DETAILED CORRESPONDANCE
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 claims
This final office action on merits is in response to the communication received on 04/10/2026. Claims 2-3, 7-8, 12-14, 19-20 are cancelled. Amendments to claims 1, 4-5, 9-10, 15-17 are acknowledged and have been carefully considered. Claims 1, 4-6, 9-11, and 15-18 are pending and considered below.
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, 4-6, 9-11, and 15-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 1, 4-6, and 11 are drawn to a method, claim 9 is drawn to an apparatus, claims 10 and 15-18 are drawn to a device. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claim 1 recites the limitations of verifying the login information in accordance with user information about a target user; invoking a data filtering rule corresponding to the health detection data, the data filtering rule comprising one or more of a numerical range rule, a data collection state rule or a data format rule; and eliminating abnormal data in the health detection data in accordance with the data filtering rule; establishing a biological model of the target user in accordance with the health detection data, wherein the biological model comprises a plurality of detection sub-models corresponding to different detection items; obtaining an association relationship among at least a part of the detection sub-models; verifying a sub-model matching degree of each detection sub-model in accordance with reference data matching the target user and the association relationship; generating a verification result of the biological model in accordance with the sub-model matching degree; and generating a health detection result of the target user in accordance with the verified biological model. These limitations, as drafted, are processes that, under their broadest reasonable interpretations, recites acts of observation, evaluation, comparison, analysis, and judgement that can practically be performed in the human mind or with the aid of pen and paper. For example, a person can compare login information with known user information, apply rules to identify abnormal data, organized health information into a health profile, identify relationships among health indicators, compare health information with reference information, determine degree to which the information corresponds to the reference information, determine the degree to which the information corresponds to the reference information, determine whether the resulting model is reliable, and generate a health assessment based on the evaluated information. The claim’s recitation that the health detection data is received from the Internet-of-Things health detection terminal” does not remove these limitations from the mental process grouping because the source of the information being evaluated does not alter the nature of the recited evaluation itself. Thus, the claim recites a mental process which is an abstract idea.
Independent claims 9 and 10 recite identical or nearly identical steps with respect to claim 1 (and therefore also recite limitations that fall within this subject matter grouping of abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis.
Under Step 2A Prong Two
The claimed limitations, as per method claim 1, include:
receiving login information from the Internet-of-Things health detection terminal;
verifying the login information in accordance with user information about a target user; and
transmitting a verification result for the login information to the Internet-of-Things health detection terminal;
receiving health detection data associated with the target user from the Internet-of- Things health detection terminal;
invoking a data filtering rule corresponding to the health detection data, the data filtering rule comprising one or more of a numerical range rule, a data collection state rule or a data format rule; and
eliminating abnormal data in the health detection data in accordance with the data filtering rule;
establishing a biological model of the target user in accordance with the health detection data, wherein the biological model comprises a plurality of detection sub-models corresponding to different detection items;
obtaining an association relationship among at least a part of the detection sub-models;
verifying a sub-model matching degree of each detection sub-model in accordance with reference data matching the target user and the association relationship;
generating a verification result of the biological model in accordance with the sub-model matching degree; and
generating a health detection result of the target user in accordance with the verified biological model.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim as a whole merely describes how to generally “apply” the concept of evaluating health information to verify a biological model and generating a health assessment in a computer environment. The claimed computer components (i.e., the Internet-of-Things health detection terminal) are recited at a high level of generality and are merely invoked as tools to perform an existing process of collecting health information, analyzing the health information, comparing health information to reference information, determining whether the information is reliable, and generating health assessment based on the information. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, this additional element does not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 1 is not integrated into a practical application. The claim recites the additional elements of receiving login information, transmitting a verification result for the login information, and receiving health detection data associated with the target user. These limitations are recited at a high level of generality (i.e., as a general means of receiving and transmitting information), and amounts to merely to mere data gathering and outputting a result), which are forms of insignificant extra-solution activities. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claim as a whole merely describes how to generally “apply” the concept of evaluating health information to verify a biological model and generating a health assessment in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claim 1, under step 2B, the additional elements of receive a message including admission data associated with an admission of receiving login information, transmitting a verification result for the login information, and receiving health detection data associated with the target user have been evaluated. As noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis without a technological improvement does not add significantly more to an abstract idea. The use of the healthcare data management method is no more than collecting information before evaluating health information, verifying a biological model, and generating a health assessment, and outputting a result and does not integrate the abstract idea into a practical application. Therefore, the claim does not recite an inventive concept and is not patent eligible.
Claim 4, 6, 15-16, and 18 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 5 and 17 recite the additional element of obtaining association information about the health detection data (claim 5 and 17), and the processor (claim 17). However, ese additional elements amount to implementing an abstract idea on a generic computing device or mere data gathering (i.e., an insignificant extra-solution activity). As such, these additional elements, when considered individually or in combination with the prior devices, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-6, 9-11, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN Patent Publication CN 113488137 A), referred to hereinafter as Chen, in view of Bordin et al. (International Publication No. WO 2019/153039 A1), referred to hereinafter as Bordin and Campbell et al. (International Publication No. WO 1998/035609 A1), referred to hereinafter as Campbell.
