DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Acknowledgement is made of applicant’s claim for foreign priority to 27 August 2021 under 35 U.S.C. 119(a)-(d).
Response to Amendment
Claims 1-20 were previously pending in this application. The amendment filed 26 November 2025 has been entered and the following has occurred: Claims 1, 10, & 20 have been amended. Claim 6 have been cancelled.
Claims 1-5 & 7-20 remain pending in the application.
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 1-5 & 7-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claims recite subject matter within a statutory category as a process (claims 1-5 & 7-19) and machine (claim 20) which recite steps of:
obtaining, by an electronic device, a first intervention plan generated based on user data of a first user;
performing, by electronic device, model training based on individual training data of the first user to obtain a first individual health benefit prediction model, the individual training data of the first user comprising an execution status of a historical intervention plan of the first user, a value of the health indicator before the historical intervention plan of the first user is executed, and a value of the health indicator after at least a part of the historical intervention plan of the first user is executed;
inputting, by the electronic device, the user health data in the user data and the at least a part of the first intervention plan into the first individual health benefit prediction model to predict the predicted value of the health indicator obtained after the at least a part of the first intervention plan is completed; and
generating, by the electronic device, a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed.
These steps of obtaining a first intervention plan generated based on user data of a first user, inputting user health data and at least first part of an intervention plan into a computational model, and generating a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed., as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind, but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the obtaining a first intervention plan generated based on user data of a first user language, obtaining a first intervention plan in the context of this claim encompasses a mental process of a person either generating or merely obtaining an intervention/treatment plan for a patient, such as via medical records or from a provider. Similarly, the limitations of inputting user health data and at least part of an intervention plan into a computational model and subsequently generating a predicted value of a health indicator obtained after a part of the first intervention plan is completed, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, such as a person plugging data inputs into a computational model, such as a digital or computer-generated model, to evaluate and re-evaluating the treatment plan/health values for a patient after each part of an intervention plan is completed by a patient to determine optimal treatment pathways. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the independent claims recite an abstract idea.
These steps of obtaining a first intervention plan generated based on user data of a first user, user health data and at least first part of an intervention plan into a computational model, and generating a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. MPEP 2106.04(a)(2)(II) describes various methods of organizing human activity, including fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). The steps recited above amount to certain methods of organizing human activity, at least by including interpretation of managing a treatment and/or intervention plan for a patient, under broadest reasonable interpretation, which includes aspects of managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). That is, the steps recited effectively managing the rules or instructions for a patient to follow regarding an intervention/treatment plan, and utilizing computational models in order to generate predicted health indicators in response to the patient performing said rules or instructions. As such, the steps recited also fall within the “Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the independent claims recite an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-5 & 7-19, reciting particular aspects of how obtaining varying data, generating values of health indicators, generating supplemental intervention plans, etc. may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of an electronic device, a memory, and/or a processor amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s specification [0083] for an electronic device; [0083] for a memory; [0083] for a processor, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of obtaining a first intervention plan generated based on user data of a first user amounts to mere data gathering; recitation of inputting the user health data in the user data and the at least a part of the first intervention plan into the first individual health benefit prediction model to predict the predicted value of the health indicator obtained after the at least a part of the first intervention plan is completed, generating a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed amounts to selecting a particular data source or type of data to be manipulated; recitation of performing model training based on individual training data of the first user to obtain a first individual health benefit prediction model, generating a predicted value of a health indicator amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of a health management method/device and/or a health indicator/intervention plan, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-5 & 7-19, which generally recite limitations relating to performance of steps by an electronic device; an intelligent wearable device; a health check device; an intelligent fitness device, a prediction model, additional limitations which amount to invoking computers as a tool to perform the abstract idea see Applicant’s specification [0083] for an electronic device; [00130] for a an intelligent wearable device; [00130] for a health check device; [00130] for an intelligent fitness device; [00118] for a prediction model, see MPEP 2106.05(f); claims 2, 5, 8-9, 13, 15, & 18-19, which recite limitations relating to obtaining the first intervention plan generated based on user data of the first user, obtaining the user data of the first user, obtaining a predicted value of a health indicator, obtaining one or more cycles of the intervention plan, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 3-13 & 15-19, which recite limitations relating to specifying the user data user information, risk factors, model training data, health indicator, the intervention plan, and/or adjusting the intervention plan, generating subsequent intervention plans and/or health indicators, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated, claims 10 & 14, which recite limitations relating to the electronic device being located inside or within the intelligent wearable device, the health check device, or the intelligent fitness device, additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as obtaining a first intervention plan generated based on user data of a first user, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); performing model training based on individual training data of the first user to obtain a first individual health benefit prediction model, generating a predicted value of a health indicator, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); maintaining one or more patent health indicators and/or medical intervention plan, such as in an electronic health record of the patient, performing model training based on individual training data of the first user to obtain a first individual health benefit prediction model which includes updating of model constraints based on training data inputted into the model, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing computerized instructions for performance of the steps recited, storing one or more intervention plans, storing one or more user data, storing one or more health indicator values, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); obtaining a first intervention plan generated based on user data of a first user, which could include, under BRI, extraction/parsing of said intervention plan from a medical record, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-5 & 7-19, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2, 5, 8-9, 13, 15, & 18-19, which recite limitations relating to obtaining the first intervention plan generated based on user data of the first user, obtaining the user data of the first user, obtaining a predicted value of a health indicator, obtaining one or more cycles of the intervention plan, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 3-5, 7-13 & 15-19, which recite limitations relating to specifying the user data user information, risk factors, model training data, health indicator, the intervention plan, and/or adjusting the intervention plan, generating subsequent intervention plans and/or health indicators, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 17-18, which recite limitations relating to maintaining one or more model training parameters and/or intervention plan parameters, e.g. intervention plan cycles, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 2-5 & 7-19, which recite limitations relating to storing computerized instructions for performance of the steps recited, storing obtained user data predicted values of a health indicator, intervention plans, etc., e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 2, 5, & 8-9 which recite limitations relating to obtaining an intervention plan, user data, which under BRI includes extraction/parsing of said data from a medical record, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 7-8, & 19-20 is rejected under 35 U.S.C. 103 as being unpatentable over Fleming et al. (U.S. Patent Publication No. 2019/0006040), hereinafter “Fleming”, in view of Shenzen et al. (Chinese Patent Application CN113241183A), hereinafter “Shenzen”.
