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 .
DETAILED ACTION
The amendment filed on July 15, 2025 has been entered and considered. Claims 1amd 13 are amended. Claims 11-12 are canceled. Claims 1-10 and 13-15 are pending and under examination in this Office action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 15, 2025 has been entered.
Response to Amendment
The rejections to claims 1-11 and 13-15 under 35 U.S.C. 112(a), written description support and 112(b) are now withdrawn in view of the claim amendment.
The rejection to claim 11 under 35 U.S.C. 112(b) is now withdrawn in view of the claim cancellation.
In view of the claim amendment, new grounds of rejections are now made under 35 U.S.C. 101, 112(a), written description support, 112(b) and 103. Please refer to the respective rejection sections for the details.
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 1-10 and 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1-10 are directed to an “device” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Claims 13-15 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claims 1 and 13 recite (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), substantially as follows: starting p.2, line 25: “applying the collected patient information and the blood pulse parameter to a trained machine learning model”, “estimating real time set of traditional medicine system parameters based on results of the trained machine learning model”, “ determining a health score and updating the health score”, “comparing…”, “identifying a disease…”, “generating a recommendation message”, “perform one or more operations based the generated recommendation message”.
In claims 1 and 13, the above recited steps can be practically performed in the human mind, with the aid of a pen and paper or with a generic computer, in a computer environment, or merely using the generic computer as a tool to perform the steps.
In regard to “applying the collected patient information and the blood pulse parameter to a trained machine learning model”, “estimating real time set of traditional medicine system parameters based on results of the trained machine learning model” – under the broadest reasonable interpretation, to apply input data to a trained machine learning model and to obtain output data requires steps that cover a mental process. For example, one may reference to a look up table or use common medical knowledge to judge what disease a subject may have depending on the patient information and the blood pulse parameters of the subject. The disease may be presented as a set of parameters.
In regard to “determining a health score based on the blood pulse parameters” and “updating the health score based on the real time set of the traditional medicine system parameters, time of day, season, and diet of the patient” – a person can perform this step as a mental step, with pen and paper or a generic computer. A person may observe the blood pulse parameters and assign a score according to the magnitude of the parameters. A person can further adjust the score by considering other parameters. For example, one can assign the resting blood pulse with a score, and one would expect that the score to be increased when the resting state is altered to a running state during the day, or decreased when the resting state is altered to a sleeping state at night.
In regard to “comparing…” and “identifying a disease…” – these steps can be performed mentally. One can visually observe and compare the real time parameters with the reference. One can mentally determine, for example, if the real time parameter is higher than the reference, that the subject is in a diseased status.
There is nothing recited in the claim to suggest an undue level of complexity in how the oscillometric envelope, the feature value and the bio-information to be identified and how a reconstruction process to be selected. Therefore, a person would be able to perform the identification and selection mentally or with a generic computer.
Prong Two: Claims 1 and 13 do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) additional steps of the one or more light sources, the one or more sensors, various processors, a memory, a display providing a GUI, collecting patient information from an interactive AI-based questionnaire, and collect blood pulse parameters that include both static and dynamic parameters, and (2) generate recommendation message, and perform one or more operations based on the generated recommendation message, which comprises generating alerts, new treatment plan or new diet plan.
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details).
The step in (2) represents merely notification outputting by a processor as a post-solution activity and is recited at a high level of generality. The recommendation is generated based on the identified disease. The operations are merely generating alerts or medical plans. However, there is no recitation in regard to how the patient is to be treated, or what the treatment is, but instead merely covers any possible treatment hat a medical professional decides to administer to the patient. As such, there is no meaningful constraints on the recommendation step such that any particular treatment or prophylaxis consideration would apply.
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated bio-information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1 and 13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite (1) additional steps of the one or more light sources, the one or more sensors, various processors, a memory, a display providing a GUI, collecting patient information from an interactive AI-based questionnaire, and collect blood pulse parameters that include both static and dynamic parameters, and (2) generate recommendation message, and perform one or more operations based on the generated recommendation message, which comprises generating alerts, new treatment plan or new diet plan.
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details).
