DETAILED ACTION
Acknowledgements
This office action is in response to the claims filed June 04, 2026.
Claims 1, 3-11, 13-18, and 20 are pending.
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 .
Request for Continued Examination
Claims 1, 3-11, 13-18, and 20 are pending.
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, 3-11, 13-18, and 20 are rejected to under 35 U.S.C 101 as not being directed to eligible subject matter based on the grounds set out in detail below:
Independent Claims 1, 11, 18, and 20:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
Independent claims 1 and 20 fall within the statutory category of method.
Independent claim 11 falls within the statutory category of machine.
Independent claim 18 falls within the statutory category of article of manufacture.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Independent claims 1, 11, 18, and 20 (claim 1 being representative) claimed invention is directed to an abstract idea without significantly more.
The claim elements which set forth the abstract idea in the independent claims (claim 1 being representative of claims 11 and 18 and 20) is:
collect a first data element;
collect a second data element;
applying, inferencing over at least the first data element and the second data element to derive a user condition indication for a user;
and on detecting, in the user condition indication, a predictive value above a threshold indicating a user condition requiring notification,
emitting a message at an output, where the user condition requiring notification comprises one or both of a physical and mental condition, wherein collecting the first data element and the second data element and applying inferencing is a trusted execution environment
The abstract idea in the claims is “certain methods of organizing human activity” as it is following rules and instructions to derive a user condition (MPEP § 2106.04(a)(2), subsection II)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For Independent claims 1, 11, 18, and 20 judicial exception is not integrated into a practical application.
Independent claim 1 recites the additional elements below:
a computing device
a biosensor device
a first transceiver
a storage and processing location
a second transceiver
machine learning model
a secure zone of the computing device, the secure zone generated in response to successful attestation of a boot-up sequence of the computing device.
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a computing device, is executing the abstract idea and claimed as a general computing element as “apply-it”
The additional element, a biosensor device, is recited as generally linking the data to the environment of biosensing
The additional element, a first transceiver, is executing the abstract idea and claimed as a general computing element as “apply-it” to send and receive data
The additional element, a storage and processing location, is executing the abstract idea and claimed as a general computing element as “apply-it” to store and analyze data
The additional element, a second transceiver, is executing the abstract idea and claimed as a general computing element as “apply-it” to send and receive data
The additional element, a machine learning model, is executing the abstract idea and claimed as “apply-it” to analyze data
The additional element, a secure zone of the computing device, the secure zone generated in response to successful attestation of a boot-up sequence of the computing device, is recited as a tool or equivalent as “apply-it” to keep data private
Independent claim 11 does not recite additional elements not already recited in the independent claim 1.
Independent claim 18 recites the additional claim elements below not already recited in claim 1:
A computer program product stored on a non-transitory computer readable medium and comprising computer program code
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, A computer program product stored on a non-transitory computer readable medium and comprising computer program code, is executing the abstract idea and claimed as a general computing element as “apply-it”
Independent claim 20 recites the additional claim elements below not already recited in claim 1:
Artificial neural network
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, Artificial neural network, is executing the abstract idea and claimed as “apply-it” to analyze data
Accordingly, independent claims 1, 11, 18, and 20 as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are merely generally linking and/or applying the abstract idea and therefore, do not amount to significantly more. The claims are patent ineligible.
Dependent Claims 3-10 and 13-17:
Eligibility Step 1 (does the subject matter fall within a statutory category?):
The dependent claims 3-10 falls within the statutory category of method.
The dependent claims 13-17 fall within the statutory category of machine.
Eligibility Step 2A-1 (does the claim recite an abstract idea, law of nature, or natural phenomenon?): Dependent claims 3-10 and 13-17 claimed invention is directed to an abstract idea without significantly more. The claims continue to limit the independent claim 1, 11, and 18 abstract idea by (1) further limiting the determination of the user condition, (2) further limiting the collection of the data, and (3) the types of data. Therefore, the dependent claims inherit the same abstract idea which is “certain methods of organizing human activity” as it is following rules and instructions to derive a user condition (MPEP § 2106.04(a)(2), subsection II)
Eligibility Step 2A-2 (does the claim recite additional elements that integrate the judicial exception into a practical application?): For claims 3-10 and 13-17 this judicial exception is not integrated into a practical application.
The dependent claims recite the additional elements below not already recited in the independent claims:
A wearable device
A sensor
Examiner takes the applicable considerations stated in MPEP 2106.04 (d) and analyzes them below in light of the instant applications disclosure and claim elements as a whole.
The additional element, a wearable device, is claimed as “apply-it” to gather data
The additional element, a sensor, is claimed as “apply-it” to gather data
Accordingly, the dependent claims as a whole do not integrate the recited abstract idea into a practical application (MPEP 2106.05(f) and 2106.04(d)(1).
Eligibility Step 2B (Does the claim amount to significantly more?): The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as analyzed above in step 2A prong 2, are merely applying the abstract idea and therefore, do not amount to significantly more. The claims are patent ineligible.
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.
Claims 1, 3-11, 13-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Pushpala et. al (hereinafter Pushpala) (US20250057479A1) in view of Chang (WO2023038222A1) and in further view of GAO et. al (hereinafter GAO) (CN120710676A)
As per claim 1, Pushpala teaches:
A method of operating a computing device, comprising: ([0034] discloses, “FIG. 1 is a schematic diagram of a computing environment 100 in which a biomonitoring and healthcare guidance system 102 ("system 102") operates, in accordance with embodiments of the present technology. As shown in FIG. 1, the system 102 is operably coupled to one or more user devices 104 via a network 108. The system 102 is also operably coupled to at least one database or storage component 106 ("database 106"). The system 102 can include processors, memory, and/or other software and/or hardware components configured to implement the various methods described herein. For example, the system 102 can be configured to monitor a user's health state and provide information to support personalized healthcare, as described in greater detail below.”)