Regarding claim 1, Chen teaches a health data management method applied to a health management server in communication with an Internet-of-Things health detection terminal located at a publicly accessible health detection station, comprising: (Chen, page 6, “FIG. 1 is a schematic structural diagram of a health management plan recommendation system provided by an embodiment of the present application. Referring to FIG. 1, the system may include: a health management server 110, a first terminal 120, a monitoring device 130, and a household device 140. Wherein, a wired or wireless communication connection may be established between the first terminal 120 and the health management server 110, a wired or wireless communication connection may be established between the monitoring device 130 and the health management server 110, and the household equipment 140 may be connected to the health management server 110. A wired or wireless communication connection can be established between. Moreover, both the monitoring device 130 and the household device 140 can be associated with the first terminal 120. Optionally, the health management server 110 may be a server, or may be a server cluster composed of several servers, or may also be a cloud computing service center. The first terminal 120 may be a terminal with a larger display screen such as a TV or a smart screen. The monitoring device 130 may be a blood glucose meter, a blood pressure meter, a temperature gun, a wearable device, or the like. The household equipment 140 may be a refrigerator, an air conditioner, a humidifier, or the like. For example, referring to FIG. 1, the first terminal 120 may be a television, the monitoring device 130 may be a blood glucose meter, and the household equipment 140 may be a refrigerator.” And Chen, page 19 “The device backend includes TV platform, mobile phone platform and home appliance platform. The equipment layer includes first terminals with larger display screens such as televisions or smart screens, second terminals such as mobile phones, tablets, or laptops, household equipment, and monitoring equipment. Among them, the household equipment may include refrigerators, air conditioners, purifiers, fresh air fans, humidifiers, and so on. The monitoring equipment may include equipment for measuring blood pressure, blood sugar, weight, body fat, body temperature, heart rate, blood oxygen and other physical sign data.”, and Chen, page 9, “Step 205: The health management server obtains the physical examination data of the target subject from the physical examination database based on the authorization confirmation instruction. After receiving the authorization confirmation instruction, the health management server can obtain the medical examination data of the target subject from the medical examination database based on the authorization confirmation instruction. Wherein, the physical examination database may be a database connected by a health management server and a hospital or a physical examination center.”)
receiving login information from the Internet-of-Things health detection terminal (Chen, page 9, “Step 203: The first terminal displays the authorization request sent by the health management server. After receiving the authorization request sent by the health management server, the first terminal may display the authorization request on its display screen. For example, FIG. 5 is a schematic diagram of a display interface of a first terminal provided in an embodiment of the present application. As shown in FIG. 5, the first terminal may display an authorization request in the login interface of the health management APP, and the authorization request may Including the following text information: Mr. xx, we can find your physical examination report on xx through your mobile phone number. Do you authorize the data connection? If you agree, we will send a verification code to your mobile phone.”);
verifying the login information in accordance with user information about a target user (Chen, page 9, Step 204: In response to the confirmation operation for the authorization request, the first terminal sends an authorization confirmation instruction to the health management server.After viewing the authorization request, the target subject can perform a confirmation operation if it is determined to be authorized. The first terminal may further respond to the confirmation operation to send an authorization confirmation instruction to the health management server. Wherein, when the first terminal displays the authorization request, it may also display a confirmation control, and the confirmation operation may be a click operation on the confirmation control. Alternatively, the confirmation operation may also be a voice operation, for example, it may be a voice "confirm authorization". For example, referring to FIG. 5, if the target subject clicks the confirmation control (that is, the control displaying "Agree"), the first terminal may send a first confirmation instruction to the health management server. The health management server may send a verification code to the second terminal of the target subject based on the first confirmation instruction. The target subject can then input the verification code into the first terminal, and after receiving the sending operation for the verification code, the first terminal can send an authorization confirmation instruction including the verification code to the health management server.”); and
transmitting a verification result for the login information to the Internet-of-Things health detection terminal (Chen, page 9 ,“For example, referring to FIG. 5, if the target subject clicks the confirmation control (that is, the control displaying "Agree"), the first terminal may send a first confirmation instruction to the health management server. The health management server may send a verification code to the second terminal of the target subject based on the first confirmation instruction. The target subject can then input the verification code into the first terminal, and after receiving the sending operation for the verification code, the first terminal can send an authorization confirmation instruction including the verification code to the health management server. Step 205: The health management server obtains the physical examination data of the target subject from the physical examination database based on the authorization confirmation instruction.”). ;
receiving health detection data associated with the target user from the Internet-of- Things health detection terminal (Chen, page 6, “FIG. 1 is a schematic structural diagram of a health management plan recommendation system provided by an embodiment of the present application. Referring to FIG. 1, the system may include: a health management server 110, a first terminal 120, a monitoring device 130, and a household device 140. Wherein, a wired or wireless communication connection may be established between the first terminal 120 and the health management server 110, a wired or wireless communication connection may be established between the monitoring device 130 and the health management server 110, and the household equipment 140 may be connected to the health management server 110. A wired or wireless communication connection can be established between. Moreover, both the monitoring device 130 and the household device 140 can be associated with the first terminal 120. Optionally, the health management server 110 may be a server, or may be a server cluster composed of several servers, or may also be a cloud computing service center. The first terminal 120 may be a terminal with a larger display screen such as a TV or a smart screen. The monitoring device 130 may be a blood glucose meter, a blood pressure meter, a temperature gun, a wearable device, or the like. The household equipment 140 may be a refrigerator, an air conditioner, a humidifier, or the like. For example, referring to FIG. 1, the first terminal 120 may be a television, the monitoring device 130 may be a blood glucose meter, and the household equipment 140 may be a refrigerator.”);
in accordance with reference data matching the target user (Chen, page 9, “Optionally, the health management server may use a pre-trained algorithm model to analyze and process the user information of the target subject to obtain a user portrait of the target subject.”, and Chen, page 7, “After the target entity implements the health management plan, it can also use monitoring equipment to collect its own physical data. The health management server can then obtain the physical sign data of the target subject collected by the monitoring device. Among them, according to the different types of monitoring equipment, the types of the physical signs data are also different. For example, if the monitoring device is a blood glucose meter, the physical sign data can include blood sugar; if the monitoring device is a blood pressure meter, the physical sign data can include blood pressure; if the monitoring device is a temperature gun, the physical sign data can include body temperature; If the monitoring device is a wearable device, the physical sign data may include heart rate.” and Chen, page 9, “Optionally, the health management server may use a pre-trained algorithm model to analyze and process the user information of the target subject to obtain a user portrait of the target subject.”).