The step in (2) represents merely notification outputting by a processor as a post-solution activity and is recited at a high level of generality. The recommendation is generated based on the identified disease. The operation is merely providing alerts or new medical plans. However, the recommended interventions, medical remedies, the treatment plan, and the new medical plans are not recited as being implemented or executed. It is at most an instruction to apply the abstract idea and mere instructions to apply an exception cannot provide an inventive concept.
The above identified additional steps are performed by light sources, light detectors, and computer processors coupled with a memory equipped with trained machine learning model for data analysis, which are all well-known, routine and conventional tools, as evidenced by Qayyum et al., “Secure and robust machine learning for healthcare: a survey”. IEEE Reviews in Biomedical Engineering, Vol. 14, Jan 2021, pp.156-180.
In Qayyum, it teaches in p.161, Col. Right how ML is used in real-time heath monitoring, for which the system is developed by integrating mobile and cloud for monitoring of heart rate using PPG signals, which is obtained via light sensor and detectors. Different types of ML model and training method is cited in this review article for monitoring, treatment, clinical workflows and diagnosis.
Accordingly, these additional steps and tools for measuring a pulse wave signal and contact pressure, and outputting a notification amount to no more than insignificant conventional extra-solution activity. Mere insignificant conventional extra-solution activity cannot provide an inventive concept. The claims hence are not patent eligible.
Dependent Claims
The following dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons:
Describe the type of diseases identified (claims 6 and 14)
Describe the type of parameters (claims 7 and 15)
The following dependent claims merely further describe the extra-solution activities and therefore, do not amount to significantly more than the judicial exception or integrate the abstract idea into a practical application for similar reasons:
describing the light sensor, the light detector and the physiological sensor (claims 2-5);
describe further tools that are also well-known routine and conventional (the battery module - claim 8; the display – claim 9, the Bluetooth or WiFi module – claim 10);
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 and 13-15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 13 recite the limitation of “updating the health score based on the real time set traditional medicine system parameters, time of day, season, and diet of the patient”. This limitation is a computer/processor-implemented functional claim limitation as it is recited to be performed by a disease identification processor, and it refers to a generic health score. Yet the specification does not disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed functions, i.e., how does a processor analyze the real time set of the traditional medicine system parameters, the time of day, the season, and the diet of the patient to account all of them, as required by the claim recitation, to determine how the health score should be updated, in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing.
In the specification, [0085] discloses this process and refers to FIG.12, yet it does not have any further disclosure in regard to how the score is derived, nor in regard to how it is updated. FIG.12 illustrates a temporal variance of SDNM and HR before, during and after lunch yet the score has a fixed value of 84. Further, FIG.12 appears to be specifically for diabetes, there is no disclosure in regard to how this process may be implemented for a health score that is recited in the claim for a generic health condition.
It is not enough to disclose that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015). As the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention, these claims are rejected for lack of written description. For more information regarding the written description requirement, see MPEP §§ 2161, 2162-2163.07(b).
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10 and 13-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites in p.3, line 3: real time set of “traditional medicine system parameters”, and in line 11 “traditional medicine system parameters” that render the scope of the claim indefinite. It is unclear if these terms, and the identical term recited in p.2, line 1, the preamble, refer to the same. The same rejection applies to claim 13 for the substantially identical limitation recited in p.6, lines 7, 14 and 19.
Claim 1, p.3, line 18 recites “the real time set traditional medicine system parameters” that lacks proper antecedent basis. It is suggested to amend this term to –the real time set of the traditional medicine system parameters--.
Claim 1, p.2, line 4 recites “one or more patient blood pulse parameters”. Yet for the rest of the claims “the blood pulse parameters” are recited in p.2 line 21, p.3 line 1, p.3 line 9, and p.3 line 16. Further p.2 line 18 recite “blood pulse parameters”. It is unclear whether all these terms refer to the same. Consistent claim language with proper antecedent basis is required. The same rejection applies to claim 13.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-2, 5-10 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Bahrami et al, US 2017/0308671 A1, hereinafter Bahrami, in view of Bhatia et al., US 2021/0027888 A1, further in view of Oser et al., US 2020/0411185 A1, hereinafter Oser.
