DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Amendments
Claims 1-6 are currently pending in this case and have been examined and
addressed below. This communication is a Final Rejection in response to the
Amendment to the Claims and Remarks filed on 06/30/2025.
Claims 1-6 are amended claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/30/2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-6 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Step 1 – Statutory Categories of Invention:
Claims 1 and 6 are drawn to a system and method, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a system to specify a health improvement path,
wherein the health improvement path search [tool] comprises circuitry configured to: generate a first model predicting a value of a health index that is an objective variable on the basis of values of a plurality of explanatory variables; generate a second model deriving a presence probability representing a degree of easiness of presence of each of combinations of the values of the plurality of explanatory variables input to the first model and the value of the health index predicted using the first model on the basis of the values of the plurality of explanatory variables; and in a multidimensional variable space of the plurality of explanatory variables, derive the value of the health index and the presence probability corresponding to each measurement target value on the basis of the first model and the second model with a plurality of measurement target values within a predetermined range from current values of the plurality of explanatory variables set as inputs, specify a plurality of paths transitioning to each of the measurement target values from the current values as start points for transitioning between the values of the plurality of explanatory variables that are continuous to each other, specify one or a plurality of paths in which the value of the health index at an end point is improved from the current value of the health index among the plurality of paths as candidate paths, and specify a path for which a product of the presence probabilities of the measurement target values included in the path is a maximum among the candidate paths as a health improvement path.
Independent claim 6 recites a method for a step of a health improvement path searching step performed by a health improvement path search [tool], generating a first model predicting a value of a health index that is an objective variable on the basis of values of a plurality of explanatory variables; a step of generating a second model deriving a presence probability representing a degree of easiness of presence of each of combinations of the values of the plurality of explanatory variables input to the first model and the value of the health index predicted using the first model on the basis of the values of the plurality of explanatory variables; and in a multidimensional variable space of the plurality of explanatory variables, a step of deriving the value of the health index and the presence probability corresponding to each measurement target value on the basis of the first model and the second model with a plurality of measurement target values within a predetermined range from current values of the plurality of explanatory variables set as inputs, specifying a plurality of paths transitioning to each of the measurement target values from the current values as start points for transitioning between the values of the plurality of explanatory variables that are continuous to each other, specifying one or a plurality of paths in which the value of the health index at an end point is improved from the current value of the health index among the plurality of paths as candidate paths, and specifying a path for which a product of the presence probabilities of the measurement target values included in the path is a maximum among the candidate paths as a health improvement path.
These steps amount to functions performable in the mind or with pen and paper and are only concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work (MPEP § 2106.04(a)(2)(III)(B) citing the abstract idea grouping for mental processes with or without physical aid).
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
The claims recite the additional elements of health improvement path search system, a database to store the plurality of explanatory variables acquired through a health examination, a health improvement path search device, and an information processing system.
These elements are recited at a high-level of generality such that it amounts to
mere instructions to apply the exception because this is an example of applying the
abstract idea by use of general-purpose computer which does not integrate the abstract
idea into a practical application.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
As discussed above with the respect to integration of the abstract idea into a
practical application, the additional elements of health improvement path search system, a database to store the plurality of explanatory variables acquired through a health examination, a health improvement path search device, and an information processing system.
The specification recites: “[Para. 0014] The health improvement path search device 1 includes a database 11, a first model generating unit 12, a second model generating unit 13, and a path searching unit 14. [Para. 0030] As illustrated in FIG. 6, the health improvement path search device 1 is configured using an information processing device 100 that includes one or a plurality of processors 103, a memory 104, a storage 105, and an input/output port 106. ”
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
For the reasons stated, these claims are consequently rejected under 35 U.S.C. § 101.
Analysis of Dependent Claims
Dependent claim 2 recites wherein the health improvement path search device specifies a path for which the value of the health index is improved the most as the candidate path among paths for which the value of the health index is improved from the current value of the health index.
Dependent claim 3 recites wherein the health improvement path search device specifies a path for which the value of the health index coincides with a target value of the health index set in advance as the candidate path among paths for which the value of the health index is improved from the current value of the health index.
Dependent claim 4 recites wherein, after a first process of selecting the values of the plurality of explanatory variables that are approximated to reference values as measurement target values with the current values set as the reference values is performed, the health improvement path search device repeatedly performs a second process of selecting the values of the plurality of explanatory variables that are approximated to the reference value as the measurement target values with the measurement target value of which the presence probability at the time of being input to the second model is the highest among the selected measurement target values set as the new reference value.