generating a health detection result of the target user in accordance with the verified biological model (Chen, page 11, “Assume that the physical examination report of the target subject obtained by the health management server shows that the target subject has a confirmed record of hypertension, and the physical sign data obtained by the health management server includes the blood pressure of the target subject collected by the sphygmomanometer.”, Chan, page 9, “Step 206: The health management server generates a health management plan of the target subject based on the acquired user information of the target subject. Among them, the user information includes questionnaire response data and physical examination data. The health management server may generate a user portrait of the target subject based on the user information. The user portrait may include at least one user tag, and each user tag may be used to indicate a subject type. For example, the user portrait may include at least one of the following user tags: people with high blood pressure, people with higher exercise intensity, and people with drier living environment. After that, the health management server can generate the health management plan of the target subject based on the user portrait. The health management plan may include at least one of the following plans: exercise plan, diet plan, physical examination plan, medication plan, sleep plan, air adjustment plan, drinking water plan, and home inspection plan.).
Chen fails to explicitly teach invoking a data filtering rule corresponding to the health detection data, the data filtering rule comprising one or more of a numerical range rule, a data collection state rule or a data format rule; eliminating abnormal data in the health detection data in accordance with the data filtering rule; establishing a biological model of the target user in accordance with the health detection data, wherein the biological model comprises a plurality of detection sub-models corresponding to different detection items; obtaining an association relationship among at least a part of the detection sub-models; verifying a sub-model matching degree of each detection sub-model; the association relationship; generating a verification result of the model in accordance with the sub-model matching degree.
Bordin teaches invoking a data filtering rule corresponding to the health detection data, the data filtering rule comprising one or more of a numerical range rule, a data collection state rule or a data format rule (Bordin [0008] “This unique NEDA resource collates all echocardiographic measurement and report data contained in the echocardiographic database of participating centers. Each database is then remotely transferred into the Master NEDA Database via a“vendor-agnostic” , automated data extraction process that transfers every measurement for each echocardiogram performed into a standardized NEDA data format (according to the NEDA Data Dictionary ). Each individual contributing to NEDA is given a unique identifier along with their demographic profile (date of birth and sex) and all data recorded with their echocardiogram.”); and
eliminating abnormal data in the health detection data in accordance with the data filtering rule (Bordin [0079] “The AI predictions for the continuity-derived aortic valve area were then evaluated in the clinical context of classifying severe AS. Initially, the test set data was filtered to only consider studies with a known aortic valve area calculated using the continuity equation and used this to label the studies as“severe AS’’ or“not severe AS’’);
verifying a sub-model matching degree (Bordin [0028] “The method may comprise the further step of (h) validating the disease model. Validating the disease model may comprise analysing the validation dataset using the disease model, wherein the records of the validation dataset comprise disease data associated with patient data. Validating the disease model may further comprise determining a validation error comprising a probability of correctly predicting a patient disease state in the records of the validation set.” and Chen, page 9, “Optionally, the health management server may use a pre-trained algorithm model to analyze and process the user information of the target subject to obtain a user portrait of the target subject.”, and Bordin [0192] “Model validation 513 makes use of the test data 505 to evaluate the performance of the trained AI model 511 on new, previously unseen data. The outputs of the validation process 513 largely depend on the particular application and the data source 501 used which are tested against particular performance metrics 515 to evaluate the predictive power (e.g. the probability that a predicted data measurement will be within defined tolerance levels as compared with the actual measurement data). An example of a useful performance metric 515 is Root-Mean-Square Error of predicted measurements compared with known data in the test data 505.”); and
generating a verification result of the model in accordance with the sub-model matching degree (Bordin [0192] “Model validation 513 makes use of the test data 505 to evaluate the performance of the trained AI model 511 on new, previously unseen data. The outputs of the validation process 513 largely depend on the particular application and the data source 501 used which are tested against particular performance metrics 515 to evaluate the predictive power (e.g. the probability that a predicted data measurement will be within defined tolerance levels as compared with the actual measurement data). An example of a useful performance metric 515 is Root-Mean-Square Error of predicted measurements compared with known data in the test data 505.”).