Claims 1 and 13. Bahrami teaches in FIG.1 and FIG.7 a wearable device and a method for estimating traditional medicine system parameters,
the wearable device ([0113]: biometric time series based patient data generated by personal sensors or wearable computers; FIG.7) comprising:
one or more light sources (1010) configured to stimulate skin of a patient through light rays; one or more sensors (1030) configured to capture one or more patient blood pulse parameters ([0127]: LED sensors 1010 includes four different types of LED light sources configured to emit light at different wavelengths and photosensors 1030 include photosensors configured to detect the different wavelengths of light emitted by the four different types of LED light sources after passing through a user’s finger placed in the finger probe 1040; and [0130]: FIG.12, the one or more red, IR and NIR LEDs are used to determine the volume of blood being measured including a detection of pulse rate, PPG and blood oxygenation) – the pulse rate is considered the “one or more patient blood pulse parameters” as claimed, wherein
one or more sensors (1030) comprises one or more light sensors, a detector, and one or more physiological sensors ([0129]: signals generated from the sensors in device 1000 are processed using one or more of the techniques…to detect noninvasively systolic, diastolic and glucose in addition to pulse, blood oxygenation, PPG, and temperature) – the one or more sensors are the “one or more light sensors” that are also detectors as claimed. The sensors that detect the above various physiological parameters are the “one or more physiological sensors” as claimed;
a hardware; a display providing a user interface; and a memory coupled to the hardware processor, wherein the memory comprises a set of program instructions in the form of a plurality of subsystems, configured to be executed by the hardware processor ([0135]: at least one non-transitory computer-readable storage medium encoded with a computer program, which when executed on a processor, performs functions of the embodiments of the present invention; and FIG.2: various subsystems performing various functions; [0128]: device 1000 also includes display 1050. FIG.11 illustrates a portion of a user interface), wherein the plurality of subsystems comprises:
a medical input data collection processor configured to
collect patient information ([0105]-[0111]: Patient Clinical Data: demographics, family history, immunizations, diagnoses and other related patient assessments; [0030]: FIG.1: (1) clinical data 110) from an interactive artificial intelligence-based questionnaire using the user interface ([0030]: FIG.1: (2) a Personalized Health Risk Assessment Profile (PHRAP) 112, and (6) a Contextual Health Information Model (CHIM) 116 to manage relationships among information artifacts that are dynamically influenced by their contexts; [0030]: FIG.1: (8) a Machine Learning and Analytical Engine 124 used for one or more of disease diagnosis, prognosis and prediction of health status and predictive analytic) – a contextual model with a machine learning engine that are dynamically influenced by the context is considered an interactive AI-based questionnaire; and
collect blood pulse parameters from the one or more sensors ([0030]: FIG.1: (4)Dynamic longitudinal biomedical data generated as Time Series Data (DTSD) 114) comprising pulse rate, pulse rate variability, pulse pressure, pulse transit time, pulse morphology ([0128]: a plurality of vital signs recorded with device 1000 including, but not limited to, pulse rate 1110,…blood pressure 1118, and PPG 1120), wherein the blood pulse parameters include both
static parameters captured when the patient is at rest ([0036]: the personalized health status model is configured to adapt based on individual characteristics…Some of the characteristics are relatively static, such as the genetic information or users education, preferences, and health classification) and
dynamic markers captured during exertion ([0036]: many other [characteristics] are highly dynamic (e.g. current locations, average Glucose level for current day, etc.) that change significantly depending on the situation);
a health status computation processor configured to:
apply the collected patient information from the interactive artificial intelligence-based questionnaire, and the blood pulse parameters associated with the patient on to a trained machine learning model ([0030]: FIG.1: (8) a Machine Learning and Analytical Engine 124; [0070]: Machine learning that uses one or more of dynamic patient generated time series data (DTSD), clinical data from an HER, personalized health risk assessment profile (PHRAP) data, and data captured based on Contextual Health Information Model (CHIM) to learn the personalized Health Status model can be used in real time for improving detection, diagnosis, and therapeutic monitoring of disease; FIG.2);
estimate real time set of traditional medicine system parameters based on the results of the trained machine learning model ([0030]: FIG.1: (8) a Machine Learning and Analytical Engine 124 used for one or more of disease diagnosis, prognosis and prediction of health status and predictive analytic; and [0070]: Machine learning…can be used in real time for improving detection, diagnosis, and therapeutic monitoring of disease; FIG.2; and [0076]: applying predictive analytics operated by traditional medicine to medicine widens the training data set beyond an individual’s experience so that individual patients can be better treated), wherein
the trained machine learning model is trained using a plurality of training datasets ([0056]: The generated data may be used as starting point to initially train machine learning techniques),