Dependent claim 5 recites wherein the health improvement path search device specifies a shortest path randomly transitioning between the measurement target values from a start point to an end point as a random path and specifies a path for which a product of presence probabilities of measurement target values included in the path is a maximum and is equal to or larger than a product of presence probabilities of measurement target values included in the random path as the health improvement path among the candidate paths.
Each of these steps of the preceding dependent claims 2-5 only serve to further limit or specify the features of independent claim 1 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
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.
Claim(s) 1-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yanuar (Bayesian structural equation modeling for the health index) in view of Langheier (US 20060173663 A1).
As per Claim 1, Yanuar teaches a health improvement path search system comprising:
generate a first model predicting a value of a health index that is an objective variable on the basis of values of the plurality of explanatory variables; ([Abstract] To model the health index of an individual based on classical structural equation modeling (SEM) (i.e. first model). [Pg. 3 2. Data and instrument] The health index which is assumed to be related to mental health, socio-demography and lifestyle (i.e. objective variable) could also be measured directly based on certain indicators. The indicators of the health index are blood pressure, cholesterol level, body mass index (BMI) and the number of common health problems that the respondent ever had (i.e. plurality of explanatory variables.)
generate a second model deriving a presence probability representing a degree of easiness of presence of each of combinations of the values of the plurality of explanatory variables input to the first model and the value of the health index predicted using the first model on the basis of the values of the plurality of explanatory variables; ([Abstract] The Bayesian SEM approach allows the user to use the prior information (i.e. health index) for updating the current information on the parameter. [Pg. 8 4. Results] In the Bayesian analysis, it is important to test the sensitivity of the Bayesian analysis with respect to the choice of the priors. In order to achieve this goal, we consider the model comparison with three types of prior inputs (i.e. explanatory variables). In assigning the values for hyper parameter, we take small variance (i.e. presence probability) for each parameter, since we have confidence to have good prior information about a parameter.)
Yanuar does not explicitly teach, however Langheier teaches
a database configured to store the plurality of explanatory variables acquired through a health examination, ([Para. 0039] Predictive modeler 100 may utilize clinical data that is in non-standardized formats as well as data in standardized formats to generate predictive models. Older datasets stored in databases. Some datasets contain data with standard terminology according to the Unified Medical Language System (UMLS) inclusive of SNOMED, and transmission of secure encrypted data in Predictive Model Markup Language (PMML; based on XML), and in Extensible Markup Language (XML). Tagging of transported data in this manner allows for the automation recalculating models based on new factors (i.e. if blood sample from the patient cohort are then analyzed for SNPs) or new patient data (10 new patients enter the cohort over the timeframe of 2005 to 2010). [Para. 0040] The lead statistics system administrator or clinical researcher can choose factors and patient criteria to be selected in the ongoing dynamic modeling, and database queries will be automatically generated to extract this information from datasets 214-226. [Para. 0041] Data will be transformed and re-organized into a standard framework. The prepared input is a text file containing "n" rows and "p" columns, where n is the number of patients and p is the total number of variables is the dataset. In the process, variables are relabeled, turned into numerical values (for example gender is recoded as 0/1 instead of Male/Female) [Para. 0047] If there are 300 variables in the dataset and the current model has 3 predictors, the next step will choose amongst the 297 possible models with one more variable and the 3 models with one less variable.)
and a health improvement path search device configured to specify a health improvement path, wherein the health improvement path search device comprises circuitry configured to: ([Para. 0015] Developing and using predictive models can be implemented as a computer program product comprising computer executable instructions embodied in a computer readable medium. Exemplary computer readable media include chip memory devices (i.e. health improvement path search device), disk memory devices (i.e. health improvement path search device), programmable logic devices (i.e. health improvement path search device), application specific integrated circuits, and downloadable electrical signals. In addition, a computer program product may be located on a single device (i.e. health improvement path search device) or computing platform or may be distributed across multiple devices (i.e. health improvement path search device) or computing platforms. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy (i.e. health improvement path).)
in a multidimensional variable space of the plurality of explanatory variables, derive the value of the health index and the presence probability corresponding to each measurement target value on the basis of the first model and the second model with a plurality of measurement target values within a predetermined range from current values of the plurality of explanatory variables set as inputs, ([Para. 0047] In large dimensional problems (large number of possible predictors) predictive modeler 100 executes a stepwise approach that searches the model space in a forward/backward manner. Starting from the null model (model with no predictive variable), each step compares the predictive score of all models generated by adding a variable and by deleting one. For example, if there are 300 variables in the dataset and the current model has 3 predictors, the next step will choose amongst the 297 possible models with one more variable and the 3 models with one less variable. Examiner interprets this to be indicative of in a multidimensional variable space of the plurality of explanatory variables. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models (i.e. outputs of first and second model) into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.)