first operating a first transceiver in the computing device to collect a first biosensor device data element into a storage and processing location; second operating the first or a second transceiver to collect a second biosensor device data element into the storage and processing location; ([0169] discloses, “In some embodiments, the biosensor 800 includes one or more other sensors 808 ("Other"), such as temperature sensors, optical sensors, bioimpedance sensors, biopotential sensors, ECG sensors, accelerometers, gyroscopes, and/or any of the other sensor types described herein. Optionally, the biosensor 800 can also include an identifier module 810 ("ID"), which can include a programmable memory storing information such as: the types of the microneedle arrays 802, 804, 806; the types of analytes detected by the first microneedle arrays 802; the configuration of the microneedle arrays 802, 804, 806; the types of the other sensors 808; a sensor ID; a lot ID; manufacturing date; expiration date; and/or any other suitable information, as described elsewhere herein.” And see [0046]discloses, “In some embodiments, one or more users can access the system 102 via the user devices 104, e.g., to send data to the system 102 (e.g., health-related information and/or contextual information) and/or receive data from the system 102 (e.g., predictions, notifications, recommendations, instructions, support, etc.).” and see [0038] discloses, “In some embodiments, the system 102 receives input data from one or more user devices 104. The user devices 104 can be any device associated with a user (e.g.. a patient), and can be used to obtain healthcare information, contextual information, and/or any other relevant information relating to the user and/or any other users (e.g., appropriately anonymized user data). In the illustrated embodiment, for example, the user devices 104 include at least one biosensor 104a (e.g., blood glucose sensors, pressure sensors, heart rate sensors, sleep trackers, temperature sensors, motion sensors, or other biomonitoring devices), at least one mobile device 104b (e.g., a smartphone or tablet computer), and, optionally, at least one wearable device 104c (e.g., a smartwatch, fitness tracker).” And see [0044] discloses, “For example, Table 2 below illustrates exemplary health and/or behavioral data that may be provided to the system 102 and/or stored in the database 106. The data in Table 2 can be generated by one or more user devices 104, as previously described. Each entry in Table 2 is labeled with a user ID, and includes a time stamp indicating when the data was obtained, the type of data, and the data value.” and [0248] / examiner notes the disclosure teaches a computer system which sends and receives data for communication biosensor data where the biosensor data can be more than one type of data element (e.g. optical, physiological, mental, etc.) and this data is stored in memory and modules used to process the stored data)
applying, in the storage and processing location, machine learning model inferencing over at least the first biosensor device data element and the second biosensor device data element to derive a user condition indication for a biosensor device user; ([0034] discloses, “For example, the system 102 can be configured to monitor a user's health state and provide information to support personalized healthcare, as described in greater detail below.” And see [0035] discloses, “The health state can be any status, condition, parameter, etc., that is associated with or otherwise related to the user's health. In some embodiments, the system 102 receives input data and performs monitoring, processing, analysis, forecasting, interpretation, etc., of the input data in order to generate instructions, notifications, recommendations, support, and/or other information to the user that may be useful for self-care of diseases or conditions, such as chronic conditions (e.g., diabetes (type 1 and type 2), pre-diabetes, hypertension, hyperlipidemia, etc.), acute conditions, etc. For example, the system 102 can be used to identify, manage, and/or monitor a variety of different diseases, conditions, and/or other health states, including, but not limited to: diabetes and associated conditions (e.g., hypoglycemia, hyperglycemia, ketoacidosis), liver diseases (e.g., hepatitis A, hepatitis B, hepatitis C, fatty liver disease, cirrhosis, liver failure), cardiovascular diseases (e.g., congestive heart failure, coronary artery disease, peripheral vascular disease, hypertension, arrhythmia, cardiomyopathy), cancer (e.g., bladder cancer, breast cancer, colorectal cancer, endometrial cancer, kidney cancer, leukemia, liver cancer, lung cancer, skin cancer, lymphoma, pancreatic cancer, prostate cancer, thyroid cancer), lung diseases (e.g., asthma, chronic obstructive pulmonary disease, hypoxia, bronchitis, cystic fibrosis), kidney diseases (e.g., chronic kidney disease), brain conditions (e.g., acute brain conditions, chronic brain conditions), ophthalmological diseases, intoxication, dehydration, hyponatremia, shock, heat stroke, infection, sepsis, trauma, water retention, bleeding, endocrine disorders, muscle breakdown, malnutrition, body function (e.g., lung functions, heart functions, kidney functions, thyroid functions, adrenal functions, etc.), women's health (e.g., gynecological diseases and conditions such as polycystic ovary syndrome (PCOS), pregnancy, fertility), drug use (e.g., smoking, alcohol, or other drugs), physical performance (e.g., athletic performance), anaerobic activity, weight loss or gain, obesity, nutrition, eating disorders, metabolism (e.g., lipid metabolism, protein metabolism, aerobic metabolism), wellness, mental health, focus, stress, effects of medication, medication levels, health indicators, and/or user compliance. For example, the embodiments herein can be used to diagnose, monitor, track, and/or provide digital therapy using behavior change, drug or therapy titration, risk assessment, or the like.” And see [0047] discloses, “As discussed further below, the system 102 can analyze the obtained input data, including historical data, current real-time data, continuously supplied data, calibration data, and/or any other data (e.g., using a statistical analysis, machine learning analysis, etc.), and generate output data. The output data can include predictions of a user's health state, correlations between data, interpretations, recommendations, notifications, instructions, support, and/or other information related to the obtained input data. In some embodiments, the output data provides information to assist the user in adjusting their behavior (e.g., diet, exercise, sleeping, etc.) to enhance outcomes, to reduce, limit, or avoid health care provider intervention, etc.” and see [0036] and [0038])
and on detecting, in the user condition indication, a predictive value above a threshold indicating a user condition requiring notification, emitting a message at an output of the computing device, where the user condition requiring notification comprises one or both of a physical and mental condition,…[…]…([0191] discloses, “At step 1050, the method 1000 can optionally include generating one or more target ranges for a health parameter. In some embodiments, a user can identify upper and/or lower limits that the user desires to stay between (e.g., from 70 mg/dL to 140 mg/dL, or from 70 mg/dL to 170 mg/dL for blood glucose values). This information can be used to help the user to interpret the forecast in terms of whether the forecast was in line with healthy values, above, below, etc. In embodiments where the target range may vary throughout the day (e.g., blood glucose target ranges may shift depending on when the user eats, performs various activities, etc.), multiple different target ranges can be generated for different time periods.” And see[0192] discloses, “At A step 1060, the method 1000 can optionally include combining forecast(s), confidence interval(s), and/or target range(s) for output to the user, e.g., via display on user interface of a user device. The output can inform the user of likely near-term health parameter values and their uncertainties, can provide a useful reference for comparison, and/or can allow the user to make decisions about whether or not to change plans and/or take any action.” And see [0193] discloses, “At step 1070, the method 1000 can optionally include interpreting the forecast(s). For example, the forecast values can be compared to the target range at the various forecast times. If more than a threshold percentage (e.g., 10% or 25%) of the forecast values are above the target range, the forecast can be labeled "high." The system 100 can generate a message for display to the user (e.g., "likely to go higher than recommended within 4 hours," "likely to remain within healthy levels for the next 8 hours"). The determination can also be used as an input to automatically select a support message that can provide the user with various actions that the user can undertake.”)
