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 .
Claims 1,4-7,8-9,12-16,19-20 are pending. Claims 1,4-7,8-9,12-16,19-20 are rejected herein.
Priority
This application does not claim priority to another application, the effective priority date is 29 June 2023.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1,4-7,8-9,12-16,19-20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1: Statutory Categories
Claims 1,8,16 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more. The claims are directed to a method of, a computer program product comprising a computer readable storage medium having program instructions to/for, and system for generating patient treatment recommendations. All are within a statutory class for subject matter eligibility purposes.
Step 2A Prong One: Abstract Idea
The limitations of (claim 1 being representative): […receiving…] the data pertaining to a use of the medical treatment and a state of the patient, the medical treatment having been administered via a wearable medical device of the patient, the state of the patient being automatically revied […], wherein change to the state of the patient are automatically received […]; identifying a subset of the data pertaining to the state of the patient as context, the context including the physical condition of the patient and environmental data including atmospheric data associated with the patient, wherein different physical conditions of the patient associated with different environmental data are identified, wherein the different environmental data are representative of different environment in which the medical treatment has been administered […]; learning a function that relates the context to a reward derived from the medical treatment, the subset of the data having been identified as the context based on an impact the subset of data has on the reward, the reward being at least a pain level of the patient, mood level of the patient, number of steps taken by the patient in a given time period, and alertness of the patient; using the function based on a current state of the patient and current environmental data including current atmospheric data to identify a type of treatment to deliver to the patient; and transmitting the type of treatment that is approved […] to the patient, wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information… as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) and/or mathematical concepts but for the recitation of generic computer components.
That is, other than reciting (claim 1) a computer, and (claim 8) a computer program product comprising a computer readable storage medium having program instructions embodied therewith, and (claim 16) at least one processor and one memory device, the claimed invention amounts to managing personal behavior or interactions between people (i.e., a person following a series of rules or steps) and/or mathematical concepts, which falls under certain methods of organizing human activity and/or mathematical concepts. Note that recitations in the claim were identified as part of the mathematical concepts grouping. For example, but for the various general-purpose computer elements, the claims encompass a person receiving data and generating treatment recommendations via a learning function. The Examiner notes that “certain methods of organizing human activity” includes a person’s interaction with a computer (MPEP 2106.04(a)(2)(II)). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people and/or mathematical concepts but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas and/or mathematical concepts grouping. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Practical Application
The judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of (claim 1) a computer, and (claim 8) a computer program product comprising a computer readable storage medium having program instructions embodied therewith, and (claim 16) at least one processor and one memory device that implements the abstract idea. These additional elements are not exclusively described by the applicant and are recited at a high-level of generality (i.e., a generic general-purpose computer or components thereof) such that they amount no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The independent claims recite a wearable medical device. The wearable medical device generally links the judicial exception to a particular technological environment. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
The claim further recites the additional element of receiving data associated with a patient undergoing medical treatment… Receiving data associated with a patient undergoing medical treatment is recited with high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, even if it recites further the type of data, and as such the limitation is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application.
Step 2B: Significantly More
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a general -purpose computer (and/or components thereof) to perform the noted steps amounts to no more than mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). As such the claims are not patient eligible.
The independent claims recite a wearable medical device. The wearable medical device generally links the judicial exception to a particular technological environment. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
The claim further recites the additional element of receiving data associated with a patient undergoing medical treatment… Receiving data associated with a patient undergoing medical treatment is recited with high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, even if it recites further the type of data, and as such the limitation is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application or provide significantly more.
Dependent Claims and Additional Elements
Dependent claims (4-7,9,12-15,19-20) are rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide an inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination. Claim 4 merely describes wherein the reward is related to a metric structured as a multi-dimensional vector describing certain patient features. Claim 5 merely describes wherein the reward is a scalar function of the context. Claim 6 merely describes wherein the subject of data includes certain features of the received data. Claim 7 merely describes wherein the function is learned using a certain algorithm with respect to super-gaussian noise. Claim 9 merely describes treatment options, and an instance of the type of treatment is selected based on the context of the patient in a given time. Claim 12 merely describes merely describes wherein the reward is related to a metric structured as a multi-dimensional vector describing certain patient features. Claim 13 merely describes wherein the reward is a scalar function of the context. Claim 14 merely describes wherein the subject of data includes certain features of the received data. Claim 15 merely describes wherein the function is learned using a certain algorithm with respect to super-gaussian noise. Claim 19 merely describes wherein the reward is related to a metric structured as a multi-dimensional vector describing certain patient features. Claim 20 merely describes wherein the function is learned using a certain algorithm with respect to super-gaussian noise.
