluate the efficacy of a treatment without the possible corruption that the noise signal may cause, e.g. see Cheng [0144].
But the combination of White and Cheng does not teach and Inam teaches the following:
wherein generating the optimal sequence of treatment actions is performed using a policy generator network with a transformer-based architecture (The system determines review metrics that correspond to analysis results of the necessity or justifiability of certain clinical treatment procedures, e.g. see Inam [0184], wherein the calculation of the review metrics is performed through machine learning implementations including neural networks such as a transformer network, e.g. see Inam [0179] and [0194].);
wherein the differentiating between the optimal sequence of treatment actions and real-world high-performance treatment sequences is performed by employing a discriminator network (The system determines measurements that will likely provide optimal results using decisions made by trained machine learning models, e.g. see Inam [0082] and [0098], wherein the machine learning implementations include neural networks such as discriminator neural networks, e.g. Inam [0178].);
wherein estimating the final healthcare KPI results is performed using a performance prediction network (The machine learning framework is used to perform an analysis of treatment data (i.e. KPI results), e.g. see Inam [0182], wherein the machine learning framework may include a transformer network (i.e. a performance prediction network), e.g. see Inam [0179].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White and Cheng to incorporate the machine learning techniques to perform treatment evaluations as taught by Inam in order to perform a quick clinical data analysis so as to detect errors, e.g. see Inam [0077]-[0078].
Regarding Claim 3, the combination of White, Cheng, and Inam teaches the limitations of Claim 1, and Inam further teaches the following:
The method of claim 1, wherein the discriminator network utilizes a neural network architecture to minimize discrepancies between generated action sequences and real-world high-performance sequences (The system utilizes a neural network to minimize a loss metric between predictions made by the neural network and labeled data, e.g. see Inam [0088] and [0155].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White and Cheng to incorporate the neural network as taught by Inam in order to perform a quick clinical data analysis so as to detect errors, e.g. see Inam [0077]-[0078].
Regarding Claim 4, the combination of White, Cheng, and Inam teaches the limitations of Claim 1, and Inam further teaches the following:
The method of claim 1, wherein the performance prediction network employs a transformer-based architecture to estimate the final KPI results (The machine learning framework is used to perform an analysis of treatment data (i.e. KPI results), e.g. see Inam [0182], wherein the machine learning framework may include a transformer network (i.e. a performance prediction network), e.g. see Inam [0179].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White and Cheng to incorporate the transformer network to analyze the health data as taught by Inam in order to perform a quick clinical data analysis so as to detect errors, e.g. see Inam [0077]-[0078].
Regarding Claim 5, the combination of White, Cheng, and Inam teaches the limitations of Claim 1, and Inam further teaches the following:
The method of claim 1, further comprising training an environment simulator using variational autoencoder techniques to simulate future states of the process based on current actions and states (The machine learning framework includes an auto-encoder using a dense layer of the network to correlate with probability for a future event through a support vector machine based on training between similar records and output, e.g. see Inam [0277].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White and Cheng to incorporate the autoencoder as taught by Inam in order to perform a quick clinical data analysis so as to detect errors, e.g. see Inam [0077]-[0078].
Regarding Claim 8, the combination of White, Cheng, and Inam teaches the limitations of Claim 1, and White further teaches the following:
The method of claim 1, wherein the optimal sequence of actions to optimize the KPI include generated treatment action sequences for a patient's healthcare plan (The system utilizes evidence based medicine (EBM) guidelines, medical billing data, and data collected from sensors to generate an ideal treatment plan, e.g. see White [0045]-[0046] and [0091].).
Regarding Claim 9, White teaches the following: A computer-implemented method for optimizing healthcare outcomes using adversarial imitation deep learning, comprising:
generating an optimal sequence of treatment actions based on the patient data (The system includes evidence based medicine (EBM) guidelines and medical billing data (i.e. patient data) that is used to generate an ideal treatment plan, e.g. see White [0045]-[0046].);
differentiating between the optimal sequence of treatment actions and real-world high-performance treatment sequences (The system analyzes an actual treatment plan (i.e. a real-world high-performance treatment sequence) relative to the ideal treatment plan, e.g. see White [0050]-[0051].);
estimating final healthcare KPI results based on the optimal sequence of treatment actions (The analysis of the actual treatment plan relative to the ideal treatment plan produces a compliance score (i.e. final healthcare KPI results) that serves as a proxy for quality of care, e.g. see White [0036] and [0051].); and
applying the optimal sequence of treatment actions to a patient's care plan to optimize healthcare KPI in real-time (When it is determined that the compliance score for a patient treatment does not meet a threshold, the system engages in remedial actions to improve quality of care, e.g. see White [0036], [0042], and [0055], wherein the system receives and processes data in real-time, e.g. see White [0037], [0105], and [0113].).