each of the plurality of training datasets comprising inputs and corresponding reference values, the inputs comprising patient information from one or more inputs from the interactive artificial intelligence-based questionnaire, and the blood pulse parameters associated with the patient ([0069]: using machine learning techniques to analyze personal biomedical baseline along other dimensions to predict future health status; and claim 13: using a machine learing technique to train the personalized health status model based, least in part, on one or more of the clinical data, the patient generated data, and the contextual information) – the “personal biomedical baseline” is considered the “corresponding reference values” as claimed, the “clinical data” is considered the “blood pulse parameters associated with the patient” as claimed, and the “patient generated data and the contextual information” is considered the “one or more inputs from the interactive AI-based questionnaire” as claimed,
the reference values comprising traditional medicine system parameters corresponding to the inputs (claim 7: generate at a first time, a personalize baseline…and store the personalized baseline measure; and claim 8: determining a health status…by detecting, at a second time, a derivation in the patient generated data from the personalized baseline measure for the at least one biomarker)
a disease identification processor configured to:
compare the real time set of traditional medicine system parameters with pre-stored real time set of traditional medicine system parameters; identify a disease, the compared results and based on pre-stored disease database (claim 7: generate at a first time, a personalize baseline…and store the personalized baseline measure; and claim 8: determining a health status…by detecting, at a second time, a derivation in the patient generated data from the personalized baseline measure for the at least one biomarker);
generate a recommendation message to the patient based on the identified disease (claim 1: output an indication of the health status to provide the health awareness of the medical condition to the individual).
Bahrami does not teach that (1) the user interface is a graphical user interface, and the patient information is collected from an interactive artificial intelligence-based questionnaire using the graphical user interface, (2) determine a health score based on the blood pulse parameters associated with the patient; update the health score based on the real time set traditional medicine system parameters, time of day, season, and diet of the patient; and the disease is determined based on the updated health score, (3) the recommendation message comprises of medical diagnosis of the disease, health parameters, therapeutic interventions, clinical interventions, one or more medical remedies, and treatment plan, (4) perform one or more operations based on the generated recommendation message and the patient prior approval, and (5) the one or more operations comprises generating alerts for the patient representatives, generating alerts for medical representatives, generating new treatment plan and generating new diet plan.
However, in regard to feature (1), in an analogous AI-based personal health monitoring field of endeavor, Bhatia teaches
the user interface is a graphical user interface, and the patient information is collected from an interactive artificial intelligence-based questionnaire using the graphical user interface ([0082]: Unique user login credentials to determine the type of user. The graphical user interface module 1012 may be configured to facilitate graphic user interface processing of the patient user device 1002…the graphical user interface module 1012 includes an interactive wizard based GUI, and a modular user-dependent workflow) – a wizard based GUI is a type of user interface that guides users through a multi-step process on a user-interactive interface, hence is considered the “interactive artificial intelligence-based questionnaire” as claimed.
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to have the device or method of Bahrami employ such a feature associated with a graphical user interface as taught in Bhatia for the well-recognized advantage of a GUI of providing a user-friendly tool that is interactive, intuitive and easy to use.
In regard to the feautures (2)-(5), in an analogous AI-based personal health monitor field of endeavor, Oser teaches those features.
Oser first teaches a wearable device ([0031]: a wearable computing device) that performs AI-based personal health monitoring in Abstract, FIG.2A and 2B: a method and system for AI-based personal health condition monitoring and improvement. Signals encoding physiological data, behavioral data, environmental stress data, emotional data and cognitive data of the use are received and processed with a model to determine characterization associated with a medical condition for diagnosis. Content of the treatment is modulated based on the characterization, and the treatment is administered.
The method and system of Oser further teaches the above identified feature (1), such that the one or more patient health parameters is associated with an interactive artificial intelligence based questionnaire (FIG.5, and [0086]: physical illness narrative module and symptom assessment; [0136]: engagement can be promoted using one or more of: artificial reality tools…; artificial intelligence-based coaching elements for driving interaction with the subject; and [0142] teaches a list of AI-based algorithm for implementing the invention).