The dependent claims recite a wearable medical device. The wearable medical device generally links the judicial exception to a particular technological environment. Additional elements that generally link the judicial exception to a particular technological environment or field of use cannot serve to integrate the exception into a practical application or provide significantly more. See MPEP 2106.04(d)(l), Relevant Consideration for Evaluating Whether Additional Elements Integrate A Judicial Exception Into A Practical Application, and MPEP 2106.05(h).
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 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.
Claim(s) 1,4,6,8-9,12,14,16,19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0006068 A1 (hereafter Shields) in view of US 2024/0395388 A1 (hereafter Fortkort) in view of Zapol (Systems and methods of producing nitrogen oxide in outpatient conditions).
Regarding Claim 1
Shields teaches:
[…]
identifying a subset of the data pertaining to the state of the patient as context, the context including the physical condition of the patient and environmental data including atmospheric data associated with the patient, [Shields teaches at the Abstract the system includes a patient data display device programmed to receive and display data associated with the patient; an environmental assessment device configured to capture visual, aural, or other ambient environmental data associated with an emergency treatment site associated with the patient; a patient monitoring device configured to be positioned on the patient having multiple sensors programmed to collect physiological data or vitals data associated with the patient; and a patient data processing device configured with a speech-to-text module. This teaches identifying a subset of data pertaining to the state of the patient as context, the context including the physical condition of the patient and environmental data including atmospheric (interpreted to be the aural data) data associated with the patient. Shield teaches at para. [0031] in certain embodiments, vitals data (among other types of data) will be used in developing rules-based and machine learning algorithms for alerting the EMS/prehospital team to patient-specific conditions such as, but not limited to, patient deterioration, best practices for certain conditions, most appropriate ED destination, trauma level or specialty care needs. This teaches identifying a subset of the data pertaining to the patient state as context.]
wherein different physical conditions of the patient associated with different environmental data are identified, [Shields teaches at the Abstract the system includes a patient data display device programmed to receive and display data associated with the patient; an environmental assessment device configured to capture visual, aural, or other ambient environmental data associated with an emergency treatment site associated with the patient; a patient monitoring device configured to be positioned on the patient having multiple sensors programmed to collect physiological data or vitals data associated with the patient; and a patient data processing device configured with a speech-to-text module. Shield teaches at para. [0031] in certain embodiments, vitals data (among other types of data) will be used in developing rules-based and machine learning algorithms for alerting the EMS/prehospital team to patient-specific conditions such as, but not limited to, patient deterioration, best practices for certain conditions, most appropriate ED destination, trauma level or specialty care needs. This teaches wherein different environmental data are identified that are associated with different physical conditions of the patient. Shields teaches at para. [0031] for example, condition alerts will be generated such as for patient conditions including stroke, STEMI alerts, and patient risk for certain conditions (e.g., stroke or MI). The condition alerts for the conditions listed are a form of identification. Collectively, Shields teaches wherein different physical conditions of the patient associated with different environmental data are identified.]
wherein the different environmental data are representative of different environment in which the medical treatment has been administered via the wearable medical device of the patient; [Shields teaches at col. [0022] in one aspect, wearable vital sensor devices (one example is a “Vital Vest” device described herein) can be equipped on or worn by a patient. Shields teaches at the Abstract the system includes a patient data display device programmed to receive and display data associated with the patient; an environmental assessment device configured to capture visual, aural, or other ambient environmental data associated with an emergency treatment site associated with the patient; a patient monitoring device configured to be positioned on the patient and having multiple sensors programmed to collect physiological data or vitals data associated with the patient and a patient data processing device configured with a speech-to-text module.]
[…].