Claim 1 –
Regarding Claim 1, Fleming discloses a health management method comprising:
obtaining, by an electronic device, a first intervention plan generated based on user data of a first user (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data);
generating, by the electronic device, a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed (See Fleming Par [0052] which discloses each digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., comprises information specific to the user and/or specific to the diabetes risk level for the user; See Fleming Par [0050] which discloses the server determining a diabetes risk score based at least on instant sensor data, historical sensor data, risk factors, etc.; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0055] which discloses detecting or predicting the risk level, i.e. health indicator, for a diabetic patient from varying patient data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed).
While Fleming generally discloses the use of a learning model for determinations and generations of treatment plans, as well as, predictions of treatment effects, Fleming does not explicitly mention training a model based on an execution status of a historical intervention plan of the first user, a value of the health indicator before the historical intervention plan of the first user is executed, and a value of the health indicator after the at least a part of the historical intervention plan of the first user is executed to determine individual health benefit.
Therefore, Shenzen discloses the generating, by the electronic device, the predicted value of the health indicator obtained after the at least a part of the first intervention plan is completed comprises:
performing, by the electronic device, model training based on individual training data of the first user, to obtain a first individual health benefit prediction model, wherein the individual training data of the first user comprises an execution status of a historical intervention plan of the first user, a value of the health indicator before the historical intervention plan of the first user is executed, and a value of the health indicator after the at least a part of the historical intervention plan of the first user is executed (See Shenzen p. 19 which discloses a model estimating a treatment effect of each reference treatment scheme based on the corresponding physiological state of each reference treatment scheme, such that the therapeutic outcome of the reference treatment regimen is estimated based on the physiological state of the biological tissue after application of the reference treatment regimen, and the higher the score is, the better the treatment effect is, and the treatment outcome can be measured as the difference between the physiological state corresponding to the reference treatment regimen and the healthy physiological state, such that the smaller the difference between the physiological state and the healthy physiological state corresponding to the reference treatment regimen, i.e. health indicator values before and after execution of a historical intervention plan/regimen, the better the corresponding treatment outcome, and the higher the corresponding score, and further specifically states that a reference treatment plan predicted for a target biological tissue and a physiological state of the biological tissue after application of the reference treatment plan are visually displayed, such that the predicted reference treatment protocol and the corresponding physiological state are visualized and displayed; See Shenzen p. 20 which discloses the reference treatment protocols are evaluated to compare the merit of the reference treatment protocols, so as to give a recommended grade of the reference treatment protocols and improve the reference value of the reference treatment protocols and future treatment protocols); and
inputting, by the electronic device, the user health data in the user data and the part and/or all of the first intervention plan into the first individual health benefit prediction model, to predict the predicted value of the health indicator obtained after the part and/or all of the first intervention plan is completed (See Shenzen p. 19 which discloses a model estimating a treatment effect of each reference treatment scheme based on the corresponding physiological state of each reference treatment scheme, such that the therapeutic outcome of the reference treatment regimen is estimated based on the physiological state of the biological tissue after application of the reference treatment regimen, and the higher the score is, the better the treatment effect is, and the treatment outcome can be measured as the difference between the physiological state corresponding to the reference treatment regimen and the healthy physiological state, such that the smaller the difference between the physiological state and the healthy physiological state corresponding to the reference treatment regimen, i.e. health indicator values before and after execution of a historical intervention plan/regimen, the better the corresponding treatment outcome, and the higher the corresponding score, and further specifically states that a reference treatment plan predicted for a target biological tissue and a physiological state of the biological tissue after application of the reference treatment plan are visually displayed, such that the predicted reference treatment protocol and the corresponding physiological state are visualized and displayed; See Shenzen p. 20 which discloses the reference treatment protocols are evaluated to compare the merit of the reference treatment protocols, so as to give a recommended grade of the reference treatment protocols and improve the reference value of the reference treatment protocols or future treatment protocols).
The disclosure of Shenzen is directly applicable to the disclosure of Fleming, because both disclosures share limitations and capabilities, such as being directed towards generation of treatment plans for a patient based on patient data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Fleming which generally discloses the use of a learning model for determinations and generations of treatment plans, as well as, predictions of treatment effects to further include training a model based on an execution status of a historical intervention plan of the first user, a value of the health indicator before the historical intervention plan of the first user is executed, and a value of the health indicator after the at least a part of the historical intervention plan of the first user is executed to determine individual health benefit, as disclosed by Shenzen, because this allows for optimizing the therapeutic outcome of reference treatment regimens by comparing the merit of the reference treatment protocols, so as to give a recommended grade of the reference treatment protocols and improve the reference value of the reference treatment protocols (See Shenzen p. 19-20)
Claim 2 –
Regarding Claim 2, Fleming and Shenzen disclose the method of claim 1 in its entirety. Fleming further discloses a method, wherein:
obtaining, by an electronic device, the first intervention plan generated based on user data of the first user comprises:
obtaining, by the electronic device, the user data of the first user (See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0050] which specifically discloses the risk score being based on one or more of user data and/or known risk factors of the user);
recognizing, by the electronic device, a user health risk factor based on the user data (See Fleming Par [0008] which discloses using various data to predict the risk level of a diabetic patient and, based on the risk level recommends optimal meals, recipes, activities, and the like, and detection and/or or prediction of a patient's diabetic risk level is based on patient history and analysis (e.g., using deep learning and visual analysis) of the patient's speech, facial expression, heart rate, etc. captured via one or more sensors on a user device; See Fleming Par [0050] which specifically discloses the risk score being based on one or more of user data and/or known risk factors of the user; See Fleming Par [0055] which discloses detecting or predicting the risk level, i.e. health indicator, for a diabetic patient from varying patient data); and
generating, by the electronic device, the first intervention plan for the user health risk factor (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data/user risk level).