But White does not teach and Cheng teaches the following:
receiving patient data from one or more medical sensors monitoring a patient (The system receives patient data, for example cardiac signals, from a plurality of sensors, e.g. see Cheng [0130].);
processing the patient data to remove irrelevant data based on correlation to a healthcare key performance indicator (KPI) (The system removes noise signal (i.e. irrelevant data) in order to evaluate the efficacy of the treatment (i.e. a KPI) without the possible corruption that the noise signal may cause, e.g. see Cheng [0144].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify White to incorporate receiving the patient data and removing the irrelevant portions of the patient data as taught by Cheng in order to evaluate the efficacy of a treatment without the possible corruption that the noise signal may cause, e.g. see Cheng [0144].
But the combination of White and Cheng does not teach and Inam teaches the following:
wherein generating the optimal sequence of treatment actions is performed using a policy generator network with a transformer-based architecture (The system determines review metrics that correspond to analysis results of the necessity or justifiability of certain clinical treatment procedures, e.g. see Inam [0184], wherein the calculation of the review metrics is performed through machine learning implementations including neural networks such as a transformer network, e.g. see Inam [0179] and [0194].);
wherein the differentiating between the optimal sequence of treatment actions and real-world high-performance treatment sequences is performed by employing a discriminator network (The system determines measurements that will likely provide optimal results using decisions made by trained machine learning models, e.g. see Inam [0082] and [0098], wherein the machine learning implementations include neural networks such as discriminator neural networks, e.g. Inam [0178].);
wherein estimating the final healthcare KPI results is performed using a performance prediction network (The machine learning framework is used to perform an analysis of treatment data (i.e. KPI results), e.g. see Inam [0182], wherein the machine learning framework may include a transformer network (i.e. a performance prediction network), e.g. see Inam [0179].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White and Cheng to incorporate the machine learning techniques to perform treatment evaluations as taught by Inam in order to perform a quick clinical data analysis so as to detect errors, e.g. see Inam [0077]-[0078].
Regarding Claim 10, the combination of White, Cheng, and Inam teaches the limitations of Claim 9, and White further teaches the following:
The method of claim 9, wherein the patient data includes real-time data and historical data (The system receives and processes data in real-time, e.g. see White [0037], [0105], and [0113], and includes historical data, e.g. see White [0028], [0038], [0073], and [0117].).
Regarding Claim 13, White teaches the following: A system for optimizing healthcare outcomes using adversarial imitation deep learning, comprising:
a hardware processor (The system includes a processor, e.g. see White [0003] and [0073].); and
a memory storing instructions that, when executed by the hardware processor (The system includes a memory storing software instructions that cause the processor to execute functions, e.g. see White [0003] and [0073].), cause the hardware processor to:
generate an optimal sequence of medical interventions based on the patient data (The system includes evidence based medicine (EBM) guidelines and medical billing data (i.e. patient data) that is used to generate an ideal treatment plan (i.e. an optimal sequence of medical interventions), e.g. see White [0045]-[0046].);
differentiating between the optimal sequence of medical interventions and real-world high-performance intervention sequences (The system analyzes an actual treatment plan (i.e. a real-world high-performance intervention sequence) relative to the ideal treatment plan, e.g. see White [0050]-[0051].);
estimate healthcare outcome results based on the optimal sequence of medical interventions (The analysis of the actual treatment plan relative to the ideal treatment plan produces a compliance score (i.e. healthcare outcome results) that serves as a proxy for quality of care, e.g. see White [0036] and [0051].); and
apply action sequences to optimize the healthcare outcome metric in real-time (When it is determined that the compliance score for a patient treatment does not meet a threshold, the system engages in remedial actions to improve quality of care, e.g. see White [0036], [0042], and [0055], wherein the system receives and processes data in real-time, e.g. see White [0037], [0105], and [0113].).
But White does not teach and Cheng teaches the following: wherein the processor is further configured to:
receive patient data from one or more medical sensors monitoring a patient (The system receives patient data, for example cardiac signals, from a plurality of sensors, e.g. see Cheng [0130].);
process the patient data to remove irrelevant data based on correlation to a healthcare outcome metric (The system removes noise signal (i.e. irrelevant data) in order to evaluate the efficacy of the treatment (i.e. a KPI) without the possible corruption that the noise signal may cause, e.g. see Cheng [0144].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify White to incorporate receiving the patient data and removing the irrelevant portions of the patient data as taught by Cheng in order to evaluate the efficacy of a treatment without the possible corruption that the noise signal may cause, e.g. see Cheng [0144].
But the combination of White and Cheng does not teach and Inam teaches the following:
wherein generating the optimal sequence of medical intervention actions is performed using a policy generator network with a transformer-based architecture (The system determines review metrics that correspond to analysis results of the necessity or justifiability of certain clinical treatment procedures, e.g. see Inam [0184], wherein the calculation of the review metrics is performed through machine learning implementations including neural networks such as a transformer network, e.g. see Inam [0179] and [0194].);
wherein the differentiating between the optimal sequence of medical interventions and real-world high-performance intervention sequences is performed by employing a discriminator network (The system determines measurements that will likely provide optimal results using decisions made by trained machine learning models, e.g. see Inam [0082] and [0098], wherein the machine learning implementations include neural networks such as discriminator neural networks, e.g. Inam [0178].);
wherein estimating the healthcare outcome results is performed using a performance prediction network (The machine learning framework is used to perform an analysis of treatment data (i.e. KPI results), e.g. see Inam [0182], wherein the machine learning framework may include a transformer network (i.e. a performance prediction network), e.g. see Inam [0179].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White and Cheng to incorporate the machine learning techniques to perform treatment evaluations as taught by Inam in order to perform a quick clinical data analysis so as to detect errors, e.g. see Inam [0077]-[0078].