Shields may not explicitly teach:
A computer-implemented method comprising: receiving data associated with a patient undergoing a medical treatment, the data pertaining to a use of the medical treatment and a state of the patient, the medical treatment having been administered via a wearable medical device of the patient, the state of the patient being automatically received via at least one sensor of the wearable medical device, wherein changes to the state of the patient are automatically received via that least one sensor of the wearable medical device;
[…]
learning a function that relates the context to a reward derived from the medical treatment, the subset of the data having been identified as the context based on an impact the subset of the data has on the reward, the reward being at least a pain level of the patient, mood level of the patient, number of steps taken by the patient in a given time period, and alertness of the patient;
[…]
using the function based on a current state of the patient and current environmental data including current atmospheric data to identify a type of treatment to deliver to the patient;
and transmitting the type of treatment that is approved to the wearable medical device of the patient, responsive to an approval, causing the wearable medical device to automatically adjust the type of treatment to the patient.
Fortkort teaches:
A computer-implemented method comprising: receiving data associated with a patient undergoing a medical treatment, [Fortkort teaches at para. [0034] point (c) a server for receiving wellness data from the PBM device and providing personalized treatment recommendations. Collectively, Fortkort teaches receiving data associated with a patient undergoing a medical treatment.]
the data pertaining to a use of the medical treatment and a state of the patient, [Fortkort teaches at para. [0141] this data will include physiological parameters (such as, for example, heart rate, skin temperature, etc.), lifestyle factors (such as for example, diet, exercise, stress levels), genetic data, treatment objectives (such as, for example, reducing pain, improving mood, healing a skin condition) and past responses to PBM treatments. Collectively, this teaches the data pertaining to a use of the medical treatment and a state of the patient.]
the medical treatment having been administered via a wearable medical device of the patient, [Fortkort teaches at para. [0034] point (c) a server for receiving wellness data from the PBM device and providing personalized treatment recommendations. Fortkort teaches at Figure 5 Item 451 a VR system (glasses with a strap) that is wearable. Fortkort teaches at para. [0305] furthermore, visual cue will be provided in the VR environment guide users in the correct application of PBM device, offering clear directional indicators for device placement, and displaying prompts or instructions for treatment. Collectively, Fortkort teaches the medical treatment having been administered via a wearable medical device of the patient.]
the state of the patient being automatically received via at least one sensor of the wearable medical device, [Fortkort teaches at Figure 5 Item 451 a VR system (glasses with a strap) that is wearable. Fortkort teaches at para. [0305] furthermore, visual cue will be provided in the VR environment guide users in the correct application of PBM device, offering clear directional indicators for device placement, and displaying prompts or instructions for treatment. Fortkort teaches at para. [0034] point (c) a server for receiving wellness data from the PBM device and providing personalized treatment recommendations. Fortkort teaches at para. [0141] this data will include physiological parameters (such as, for example, heart rate, skin temperature, etc.), lifestyle factors (such as for example, diet, exercise, stress levels), genetic data, treatment objectives (such as, for example, reducing pain, improving mood, healing a skin condition) and past responses to PBM treatments. Collectively, this teaches and the state of the patient being automatically received via at least one sensor of the wearable medical device.]
wherein changes to the state of the patient are automatically received via that least one sensor of the wearable medical device; [Fortkort teaches at para. [0033] in another aspect, a method is provided for providing a subscription-based wellness promotion service. Fortkort teaches at para. [0034] point (c) a server for receiving wellness data from the PBM device and providing personalized treatment recommendations. Fortkort teaches at Figure 5 Item 451 a VR system (glasses with a strap) that is wearable. Fortkort teaches at para. [0305] furthermore, visual cue will be provided in the VR environment guide users in the correct application of PBM device, offering clear directional indicators for device placement, and displaying prompts or instructions for treatment. This teaches receiving data associated with a patient undergoing a medical treatment. This teaches the medical treatment having been administered via a wearable medical device of the patient. Fortkort teaches at para. [0141] for example, the system will collect relevant wellness data and treatment objective from the user. Fortkort teaches at para. [0141] this data will include physiological parameters (such as, for example, heart rate, skin temperature, etc.), lifestyle factors (such as for example, diet, exercise, stress levels), genetic data, treatment objectives (such as, for example, reducing pain, improving mood, healing a skin condition) and past responses to PBM treatments. Collectively, this teaches receiving data associated with a patient undergoing a medical treatment, the data pertaining to a use of the medical treatment and a state of the patient. The state of the patient and data pertaining to a use of the medical treatment is interpreted to be the physiological parameters (heart rate, skin, temperature etc.) of the patient, for example, the temperature.]