Campbell teaches establishing a biological model of the target user in accordance with the health detection data, wherein the biological model comprises a plurality of detection sub-models corresponding to different detection items (Campbell, page 10-11, “In a representative embodiment, the present invention is directed to computer-based system that uses a series of statistical analysis steps for creating mathematical-statistical functions that can be used to estimate an individual's risk of acquiring a specified biological condition within a specified time period or age interval and to identify individuals that are at highest risk. Prior to Phase I of the subject method, the available subjects may be randomly assigned to a Training Sample or an Evaluation Sample; Phases I-Ill operate on data from the Training Sample and Phase IV operates on data from the Evaluation Sample. Phase I is a Screening Phase that uses correlation, logistic regression, mixed model, and other analyses to select a large subset of biomarkers that have potentially useful information for risk estimation.”, Campbell, page 12, “Mixed model analysis is a term for statistical methods used for the analysis of expected-value relationships be1ween correlated dependent variables (multivariate measurements or IO observations, longitudinal measurements/observations of one variable, and/or longitudinal multivariate measurements/observations) and "independent variables" that can include covariates. such as age, classification variables (representing group membership) and also used for analysis of structures and parameters representing covariances among correlated measurements/observations. The term "mixed models" includes fixed-effects models, random-effects models. and mixed-effects models. Mixed models may have linear or nonlinear structures in the expected-value model and/or in the covariance model. A mixed model analysis typically includes estimation of expected value parameters (often denoted Pl and covariance matrix parameters (often of the form :1: = Zt.Z'+V, where t. and V are matrices of unknown parameters). A mixed model analysis may also include predictors of random subject effects (often denoted d, for the k-th subject) and so-called "best linear unbiased predictors" (or "BLUPs") for individual subjects. A mixed model analysis typically includes procedures for testing hypotheses about expected value parameters and/or covariance parameters and for constructing confidence regions for parameters.”, and
Campbell, page 31-32, “Step 4: Initiate the set of Candidate Biomarkers by including any Potential Biomarkers that on the basis of previous research and experience, are confidently believed to be related to the specified biological condition. The objective of this step is to utilize prior information on biomarkers that are potentially important discriminants for the specified biological condition. For example, if the specified biological condition is acquiring coronary heart disease (CHD) within a specified time, previous research has shown that values of serum cholesterol, systolic blood pressure, glucose intolerance, or cigarette smoking (to name just a few) are related to onset of CHD and should be copied from the list of Potential Biomarkers to the list of Candidate Biomarkers. Any reliable source of intonation or ·educated guess' may be relied upon to select the subset of biomarkers known or believed to be related to the specified biological condition. Although the identity of the biomarkers initially selected is not critical to determining the identity of the subset that is ultimately selected for use in discrimination. the initial selection of biomarkers that are ultimately confirmed by this system as having the greatest statistical significance for predicting the specified biological condition will assist in providing more rapid convergence, to the empirically determined subset. In other words, the more educated the initial selection, the more rapid the convergence. Step 5: Add to the List of Candidate Biomarkers any Potential Biomarkers that are “statistically significant” correlated with the “known important” biomarkers from Step 4. Data from the training sample are used to compute a correlation coefficient between each previously identified Candidate Biomarker (which are "known important” biomarkers) and each Potential Biomarker. Any statistically valid correlation coefficient may be used. The goal is to identify biomarkers that may be good discriminators. A correlate of a "known important" biomarker may be a better discriminator than the "known important" biomarker itself. At the least, correlates of known important biomarkers should be included in the initial analyses.”);
obtaining an association relationship among at least a part of the detection sub-models (Campbell, page 11, “Correlation analysis is a term for statistical methods used for estimating the strength of the linear relationship between two or more variables. Correlation, as used here, can include a variety of types of correlation, including but not limited to: Pearson product-moment correlations, Spearman's p, Kendall's T. the Fisher-Yates rF, and others.” and Campbell, page 12, “Mixed model analysis is a term for statistical methods used for the analysis of expected-value relationships be1ween correlated dependent variables (multivariate measurements or IO observations, longitudinal measurements/observations of one variable, and/or longitudinal multivariate measurements/observations) and "independent variables" that can include covariates. such as age, classification variables (representing group membership) and also used for analysis of structures and parameters representing covariances among correlated measurements/observations. The term "mixed models" includes fixed-effects models, random-effects models. and mixed-effects models. Mixed models may have linear or nonlinear structures in the expected-value model and/or in the covariance model. A mixed model analysis typically includes estimation of expected value parameters (often denoted Pl and covariance matrix parameters (often of the form :1: = Zt.Z'+V, where t. and V are matrices of unknown parameters). A mixed model analysis may also include predictors of random subject effects (often denoted d, for the k-th subject) and so-called "best linear unbiased predictors" (or "BLUPs") for individual subjects. A mixed model analysis typically includes procedures for testing hypotheses about expected value parameters and/or covariance parameters and for constructing confidence regions for parameters.”, and Campbell, page 31-32, “Step 4: Initiate the set of Candidate Biomarkers by including any Potential Biomarkers that on the basis of previous research and experience, are confidently believed to be related to the specified biological condition. The objective of this step is to utilize prior information on biomarkers that are potentially important discriminants for the specified biological condition. For example, if the specified biological condition is acquiring coronary heart disease (CHD) within a specified time, previous research has shown that values of serum cholesterol, systolic blood pressure, glucose intolerance, or cigarette smoking (to name just a few) are