However, Pushpala does not teach:
wherein the storage and processing location for collecting the first biosensor device data element and the second biosensor device data element and applying machine learning model inferencing is a secure zone of the computing device, the secure zone defining a trusted execution environment generated in response to successful attestation of a boot-up sequence of the computing device.
However, CHANG teaches:
wherein the storage and processing location for collecting the first biosensor device data element and the second biosensor device data element and applying machine learning model inferencing is a secure zone of the computing device. (see page 1 paragraph 5 and see page 16 paragraphs 1-2 discloses, “In operation 1040, the processor 120 executes a second application (eg, applet 2 323b of FIG. 3 ) accessible to the biosensor (eg, the sensor 340 of FIG. 3 ) when the TEE 320 is provided in the first memory. According to the execution of the second application, biometric information (eg, the second biometric information of FIG. 5 ) may be acquired from the biometric sensor and stored at least temporarily in the secure area. Additionally, the second application compares the second biometric information obtained from the biometric sensor with the first biometric information stored in the security area of the second memory and generates an authentication result indicating whether the second biometric information is valid or not based on the comparison result. may be. In operation 1050, the processor 120 may transfer the authentication result performed by the second application to the first virtual machine granted access to the TEE 320. Accordingly, the first virtual machine may transfer the authentication result to the first application. Additionally, the processor 120 may transmit the biometric information acquired from the biometric sensor to the first virtual machine along with the authentication result. Accordingly, the first virtual machine may perform a reconfirmation operation of determining whether the authentication result of the second application is correct by using the biometric information acquired from the biometric sensor.” And see page 3 paragraph 8 discloses, “The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, an image signal processor or a communication processor) may be implemented as part of other functionally related components (eg, the camera module 180 or the communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Pushpala’s teachings with CHANG’s teachings, the motivation being PushPala teaches encryption of data and keeping data secure (e.g. [0247] and see [0139]) therefore, it would be obvious to one of ordinary skill that health data security would be improved by applying a secure zone explicitly with a secure computing element and further improve following healthcare data protection laws thus protecting cryptographic keys and overall security of encryption.
However, CHANG does not explicitly teach:
the secure zone defining a trusted execution environment generated in response to successful attestation of a boot-up sequence of the computing device.
However, GAO does teach:
the secure zone defining a trusted execution environment generated in response to successful attestation of a boot-up sequence of the computing device. (page 7 para. 7-8 discloses, “In the embodiments of the present application, the execution subject can be an independent security module (or security component), that is, a security module in a TEE based on ARM TrustZone technology. For example, the security module is securely booted along with the BIOS during the power-on process of the ARM server. The security module can be deployed in either S-EL0 or S-EL1 of the ARM TrustZone architecture, without limitation here. In an embodiment of the present application, a remote attestation process supporting multiple user TEE OS is provided. The virtualization component (i.e., S-EL2 component) in the cloud server (i.e., ARM server) can send a loading message to the security module for determining the target trusted kernel system to be loaded from multiple user trusted kernel systems, i.e., a loading message for determining the target user TEE OS from multiple user TEE OS. The independent security module provided in the embodiment of the present application receives a loading message from the S-EL2 component in the ARM server, and then generates a remote attestation report for providing the verification content of the target user TEE OS based on the loading message, and outputs the remote attestation report to the remote attestation verification component running in the non-secure world operating environment in the ARM server, so that the remote attestation verification component remotely attests the target user TEE OS based on the remote attestation report. Among them, a preset security architecture (i.e., ARM TrustZone architecture) is deployed in the ARM server, and a secure world operating environment and a non-secure world operating environment are created on the ARM TrustZone architecture. The S-EL2 component for providing physical isolation for multiple user TEE OS runs in the secure world operating environment.”
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Pushpala’s teachings and CHANG’s teachings with GAO’s teachings, the motivation being PushPala teaches encryption of data and keeping data secure (e.g. [0247] and see [0139]) therefore, it would be obvious to one of ordinary skill that health data security would be improved by applying a secure zone explicitly with a secure computing element and further improve keeping secure zones separate and authenticated by attestation after boot-up following healthcare data protection laws thus protecting cryptographic keys and overall security of encryption.
As per claim 3, Pushpala teaches:
The method of claim 1, wherein the detecting a predictive value above a threshold indicating a user condition requiring notification comprises identifying in the first biosensor device data element and the second biosensor device data element a combination of values together indicative of the user condition requiring notification. ([0193] discloses, “At step 1070, the method 1000 can optionally include interpreting the forecast(s). For example, the forecast values can be compared to the target range at the various forecast times. If more than a threshold percentage (e.g., 10% or 25%) of the forecast values are above the target range, the forecast can be labeled "high." The system 100 can generate a message for display to the user (e.g., "likely to go higher than recommended within 4 hours," "likely to remain within healthy levels for the next 8 hours"). The determination can also be used as an input to automatically select a support message that can provide the user with various actions that the user can undertake.”)