using the function based on a current state of the patient and current environmental data including current atmospheric data to identify a type of treatment to deliver to the patient; [Fortkort teaches at para. [0133] various reinforcement learning algorithms will be utilized in the systems and methodologies disclosed therein. Fortkort teaches at para. [0133] these algorithms will be utilized, for example, to continually refine the treatment recommendations as new data is received. This teaches using the function based on a current state of the patient and current environmental data (environmental data is taught below). Fortkort teaches at para. [0192] various types of wellness data will be utilized in the systems and methodologies disclosed therein. Fortkort teaches at par.[0192] these will include, without limitation…environmental data (such as, for example, data about the individual’s living and working conditions;…allergies (Such as, for example, information about food, drug, environmental, or other allergies);… Fortkort teaches at para. [0240] environmental sensors such as humidity, temperature, or air quality sensors will be included to measure the conditions in the user’s environment. This teaches using the function based on the current environmental data including current atmospheric data to identify a type of treatment to deliver to the patient. Fortkort teaches at para. [0134] the agent receives rewards or penalties (positive or negative rewards) as feedback for its actions and learns to make better decisions over time by maximizing the total reward. Fortkort teaches at para. [0141] for example, the system will collect relevant wellness data and treatment objective from the user. Fortkort teaches at para. [0141] this data will include physiological parameters (such as, for example, heart rate, skin temperature, etc.), lifestyle factors (such as for example, diet, exercise, stress levels), genetic data, treatment objectives (such as, for example, reducing pain, improving mood, healing a skin condition) and past responses to PBM treatments. Collectively, this teaches using the function based on a current state of the patient and current environmental data to identify a type of treatment to deliver to the patient.]
[…]
learning a function that relates the context to a reward derived from the medical treatment, the subset of the data having been identified as the context based on an impact the subset of the data has on the reward, the reward being at least a pain level of the patient, mood level of the patient, number of steps taken by the patient in a given time period, and alertness of the patient; [Fortkort teaches at para. [0133] various reinforcement learning algorithms will be utilized in the systems and methodologies disclosed therein. Fortkort teaches at para. [0133] these algorithms will be utilized, for example, to continually refine the treatment recommendations as new data is received. Fortkort teaches at para. [0134] the agent receives rewards or penalties (positive or negative rewards) as feedback for its actions and learns to make better decisions over time by maximizing the total reward. Fortkort teaches at para. [0141] for example, the system will collect relevant wellness data and treatment objective from the user. Fortkort teaches at para. [0141] this data will include physiological parameters (such as, for example, heart rate, skin temperature, etc.), lifestyle factors (such as for example, diet, exercise, stress levels), genetic data, treatment objectives (such as, for example, reducing pain, improving mood, healing a skin condition) and past responses to PBM treatments. Fortkort teaches at para. [0409] each PBM device connected to the system implements regular wellness data in which it collects wellness data from its associated user. Fortkort teaches at para. [0409] this data will include metrics related to the PBM treatment itself (Such as, for example, changes in heart rate, blood pressure, or skin temperature), and subjective user feedback (such as, for example, use feedback relating to pain levels, mood, or sleep quality). Fortkort teaches at para. [0192] various types of wellness data will be utilized in the systems and methodologies disclosed therein. Fortkort teaches at para.[0192] these will include, without limitation, biometric data (such as, for example, heart rate, blood pressure, body mass index (BMI), cholesterol levels, blood glucose levels, and other vital signs); physical activity data (such as, for example, the number of steps taken per day, amount of vigorous or moderate exercise, typical of physical activity, and sedentary time); sleep data (such as, for example, total sleep time, sleep efficiency, number and duration of awakenings, and stages of sleep); nutrition data (such as, for example, information about diet such as caloric intake, macronutrient distribution, hydration, and consumption of fruits, vegetables, or other specific foods); stress levels (such as, for example, self-reported stress levels or physiological indicators of stress such as cortisol levels); mental health data (such as, for example, self-reported mood, anxiety levels, depressive symptoms, and cognitive function); social wellness data (such as, for example, self-reported data about social activity, relationship satisfaction, feelings of loneliness or connection, and involvement in the community); substance use data (such as, for example, consumption of alcohol, tobacco, caffeine, or other substances); medical history (such as, for example, past diagnoses, surgical history, medication use, and family medical history); genomic data (such as, for example, data derived from genetic testing, such as genetic predispositions to certain diseases); environmental data (such as, for example, data about the individual's living