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 . This non-final office action on merits is in response to the Patent Application filed on 12/20/2024.
Status of claims
Claims 1-11 are pending and considered below. This application is a 371 of PCT/CN2023/093234 filed on 05/10/2023, which claims the benefit to CN Application No. CN202210713607.9 filed on 06/22/2022.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 06/05/2025 has been acknowledged. The submission 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 1-20 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 1-8, and 11-12 are drawn to a method, claim 9 is drawn to an apparatus, claims 10 and 13-20 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 limitation of establishing a biological model of the target user in accordance with the health detection data; and verifying the biological model, 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 interpretation, cover performance of the limitations in the mind or by using a pen and paper. The claim encompasses a user simply reviewing health measurements, constructing a conceptual health profile, comparing measurements to known reference values, and formulating a corresponding health assessment in their mind or by using a pen and paper. 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 the steps of:
receiving health detection data associated with a target user from the Internet-of-Things health detection terminal;
establishing a biological model of the target user in accordance with the health detection data; and
verifying the biological model, 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 recites the additional element of receiving health detection data associated with a target user from the Internet-of-Things health detection terminal. This limitation is recited at a high level of generality (i.e., as a general means of collecting physiological measurements before performing the modeling and verification steps), and amounts to merely data gathering, which is a form of insignificant extra-solution activity. Accordingly, even in combination, this additional element does 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 an additional element that is sufficient to amount to significantly more than the judicial exception. For the providing limitation that was considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. 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 step is no more than collecting information before performing the modeling and verification steps and does not integrate the abstract idea into a practical application. For these reasons, there is no inventive concept and the claim is not patent eligible.
Claims 2-7, and 12-19 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 8, 11, and 20 recite the additional element of receiving login information from the Internet-of-Things health detection terminal (claim 8 and 20), transmitting a verification result for the login information to the Internet-of-Things health detection terminal (claim 8 and 20), the program is executed by a processor (claim 11 and 20). However, this additional element amounts to implementing an abstract idea on a generic computing device or mere data gathering and displaying results (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 claim is 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 1-20 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.
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, 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.”):
receiving health detection data associated with a 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.”);
establishing a biological model of the target user in accordance with the health detection data (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
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.”).
Chen fails to explicitly teach verifying the model.
Bordin teaches verifying the model (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.”).
Chen teaches a health management system including a health management server in communication with monitoring devices such as a blood glucose meter and blood pressure meter, and therefore teaches receiving health detection data from an IoT health detection terminal. Chen further teaches that the health management server analyzes the collected user data using a pre-trained algorithm model to generate a “user portrait”, which corresponds to establishing a biological model in accordance with the received health detection data. Chen also teaches generating a health result, such as a hypertension determination, based on the analyzed and modeled physiological data. Bordin teaches validating or verifying a health or disease model by applying the model to reference data and determining a validation error or prediction probability, thereby teaching the step of verifying the biological model prior to generating a health detection result.
It would have been obvious to one of ordinary skill in the art to incorporate Bordin’s model verification techniques into Chen’s health management system because both references address computerized analysis of physiological data and seek to improve the accuracy and reliability of user specific health assessments. A POSITA would have been motivated to combine Chen’s model generation functionality with Bordin’s verification procedures to ensure that the modeled health information is accurate before producing a health detection result, yielding a predictable improvement.
Regarding claim 2, Chen and Bordin teach the invention in claim 1, as discussed above, and further teach wherein the biological model comprises a plurality of detection sub-models corresponding to different detection items, wherein the verifying the biological model comprises (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.” and
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.”:
verifying a sub-model matching degree of each detection sub-model in accordance with reference data matching the target user (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
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 biological model in accordance with the sub-model matching degree (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.” 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.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to use Chen’s multiple types of physiological measurements (blood sugar, blood pressure, etc.) as separate detection sub-models within the user model taught by Chen, because Chen teaches that different monitoring devices produce different types of physical sign data and that the server analyzes that data using an algorithmic user model. Further, Bordin teaches validating a model by comparing each component of the model against reference or test datasets and computing performance or error metrics, which corresponds to verifying a matching degree for each sub-model. A PHOSITA would have been motivated to combine Chen and Bordin because both references address processing physiological data using algorithmic models, and combining them predictably yields a health data system that models each measurement type separately and verifies each one against reference data before generating results.
Regarding claim 3, Chen and Bordin teach the invention in claim 2, as discussed above, and further teach wherein the verifying the sub-model matching degree of each detection sub-model in accordance with the reference data matching the target user comprises (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
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.”):
obtaining an association relationship among at least a part of the detection sub-models (Bordin, [0049] “Figure 8 shows graphical results of an embodiment of the AI-assisted echocardiography methods and systems as disclosed herein for prediction of severe Aortic Stenosis (AS) in the NEDA records for the general population; Figures 9A, 9B, 9C and 9D shows graphical results of the prediction accuracy of an embodiment of the AI-assisted echocardiography methods and systems as disclosed herein for prediction of severe Aortic Stenosis (AS) in the NEDA records for subsets of the general population, respectively for ejection fraction value (EF) of <= 50%, EF <=40%, EF <= 35%, and EF <=30%” 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.”) ; and
verifying the sub-model matching degree of each detection sub-model in accordance with the association relationship (Chen, page 14 “Optionally, the first terminal may also display the user information of the target subject in the user information interface of the health management APP. FIG. 17 is a schematic diagram of a user information interface provided by an embodiment of the present application, and FIG. 18 is a schematic diagram of another user information interface provided by an embodiment of the present application. As shown in Figure 17 and Figure 18, the user information interface may display a comprehensive score of the target subject’s health status, and at least one label used to characterize the target user’s health status and/or living habits, for example, it may include Labels as follows: normal blood sugar, normal uric acid, high blood pressure, and persistent exercise, etc.”, 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.).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to use associations among the different physiological parameters collected by Chen’s monitoring devices, because Chen teaches that different health indicators (blood sugar, blood pressure, etc.) are collected for the same user and displayed together in a comprehensive health profile. Bordin further teaches evaluating and correlating different model outputs across population subsets (ejection fraction groups, aortic stenosis severity, etc.), which constitutes identifying relationships among sub-models and using those relationships during model validation. A PHOSITA would have been motivated to combine Chen with Bordin because both references teach analyzing multiple health indicators together, and applying the cross parameter associations during verification predictably improves the accuracy of the overall health status evaluation.