As per claim 4, Pushpala teaches:
The method of claim 1, wherein the first biosensor device data element and the second biosensor device data element are collected from the same biosensor device. ([0053] discloses, “The systems and methods of the present technology can use one or more biosensors (also referred to herein as "biosensor devices" "sensors," or "sensor devices") to generate user data, such as data indicative of a user's health state. The biosensors described herein can be or include various types of sensors, such as chemical sensors, electrochemical sensors, optical sensors (e.g., optical enzymatic sensors, opto-chemical sensors, fluorescence-based sensors, etc.), spectrophotometric sensors, spectroscopic sensors, polarimetric sensors, calorimetric sensors, iontophoretic sensors, radiometric sensors, and the like, and combinations thereof. The biosensors can be implanted sensors, nonimplanted sensors, invasive sensors, minimally invasive sensors, non-invasive sensors, wearable sensors, etc. The biosensors can be disposable sensors, reusable sensors, or can include any suitable combination of disposable and reusable components (e.g., a disposable sensor portion for monitoring specific condition(s) and a reusable electronics portion for receiving and processing the sensor data).” And see [0054] discloses, “The number, configuration, and/or functionality of the biosensors can be selected based on desired sensing capabilities. For example, the biosensors described herein can be configured to sense any suitable combinations of the following health parameters: glucose, gases (e.g. oxygen, carbon dioxide, etc.), electrolytes (e.g., bicarbonate, potassium, sodium, magnesium, chloride, lactic acid), BUN, creatinine, ketones, cholesterol, triglycerides, alcohols, amino acids, neurotransmitters, hormones, disease biomarkers (e.g., cancer biomarkers, cardiovascular disease biomarkers), drugs, pH, cell count, vitals (e.g., heart rate, body temperature (such as skin temperature), blood pressure (such as systolic and/or diastolic blood pressure), respiratory rate), cardiovascular data (e.g., pacemaker data, arrhythmia data), body function data, meal or nutrition data (e.g., number of meals; timing of meals; number of calories; amount of carbohydrates, fats, sugars, etc.), physical activity or exercise data (e.g., time and/or duration of activity; activity type such as walking, running, swimming; strenuousness of the activity such as low, moderate, high; etc.), sleep data (e.g., number of hours of sleep, average hours of sleep, variability of hours of sleep, sleep-wake cycle data, data related to sleep apnea events, sleep fragmentation (such as fraction of nighttime hours awake between sleep episodes, etc.)), stress level data (e.g., cortisol and/or other chemical indicators of stress levels, perspiration), alc data, user location (e.g., GPS coordinates, elevation data), air pressure, humidity, temperature, air quality, and/or the like.” And see [0057] FIGS. 2-3R and the accompanying description provide various examples of biosensors that are suitable for use with the biomonitoring and healthcare guidance system 102 of FIG. 1. Specifically, FIG. 2 provides a general overview of the components of a biosensor, and FIGS. 3A-3R provide a representative example of a biosensor. Any of the features of the embodiments of FIGS. 2-3R can be combined with each other and/or with any of the other systems and devices described herein. [0058] discloses, “FIG. 2 is a schematic illustration of a biosensor device 200 ("device 200") configured in accordance with embodiments of the present technology. The device 200 can be a wearable patch sensor configured to be applied to a user's body in order to obtain user health data in a noninvasive or minimally-invasive manner. The device 200 can be used in any of the systems and methods described herein (e.g., as the biosensor 104a of FIG. 1). The device 200 includes a patch 202 (also referred to as a "patch portion," "base portion," or "sensing component") and a pod 204 (also referred to as a "pod portion," "capsule portion," or "electronics component"). The patch 202 can be coupled to the pod 204 (e.g., releasably coupled or permanently affixed) to form the device 200.” And see [0059] discloses, “The patch 202 can include a substrate 206 configured to couple to the user's body (e.g., to the surface of the skin) via adhesives or other suitable temporary attachment techniques. The base portion also includes at least one array of microneedles 208 coupled to and/or supported by the substrate 206. The microneedles 208 can be configured to penetrate into the user's skin to access interstitial fluid therein. In some embodiments, when the device 200 is applied to the skin, the microneedles 208 extend only into the stratum corneum and epidermis, and do not penetrate into the dermis or hypodermis (subcutaneous tissue). This approach can reduce or avoid pain and/or discomfort, while still providing accurate detection of analytes in the epidermal interstitial fluid. The microneedles 208 can be configured to detect one or more analytes in the interstitial fluid, such as glucose, gases, electrolytes, BUN, creatinine, ketones, alcohols, amino acids, neurotransmitters, hormones, biomarkers, drugs, pH, cell count, and/or any of the other analytes described herein. Each microneedle 208 can be configured to detect a single analyte, or some or all of the microneedles 208 can be configured to detect multiple analytes (e.g., two, three, four, five, or more different analytes). Optionally, some or all of the microneedles 208 can be configured to detect physiological parameters, such as electrical properties (e.g., biopotential, bioimpedance), body temperature, etc.”)
As per claim 5, Pushpala teaches:
The method of claim 1, wherein the first biosensor device data element and the second biosensor device data element form a time series and the detecting a predictive value above a threshold indicating a user condition requiring notification is responsive to a change over the time series. (see [0193] discloses, “At step 1070, the method 1000 can optionally include interpreting the forecast(s). For example, the forecast values can be compared to the target range at the various forecast times. If more than a threshold percentage (e.g., 10% or 25%) of the forecast values are above the target range, the forecast can be labeled "high." The system 100 can generate a message for display to the user (e.g., "likely to go higher than recommended within 4 hours," "likely to remain within healthy levels for the next 8 hours"). The determination can also be used as an input to automatically select a support message that can provide the user with various actions that the user can undertake.” And see [0226] discloses, “FIG. 13A illustrates a user interface 1300a for tracking a user's health parameter values. In the illustrated embodiment, the user interface 1300a includes a timeline or graph showing a series of alc values over time, as well as a chronological feed showing trends in recent alc values (e.g., percent change relative to the previous values). Optionally, the user interface 1300a can also include a link to resources providing information regarding alc measurements.”) and see [0227] discloses, “FIG. 13B illustrates another user interface 1300b for tracking a user's health parameters. The user interface 1300b can be generally similar to the user interface 1300a of FIG. 13A, except that the user interface 1300b displays the user's diastolic and systolic blood pressure values over time. The user interface 1300b can include a graph showing a series of blood pressure measurements overlaid onto a target blood pressure range (e.g., 80 mmHg to 120 mmHg).” And see [0228] discloses, “FIG. 13C illustrates yet another user interface 1300c for tracking a user's health parameters. The user interface 1300c can be generally similar to the user interface 1300a of FIG. 13A, except that the user interface 1300c displays the user's weight over time, as well as the change in weight (e.g., percentage of weight lost) relative to previous weight measurements.” And see [0229] discloses, “FIG. 13D illustrates a user interface 1300d for