and working conditions, exposure to pollutants or allergens, and access to green spaces); occupational data (such as, for example, job satisfaction, work-related stress, ergonomic factors, and occupational hazards); self-reported symptoms (such as, for example, data about pain, fatigue, digestive issues, and other symptoms); immunization record (such as, for example, information about vaccinations received); health screening results (such as, for example, the results from health screenings such as mammograms, colonoscopies, skin checks, and the like); sexual health data (such as, for example, information about sexual activity, practices, and sexual health conditions or concerns); allergies (such as, for example, information about food, drug, environmental, or other allergies); menstrual cycle data (such as, for example, information about the menstrual cycle, symptoms, and related conditions (like PCOS or endometriosis)); bone density (such as, for example, information related to bone health, especially relevant for older users); and mindfulness and meditation practices (such as, for example, information about frequency, duration, and types of mindfulness or meditation practices). The number and duration of awakenings is interpreted as alertness of the patient. The anxiety level is also interpreted as the mood level. Collectively, this teaches learning a function that relates the context to a reward derived from the medical treatment, the subset of the data having been identified as the context based on an impact the subset of the data has on the reward, the reward being at least a pain level of the patient, mood level of the patient, number of steps taken by the patient in a given time period, and alertness of the patient.]
[…]
and transmitting the type of treatment that is approved to the wearable medical device of the patient, [Fortkort teaches as para. [0048] as previously noted, a software client 1 is installed on each PBM device, or on a user device in communication with the PBM device. Fortkort teaches at para. [0048] the software client collects data from the user, such as user input data and sensor data from the PBM device, and transmits this information to the server via the cloud. Fortkort teaches at para. [0048] the PBM device is also adapted to receive treatment recommendations from the server and present them to the user in a user-friendly manner by way of a user interface. Fortkort teaches at Figure 5 Item 451 a VR system (glasses with a strap) that is wearable. Fortkort teaches at para. [0305] furthermore, visual cue will be provided in the VR environment guide users in the correct application of PBM device, offering clear directional indicators for device placement, and displaying prompts or instructions for treatment. This teaches transmitting the type of treatment that is approved to the wearable medical device of the patient. ]
responsive to an approval, causing the wearable medical device to automatically adjust the type of treatment to the patient, [Fortkort teaches at para. [0047] the PBM devices are the physical devices used to administer PBM therapy. Fortkort teaches at para. [0369] in a wellness system, these will be used to automatically implement personalized treatment plans. Fortkort teaches at para. [0369] for example, if certain health data parameters are met, the smart contract will automatically adjust the treatment regimen on the PBM device. The meeting of certain health data parameters is interpreted as approval.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the computer based tools and techniques for optimizing emergency medical treatment of Shields to the AI augmented photobiomodulation wellness system with a community of users of Fortkort with the motivation of treating or preventing Alzheimer’s Disease (Fortkort at para. [0008]).
Shields/Fortkort may not explicitly teach:
wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information.
Zapol teaches:
wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information. [Zapol teaches at para. [0087] Figure 65 is a typical flowchart of a method for ensuring that a portable NO production device is used appropriately in conjunction with other therapy. Zapol teaches at para. [00107] in some embodiments, the device will comprise a cannula with two channels – with one channel for NO and one channel for O2. Zapol teaches at para. [0107] in some embodiments two gases are mixed at the base of the nose before exiting the cannula. Zapol teaches at para. [00116] the non-stationary device, which introduces a high concentration of NO into the O2 stream in the non-stationary device, will offer reduced levels of NO2 in the patient, as shown in the embodiment of the NO producing device shown in Figure sixteen. Collectively, Zapol teaches wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information. Zapol teaches at para. [0126] a base station will comprise various sensors. Zapol teaches at para. [0126] in some embodiments, the base station will include one or more gas analysis sensors to verify the calibration of the NO production device. Collectively, Zapol teaches wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information. The non-stationary information is the results/actions of the one or more gas analysis sensors to verify the calibration of the NO production device.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the computer based tools and techniques for optimizing emergency medical treatment of Shields to the AI augmented photobiomodulation wellness system with a community of users of Fortkort to the systems and methods of producing nitrogen oxide in outpatient conditions of Zapol with the motivation of facilitating a wearable system for producing and delivering nitric oxide for use both in a hospital and outside it (Zapol at para. [004]).