Claim 3 –
Regarding Claim 3, Fleming and Shenzen disclose the method of claim 1 in its entirety. Fleming further discloses a method, wherein:
the user data comprises one or more of basic user information, user behavior data, or user health data (See Fleming Par [0025] which discloses instant sensor data including blood sugar, temperature, weight, risk level, heart rate, etc., constituting user information and/or user health data; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0050] which specifically discloses the risk score being based on one or more of user data and/or known risk factors of the user; See Fleming Par [0060] which discloses that user data including user interface sentiment and engagement may be analyzed, constituting user behavior; See Fleming Par [0074] which includes inputting family size and/or demographics of the user).
Claim 4 –
Regarding Claim 4, Fleming and Shenzen disclose the method of claim 3 in its entirety. Fleming further discloses a method, wherein:
the basic user information comprises one or more of an age and/or a gender (See Fleming Par [0071] which discloses user information comprising the patient/user being 40-years old and a woman);
the user behavior data comprises at least one of exercise data, stress data, sleep data, diet data, drinking data, or smoking data (See Fleming Par [0071] which discloses user behavior data including fatigue, i.e. sleep data, weight gain, and stress); and
the user health data comprises at least one of body weight data, body composition data, blood pressure data, blood glucose data, and blood lipid data (See Fleming Par [0025] which discloses instant sensor data including blood sugar, i.e. glucose, temperature, weight, risk level, heart rate, etc.,).
Claim 5 –
Regarding Claim 5, Fleming and Shenzen disclose the method of claim 2 in its entirety. Fleming further discloses a method, wherein:
the user data comprises basic user information and further comprises one or more of user behavior data or user health data, the basic user information comprising one or more of an age or a gender (See Fleming Par [0071] which discloses user information comprising the patient/user being 40-years old and a woman; See Fleming Par [0025] which discloses instant sensor data including blood sugar, temperature, weight, risk level, heart rate, etc., constituting user information and/or user health data; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0050] which specifically discloses the risk score being based on one or more of user data and/or known risk factors of the user; See Fleming Par [0060] which discloses that user data including user interface sentiment and engagement may be analyzed, constituting user behavior; See Fleming Par [0074] which includes inputting family size and/or demographics of the user); and
the recognizing, by the electronic device, a user health risk factor based on the user data comprises:
obtaining, by the electronic device, from risk factor correspondences of a plurality of groups, a risk factor correspondence of a first group corresponding to the basic user information, wherein different groups correspond to different age ranges and/or genders, and the risk factor correspondence of the first group comprises a correspondence between one or more health risk factors and a corresponding preset condition of the one or more health risk factors, and comprises a correspondence between a first health risk factor and a first preset condition (See Fleming Par [0071] which discloses user information comprising the patient/user being 40-years old and a woman, thereby including age and gender; See Fleming Par [0025] which discloses instant sensor data including blood sugar, temperature, weight, risk level, heart rate, etc., constituting user information and/or user health data; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0050] which specifically discloses the risk score being based on one or more of user data and/or known risk factors of the user; See Fleming Par [0060] which discloses that user data including user interface sentiment and engagement may be analyzed, constituting user behavior; See Fleming Par [0074] which includes inputting family size and/or demographics of the user; See Fleming Par [0055] which discloses predicting risk level according to past history (individual, cohorts or those connected in a social network) and/or context of the person, i.e. a group); and
determining, by the electronic device, the user health risk factor based on the user behavior data and/or the user health data and with reference to the risk factor correspondence of the first group, wherein when one or more of the user behavior data or the user health data meet the first preset condition, the user health risk factor comprises the first health risk factor (See Fleming Par [0071] which discloses user information comprising the patient/user being 40-years old and a woman, thereby including age and gender; See Fleming Par [0025] which discloses instant sensor data including blood sugar, temperature, weight, risk level, heart rate, etc., constituting user information and/or user health data; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0050] which specifically discloses the risk score being based on one or more of user data and/or known risk factors of the user; See Fleming Par [0060] which discloses that user data including user interface sentiment and engagement may be analyzed, constituting user behavior; See Fleming Par [0074] which includes inputting family size and/or demographics of the user; See Fleming Par [0055] which discloses predicting risk level according to past history (individual, cohorts or those connected in a social network) and/or context of the person, i.e. a group).
Claim 7 –
Regarding Claim 7, Fleming and Shenzen disclose the method of claim 1 in its entirety. Fleming further discloses a method, wherein:
the health indicator comprises at least one of a body weight, a body mass index, a body fat percentage, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, total cholesterol, and triglyceride (See Fleming Par [0025] which discloses instant sensor data including blood sugar, temperature, weight, risk level, heart rate, etc., constituting user information and/or user health data; See Fleming Par [0004] which discloses various health indicators including fasting plasma glucose (FPG)).
Claim 8 –
Regarding Claim 8, Fleming and Shenzen disclose the method of claim 1 in its entirety. Fleming further discloses a method, further comprising:
displaying, by the electronic device, the predicted value of the health indicator obtained after the at least a part of the first intervention plan is completed (See Fleming Par [0009] which discloses given the determined current state, an amelioration action (or more than one such actions) will be triggered and the predicted effect shown through visual or other indicators, e.g. a user interface exhibiting display color and intensity).