displaying health event data. The user interface 1300d can show a plurality of timelines for multiple days (e.g., the current day and/or the past few days). Each timeline can be annotated with visual indicators (e.g., icons) representing events that occurred during that day. In the illustrated embodiment, for example, the events include blood glucose measurements, insulin intake, food intake, and activity. The size of the visual indicators can also provide information regarding the event, e.g., larger icons can mean a higher blood glucose level, a higher insulin dose, more food intake, more activity, etc.” and see [0230] discloses, “FIG. 13E illustrates another user interface 1300e for displaying health event data. The information shown on the user interface 1300e can be similar to the information shown on the user interface 1300d of FIG. 13D, except that the user interface 1300e displays health events for a single day. In the illustrated embodiment, the user interface 1300e displays an annotated timeline with the health events that occurred during the day, as well as a chronological feed providing more details on each event (e.g., blood glucose concentration, carbohydrates consumed, amount of insulin taken, duration of activity, etc.).” and see [0231] discloses, “FIG. 13F illustrates a user interface 1300f for displaying time-in-range data. The user interface 1300f can show a plurality of timelines for multiple days (e.g., the current day and/or the past few days). Each timeline can be annotated with visual indicators (e.g., icons) representing measured values for a health parameter during that day. The timelines can also include graphics (e.g., a highlighted bar) showing the total range of values for that day. The user interface 1300f can also display the targeted range for that health parameter, e.g., as a highlighted region or other graphic overlaid onto the individual timelines, so the user can visualize how their actual health parameter values compare to the target range. The user interface 1300f can also display the amount of time the measured values were in the targeted range, below the targeted range, and/or above the targeted range.” See also [0200] and [0214]-[0216])
As per claim 6, Pushpala teaches:
The method of claim 1, wherein the first biosensor device data element and the second biosensor device data element form a time series and the detecting a predictive value above a threshold indicating a user condition requiring notification is responsive to an anomaly with respect to historical data in the time series. (see [0193] discloses, “At step 1070, the method 1000 can optionally include interpreting the forecast(s). For example, the forecast values can be compared to the target range at the various forecast times. If more than a threshold percentage (e.g., 10% or 25%) of the forecast values are above the target range, the forecast can be labeled "high." The system 100 can generate a message for display to the user (e.g., "likely to go higher than recommended within 4 hours," "likely to remain within healthy levels for the next 8 hours"). The determination can also be used as an input to automatically select a support message that can provide the user with various actions that the user can undertake.” And see [0226] discloses, “FIG. 13A illustrates a user interface 1300a for tracking a user's health parameter values. In the illustrated embodiment, the user interface 1300a includes a timeline or graph showing a series of alc values over time, as well as a chronological feed showing trends in recent alc values (e.g., percent change relative to the previous values). Optionally, the user interface 1300a can also include a link to resources providing information regarding alc measurements.”) and see [0227] discloses, “FIG. 13B illustrates another user interface 1300b for tracking a user's health parameters. The user interface 1300b can be generally similar to the user interface 1300a of FIG. 13A, except that the user interface 1300b displays the user's diastolic and systolic blood pressure values over time. The user interface 1300b can include a graph showing a series of blood pressure measurements overlaid onto a target blood pressure range (e.g., 80 mmHg to 120 mmHg).” And see [0228] discloses, “FIG. 13C illustrates yet another user interface 1300c for tracking a user's health parameters. The user interface 1300c can be generally similar to the user interface 1300a of FIG. 13A, except that the user interface 1300c displays the user's weight over time, as well as the change in weight (e.g., percentage of weight lost) relative to previous weight measurements.” And see [0229] discloses, “FIG. 13D illustrates a user interface 1300d for displaying health event data. The user interface 1300d can show a plurality of timelines for multiple days (e.g., the current day and/or the past few days). Each timeline can be annotated with visual indicators (e.g., icons) representing events that occurred during that day. In the illustrated embodiment, for example, the events include blood glucose measurements, insulin intake, food intake, and activity. The size of the visual indicators can also provide information regarding the event, e.g., larger icons can mean a higher blood glucose level, a higher insulin dose, more food intake, more activity, etc.” and see [0230] discloses, “FIG. 13E illustrates another user interface 1300e for displaying health event data. The information shown on the user interface 1300e can be similar to the information shown on the user interface 1300d of FIG. 13D, except that the user interface 1300e displays health events for a single day. In the illustrated embodiment, the user interface 1300e displays an annotated timeline with the health events that occurred during the day, as well as a chronological feed providing more details on each event (e.g., blood glucose concentration, carbohydrates consumed, amount of insulin taken, duration of activity, etc.).” and see [0231] discloses, “FIG. 13F illustrates a user interface 1300f for displaying time-in-range data. The user interface 1300f can show a plurality of timelines for multiple days (e.g., the current day and/or the past few days). Each timeline can be annotated with visual indicators (e.g., icons) representing measured values for a health parameter during that day. The timelines can also include graphics (e.g., a highlighted bar) showing the total range of values for that day. The user interface 1300f can also display the targeted range for that health parameter, e.g., as a highlighted region or other graphic overlaid onto the individual timelines, so the user can visualize how their actual health parameter values compare to the target range. The user interface 1300f can also display the amount of time the measured values were in the targeted range, below the targeted range, and/or above the targeted range.” See also [0200] and [0214]-[0216] / examiner notes the anomaly is defined as something which deviates from normal indicated by the threshold being exceeded or other examples given in the disclosure)
As per claim 7, Pushpala teaches:
The method of claim 1, wherein applying machine learning model inferencing over at least the first biosensor device data element and the second biosensor device data element comprises operating a trained neural network. ([0049] discloses, “In some embodiments, the system 102 is configured to analyze the input data and generate the output data using one or more machine learning models. The machine learning models can include…[…]…artificial neural networks (e.g., perceptron, multilayer perceptrons, backpropagation, stochastic gradient descent, Hopfield networks, radial basis function networks), deep learning algorithms (e.g., convolutional neural networks, recurrent neural networks, long short-term memory networks, stacked autoencoders,..[…]…” and see [0244] discloses, “The system can include one or more machine learning models trained based on user data (e.g., a user's data, a group of users, etc.). In certain embodiments, an analysis module can be configured with one or more algorithms to generate personalized information using statistics, machine learning, AI, neural networks, or the like.”)