Oser further teaches that
(2) determine a health score based on the blood pulse parameters associated with the patient; and update the health score based on the real time set traditional medicine system parameters, time of day, season, and diet of the patient, and the disease is determined based on the updated health score ([0059]: in relation to performing the pre-assessment and/or onboarding process, the online system and/or other system components can implement surveying tools…Survey tools can be delivered through an application executing on the client device fo the subject…The system can include architecture for receiving data derived from the subject (e.g., through sensor components, through survey components, associated with…other characteristics), processing the data with one or more models, and returning scores (e.g., measures of symptom severity, etc). Scores can also be used for tagging user data with symptom severity, in relation to model aspects and model training/refinement; and [0122]: FIG.8D depicts architecture o th conditional branching model for a pathway targeted to anxiety and depression (e.g., with a GAD-7 score greater than or equal to 1); FIG.3B illustrates updating the health score based on the real time assessment; [0050] teaches a list of conditions being included for assessment: dietary characteristics, [0051]: environmental stress data for pre-assessment; and [0108]: the health behavior in related with sleep),
(3) the recommendation message comprises of medical diagnosis of the disease (FIG.3B: symptom severity), health parameters (FIG.3B: severity score), therapeutic interventions, clinical interventions, one or more medical remedies, and treatment plan ([0053]: FIG.3B, step 301 can be implemented through an application executing at a mobile device or other device associated with the user, where the application prompts inputs from the user pertaining to various symptoms…and generated a report indicating severity of the health condition; and),
(4) perform one or more operations based on the generated recommendation message and the patient prior approval ([0125]: monitoring can be performed using survey components delivered with interactive interventions of the intervention regimen, where the user is prompted and provided with interactive elements that allow the subject to provide self-report data indicating progress statuses), and
(5) the one or more operations comprises generating alerts for the patient representatives, generating alerts for medical representatives, generating new treatment plan and generating new diet plan ([0126]: a predictive model that outputs indications of one or more of symptom severity predications, predictions of subject states, indications of predicted success of the subject in achieving goals, and/or other predictions).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to have the device or method of Bahrami and Bhatia combined employ such features (2)-(5) identified above as taught in Oser for the advantage of “providing a model that can be structured and ultimately refined for receiving data objects with a return of a set of outputs comprising a section of treatment subcomponents tagged with efficacy indicators” that “improves changes of success in outcome”, as suggested in Oser, [0139] and [0140] and further “improving effectiveness of provided treatments and increase success of the subject in achieving his/her goals”, as suggested in Oser, [0132].
In regard to Claim 2, Bahrami further teaches that
the one or more light sensors is configured to capture patient blood pulse waveform and collect data about underlying patient blood pulsations on application of the light rays ([0129]: signals generated from the sensors in device 1000 are processed using one or more of the techniques…to detect noninvasively systolic, diastolic and glucose in addition to pulse, blood oxygenation, PPG, and temperature).
In regard to Claim 5, Oser further teaches that the one or more physiological sensors configured to capture physiological parameters and movement parameters of the patient, wherein the one or more physiological sensors comprises accelerometer, galvanic sensor, barometer and temperature sensor ([0035]: sensing components associated with one or more of: activity of a subject (e.g., through accelerometer, gyroscopes…).
In regard to Claims 6 and 14, Bahrami further teaches that the identified diseases comprise diabetes ([0102]: chronic conditions such as diabetes; Table 1), cardiovascular diseases ([0027]: cardiovascular diseases) and Oser further teaches that the identified diseases comprise gastro-intestinal diseases ([0031]: GI health condition).
In regard to Claims 7 and 15, Oser further teaches that the captured one or more patient health parameters comprises patient phenotypic features of the patient ([0053]: identify the user as having a certain state of severity (e.g., expression, phenotype, etc)), wherein the patient phenotypic features comprise anatomic features, physical, physiological features, psychological features ([0053]: user inputs pertaining to various symptoms (e.g., pain, detecation, abdominal distension, digestive issues, cognitive symptoms, behavioral effects, etc); [0076]: symptoms that have been affected by behavioral, mental and emotional factors; and [0035]: sensing data associated with activity of a subject).