Claim 19 –
Regarding Claim 19, Fleming and Shenzen disclose the method of claim 1 in its entirety. Fleming further discloses a method, further comprises:
estimating, by the electronic device, the age of the first user based on the user data, and using the age as an estimated user age of the first user (See Fleming Par [0071] which discloses user information comprising the patient/user being 40-years old) wherein
the user data comprises one or more of the user behavior data or the user health data (See Fleming Par [0025] which discloses instant sensor data including blood sugar, temperature, weight, risk level, heart rate, etc., constituting user information and/or user health data; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0050] which specifically discloses the risk score being based on one or more of user data and/or known risk factors of the user; See Fleming Par [0060] which discloses that user data including user interface sentiment and engagement may be analyzed, constituting user behavior; See Fleming Par [0074] which includes inputting family size and/or demographics of the user); and
generating, by the electronic device, a predicted value of an estimated user age of the first user obtained after the at least a part of the first intervention plan is completed (See Fleming Par [0071] which discloses user information comprising the patient/user being 40-years old; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed).
Claim 20 –
Regarding Claim 20, Fleming discloses an electronic device, comprising:
a memory storing instructions (See Fleming Par [0035]); and
at least one processor in communication with the memory, the at least one processor configured, upon execution of the instructions to perform the following steps (See Fleming Par [0035]):
obtaining a first intervention plan generated based on user data of a first user (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data); and
generating a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed (See Fleming Par [0052] which discloses each digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., comprises information specific to the user and/or specific to the diabetes risk level for the user; See Fleming Par [0050] which discloses the server determining a diabetes risk score based at least on instant sensor data, historical sensor data, risk factors, etc.; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0055] which discloses detecting or predicting the risk level, i.e. health indicator, for a diabetic patient from varying patient data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed).
While Fleming generally discloses the use of a learning model for determinations and generations of treatment plans, as well as, predictions of treatment effects, Fleming does not explicitly mention training a model based on an execution status of a historical intervention plan of the first user, a value of the health indicator before the historical intervention plan of the first user is executed, and a value of the health indicator after the at least a part of the historical intervention plan of the first user is executed to determine individual health benefit.
Therefore, Shenzen discloses the generating, by the electronic device, the predicted value of the health indicator obtained after the at least a part of the first intervention plan is completed comprises:
performing, by the electronic device, model training based on individual training data of the first user, to obtain a first individual health benefit prediction model, wherein the individual training data of the first user comprises an execution status of a historical intervention plan of the first user, a value of the health indicator before the historical intervention plan of the first user is executed, and a value of the health indicator after the at least a part of the historical intervention plan of the first user is executed (See Shenzen p. 19 which discloses a model estimating a treatment effect of each reference treatment scheme based on the corresponding physiological state of each reference treatment scheme, such that the therapeutic outcome of the reference treatment regimen is estimated based on the physiological state of the biological tissue after application of the reference treatment regimen, and the higher the score is, the better the treatment effect is, and the treatment outcome can be measured as the difference between the physiological state corresponding to the reference treatment regimen and the healthy physiological state, such that the smaller the difference between the physiological state and the healthy physiological state corresponding to the reference treatment regimen, i.e. health indicator values before and after execution of a historical intervention plan/regimen, the better the corresponding treatment outcome, and the higher the corresponding score, and further specifically states that a reference treatment plan predicted for a target biological tissue and a physiological state of the biological tissue after application of the reference treatment plan are visually displayed, such that the predicted reference treatment protocol and the corresponding physiological state are visualized and displayed; See Shenzen p. 20 which discloses the reference treatment protocols are evaluated to compare the merit of the reference treatment protocols, so as to give a recommended grade of the reference treatment protocols and improve the reference value of the reference treatment protocols and future treatment protocols); and
inputting, by the electronic device, the user health data in the user data and the part and/or all of the first intervention plan into the first individual health benefit prediction model, to predict the predicted value of the health indicator obtained after the part and/or all of the first intervention plan is completed (See Shenzen p. 19 which discloses a model estimating a treatment effect of each reference treatment scheme based on the corresponding physiological state of each reference treatment scheme, such that the therapeutic outcome of the reference treatment regimen is estimated based on the physiological state of the biological tissue after application of the reference treatment regimen, and the higher the score is, the better the treatment effect is, and the treatment outcome can be measured as the difference between the physiological state corresponding to the reference treatment regimen and the healthy physiological state, such that the smaller the difference between the physiological state and the healthy physiological state corresponding to the reference treatment regimen, i.e. health indicator values before and after execution of a historical intervention plan/regimen, the better the corresponding treatment outcome, and the higher the corresponding score, and further specifically states that a reference treatment plan predicted for a target biological tissue and a physiological state of the biological tissue after application of the reference treatment plan are visually displayed, such that the predicted reference treatment protocol and the corresponding physiological state are visualized and displayed; See Shenzen p. 20 which discloses the reference treatment protocols are evaluated to compare the merit of the reference treatment protocols, so as to give a recommended grade of the reference treatment protocols and improve the reference value of the reference treatment protocols or future treatment protocols).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of Fleming which generally discloses the use of a learning model for determinations and generations of treatment plans, as well as, predictions of treatment effects to further include training a model based on an execution status of a historical intervention plan of the first user, a value of the health indicator before the historical intervention plan of the first user is executed, and a value of the health indicator after the at least a part of the historical intervention plan of the first user is executed to determine individual health benefit, as disclosed by Shenzen, because this allows for optimizing the therapeutic outcome of reference treatment regimens by comparing the merit of the reference treatment protocols, so as to give a recommended grade of the reference treatment protocols and improve the reference value of the reference treatment protocols (See Shenzen p. 19-20)
Claims 9-18 are rejected under 35 U.S.C. 103 as being unpatentable over Fleming in view of Shenzen, further in view of Mason et al. (U.S. Patent Publication No. 2021/0134416), hereinafter “Mason”.