As per claim 8, Pushpala teaches:
The method of claim 1, wherein the collecting of at least one of the first biosensor device data element and the second biosensor device data element comprises collecting data from a wearable device. ([0038] discloses, “In some embodiments, the system 102 receives input data from one or more user devices 104. The user devices 104 can be any device associated with a user (e.g.. a patient), and can be used to obtain healthcare information, contextual information, and/or any other relevant information relating to the user and/or any other users (e.g., appropriately anonymized user data). In the illustrated embodiment, for example, the user devices 104 include at least one biosensor 104a (e.g., blood glucose sensors, pressure sensors, heart rate sensors, sleep trackers, temperature sensors, motion sensors, or other biomonitoring devices), at least one mobile device 104b (e.g., a smartphone or tablet computer), and, optionally, at least one wearable device 104c (e.g., a smartwatch, fitness tracker). In other embodiments, however, one or more of the user devices 104a-c can be omitted and/or other types of user devices can be included, such as computing devices (e.g., personal computers, laptop computers, etc.). Moreover, although FIG. 1 illustrates the biosensor(s) 104a as being separate from the other user devices 104, in other embodiments the biosensor(s) 104а can be incorporated into another user device 104. Additional examples of biosensors 104a suitable for use with the present technology are described in greater detail below.”)
As per claim 9, Pushpala teaches:
The method of claim 1, wherein the collecting of at least one of the first data element and the second biosensor device data element comprises collecting data from a sensor integrated into the computing device. (see Fig. 1 and see [0038] discloses, “In some embodiments, the system 102 receives input data from one or more user devices 104. The user devices 104 can be any device associated with a user (e.g.. a patient), and can be used to obtain healthcare information, contextual information, and/or any other relevant information relating to the user and/or any other users (e.g., appropriately anonymized user data). In the illustrated embodiment, for example, the user devices 104 include at least one biosensor 104a (e.g., blood glucose sensors, pressure sensors, heart rate sensors, sleep trackers, temperature sensors, motion sensors, or other biomonitoring devices), at least one mobile device 104b (e.g., a smartphone or tablet computer), and, optionally, at least one wearable device 104c (e.g., a smartwatch, fitness tracker). In other embodiments, however, one or more of the user devices 104a-c can be omitted and/or other types of user devices can be included, such as computing devices (e.g., personal computers, laptop computers, etc.). Moreover, although FIG. 1 illustrates the biosensor(s) 104a as being separate from the other user devices 104, in other embodiments the biosensor(s) 104а can be incorporated into another user device 104. Additional examples of biosensors 104a suitable for use with the present technology are described in greater detail below.”)
As per claim 10, Pushpala teaches:
The method of claim 9, wherein the collecting data from a sensor integrated into the computing device comprises collecting image data. ([0036] discloses, “Health-related information can also include medical history data (e.g., weight, age, sleeping patterns, medical conditions, cholesterol levels, triglyceride levels, disease type, family history, user health history, diagnoses, tobacco usage, alcohol usage, etc.), diagnostic data (e.g., molecular diagnostics, imaging), medication data (e.g., timing and/or dosages of medications such as insulin), personal data (e.g., name, gender, demographics, social network information, etc.), and/or any other data, and/or any combination thereof.” And see [0053] discloses, “The systems and methods of the present technology can use one or more biosensors (also referred to herein as "biosensor devices" "sensors," or "sensor devices") to generate user data, such as data indicative of a user's health state. The biosensors described herein can be or include various types of sensors, such as chemical sensors, electrochemical sensors, optical sensors (e.g., optical enzymatic sensors, opto-chemical sensors, fluorescence-based sensors, etc.), spectrophotometric sensors, spectroscopic sensors, polarimetric sensors, calorimetric sensors, iontophoretic sensors, radiometric sensors, and the like, and combinations thereof. The biosensors can be implanted sensors, nonimplanted sensors, invasive sensors, minimally invasive sensors, non-invasive sensors, wearable sensors, etc. The biosensors can be disposable sensors, reusable sensors, or can include any suitable combination of disposable and reusable components (e.g., a disposable sensor portion for monitoring specific condition(s) and a reusable electronics portion for receiving and processing the sensor data).”)
As per claim 20, Pushpala teaches:
A method of operating a computing device,…[…]… comprising: determining in an artificial neural network a correlation between at least a first biosensor device data element, a second biosensor device data element, and a physical condition indication for a biosensor device user; ([0047] discloses, “As discussed further below, the system 102 can analyze the obtained input data, including historical data, current real-time data, continuously supplied data, calibration data, and/or any other data (e.g., using a statistical analysis, machine learning analysis, etc.), and generate output data. The output data can include predictions of a user's health state, correlations between data, interpretations, recommendations, notifications, instructions, support, and/or other information related to the obtained input data. In some embodiments, the output data provides information to assist the user in adjusting their behavior (e.g., diet, exercise, sleeping, etc.) to enhance outcomes, to reduce, limit, or avoid health care provider intervention, etc.” and see [0184]-[0186] and see [0049])
and adjusting the weighting of the artificial neural network according to the correlation to establish a prediction threshold for a physical condition requiring a notification message to be emitted.([0192] discloses, “At A step 1060, the method 1000 can optionally include combining forecast(s), confidence interval(s), and/or target range(s) for output to the user, e.g., via display on user interface of a user device. The output can inform the user of likely near-term health parameter values and their uncertainties, can provide a useful reference for comparison, and/or can allow the user to make decisions about whether or not to change plans and/or take any action. [0193] At step 1070, the method 1000 can optionally include interpreting the forecast(s). For example, the forecast values can be compared to the target range at the various forecast times. If more than a threshold percentage (e.g., 10% or 25%) of the forecast values are above the target range, the forecast can be labeled "high." The system 100 can generate a message for display to the user (e.g., "likely to go higher than recommended within 4 hours," "likely to remain within healthy levels for the next 8 hours"). The determination can also be used as an input to automatically select a support message that can provide the user with various actions that the user can undertake.”)
However, Pushpala does not teach:
in a secure zone of the computing device,
wherein the secure zone defining a trusted execution environment generated in response to successful attestation of a boot-up sequence of the computing device.