Regarding claim 4, Chen and Bordin teach the invention in claim 2, 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-mode (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.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to include blood pressure, blood glucose, blood oxygen, body fat, body composition, bone substance, lung function, arteriosclerosis, or electrocardio sub-models within the biological model because Chen teaches collecting these exact physiological parameters from different monitoring devices (blood glucose meter, blood pressure meter, temperature gun, wearable device, etc.) and analyzing them using an algorithmic user model to form a comprehensive user portrait. Since these measurement types were known and routinely used together in health monitoring systems, representing each one as a corresponding “sub-model” is organizing Chen’s existing data sources into labeled categories. A PHOSITA would have had a clear motivation to do so because it reflects standard practice in structuring heterogeneous physiological data for modeling and verification, yielding predictable results.
Regarding claim 5, Chen and Bordin 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 (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 (Chen, page 7, “Step 105: The health management server obtains the operating data of the household equipment associated with the first terminal. 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. The association of the household equipment with the first terminal may refer to: the household equipment establishes a communication connection with the first terminal, and the communication connection may be a Bluetooth connection or may be a wireless-fidelity (WIFI) connection. Depending on the type of household equipment, the type of the operating data is also different. For example, if the household equipment is an air conditioner, the operating data may include the temperature and wind speed of the air conditioner; if the household equipment is a refrigerator, the operating data may include the types and quantities of ingredients stored in the refrigerator.”, 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 [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.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to obtain environmental and physical factors associated with the user (such as dietary information, exercise preference, lifestyle information, and living environment) because Chen teaches that the health management server gathers user information including health status, lifestyle, diet, exercise habits, and environmental data from household equipment linked to the user’s terminal. Bordin further teaches using confidence measures and validation techniques tied to patient associated data to determine whether model measurements should be trusted. A PHOSITA would have been motivated to combine these teachings to improve the reliability of model verification by incorporating available user context information into the validation process, resulting in predictable improvements in accuracy.
Regarding claim 6, Chen and Bordin 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.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to treat collection period, weather, geographical environment, food-intake information, and health information as environmental or physical factors because Chen discloses collecting user lifestyle information, dietary preference information, exercise preference information, and living environment information as part of its health management framework. These categories directly map onto the claimed environmental and physical factors and represent standard contextual attributes used in health data analysis. A PHOSITA would have had a clear motivation to incorporate these known factors into the verification process, as they influence physiological measurements and would predictably improve accuracy in assessing the validity of health related data.
Regarding claim 7, Chen and Bordin teach the invention in claim 1, as discussed above, and further teach wherein subsequent to receiving the health detection data associated with the target user from the Internet- of-Things health detection terminal, the health data management method further comprises (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.”):
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’’).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to apply data filtering rules to health measurement data, as Chen teaches receiving heterogeneous physiological measurements from various monitoring devices and sending them to the server, which necessarily requires handling data integrity issues across different devices and formats. Bordin teaches standardizing incoming medical measurement data using predefined data format rules (NEDA Data Dictionary) and filtering out abnormal or incomplete records before model evaluation. A PHOSITA would have been motivated to combine Chen’s multi device data acquisition with Bordin’s data filtering and anomaly removal techniques to ensure data quality prior to health model processing, a predictable and well understood improvement in health analytics systems.
Regarding claim 8, Chen and Bordin teach the invention in claim 1, as discussed above, and further teach wherein prior to receiving the health detection data associated with the target user from the Internet-of-Things health detection terminal, the health data management method further comprises (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.”):
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 the 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.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to require user login verification prior to receiving health detection data because Chen teaches that the health management server and first terminal display authorization requests, receive login related confirmation inputs, transmit verification codes, and verify a user’s identity before retrieving personal health information. These steps correspond to receiving login information, verifying it with user data, and returning a verification result. A PHOSITA would have been motivated to implement such authentication requirements because secure access to personal health data is a well established necessity in networked health monitoring systems, and combining Chen’s login and verification workflow with the claimed method yields predictable results in maintaining data security and proper user and data association.
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 and Bordin teach the invention in claim 3, as discussed above, and further teach wherein the program is executed by a processor so as to implement the steps of the health data management method according to claim 1 (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.”).
Therefore, it would be obvious to a PHOSITA before the effective filing date of the invention to store the program on a computer readable storage medium, because Chen teaches providing a computer readable storage medium storing a program that is executed by a processor to implement its health-management methods. The resulting structure is nothing more than the standard computer readable medium implementation of a method, yielding predictable results.
Claims 12, 15 and 16 are analogous to claim 4, thus claims 12, 15, and 16 are similarly analyzed and rejected in a manner consistent with the rejection of claim 4.
Claims 13-14 are analogous to claims 2-3, thus claims 13-14 are similarly analyzed and rejected in a manner consistent with the rejection of claims 2-3.
Claims 17-20 are analogous to claims 5-8, thus claims 17-20 are similarly analyzed and rejected in a manner consistent with the rejection of claims 5-8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685