Claim 9 –
Regarding Claim 9, Fleming and Shenzen disclose the method of claim 8 in its entirety. Fleming further discloses a method, wherein:
the predicted value of the health indicator obtained after the at least a part of the first intervention plan is completed comprises predicted values of the health indicator obtained after at least a part of cycles in the intervention plans of N cycles in the first intervention plan are separately completed (See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed); and
the displaying, by the electronic device, the predicted value of the health indicator obtained after the at least a part of the first intervention plan is completed comprises:
displaying, by the electronic device, a change trend of the health indicator obtained after the at least a part of the first intervention plan is completed, the change trend of the health indicator comprising the predicted values of the health indicator obtained after the at least a part of cycles in the intervention plans of N cycles in the first intervention plan are separately completed (See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed; See Fleming Par [0009] which discloses given the determined current state, an amelioration action (or more than one such actions) will be triggered and the predicted effect shown through visual or other indicators, e.g. a user interface exhibiting display color and intensity).
While Fleming and Shenzen disclose a predicted value of a health indicator being obtained after at least a part of the first intervention plan is completed, Fleming and Shenzen do not explicitly disclose the intervention plan comprising multiple cycles or sessions as given by:
the first intervention plan comprises intervention plans of N cycles, N is a positive integer greater than 1.
However, Mason discloses the first intervention plan comprises intervention plans of N cycles, N is a positive integer greater than 1 (See Mason Par [0025] which discloses the treatment plan including one or more treatment sessions, i.e. cycles, such that each treatment session can comprise session periods, including a particular exercise or regimen for rehabilitation purposes, such as each session period comprising a particular exercise directed to one or more of strength training, range of motion training, cardiovascular training, endurance training, and the like). The disclosure of Mason is directly applicable to the combined disclosure of Fleming and Shenzen because both disclosures share limitations and capabilities, such as being directed towards developing treatment plans for a user/patient in view of patient data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of het claimed invention to modify the disclosure of Fleming and Shenzen to further include the intervention plan comprising multiple cycles or sessions, as disclosed by Mason, because the intervention plan may comprise multiple training protocols that need to span over multiple sessions, such as each session period comprising a particular exercise directed to one or more of strength training, range of motion training, cardiovascular training, endurance training, and the like (See Mason Par [0025]).
Claim 10 –
Regarding Claim 10, Fleming and Shenzen disclose the method of claim 1 in its entirety. Fleming further discloses a method, wherein:
delivering, by the electronic device, a wearable intervention sub-plan in the first intervention plan to an intelligent wearable device, wherein the wearable intervention sub-plan is a plan that is in the first intervention plan and that is to be executed by the intelligent wearable device (“that is to be executed by…” is understood to be a whereby clause, i.e. an intended result of a process step positively recited that it simply expresses the intended result of a process step positively recited, therefore see Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data; See Fleming Par [0052] which discloses each digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., comprises information specific to the user and/or specific to the diabetes risk level for the user; See Fleming Par [0050] which discloses the server determining a diabetes risk score based at least on instant sensor data, historical sensor data, risk factors, etc.; See Fleming Par [0045] which discloses obtaining instant sensor data, such as varying data in real-time; See Fleming Par [0055] which discloses detecting or predicting the risk level, i.e. health indicator, for a diabetic patient from varying patient data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed);
delivering, by the electronic device, a check intervention sub-plan in the first intervention plan to a health check device, wherein the check intervention sub-plan is a plan that is in the first intervention plan and that is to be executed by the health check device (“that is to be executed by…” is understood to be a whereby clause, i.e. an intended result of a process step positively recited that it simply expresses the intended result of a process step positively recited, therefore see Fleming Par [0079] which discloses a health check, such as a 3-month medical check-up, following implementation of a health plan, such that a regulator stores daily glucose and risk levels for purposes of said check-up, thereby constituting a check intervention sub-plan to check the implementation of said plan and the effectiveness after 3-months being implemented by a health-check device); and
Fleming and Shenzen do not seem to further disclose:
delivering, by the electronic device, a fitness intervention sub-plan in the first intervention plan to an intelligent fitness device, wherein the fitness intervention sub-plan is a plan that is in the first intervention plan and that is to be executed by the intelligent fitness device
However, Mason discloses delivering, by the electronic device, a fitness intervention sub-plan in the first intervention plan to an intelligent fitness device, wherein the fitness intervention sub-plan is a plan that is in the first intervention plan and that is to be executed by the intelligent fitness device (“… that is to be executed by…” is understood to be a whereby clause, i.e. an intended result of a process step positively recited that it simply expresses the intended result of a process step positively recited, therefore, see Mason Par [0025] which discloses a treatment plan possibly including one or more treatment protocols with associated session periods, and each session could include a particular exercise, i.e. fitness, for treating the body part of the patient, such as for post-operative rehabilitation after a knee surgery, and said treatment plan can also include information pertaining a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof, such that the overall treatment plan includes several regimens, thereby reading on “sub-plans” of the overall intervention plan; See Mason Par [0075] which discloses a patient having high blood pressure, such that the treatment device itself may monitor and update said exercises or activities for the user based on updated data being received by the device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of het claimed invention to modify the disclosure of Fleming and Shenzen to further include delivering a fitness intervention sub-plan in the first intervention plan to an intelligent fitness device, as disclosed by Mason, because certain treatment plans contain multiple plans or portions for treatment purposes, such as medicine, therapy, exercise, etc., and thereby allows for a patient to combine both medicine and therapy/exercise for certain procedures/conditions of the patient, such as post-operative rehabilitation, such that the device can iteratively monitor and update the treatment plan for the patient based on updated data being received by said device (See Mason Par [0025] & [0075]).