specify a plurality of paths transitioning to each of the measurement target values from the current values as start points for transitioning between the values of the plurality of explanatory variables that are continuous to each other, ([Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch (i.e. plurality of paths). The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.)
specify one or a plurality of paths in which the value of the health index at an end point is improved from the current value of the health, ([Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.)
and specify a path for which a product of the presence probabilities of the measurement target values included in the path is a maximum among the candidate paths as a health improvement path. ([Para. 0008] Evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy (i.e. maximum). Examiner interprets that the optimal intervention strategy is indicative of maximum presence probability.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of determining the health index and probabilities of health indicators as taught by Yanuar and incorporate the decision tree as taught by Langheier, with the motivation of using predictive models to predict a plurality of medical outcomes and optimal intervention strategies (Langheier Para. 0002).
As per Claim 2, Yanuar/ Langheier teach the health improvement path search system according to claim 1, Langheier further teaches wherein the health improvement path search device specifies a path for which the value of the health index is improved the most as the candidate path among paths for which the value of the health index is improved from the current value of the health index. ([Para. 0008] Evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy (i.e. maximum). Examiner interprets the highest sum of the probabilities to be indicative of improved the most.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of determining the health index and probabilities of health indicators as taught by Yanuar and incorporate the decision tree as taught by Langheier, with the motivation of using predictive models to predict a plurality of medical outcomes and optimal intervention strategies (Langheier Para. 0002).
As per Claim 3, Yanuar/ Langheier teach the health improvement path search system according to claim 1, Langheier further teaches wherein the health improvement path search device specifies a path for which the value of the health index coincides with a target value of the health index set in advance as the candidate path among paths for which the value of the health index is improved from the current value of the health index. ([Para. 0008] Evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy (i.e. target path).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of determining the health index and probabilities of health indicators as taught by Yanuar and incorporate the decision tree as taught by Langheier, with the motivation of using predictive models to predict a plurality of medical outcomes and optimal intervention strategies (Langheier Para. 0002).
As per Claim 4, Yanuar/ Langheier teach the health improvement path search system according to claim 1, Langheier further teaches wherein, after a first process of selecting the values of the plurality of explanatory variables that are approximated to reference values as measurement target values with the current values set as the reference values is performed, the health improvement path search device repeatedly performs a second process of selecting the values of the plurality of explanatory variables that are approximated to the reference value as the measurement target values with the measurement target value of which the presence probability at the time of being input to the second model is the highest among the selected measurement target values set as the new reference value. ([Para. 0008] Evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. [Para. 0123] Display risk scores associated with different treatment regimens, receive input from a user to modify treatment regimens, and automatically update risk scores based on the modified treatment regimens (i.e. second process). [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of determining the health index and probabilities of health indicators as taught by Yanuar and incorporate the decision tree as taught by Langheier, with the motivation of using predictive models to predict a plurality of medical outcomes and optimal intervention strategies (Langheier Para. 0002).
As per Claim 5, Yanuar/ Langheier teach the health improvement path search system according to claim 1, Langheier further teaches wherein the health improvement path search device specifies a shortest path randomly transitioning between the measurement target values from a start point to an end point as a random path and specifies a path for which a product of presence probabilities of measurement target values included in the path is a maximum and is equal to or larger than a product of presence probabilities of measurement target values included in the random path as the health improvement path among the candidate paths. ([Para. 0008] Evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy (i.e. maximum).)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of determining the health index and probabilities of health indicators as taught by Yanuar and incorporate the decision tree as taught by Langheier, with the motivation of using predictive models to predict a plurality of medical outcomes and optimal intervention strategies (Langheier Para. 0002).
As per Claim 6, Yanuar teaches a health improvement path search method performed by an information processing system, the health improvement path search method comprising:
a step of generating a first model predicting a value of a health index that is an objective variable on the basis of values of the plurality of explanatory variables; ([Abstract] To model the health index of an individual based on classical structural equation modeling (SEM) (i.e. first model). [Pg. 3 2. Data and instrument] The health index which is assumed to be related to mental health, socio-demography and lifestyle (i.e. objective variable) could also be measured directly based on certain indicators. The indicators of the health index are blood pressure, cholesterol level, body mass index (BMI) and the number of common health problems that the respondent ever had (i.e. plurality of explanatory variables).)
a step of generating a second model deriving a presence probability representing a degree of easiness of presence of each of combinations of the values of the plurality of explanatory variables input to the first model and the value of the health index predicted using the first model on the basis of the values of the plurality of explanatory variables; ([Abstract] The Bayesian SEM approach allows the user to use the prior information (i.e. health index) for updating the current information on the parameter. [Pg. 8 4. Results] In the Bayesian analysis, it is important to test the sensitivity of the Bayesian analysis with respect to the choice of the priors. In order to achieve this goal, we consider the model comparison with three types of prior inputs (i.e. explanatory variables). In assigning the values for hyper parameter, we take small variance (i.e. presence probability) for each parameter, since we have confidence to have good prior information about a parameter.)