In regard to Claim 8, Oser further teaches a battery module configured to the wearable device ([0032]: a wrist-borne wearable computing device) – a wristband type of wearable device has a built-in battery module to supply power to the device in order for it to function in a mobile fashion.
In regard to Claim 9, Oser further teaches a touch display configured to view the collected patient information, the captured one or more patient health parameters and the recommendation messages (FIG.3 and FIG. 5 illustrate a touch-screen platform providing an interactive tool to the user).
In regard to Claim 10, Oser further teaches a Bluetooth or Wi-Fi integrated module configured to enable transmit of the collected patient information, the captured one or more patient health parameters and the recommendation messages to an external device ([0037]: The network can include wired and/or wireless connections to the network. The network can implement communication linking technologies including…802.11 architecture (e.g., Wi-Fi, etc.))
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Bahrami in view of Bhatia and Oser, further in view of Fukuda et al., US 2019/0125197 A1, hereinafter Fukuda.
Claim 3. Bahrami, Bhatia and Oser combined teaches all the limitations of claim 1.
Neither of Bahrami, Bhatia and Oser teaches that the detector comprises a digital image sensor, wherein the digital sensor is configured to capture spatial information of the patient blood pulsations.
However, in an analogous biological information detection field of endeavor, Fukuda teaches that
the detector comprises a digital image sensor, wherein the digital sensor is configured to capture spatial information of the patient blood pulsations ([0114]: the camera 100 needs to detect the pulse wave signal base on the time series change of the skin color of the subject, and to estimate blood pressure and therefore, may be a digital video camera capable of taking moving images; and [0115]: the frame image analysis unit outputs the skin color level signal and the skin color wavelength data signal of each pixel…The skin color level signal indicates that the image signal of the concerned, pixel is the signal included in the predetermined skin color space, that is, in the skin color region).
To analyze the pixels in the skin color space or the skin color region in the moving images for pulse wave signals is considered “to capture spatial information of the patient blood pulsations” as claimed.
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to have the detector of Bahrami as modified employ the feature of “comprising a digital image sensor, wherein the digital sensor is configured to capture spatial information of the patient blood pulsations” as taught in Fukuda for the advantage of “stably detecting biological information of a subject”, as suggested in Fukuda, [0011].
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Bahrami in view of Bhatia and Oser, further in view of Balabine et al., US 2019/0142346 A1, hereinafter Balabine.
Claim 4. Bahrami, Bhatia and Oser combined teaches all the limitations of claim 1.
Neither of Bahrami, Bhatia and Oser teaches that the one or more physiological sensors comprises a magnetic sensor, wherein the magnetic sensor is configured to detect any real time changes in underlying patient blood.
However, in an analogous biological blood information analysis and detection field of endeavor, Balabine teaches that
the one or more physiological sensors comprises a magnetic sensor, wherein the magnetic sensor is configured to detect any real time changes in underlying patient blood ([0007]: at least one of a PPG sensor or a magnetic sensor…the PPG/magnetic sensor uses a pulse oximeter to measure changes in skin light absorption; a processor or receiving a signal from the at least one of the PPG sensor or the magnetic sensor measurements…compute trend of systolic blood pressure,…compute trend of diastolic blood pressure…).
Therefore, it would have been obvious to one of the ordinary skilled in the art before the effective filing date of the claimed invention to have the one or more physiological sensors of Bahrami as modified employ the feature of “comprising a magnetic sensor, wherein the magnetic sensor is configured to detect any real time changes in underlying patient blood” as taught in Balabine for the advantage of the magnetic sensor being a well-known and conventional means for measuring blood associated parameters.
Response to Arguments
Applicant’s arguments in regard to the rejection to claims 1-11 and 13-15 under 35 U.S.C. 112(a), written description requirement for a number of computer/processor implemented steps lacking disclosure of an algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed functions have been fully considered and they are persuasive. The rejection is now withdrawn in view of the claim amendment.
In view of the claim amendment, new grounds of rejections are now made under 35 U.S.C. 101, 112(a), written description support, 112(b) and 103. Please refer to the respective rejection sections for the details.
Conclusion
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/YI-SHAN YANG/Primary Examiner, Art Unit 3798