related to onset of CHD and should be copied from the list of Potential Biomarkers to the list of Candidate Biomarkers. Any reliable source of intonation or ·educated guess' may be relied upon to select the subset of biomarkers known or believed to be related to the specified biological condition. Although the identity of the biomarkers initially selected is not critical to determining the identity of the subset that is ultimately selected for use in discrimination. the initial selection of biomarkers that are ultimately confirmed by this system as having the greatest statistical significance for predicting the specified biological condition will assist in providing more rapid convergence, to the empirically determined subset. In other words, the more educated the initial selection, the more rapid the convergence. Step 5: Add to the List of Candidate Biomarkers any Potential Biomarkers that are “statistically significant” correlated with the “known important” biomarkers from Step 4. Data from the training sample are used to compute a correlation coefficient between each previously identified Candidate Biomarker (which are "known important” biomarkers) and each Potential Biomarker. Any statistically valid correlation coefficient may be used. The goal is to identify biomarkers that may be good discriminators. A correlate of a "known important" biomarker may be a better discriminator than the "known important" biomarker itself. At the least, correlates of known important biomarkers should be included in the initial analyses.” and Campbell, page 32, “Data from the training sample are used to compute a correlation coefficient between each previously identified Candidate Biomarker (which are "known important' biomarkers) and each Potential Biomarker. Any statistically valid correlation coefficient may be used. The goal is to identify biomarkers that may be good discriminators. A correlate of a "known important" biomarker may be a better discriminator than the "known important" biomarker itself. At the least, correlates of known important biomarkers should be included in the initial analyses. If the specified biological condition 1s actually defined by values of one or more biomarkers, (e.g., hypotension), the defining biomarkers would be "known important" biomarkers and would have been moved to the list of Candidate Biomarkers in Step 4. Correlates of the defining biomarkers would be moved to the list of Candidate Biomarkers in this Step. "Statistical significance" is used here only as a tool for deciding between "probably important" and "probably unimportant" correlates. In a representative embodiment, a traditional p-value will be computed for a correlation between a Potential Biomarker and a Candidate Biomarker. If p is less than some specified value, e.g. p<0. 05, or p<0.01, the Potential Biomarker is moved to the Candidate Biomarker list.”); and
each detection sub-model and the association relationship (Campbell, page 11, “Correlation analysis is a term for statistical methods used for estimating the strength of the linear relationship between two or more variables. Correlation, as used here, can include a variety of types of correlation, including but not limited to: Pearson product-moment correlations, Spearman's p, Kendall's T. the Fisher-Yates rF, and others.” and Campbell, page 12, “Mixed model analysis is a term for statistical methods used for the analysis of expected-value relationships be1ween correlated dependent variables (multivariate measurements or IO observations, longitudinal measurements/observations of one variable, and/or longitudinal multivariate measurements/observations) and "independent variables" that can include covariates. such as age, classification variables (representing group membership) and also used for analysis of structures and parameters representing covariances among correlated measurements/observations. The term "mixed models" includes fixed-effects models, random-effects models. and mixed-effects models. Mixed models may have linear or nonlinear structures in the expected-value model and/or in the covariance model. A mixed model analysis typically includes estimation of expected value parameters (often denoted Pl and covariance matrix parameters (often of the form :1: = Zt.Z'+V, where t. and V are matrices of unknown parameters). A mixed model analysis may also include predictors of random subject effects (often denoted d, for the k-th subject) and so-called "best linear unbiased predictors" (or "BLUPs") for individual subjects. A mixed model analysis typically includes procedures for testing hypotheses about expected value parameters and/or covariance parameters and for constructing confidence regions for parameters.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Chen's health management system to incorporate the data standardization and filtering techniques taught by Bordin. Chen teaches collecting health information from monitoring devices and generating user specific health outputs, while Bordin teaches transferring medical measurements into a standardized format and filtering data according to predetermined criteria before performing subsequent analysis. A person of ordinary skill in the art would have recognized that applying Bordin's known filtering and standardization techniques to the health data collected in Chen would improve data consistency, remove unsuitable data, and thereby improve the reliability of subsequent health assessments and recommendations.
It would have been further obvious to modify Chen in view of Campbell because Campbell teaches constructing predictive biological models from multiple biometric indicators, including blood pressure, glucose measurements, and other physiological biomarkers, and further teaches determining relationships among such indicators using correlation analysis and mixed-model analysis. A person of ordinary skill in the art would have recognized that incorporating Campbell's modeling techniques into Chen's health management system would improve the system's ability to analyze multiple types of physiological measurements and generate more robust and individualized health assessments based on known relationships among physiological indicators. The combination would merely apply known predictive modeling techniques to a known health-monitoring system and would have yielded predictable results.
It would have been further obvious to incorporate Bordin's model validation techniques into the combined Chen and Campbell system. Campbell teaches constructing biological models using biometric indicators and their relationships, while Bordin teaches validating predictive models using reference datasets, performance metrics, and known patient data to evaluate predictive accuracy. A person of ordinary skill in the art would have been motivated to validate Campbell's predictive biological models using Bordin's known validation techniques before generating health outputs in order to improve confidence in the generated results and reduce prediction errors. The resulting combination merely uses known validation techniques to improve similar health analysis systems in the same field and would have predictably resulted in more accurate and reliable health assessments for a target user.