Claim 11 –
Regarding Claim 11, Fleming, Shenzen, and Mason disclose the method of claim 10 in its entirety. Fleming and Mason further disclose a method, wherein:
the first intervention plan comprises one or more of a first exercise plan, a first diet plan, or a first health habit check-in task set (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data; See Fleming Par [0079] which discloses a health check, such as a 3-month medical check-up, following implementation of a health plan, such that a regulator stores daily glucose and risk levels for purposes of said check-up, thereby constituting a check intervention sub-plan to check the implementation of said plan and the effectiveness after 3-months);
the wearable intervention sub-plan comprises at least a part of the first exercise plan, at least a part of the first diet plan, or at least a part of the first health habit check-in task set (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed; See Fleming Par [0079] which discloses a health check, such as a 3-month medical check-up, following implementation of a health plan, such that a regulator stores daily glucose and risk levels for purposes of said check-up, thereby constituting a check intervention sub-plan to check the implementation of said plan and the effectiveness after 3-months);
the check intervention sub-plan comprises at least a part of health indicator check tasks in the first health habit check-in task set (See Fleming Par [0079] which discloses a health check, such as a 3-month medical check-up, following implementation of a health plan, such that a regulator stores daily glucose and risk levels for purposes of said check-up, i.e. health indicator check tasks, thereby constituting a check intervention sub-plan to check the implementation of said plan and the effectiveness after 3-months); and
the fitness intervention sub-plan comprises at least a part of the first exercise plan (see Mason Par [0025] which discloses a treatment plan possibly including one or more treatment protocols with associated session periods, and each session could include a particular exercise, i.e. fitness, for treating the body part of the patient, such as for post-operative rehabilitation after a knee surgery, and said treatment plan can also include information pertaining a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof, such that the overall treatment plan includes several regimens, thereby reading on “sub-plans” of the overall intervention plan; See Mason Par [0075] which discloses a patient having high blood pressure, such that the treatment device itself may monitor and update said exercises or activities for the user based on updated data being received by the device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of het claimed invention to modify the disclosure of Fleming and Shenzen to further include delivering a fitness intervention sub-plan in the first intervention plan to an intelligent fitness device, as disclosed by Mason, because certain treatment plans contain multiple plans or portions for treatment purposes, such as medicine, therapy, exercise, etc., and thereby allows for a patient to combine both medicine and therapy/exercise for certain procedures/conditions of the patient, such as post-operative rehabilitation, such that the device can iteratively monitor and update the treatment plan for the patient based on updated data being received by said device (See Mason Par [0025] & [0075]).
Claim 12 –
Regarding Claim 12, Fleming and Shenzen disclose the method of claim 10 in its entirety. Fleming and Mason further disclose a method, wherein:
the first intervention plan comprises intervention plans of N cycles, N is a positive integer greater than 1 (See Mason Par [0025] which discloses the treatment plan including one or more treatment sessions, i.e. cycles, such that each treatment session can comprise session periods, including a particular exercise or regimen for rehabilitation purposes, such as each session period comprising a particular exercise directed to one or more of strength training, range of motion training, cardiovascular training, endurance training, and the like);
the wearable intervention sub-plan is a wearable intervention sub-plan of one or all cycles in the first intervention plan (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed, thereby being one or all cycles of an intervention plan);
the check intervention sub-plan is a check intervention sub-plan of one or all cycles in the first intervention plan (See Fleming Par [0079] which discloses a health check, such as a 3-month medical check-up, following implementation of a health plan, such that a regulator stores daily glucose and risk levels for purposes of said check-up, thereby constituting a check intervention sub-plan to check the implementation of said plan and the effectiveness after 3-months being implemented by a health-check device); and
the fitness intervention sub-plan is a fitness intervention sub-plan of one or all cycles in the first intervention plan (See Mason Par [0025] which discloses a treatment plan possibly including one or more treatment protocols with associated session periods, and each session could include a particular exercise, i.e. fitness, for treating the body part of the patient, such as for post-operative rehabilitation after a knee surgery, and said treatment plan can also include information pertaining a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof, such that the overall treatment plan includes several regimens, thereby reading on “sub-plans” of the overall intervention plan; See Mason Par [0075] which discloses a patient having high blood pressure, such that the treatment device itself may monitor and update said exercises or activities for the user based on updated data being received by the device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of het claimed invention to modify the disclosure of Fleming and Shenzen to further include disclose the intervention plan comprising multiple cycles or sessions, as disclosed by Mason, because the intervention plan may comprise multiple training protocols that need to span over multiple sessions and comprise multiple types of sessions such as each session period comprising a particular exercise directed to one or more of strength training, range of motion training, cardiovascular training, endurance training, medicine, sleep, etc., (See Mason Par [0025] & [0075]).
Claim 13 –
Regarding Claim 13, Fleming and Shenzen disclose the method of claim 1 in its entirety. Fleming and Mason further disclose a method, wherein:
the first intervention plan comprises the intervention plans of N cycles, N is a positive integer greater than 1 (See Mason Par [0025] which discloses the treatment plan including one or more treatment sessions, i.e. cycles, such that each treatment session can comprise session periods, including a particular exercise or regimen for rehabilitation purposes, such as each session period comprising a particular exercise directed to one or more of strength training, range of motion training, cardiovascular training, endurance training, and the like), and the method further comprises:
obtaining, by the electronic device, one or more of actual execution data or a value of the health indicator in an execution process of a first cycle in the first intervention plan (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed, thereby being one or all cycle of the intervention plan), wherein
one or more of the actual execution data or the value of the health indicator is obtained through monitoring by one or more of the electronic device or another device in the health management system in which the electronic device is located (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed, thereby being one or all cycle of the intervention plan).