However, CHANG teaches:
in a secure zone of the computing device.(see page 1 paragraph 5 and see page 16 paragraphs 1-2 discloses, “In operation 1040, the processor 120 executes a second application (eg, applet 2 323b of FIG. 3 ) accessible to the biosensor (eg, the sensor 340 of FIG. 3 ) when the TEE 320 is provided in the first memory. According to the execution of the second application, biometric information (eg, the second biometric information of FIG. 5 ) may be acquired from the biometric sensor and stored at least temporarily in the secure area. Additionally, the second application compares the second biometric information obtained from the biometric sensor with the first biometric information stored in the security area of the second memory and generates an authentication result indicating whether the second biometric information is valid or not based on the comparison result. may be. In operation 1050, the processor 120 may transfer the authentication result performed by the second application to the first virtual machine granted access to the TEE 320. Accordingly, the first virtual machine may transfer the authentication result to the first application. Additionally, the processor 120 may transmit the biometric information acquired from the biometric sensor to the first virtual machine along with the authentication result. Accordingly, the first virtual machine may perform a reconfirmation operation of determining whether the authentication result of the second application is correct by using the biometric information acquired from the biometric sensor.” And see page 3 paragraph 8 discloses, “The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, an image signal processor or a communication processor) may be implemented as part of other functionally related components (eg, the camera module 180 or the communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.”)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Pushpala’s teachings with Chang’s teachings, the motivation being PushPala teaches encryption of data and keeping data secure (e.g. [0247] and see [0139]) therefore, it would be obvious to one of ordinary skill that health data security would be improved by applying a secure zone explicitly with a secure computing element and further improve following healthcare data protection laws thus protecting cryptographic keys and overall security of encryption.
However, CHANG does not explicitly teach:
the secure zone defining a trusted execution environment generated in response to successful attestation of a boot-up sequence of the computing device.
However, GAO does teach:
the secure zone defining a trusted execution environment generated in response to successful attestation of a boot-up sequence of the computing device. (page 7 para. 7-8 discloses, “In the embodiments of the present application, the execution subject can be an independent security module (or security component), that is, a security module in a TEE based on ARM TrustZone technology. For example, the security module is securely booted along with the BIOS during the power-on process of the ARM server. The security module can be deployed in either S-EL0 or S-EL1 of the ARM TrustZone architecture, without limitation here. In an embodiment of the present application, a remote attestation process supporting multiple user TEE OS is provided. The virtualization component (i.e., S-EL2 component) in the cloud server (i.e., ARM server) can send a loading message to the security module for determining the target trusted kernel system to be loaded from multiple user trusted kernel systems, i.e., a loading message for determining the target user TEE OS from multiple user TEE OS. The independent security module provided in the embodiment of the present application receives a loading message from the S-EL2 component in the ARM server, and then generates a remote attestation report for providing the verification content of the target user TEE OS based on the loading message, and outputs the remote attestation report to the remote attestation verification component running in the non-secure world operating environment in the ARM server, so that the remote attestation verification component remotely attests the target user TEE OS based on the remote attestation report. Among them, a preset security architecture (i.e., ARM TrustZone architecture) is deployed in the ARM server, and a secure world operating environment and a non-secure world operating environment are created on the ARM TrustZone architecture. The S-EL2 component for providing physical isolation for multiple user TEE OS runs in the secure world operating environment.”
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Pushpala’s teachings and CHANG’s teachings with GAO’s teachings, the motivation being PushPala teaches encryption of data and keeping data secure (e.g. [0247] and see [0139]) therefore, it would be obvious to one of ordinary skill that health data security would be improved by applying a secure zone explicitly with a secure computing element and further improve keeping secure zones separate and authenticated by attestation after boot-up following healthcare data protection laws thus protecting cryptographic keys and overall security of encryption.
As per claims 11 and 13-17, they are system claims which repeat the same limitations of claims 1 and 3-7 the corresponding method claims, as a collection of elements as opposed to a series of process steps. Since the teachings of Pushpala, Chang, and GAO as well as motivations to combine disclose the underlying process steps that constitute the methods of claims 1 and 3-7, it is respectfully submitted that they provide the underlying structural elements that perform the steps as well. As such, the limitations of claims 11 and 13-17 are rejected for the same reasons given above for claims 1 and 3-7.
As per claim 18, it is an article of manufacture claim which repeats the same limitations of claim 1, the corresponding method claims, as a collection of executable instructions stored on machine readable media as opposed to a series of process steps. Since the teachings of Pushpala, Chang, and GAO as well as motivations to combine disclose the underlying process steps that constitute the method of claim 1, it is respectfully submitted that they likewise disclose the executable instructions that perform the steps as well. As such, the limitations of claim 18 are rejected for the same reasons given above for claim 1.
Response to Arguments Regarding 35 U.S.C § 101 Rejection
Applicant’s arguments on pages 1-5 of remarks have been considered and are responded to below. Claims 1, 3-11, 13-18, and 20 stand rejected under 35 U.S.C. § 101 as being directed to an abstract idea without significantly more. In particular, the Office Action maintains the assertion that the claims are directed towards certain methods of organizing human activity as 'following rules and instructions to derive a user condition." (Office Action, p. 4.)
Applicant respectfully submits that this characterization is based on an over-abstraction of the claims. Rather, the claims are directed to a patent-eligible practical application of any purported abstract idea that improves the functioning and security of the computing device.
Step 2A Prong One
Applicant again respectfully disagrees with the characterization that the claims are directed to a certain method of organizing human activity under Step 2A Prong One and refers again to the arguments made in the Amendment filed March 3, 2026.
Examiner appreciates applicant’s arguments but does not find them persuasive for the same reasoning given in the office action dated 03/30/2026 in response to the amendments filed March 3, 2026.
Step 2A Prong Two
Even assuming, arguendo, an abstract idea is recited in claim 1, any purported abstract idea is integrated into a practical application under Step 2A Prong Two. The Supreme Court has long distinguished between principles themselves (which are not patent eligible) and the integration of those principles into practical applications (which are patent eligible). See, e.g., Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 80, 84, 101 USPQ2d 1961, 1968-69, 1970 (2012). A claim can integrate an abstract idea into a practical application where it recites an improvement to the functioning of a computer or any other technology or technical field. MPEP § 2106.04(d)(1). Further, a claim that recites applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception can integrate the exception into a practical application. Id. See also MPEP § 2106.05(e).
Without waiver or disclaimer, and solely in an effort to further prosecution, claim 1 has been amended to recite, in combination with the other limitations of the claim, "the storage and processing location for collecting the first biosensor device data element and the second biosensor device data element and applying machine learning model inferencing is a secure zone of the
computing device, the secure zone defining a trusted execution environment generated in response to successful attestation of a boot-up sequence of the computing device" (emphasis added).