Regarding claim 4, Chen, Bordin, and Campbell teach the invention in claim 1, as discussed above, and further teach wherein the detection sub-model comprises one or more of a blood pressure sub-model, a blood glucose sub-model, a blood oxygen sub-model, a body fat sub-model, a body composition sub- model, a bone substance sub-model, a lung function sub-model, an arteriosclerosis sub-model or an electrocardio sub-model (Campbell, page 11, “Correlation analysis is a term for statistical methods used for estimating the strength of the linear relationship between two or more variables. Correlation, as used here, can include a variety of types of correlation, including but not limited to: Pearson product-moment correlations, Spearman's p, Kendall's T. the Fisher-Yates rF, and others.” and Campbell, page 12, “Mixed model analysis is a term for statistical methods used for the analysis of expected-value relationships be1ween correlated dependent variables (multivariate measurements or IO observations, longitudinal measurements/observations of one variable, and/or longitudinal multivariate measurements/observations) and "independent variables" that can include covariates. such as age, classification variables (representing group membership) and also used for analysis of structures and parameters representing covariances among correlated measurements/observations. The term "mixed models" includes fixed-effects models, random-effects models. and mixed-effects models. Mixed models may have linear or nonlinear structures in the expected-value model and/or in the covariance model. A mixed model analysis typically includes estimation of expected value parameters (often denoted Pl and covariance matrix parameters (often of the form :1: = Zt.Z'+V, where t. and V are matrices of unknown parameters). A mixed model analysis may also include predictors of random subject effects (often denoted d, for the k-th subject) and so-called "best linear unbiased predictors" (or "BLUPs") for individual subjects. A mixed model analysis typically includes procedures for testing hypotheses about expected value parameters and/or covariance parameters and for constructing confidence regions for parameters.” and
Chen, page 7, “After the target entity implements the health management plan, it can also use monitoring equipment to collect its own physical data. The health management server can then obtain the physical sign data of the target subject collected by the monitoring device. Among them, according to the different types of monitoring equipment, the types of the physical signs data are also different. For example, if the monitoring device is a blood glucose meter, the physical sign data can include blood sugar; if the monitoring device is a blood pressure meter, the physical sign data can include blood pressure; if the monitoring device is a temperature gun, the physical sign data can include body temperature; If the monitoring device is a wearable device, the physical sign data may include heart rate.” and Chen, page 9, “Optionally, the health management server may use a pre-trained algorithm model to analyze and process the user information of the target subject to obtain a user portrait of the target subject.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the health management system of Chen in view of Campbell to implement detection sub-models corresponding to specific physiological indicators such as blood pressure and blood glucose. Chen teaches collecting multiple types of physiological measurements, including blood pressure and blood glucose data, from monitoring devices associated with a target user, while Campbell teaches constructing predictive biological models using multiple biometric indicators and evaluating such indicators through statistical and predictive modeling techniques. A person of ordinary skill in the art would have recognized that modeling individual physiological indicators separately within a larger predictive biological model would improve organization, analysis, and evaluation of the collected health data and would facilitate individualized health assessment based on specific physiological measurements. The modification would merely represent the predictable use of known biomarker modeling techniques within Chen's health management framework and would have yielded the expected benefit of improved analysis of blood pressure and blood glucose health conditions.
Regarding claim 5, Chen, Bordin, and Campbell teach the invention in claim 1, as discussed above, and further teach wherein prior to verifying a confidence level of the biological model, the health data management method further comprises (Bordin [0100] “If any AI-predicted measurements have a high enough“ confidence” output from the system, they can be used as-is, saving time.” Bordin [0028] “The method may comprise the further step of (h) validating the disease model. Validating the disease model may comprise analysing the validation dataset using the disease model, wherein the records of the validation dataset comprise disease data associated with patient data. Validating the disease model may further comprise determining a validation error comprising a probability of correctly predicting a patient disease state in the records of the validation set.” And Chen, page 9, “Optionally, the health management server may use a pre-trained algorithm model to analyze and process the user information of the target subject to obtain a user portrait of the target subject.”):
obtaining association information about the health detection data, the association information comprising at least one of an environmental factor or a physical factor of the target user (Campbell, page 11, “Correlation analysis is a term for statistical methods used for estimating the strength of the linear relationship between two or more variables. Correlation, as used here, can include a variety of types of correlation, including but not limited to: Pearson product-moment correlations, Spearman's p, Kendall's T. the Fisher-Yates rF, and others.” and Campbell, page 12, “Mixed model analysis is a term for statistical methods used for the analysis of expected-value relationships be1ween correlated dependent variables (multivariate measurements or IO observations, longitudinal measurements/observations of one variable, and/or longitudinal multivariate measurements/observations) and "independent variables" that can include covariates. such as age, classification variables (representing group membership) and also used for analysis of structures and parameters representing covariances among correlated measurements/observations. The term "mixed models" includes fixed-effects models, random-effects models. and mixed-effects models. Mixed models may have linear or nonlinear structures in the expected-value model and/or in the covariance model. A mixed model analysis typically includes estimation of expected value parameters (often denoted Pl and covariance matrix parameters (often of the form :1: = Zt.Z'+V, where t. and V are matrices of unknown parameters). A mixed model analysis may also include predictors of random subject effects (often denoted d, for the k-th subject) and so-called "best linear unbiased predictors" (or "BLUPs") for individual subjects. A mixed model analysis typically includes procedures for testing hypotheses about expected value parameters and/or covariance parameters and for constructing confidence regions for parameters.”, and Campbell, page 31-32, “Step 4: Initiate the set of Candidate Biomarkers by including any Potential Biomarkers that on the basis of previous research and experience, are confidently believed to be related to the specified biological condition. The objective of this step is to utilize prior information on biomarkers that are potentially important discriminants for the specified biological condition. For example, if the specified biological condition is acquiring coronary heart disease (CHD) within a specified time, previous research has shown that values of serum cholesterol, systolic blood pressure, glucose intolerance, or cigarette smoking (to name just a few) are related to onset of CHD and should be copied from the list of Potential Biomarkers to the list of Candidate Biomarkers. Any reliable source of intonation or ·educated guess' may be relied upon to select the subset of biomarkers known or believed to be related to the specified biological condition. Although the identity of the biomarkers initially selected is not