It would have been obvious to one of ordinary skill in the art before the effective filing date of het claimed invention to modify the disclosure of Fleming and Shenzen to further include disclose the intervention plan comprising multiple cycles or sessions, as disclosed by Mason, because the intervention plan may comprise multiple training protocols that need to span over multiple sessions, such as each session period comprising a particular exercise directed to one or more of strength training, range of motion training, cardiovascular training, endurance training, and the like (See Mason Par [0025]).
Claim 14 –
Regarding Claim 14, Fleming, Shenzen, and Mason disclose the method of claim 13 in its entirety. Fleming further discloses a method, comprising:
the another device in the health management system in which the electronic device is located comprises one or more of the intelligent wearable device, the health check device, or the intelligent fitness device (See Fleming Par [0031], [0052] which discloses the first digitized menu option, i.e. a single meal plan, daily meal plan, weekly meal plan, etc., which constitutes an intervention plan, is generated to reduce the diabetic risk of the user and is selected based on nutrition requirements of the user, and is thereby based on user data; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed, thereby being one or all cycle of the intervention plan; Fleming Par [0027], [0044], [0055], and [0075] which disclose the use of a mobile/wearable device, including an intelligent meal planner, i.e. the meal planner device comprises a wearable device).
Claim 15 –
Regarding Claim 15, Fleming, Shenzen, and Mason discloses the method of claim 13 in its entirety. Fleming and Mason further disclose a method, wherein:
the predicted value of the health indicator obtained after the first intervention plan is completed comprises the predicted value of the health indicator obtained after the first cycle in the first intervention plan is completed (See Fleming Par [0071] which discloses user information comprising the patient/user being 40-years old; See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, constituting a predicted value of a health indicator obtained after at least a part of the first intervention plan is completed); and
the method further comprises:
comparing, by the electronic device, the predicted value of the health indicator obtained after the first cycle in the first intervention plan is completed and an actual value of the health indicator obtained after an intervention plan of the first cycle is completed for a degree of consistency between the values, to obtain an intervention effect assessment result (See Mason Par [0053]-[0054] which discloses comparing treatment information to the treatment plan being performed by the user, such that expected information, which pertains to the user while the user uses the treatment device to perform the treatment plan is compared to the prediction, to determine that the treatment plan is having the desired effect, i.e. degree of consistency and if not, the treatment plan can be modified).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Fleming, Shenzen, and Mason, to further include comparing the predicted value of the health indicator obtained after the first cycle in the first intervention plan is completed and an actual value of the health indicator obtained after an intervention plan of the first cycle is completed to obtain an intervention effect assessment result, as disclosed by Mason, because this allows for determining if the treatment plan is having the desired effect, and if not, the treatment plan can be updated accordingly (See Mason Par [0053]-[0054]).
Claim 16 –
Regarding Claim 16, Fleming, Shenzen, and Mason discloses the method of claim 15 in its entirety. Fleming and Mason further disclose a method, further comprising:
generating, by the electronic device, an assessment of one or more intervention plans in the first intervention plan based on the intervention effect assessment result and with reference to one or more of the actual execution data or the value of the health indicator in the execution process of the first cycle (See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, i.e. adjusted first intervention plan after at least a first intervention plan or portion of the plan is completed, such that following plans/regimen would effectively be based on this check-up; See Mason Par [0053]-[0054] which discloses comparing treatment information to the treatment plan being performed by the user, such that expected information, which pertains to the user while the user uses the treatment device to perform the treatment plan is compared to the prediction, to determine that the treatment plan is having the desired effect, i.e. degree of consistency and if not, the treatment plan can be modified), and
using the assessment as an assessment result of the first intervention plan (See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, i.e. adjusted first intervention plan after at least a first intervention plan or portion of the plan is completed, such that following plans/regimen would effectively be based on this check-up; See Mason Par [0053]-[0054] which discloses comparing treatment information to the treatment plan being performed by the user, such that expected information, which pertains to the user while the user uses the treatment device to perform the treatment plan is compared to the prediction, to determine that the treatment plan is having the desired effect, i.e. degree of consistency and if not, the treatment plan can be modified).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combined disclosure of Fleming, Shenzen, and Mason, to further include comparing the predicted value of the health indicator obtained after the first cycle in the first intervention plan is completed and an actual value of the health indicator obtained after an intervention plan of the first cycle is completed to obtain an intervention effect assessment result, as disclosed by Mason, because this allows for determining if the treatment plan is having the desired effect, and if not, the treatment plan can be updated accordingly (See Mason Par [0053]-[0054]).
Claim 17 –
Regarding Claim 17, Fleming, Shenzen, and Mason discloses the method of claim 16 in its entirety. Fleming further discloses a method, further comprising:
adjusting, by the electronic device, a second cycle intervention plan of a second cycle in the first intervention plan based on the assessment result of the first intervention plan, wherein the second cycle is a next cycle of the first cycle (See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, i.e. adjusted first intervention plan after at least a first intervention plan or portion of the plan is completed, such that following plans/regimen would effectively be based on this check-up).
Claim 18 –
Regarding Claim 18, Fleming, Shenzen, and Mason discloses the method of claim 17 in its entirety. Fleming further discloses a method, wherein:
after the adjusting the intervention plan of the second cycle in the first intervention plan (See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, i.e. adjusted first intervention plan after at least a first intervention plan or portion of the plan is completed, such that following plans/regimen would effectively be based on this check-up), the method further comprises:
generating, by the electronic device, the predicted value of the health indicator obtained after the at least a part of an adjusted first intervention plan is completed (See Fleming Par [0079] which discloses a 3-month check-up, and shows a user’s monthly budget has not been exceeded and the health of her family is not compromised, such that the user’s risk level is predicted by the regulator, i.e. adjusted first intervention plan after at least a first intervention plan or portion of the plan is completed).