Regarding Claims 15-17, the limitations of Claims 15-17 are substantially similar to those claimed in Claims 3-5, with the sole difference being that Claims 3-5 recite a method, whereas Claims 15-17 recite a system. Specifically pertaining to Claims 15-17, Examiner notes that White teaches a system and a method, e.g. see White [0127], and hence the grounds of rejection provided above for Claim 3-5 are similarly applied to Claims 15-17.
Regarding Claim 20, the combination of White, Cheng, and Inam teaches the limitations of Claim 14, and White further teaches the following:
The system of claim 14, wherein the optimal sequence of medical interventions includes at least one of medication administration, surgical procedures, therapy sessions, and lifestyle recommendations (The ideal treatment plan can include visits to a medical care provider (i.e. therapy sessions), surgery, and/or prescriptions (i.e. medication administration), e.g. see White [0041].).
Claims 2, 12, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of White, Cheng, and Inam in view of Chawla (US 2021/0012902).
Regarding Claim 2, the combination of White, Cheng, and Inam teaches the limitations of Claim 1, but does not teach and Chawla teaches the following:
The method of claim 1, wherein the policy generator network employs a multi-head self-attention mechanism to capture temporal dependencies in the sensor data (The system receives heart rate data over time from a sensor, wherein the data is analyzed to capture inter-dependencies across time-specific representations based on a position-aware multi-head self-attention mechanism, e.g. see Chawla [0018] and [0087].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White, Cheng, and Inam to incorporate the multi-head self-attention mechanism as taught by Chawla in order to produce a higher quality analysis of the data, e.g. see Chawla [0018].
Regarding Claims 12 and 14, the limitations of Claims 12 and 14 are substantially similar to those claimed in Claim 2, with the sole difference being that Claim 2 recites a method, whereas Claim 12 recites a method specifically pertaining to health/medical data, and Claim 14 recites a system. Specifically pertaining to Claims 12 and 14, Examiner notes that White teaches a system and a method, e.g. see White [0127], and hence the grounds of rejection provided above for Claim 2 are similarly applied to Claims 12 and 14.
Claims 6, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of White, Cheng, and Inam in view of Capps (US 2021/0313066).
Regarding Claim 6, the combination of White, Cheng, and Inam teaches the limitations of Claim 5, but does not teach and Capps teaches the following:
The method of claim 5, wherein the environment simulator is used to predict consequences of potential actions during generation of the optimal sequence of actions (The system utilizes machine learning to select an optimal series of actions, wherein the actions are selected by performing simulations on possible action sequences and selecting the actions that are optimal in view of a specified goal, e.g. see Capps [0019] and [0057].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White, Cheng, and Inam to incorporate predicting the optimal series of actions based on simulating possible action sequences as taught by Capps in order to best to advise a patient with respect to their overall health and fitness, and encourage the patient in an effective way, e.g. see Capps [0013].
Regarding Claims 11 and 18, the limitations of Claims 11 and 18 are substantially similar to those claimed in Claim 6, with the sole difference being that Claim 6 recites a method, whereas Claim 11 recites a method specifically pertaining to health/medical data, and Claim 18 recites a system. Specifically pertaining to Claims 11 and 18, Examiner notes that White teaches a system and a method, e.g. see White [0127], and hence the grounds of rejection provided above for Claim 6 are similarly applied to Claims 11 and 18.
Claims 7 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of White, Cheng, and Inam in view of Kuusela (US 2018/0161596 ).
Regarding Claim 7, the combination of White, Cheng, and Inam teaches the limitations of Claim 1, but does not teach and Kuusela teaches the following:
The method of claim 1, further comprising selecting high KPI samples from historical data to train the policy generator network and the discriminator network (The system determines a treatment plan for a patient based on a model that is trained using a set of treatment plans previously devised for past patients (i.e. historical data) that are of high quality (i.e. high KPI), e.g. see Kuusela [0019].).
Furthermore, before the effective filing date, it would have been obvious to one ordinarily skilled in the art of healthcare to modify the combination of White, Cheng, and Inam to incorporate utilizing high quality training data for the prediction as taught by Kuusela in order to improve treatment planning and facilitate better treatment delivery, e.g. see Kuusela [0013].
Regarding Claim 19, the limitations of Claim 19 are substantially similar to those claimed in Claim 7, with the sole difference being that Claim 7 recites a method, whereas Claim 19 recites a system. Specifically pertaining to Claim 19, Examiner notes that White teaches a system and a method, e.g. see White [0127], and hence the grounds of rejection provided above for Claim 7 are similarly applied to Claim 19.
Conclusion
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/JOHN P GO/Examiner, Art Unit 3681