Yanuar does not explicitly teach, however Langheier teaches
a step of storing the plurality of explanatory variables acquired through a health examination in a database, ([Para. 0039] Predictive modeler 100 may utilize clinical data that is in non-standardized formats as well as data in standardized formats to generate predictive models. Older datasets stored in databases. Some datasets contain data with standard terminology according to the Unified Medical Language System (UMLS) inclusive of SNOMED, and transmission of secure encrypted data in Predictive Model Markup Language (PMML; based on XML), and in Extensible Markup Language (XML). Tagging of transported data in this manner allows for the automation recalculating models based on new factors (i.e. if blood sample from the patient cohort are then analyzed for SNPs) or new patient data (10 new patients enter the cohort over the timeframe of 2005 to 2010). [Para. 0040] The lead statistics system administrator or clinical researcher can choose factors and patient criteria to be selected in the ongoing dynamic modeling, and database queries will be automatically generated to extract this information from datasets 214-226. [Para. 0041] Data will be transformed and re-organized into a standard framework. The prepared input is a text file containing "n" rows and "p" columns, where n is the number of patients and p is the total number of variables is the dataset. In the process, variables are relabeled, turned into numerical values (for example gender is recoded as 0/1 instead of Male/Female) [Para. 0047] If there are 300 variables in the dataset and the current model has 3 predictors, the next step will choose amongst the 297 possible models with one more variable and the 3 models with one less variable.)
a health improvement path searching step performed by a health improvement path search device, the health improvement path searching step comprising: ([Para. 0015] Developing and using predictive models can be implemented as a computer program product comprising computer executable instructions embodied in a computer readable medium. Exemplary computer readable media include chip memory devices (i.e. health improvement path search device), disk memory devices (i.e. health improvement path search device), programmable logic devices (i.e. health improvement path search device), application specific integrated circuits, and downloadable electrical signals. In addition, a computer program product may be located on a single device (i.e. health improvement path search device) or computing platform or may be distributed across multiple devices (i.e. health improvement path search device) or computing platforms. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy (i.e. health improvement path).)
and in a multidimensional variable space of the plurality of explanatory variables, a step of deriving the value of the health index and the presence probability corresponding to each measurement target value on the basis of the first model and the second model with a plurality of measurement target values within a predetermined range from current values of the plurality of explanatory variables set as inputs, ([Para. 0047] In large dimensional problems (large number of possible predictors) predictive modeler 100 executes a stepwise approach that searches the model space in a forward/backward manner. Starting from the null model (model with no predictive variable), each step compares the predictive score of all models generated by adding a variable and by deleting one. For example, if there are 300 variables in the dataset and the current model has 3 predictors, the next step will choose amongst the 297 possible models with one more variable and the 3 models with one less variable. Examiner interprets this to be indicative of in a multidimensional variable space of the plurality of explanatory variables. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models (i.e. outputs of first and second model) into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.)
specifying a plurality of paths transitioning to each of the measurement target values from the current values as start points for transitioning between the values of the plurality of explanatory variables that are continuous to each other, ([Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch (i.e. plurality of paths). The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.)
specifying one or a plurality of paths in which the value of the health index at an end point is improved from the current value of the health index among the plurality of paths as candidate paths, ([Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies.)
and specifying a path for which a product of the presence probabilities of the measurement target values included in the path is a maximum among the candidate paths as the health improvement path. ([Para. 0008] Evaluate different treatment and testing strategies and the effects of these strategies on the likelihoods associated with the different outcomes, and recommend the optimal overall strategy or decision path. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy (i.e. maximum). Examiner interprets that the optimal intervention strategy is indicative of maximum presence probability.)
Therefore, it would be prima facie obvious to one of ordinary skill in the art, at the time of filing, to modify the method of determining the health index and probabilities of health indicators as taught by Yanuar and incorporate the decision tree as taught by Langheier, with the motivation of using predictive models to predict a plurality of medical outcomes and optimal intervention strategies (Langheier Para. 0002).