critical to determining the identity of the subset that is ultimately selected for use in discrimination. the initial selection of biomarkers that are ultimately confirmed by this system as having the greatest statistical significance for predicting the specified biological condition will assist in providing more rapid convergence, to the empirically determined subset. In other words, the more educated the initial selection, the more rapid the convergence. Step 5: Add to the List of Candidate Biomarkers any Potential Biomarkers that are “statistically significant” correlated with the “known important” biomarkers from Step 4. Data from the training sample are used to compute a correlation coefficient between each previously identified Candidate Biomarker (which are "known important” biomarkers) and each Potential Biomarker. Any statistically valid correlation coefficient may be used. The goal is to identify biomarkers that may be good discriminators. A correlate of a "known important" biomarker may be a better discriminator than the "known important" biomarker itself. At the least, correlates of known important biomarkers should be included in the initial analyses.”, and Campbell, page 32, “Data from the training sample are used to compute a correlation coefficient between each previously identified Candidate Biomarker (which are "known important' biomarkers) and each Potential Biomarker. Any statistically valid correlation coefficient may be used. The goal is to identify biomarkers that may be good discriminators. A correlate of a "known important" biomarker may be a better discriminator than the "known important" biomarker itself. At the least, correlates of known important biomarkers should be included in the initial analyses. If the specified biological condition 1s actually defined by values of one or more biomarkers, (e.g., hypotension), the defining biomarkers would be "known important" biomarkers and would have been moved to the list of Candidate Biomarkers in Step 4. Correlates of the defining biomarkers would be moved to the list of Candidate Biomarkers in this Step. "Statistical significance" is used here only as a tool for deciding between "probably important" and "probably unimportant" correlates. In a representative embodiment, a traditional p-value will be computed for a correlation between a Potential Biomarker and a Candidate Biomarker. If p is less than some specified value, e.g. p<0. 05, or p<0.01, the Potential Biomarker is moved to the Candidate Biomarker list.” and
Chen, page 6, “In this embodiment of the application, the health management server may obtain user information of the target subject, and the user information may include basic information of the target subject, health status information, dietary preference information, lifestyle information, exercise preference information, attention topic information, and Living environment information, etc. The health management server may then generate a health management plan of the target subject based on the user information. The health management plan may include at least one of the following plans: exercise plan, diet plan, physical examination plan, medication plan, sleep plan, air adjustment plan, drinking water plan, home inspection plan, and the like.” and Chen, page 7, “Since the household equipment is associated with the first terminal of the target subject, the operating data of the household equipment can reflect the environmental data of the environment where the target subject is in the process of executing the health management plan.”); and
generating a constraint condition for verifying the biological model in accordance with the association information (Campbell, page 11, “Correlation analysis is a term for statistical methods used for estimating the strength of the linear relationship between two or more variables. Correlation, as used here, can include a variety of types of correlation, including but not limited to: Pearson product-moment correlations, Spearman's p, Kendall's T. the Fisher-Yates rF, and others.” and Campbell, page 12, “Mixed model analysis is a term for statistical methods used for the analysis of expected-value relationships be1ween correlated dependent variables (multivariate measurements or IO observations, longitudinal measurements/observations of one variable, and/or longitudinal multivariate measurements/observations) and "independent variables" that can include covariates. such as age, classification variables (representing group membership) and also used for analysis of structures and parameters representing covariances among correlated measurements/observations. The term "mixed models" includes fixed-effects models, random-effects models. and mixed-effects models. Mixed models may have linear or nonlinear structures in the expected-value model and/or in the covariance model. A mixed model analysis typically includes estimation of expected value parameters (often denoted Pl and covariance matrix parameters (often of the form :1: = Zt.Z'+V, where t. and V are matrices of unknown parameters). A mixed model analysis may also include predictors of random subject effects (often denoted d, for the k-th subject) and so-called "best linear unbiased predictors" (or "BLUPs") for individual subjects. A mixed model analysis typically includes procedures for testing hypotheses about expected value parameters and/or covariance parameters and for constructing confidence regions for parameters.” and
Bordin [0100] “If any AI-predicted measurements have a high enough“ confidence” output from the system, they can be used as-is, saving time.”, Bordin [0028] “The method may comprise the further step of (h) validating the disease model. Validating the disease model may comprise analysing the validation dataset using the disease model, wherein the records of the validation dataset comprise disease data associated with patient data. Validating the disease model may further comprise determining a validation error comprising a probability of correctly predicting a patient disease state in the records of the validation set.” and Chen, page 9, “Optionally, the health management server may use a pre-trained algorithm model to analyze and process the user information of the target subject to obtain a user portrait of the target subject.”, and Bordin [0192] “Model validation 513 makes use of the test data 505 to evaluate the performance of the trained AI model 511 on new, previously unseen data. The outputs of the validation process 513 largely depend on the particular application and the data source 501 used which are tested against particular performance metrics 515 to evaluate the predictive power (e.g. the probability that a predicted data measurement will be within defined tolerance levels as compared with the actual measurement data). An example of a useful performance metric 515 is Root-Mean-Square Error of predicted measurements compared with known data in the test data 505.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the combined system of Chen, Campbell, and Bordin to obtain association information comprising environmental and physical factors of a target user and to generate constraint conditions for verifying a biological model based on such association information. Chen teaches collecting user specific physical factors, such as blood pressure, blood glucose, body temperature, and heart rate, as well as environmental factors, including living environment information and environmental data derived from household equipment. Campbell teaches analyzing relationships among physiological variables through correlation analysis, determining statistically significant associations among biometric indicators, and establishing reliability measures and parameter constraints for predictive models. Bordin further teaches validating predictive models using confidence values, validation errors, performance metrics, and tolerance thresholds. A person of ordinary skill in the art would have recognized that incorporating environmental and physiological association information into the model-verification process and generating validation criteria based on those relationships would improve the reliability and predictive accuracy of the resulting biological model. The modification merely applies known correlation analysis and model validation techniques to known user specific health and environmental information and would have predictably resulted in a more robust and reliable health assessment system.