As an initial matter, amended claim 1 makes clear the specific, non-conventional computing architecture. The claims recite that the "storage and processing location", where the operations of collecting the biosensor data elements and applying machine learning model inferencing occur, is a "secure zone" that defines a "trusted execution environment generated in response to successful attestation of a boot-up sequence". This is a specific computing architecture performing specific processing steps within a verifiably secure environment and is not a generic computer performing an abstract task. (See MPEP § 2106.05(e).)
The claim therefore applies any purported judicial exception in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception can integrate the exception into a practical application.
Moreover, amended claim 1 integrates any purported abstract idea into a practical application because it recites an improvement to secure computing and reliable data processing. (See MPEP § 2106.04(d)(1).) In particular, performing the operations of collecting the biosensor data elements and applying machine learning model inferencing occur within the recited a trusted execution environment provides concrete technical advantages that solve technical problems inherent in processing sensitive data.
In a first example, amended claim 1 protects data and model integrity during active processing. Conventional security techniques often focus on data at rest (encryption) or in transit.
The claimed trusted execution environment protects the first and second biosensor data elements, as well as the machine learning model itself, while they are actively being processed in memory. As disclosed in the application (e.g., see paragraphs [0062] and [0065]), this environment ensures that the machine learning model, its parameters, and the data are accessible only to the authorized process, thereby protecting them from being read or altered by other processes, malware, or even a compromised host operating system. This solves the technical problem of ensuring the integrity and confidentiality of data during computation.
In a second example, amended claim 1 ensures trustworthy and reliable machine learning output. By protecting the machine learning model itself from interference within the trusted execution environment, the method of amended claim 1 ensures that the "user condition indication" is reliable and trustworthy. This solves the technical problem of preventing malicious or accidental alteration of machine learning models, which could otherwise lead to dangerously incorrect health assessments.
In a third example, amended claim 1 enables provable security. The claimed generation of the trusted execution environment "in response to successful attestation of a boot-up sequence" provides a cryptographic chain of trust from the moment the device is turned on. This solves the technical problem of establishing verifiable trust in a computing environment before it is provisioned with sensitive health data (see paragraph [0068]). This is a specific technological process that is fundamentally different from merely applying a generic security policy. Amended claim 1 thus improves data integrity and the limitations that reflect the improvement are recited in the claims. (See MPEP 2106.05(a).) Specifically, the aforementioned technical advantages represent a specific improvement to computer functionality, particularly in the fields of secure computing and reliable data processing. The claims are not merely "applying" an abstract idea but are directed to a specific technical solution to technical problems.
Amended claim 1 therefore integrates any purported abstract idea into a practical application under Step 2A Prong Two. Similar amendments have been made to independent claims 11, 18, and 20, which are likewise subject matter eligible, as are the respective dependent claims that depend therefrom.
Applicant respectfully requests withdrawal of the rejections under 35 U.S.C. § 101.
Examiner appreciates applicant’s argument but does not find it persuasive.
The MPEP state in 2106.04(d), “Examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical applications.”
Therefore, respectfully, examiner disagrees with the applicant as the claim limitations must be reviewed in light of the specification and the specification cannot be read into the claims. The positively recited in claim 1 (as representative) are directed to a judicial exception (i.e. certain methods of organizing human activity) following rules or instructions to derive a user condition. This is abstract in substance as the physician should follow a process to review data at his or her disposal to derive a user’s condition while maintaining security and privacy of that data by following rules and instructions. Being implemented by a computer environment does not make the recited claim dispositive of being certain methods of organizing human activity. Additionally, the judicial exception (abstract idea) cannot integrate itself into a practical application but identification of any additional elements recited in the claim can be evaluated to determine if the additional elements integrate the exception into a practical application. The claims additional elements are not recited as being an improvement to a technology field or a technology confined to the computer environment in which the claims recite (see instant application [0046]). The claim construction does not recite unconventional steps or an improvement to a secure zone for data integrity to derive a user condition but rather uses a secure zone as “apply-it” to protect information.
The claims do not reflect solving a problem with a secure zone rather reflects the analysis of data to detect a user condition. This same rational applies to the further examples argued by applicant ensuring the integrity and confidentiality of information is not a problem understood to be with the computer itself as the environment is confined but rather an abstract problem with data privacy and the computer elements are used as tools such as the computer itself and the secure zone during computation. The claims also do not recite a specific cryptographic chain of trust from the moment the device is turned on rather recites a secure zone attested after boot-up of a computer. Thus there is no reflection or recitation of improvement to the computing elements themselves to improve cryptography but rather applied computer elements to secure data. Examiner maintains the claims are directed to an abstract idea and do not integrate into a practical application. Therefore, they also do not amount to significantly more.
Examiner maintains the 35 U.S.C § 101 rejection.
Response to Arguments Regarding 35 U.S.C § 102/103 Rejections
Applicant’s arguments on pages 5-7 of remarks have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Examiner maintains the 35 U.S.C § 103 rejection.
Prior Art not cited but made of record
HABIB (US20240074661A1)
A method to monitor a wearable device, the method including providing a processing system located remotely from the wearable device having a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, including; obtaining baseline biosensor samples of a user of the wearable device to establish expected biosensor outputs; obtaining continuous biosensor output samples at predetermined intervals; storing acquired baseline and continuous sensor outputs; comparing at predetermined intervals the continuous biosensor outputs to the baseline biosensor outputs; determining changes in the continuous biosensor outputs to the baseline biosensor outputs; and outputting information using machine learning associated with the changes in the continuous biosensor outputs to the baseline biosensor outputs.
Ehsani et. al (hereinafter Ehsani) (US20240008784A1)
A system for management of health conditions, such as mental health conditions, using a biosensor configured to monitor one or more biological properties of an individual and providing feedback and information based on data in real-time. A corresponding method, performed by one or more components of the system, may include monitoring a biological property of the individual, identifying an irregularity as a symptom of the health condition, and intervening with the individual to alleviate the symptom. The method may further include determining an efficacy of the intervention and adapting the method to improve the intervention. The system may be configured to predict a future symptom and preventatively intervene.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ashley Elizabeth Evans whose telephone number is (571) 270-0110. The examiner can normally be reached Monday – Friday 8:00 AM – 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mamon Obeid can be reached on (571) 270-1813. The fax phone number for the organization where this application or proceeding is assigned 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center. Should you have questions on access to the Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/ASHLEY ELIZABETH EVANS/Examiner, Art Unit 3687
/MAMON OBEID/Supervisory Patent Examiner, Art Unit 3687
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