Response to Arguments
Applicant's arguments, see pgs. 5-6 “Discussion of Section 101 Rejections” filed 07/01/2025 have been fully considered but they are not persuasive.
Applicant asserts that the present claims, as amended, are directed to a health improvement path search system and method that require a database and a health improvement path search device that performs certain steps. Each component has a specific purpose and performs one or more steps that integrate any alleged abstract idea into a practical application. Examiner respectfully disagrees. The database and the health improvement path search device are additional elements, which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception, therefore do not provide a practical application of significantly more.
Applicant asserts that the pending claims are not directed to an abstract idea that is merely human mental work. On the contrary, in the present invention, the entire set of variables of the health index is modeled, and the probability of the health state being taken is evaluated in a multidimensional variable space comprising both measured values and predicted values. Examiner interprets that the human mind is capable of a health improvement path searching step, generating a first model predicting a value of a health index that is an objective variable on the basis of values of a plurality of explanatory variables; a step of generating a second model deriving a presence probability representing a degree of easiness of presence of each of combinations of the values of the plurality of explanatory variables input to the first model and the value of the health index predicted using the first model on the basis of the values of the plurality of explanatory variables; and in a multidimensional variable space of the plurality of explanatory variables, a step of deriving the value of the health index and the presence probability corresponding to each measurement target value on the basis of the first model and the second model with a plurality of measurement target values within a predetermined range from current values of the plurality of explanatory variables set as inputs, specifying a plurality of paths transitioning to each of the measurement target values from the current values as start points for transitioning between the values of the plurality of explanatory variables that are continuous to each other, specifying one or a plurality of paths in which the value of the health index at an end point is improved from the current value of the health index among the plurality of paths as candidate paths, and specifying a path for which a product of the presence probabilities of the measurement target values included in the path is a maximum among the candidate paths as a health improvement path accordingly. Examiner notes that health improvement path search system, a database to store the plurality of explanatory variables acquired through a health examination, a health improvement path search device, and an information processing system are not treated as part of the abstract idea (See Step 2A, Prong 2 and Step 2B analysis in the above rejection). The use of electronic means for performing the abstract idea is not enough to overcome Step 2A Prong 1 (2019 Revised Patent Subject Matter Eligibility Guidance, 84 FED. REG 4 (January 7,2019) at pg. 8 footnote 54 further citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316-18 (Fed. Cir. 2016) where the electronic implementation of human activity was not adequate to overcome Step 2A Prong 1).
Applicant's arguments, see pgs. 7-8, “Discussion of Obviousness” filed 07/01/2025 have been fully considered but they are not persuasive.
Applicant asserts that Yanuar and Langheier do not teach or suggest the feature of the entire set of variables of the health index is modeled, and the probability of the health state being taken is evaluated in a multidimensional variable space comprising measured values and predicted values. Examiner respectfully disagrees.([Para. 0047] In large dimensional problems (large number of possible predictors) predictive modeler 100 executes a stepwise approach that searches the model space in a forward/backward manner. Starting from the null model (model with no predictive variable), each step compares the predictive score of all models generated by adding a variable and by deleting one. For example, if there are 300 variables in the dataset and the current model has 3 predictors, the next step will choose amongst the 297 possible models with one more variable and the 3 models with one less variable. Examiner interprets this to be indicative of in a multidimensional variable space of the plurality of explanatory variables. [Para. 0146] The decision support module 104 may automatically incorporate scores from multiple models (i.e. outputs of first and second model) into a decision tree to enable an individual to select an optimal intervention strategy. FIG. 12 illustrates an example of such a decision tree. In FIG. 12, the decision tree includes branches that correspond to outcomes related to febrile neutropenia. The % symbols on each branch correspond to probabilities associated with each branch. The circles in each branch mean that the values being calculated for the sub-branches should be added. Branches can then be compared to determine the optimal intervention strategy. The probabilities output from a predictive model used by decision support module 104 may be automatically incorporated into a decision tree, such as that illustrated in FIG. 12, to evaluate different outcomes and treatment strategies. Examiner interprets that the optimal intervention strategy is indicative of maximum presence probability. This is indicative of the entire set of variables of the health index is modeled, and the probability of the health state being taken is evaluated in a multidimensional variable space comprising measured values and predicted values.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Patricia K Edouard whose telephone number is (571)272-6084. The examiner can normally be reached Monday - Friday 7:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya M Long can be reached at 571-270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/P.K.E./Examiner, Art Unit 3682
/FONYA M LONG/Supervisory Patent Examiner, Art Unit 3682