Response to Arguments
Applicant's arguments filed 26 November 2025 have been fully considered but they are not persuasive:
Regarding 35 U.S.C. 112(b) rejections of claims 10-12, Applicant argues on p. 9-10 of Arguments/Remarks that claim 10 has been amended to overcome previous 35 U.S.C. 112(b) rejections for claim 10 and claims dependent therefrom. Examiner agrees with Applicant’s arguments. Therefore, the 35 U.S.C. 112(b) rejections for claim 10 and claims dependent therefrom (claims 11-12) have been withdrawn.
Regarding 35 U.S.C. 101 rejections of claims 1-20, Applicant argues on p. 10-11 of Arguments/Remarks that independent claims 1 & 20 produce a concrete and tangible result to a user or users, at least by generating a first intervention plan for the user(s), such as by performing physical actions. Examiner respectfully disagrees with Applicant’s arguments. The production of an intervention plan for a user does not necessarily entail a tangible and/or concrete result. That is, there is no indication or support for the intervention plan being implemented on the back end by performance/effectuation of the computer system. Rather, the output/production of the intervention plan is merely an outputted recommendation or guidelines for a user or doctor to follow in terms of a medical treatment and/or intervention for a patient. Applicant’s arguments are not further substantiated beyond merely restating the claim limitations found in independent claims, which have been determined to constitute insignificant, extra-solution activity. Therefore, independent claims 1 & 20 and claims dependent therefrom 2-19 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 101 rejections of claims 1-20, Applicant argues on p. 11 of Arguments/Remarks that independent claims 1 & 20 disclose significantly more than an abstract idea by producing a concrete and tangible result to a user or users. Applicant argues similar arguments to the arguments found above regarding generating a first intervention plan for the user(s), such as by performing physical actions producing a concrete and tangible result to a user, without further substantiation. Examiner respectfully disagrees with Applicant’s arguments. As mentioned above, there is no indication or support for the intervention plan being implemented on the back end by performance/effectuation of the computer system. Rather, the output/production of the intervention plan is merely an outputted recommendation or guidelines for a user or doctor to follow in terms of a medical treatment and/or intervention for a patient. Applicant’s arguments are not further substantiated beyond merely restating the claim limitations found in independent claims, which have been determined to constitute insignificant, extra-solution activity. Therefore, independent claims 1 & 20 and claims dependent therefrom 2-19 remain rejected under 35 U.S.C. 101.
Regarding 35 U.S.C. 102 rejections of claims 1-20, Applicant argues on p. 13-14 that Fleming does not disclose the newly amended limitations to independent claims 1 & 20, and therefore 35 U.S.C. 102 rejections made over Fleming should be withdrawn for claims 1-5, 7-8, 10-11, & 19-20. Applicant further argues on p. 14-15 that Shenzen does not cure the deficiencies of Fleming. Examiner agrees with Applicant’s arguments regarding Fleming. Therefore, the 35 U.S.C. 102 rejections have been withdrawn. However, upon further consideration, a new ground of rejection has been made under 35 U.S.C. 103 over Fleming in view of Shenzen. That is, amended independent claims 1 & 20 now incorporate limitations from previously pending, dependent claim 6, which was previously met in its entirety by Fleming and Shenzen. Therefore, amended independent claims 1 & 20 which now incorporate said limitations are similarly rejected under 35 U.S.C. 103 over Fleming in view of Shenzen. While Applicant further argues on p. 14-15 that Shenzen does not effectively disclose the limitations from previously pending, dependent claim 6 that are not found in amended independent claims 1 & 20, Applicant merely restates claim language now found independent claim 1, without further substantiation of said arguments. For instance, Applicant argues that Shenzen does not disclose model training based on individual training data for a health prediction model. Therefore, Examiner points to the 35 U.S.C. 103 rejections for claim 1 made over Fleming in view of Shenzen which effectively shows that Shenzen reads on each and every newly amended limitation in independent claims 1 & 20 that were previously found in dependent claim 6. As such, claims 1-5, 7-8, 10-11, & 19-20 remain rejected under 35 U.S.C. 103.
Regarding 35 U.S.C. 102/103 rejections of claims 1-20, Applicant argues on p. 15 of Arguments/Remarks that because independent claims 1 & 20 are purportedly allowable over the prior art, dependent claims 2-5, 7-8, 10-11, & 19 are also allowable over the prior art by virtue of dependency. Examiner respectfully disagrees with Applicant’s arguments. As shown above, independent claims 1 & 20 remain rejected under 35 U.S.C. 103. Therefore, Applicant’s arguments regarding claims 1 & 20 being purportedly allowable over the prior art and therefore dependent claims 2-5, 7-8, 10-11, & 19 also being purportedly allowable are rendered moot. As such, claims 1-5, 7-8, 10-11, & 19-20 remain rejected under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Neumann et al. (U.S. Patent Publication No. 2020/0342352) discloses a behavior modification support system including a diagnostic engine receiving at least a biological extraction from a user and generating at least a request for a behavior modification;
Applebaum et al. (U.S. Patent Publication No. 2019/0074080) discloses methods and systems for managing lifestyle and health interventions, including addressing a variety of lifestyle-related health conditions including body weight and cardiometabolic disorders such as type-II diabetes;
Pacione et al. (U.S. Patent Publication No. 2015/0289808) discloses a nutrition and activity management system is disclosed that monitors food intake and energy expenditure of an individual through the use of a body-mounted sensing apparatus, such that the system delivers relevant and predictive feedback regarding the mutual effect of the user's energy expenditure, food consumption and other measured or derived or manually input physiological contextual parameters upon progress toward a goal.
Applicant's amendment necessitated the new ground 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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/H.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684