Regarding claim 6, Chen, Bordin, and Campbell teach the invention in claim 5, as discussed above, and further teach wherein the environmental factor comprises one or more of a collection period, weather information or geographical environment; and/or the physical factor comprises one or more of food-intake information or health information (Chen, page 6, “In this embodiment of the application, the health management server may obtain user information of the target subject, and the user information may include basic information of the target subject, health status information, dietary preference information, lifestyle information, exercise preference information, attention topic information, and Living environment information, etc. The health management server may then generate a health management plan of the target subject based on the user information. The health management plan may include at least one of the following plans: exercise plan, diet plan, physical examination plan, medication plan, sleep plan, air adjustment plan, drinking water plan, home inspection plan, and the like.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate environmental and physical factors, including collection period information, weather environmental conditions, geographical or living environment information, food intake information, and health information, into the health management framework of Chen when obtaining association information for use in evaluating a user's health condition. Chen teaches collecting and utilizing user specific information, including health status information, dietary preference information, and living environment information, to generate individualized health management plans. A person of ordinary skill in the art would have recognized that additional environmental conditions affecting a user's health, such as the timing of data collection, weather conditions, and geographical environment, as well as physical factors such as food intake and health status, are well-known variables that influence physiological measurements and health outcomes. It would therefore have been obvious to consider such factors when generating association information and evaluating health models in order to improve the accuracy and reliability of the resulting health assessments and recommendations. The modification merely involves incorporating known health variables into a known health-management system to achieve the predictable result of more personalized and context aware health analysis.
Claims 9 and 10 are analogous to claim 1, thus claims 9 and 10 are similarly analyzed and rejected in a manner consistent with the rejection of claim 1.
Regarding claim 11, Chen, Bordin, and Campbell teach the invention in claim 1, as discussed above, and further teach a readable storage medium storing therein a program, wherein the program is executed by a processor so as to implement the steps of the health data management method (Chen, page 4, “In yet another aspect, a computer-readable storage medium is provided, and a computer program is stored in the computer-readable storage medium, and the computer program is loaded and executed by a processor to implement the health management plan provided in any of the above Recommended method.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to implement the combined health data management method of Chen, Bordin, and Campbell as computer executable instructions stored on a computer readable storage medium because Chen teaches storing a computer program on a computer readable storage medium and executing the program by a processor to implement a health management method. Implementing known method steps in the form of processor executable instructions stored on a non-transitory computer readable medium represents a well known and conventional technique for deploying computer implemented methods and would have yielded the predictable result of enabling the method to be executed by a computing device.
Claims 15-16 are analogous to claim 4, thus claims 15-16 are similarly analyzed and rejected in a manner consistent with the rejection of claim 4.
Claims 17-18 are analogous to claims 5-6, thus claims 17-18 are similarly analyzed and rejected in a manner consistent with the rejection of claims 5-6.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 04/10/2026 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
Applicant's arguments have been fully considered but are not persuasive. Applicant states that amended claim 1 recites a complete technical chain involving authentication, filtering, modeling, correlation verification, and result generation that addresses challenges associated with heterogeneous Internet-of-Things (IoT) sensors in publicly accessible health monitoring stations. However, the eligibility analysis is based on the claim language and the claim does not recite a specific improvement to sensor technology, network communications, authentication technology, cybersecurity technology, or computer functionality. Instead, the claim recites receiving information, evaluating the information according to rules and reference information, establishing a biological model, determining relationships among health indicators, verifying the resulting model, and generating a health assessment. This constitutes observations, evaluations, comparisons, analyses, and judgments that fall within the mental process grouping of abstract ideas.
Applicant further states that the claim addresses data quality control, heterogeneous sensor fusion, fault isolation, cross validation, and reliability control. However, the claim does not recite any specific technological mechanism for achieving these improvements. For example, the claim does not recite a specific sensor processing technique, communication protocol, authentication protocol, filtering algorithm, machine learning architecture, or other technological implementation that improves the functioning of a computer or another technology. Instead, the claim broadly recites a result, such as establishing a biological model, obtaining relationships among sub-models, verifying matching degrees, and generating a health detection result. This does not integrate the judicial exception into a practical application.
Applicant also states that the claimed combination of features produces synergistic effects. However, the additional elements recited in the claim remain (the Internet-of-Things health detection terminal and the receiving and transmitting of information) merely gather information for use in the recited abstract analysis and communicate the results of that analysis. The claim does not recite a technical computer component or an improvement to the underlying technology. Accordingly, the additional elements, individually and in combination, amount to no more than implementing the abstract idea in a generic computing environment and performing insignificant extra-solution activity. Therefore, the claims remain directed to a judicial exception without significantly more and the rejection under 35 U.S.C. § 101 is maintained.
Claim Rejections - 35 USC § 103
Applicant’s arguments traversing the prior art rejection in the previous Office Action have been fully considered. However, those arguments are rendered moot because the present rejection under 35 U.S.C. §103 relies on a different set of prior art references (Chen, Bordin, and Campbell), which teach or suggest the limitations of the claims. Accordingly, Applicant’s prior arguments are not responsive to the current grounds of rejection. The rejection of claims 1, 4-6, 9-11, and 15-18 under 35 U.S.C. §103 is therefore maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Soto et al. (U.S. Patent Publication 2021/0134415 A1) teaches physiological sensor data (heart rate, blood pressure, glucose, oxygen, etc.) collected through networked or IoT devices can be automatically analyzed by predictive models to forecast patient outcomes, detect health issues, and trigger medical device adjustments or communications.
Morinaga et al. (U.S. Publication 2015/0379226) teaches a system that acquires a user’s measurement data, computes a health evaluation value from it, and selects an image corresponding to that value, and displays the selected image.
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.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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