Regarding Claim 8
Due to its similarity to Claim 1, Claim 8 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 16
Due to its similarity to Claim 1, Claim 16 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Regarding Claim 9
Shields/Fortkort/Zapol teach the computer program product of claim 8. Shields/Fortkort/Zapol also teach:
wherein the wearable medical device includes multiple different programmed treatment options and an instance of the type of treatment is selected based on the context of the patient in a given time. [Fortkort teaches at para [0047] the PBM devices are the physical devices used to administer PBM therapy. Fortkort teaches at para. [0047] these will be handheld devices, wearable devices, or stationary devices, and are equipped with hardware capable of emitting light at specific wavelengths required for PBM. Fortkort teaches at para. [0021] the method comprises installing software client on a plurality of users; collecting wellness data from said plurality of users to form a wellness database; receiving, at a server in communication with said plurality of client PBM devices, wellness data and treatment objectives from said plurality of users via said software client; providing light therapy treatment recommendations to said plurality of client PBM devices from the server; accepting treatment objectives from each of said plurality of users by an artificial intelligence engine equipped within said server; and operating on said wellness database by said artificial intelligence engine to generate PBM recommendations for each of said plurality of users. Fortkort teaches at para. [0033] point (g) tracking wellness progress over time and adjusting treatment recommendations based on said progress. Fortkort teaches at para. [0037] last paragraph and at least one software container functioning as a user interface module, providing users with access to their wellness data, treatment recommendations, and other system features. Collectively, Fortkort teaches wherein the wearable medical device includes multiple different programmed treatment options and an instance of the type of treatment is selected based on the context of the patient in a given time.]
Regarding Claim 6
Shields/Fortkort/Zapol teach the computer-implemented method of claim 1. Shields/Fortkort/Zapol further teach:
wherein the subset of data includes a set of features from the received data. [Fortkort teaches at para. [0133] various reinforcement learning algorithms will be utilized in the systems and methodologies disclosed therein. Fortkort teaches at para. [0133] these algorithms will be utilized, for example, to continually refine the treatment recommendations as new data is received. Fortkort teaches at para. [0114] preprocessing will involve, for example, normalizing numerical features, encoding categorical variables, and handling missing data. Collectively, this teaches wherein the subset of data includes a set of features from the received data.]
Regarding Claim 14
Due to its similarity to Claim 6, Claim 14 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 6.
Regarding Claim 4
Shields/Fortkort/Zapol teach the computer-implemented method of claim 1. Shields/Fortkort/Zapol further teach:
wherein the reward is related to a metric structured as a multi-dimensional vector that describes the state of the patient related to the medical treatment the patient is undergoing.[Fortkort teaches at para. [0133] various reinforcement learning algorithms will be utilized in the systems and methodologies disclosed therein. Fortkort teaches at para. [0133] these algorithms will be utilized, for example, to continually refine the treatment recommendations as new data is received. Fortkort teaches at para. [0134] the agent receives rewards or penalties (positive or negative rewards) as feedback for its actions and learns to make better decisions over time by maximizing the total reward. Fortkort teaches at para. [0167] for instance, the data will be converted into a suitable vector space representation. Fortkort teaches at para. [0080] algorithms such as support vector machines, linear regression, or random forests will be utilized to predict the most effective treatments based on new wellness data. Fortkort teaches at para. [0086] this algorithm will be especially useful in situations where the wellness data is not linearly separable or has a high number of dimensions. Collectively, this teaches wherein the reward is related to a metric structured as a multi-dimensional vector that describes the state of the patient related to the medical treatment the patient is undergoing.]
Regarding Claim 12
Due to its similarity to Claim 4, Claim 12 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 4.
Regarding Claim 19
Due to its similarity to Claim 4, Claim 19 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 4.
Claim(s) 5,13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0006068 A1 (hereafter Shields) in view of US 2024/0395388 A1 (hereafter Fortkort) in view of Zapol (Systems and methods of producing nitrogen oxide in outpatient conditions) in view of Ameko (Offline Contextual Multi-Armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation).
Regarding Claim 5
Shields/Fortkort/Zapol teach the computer-implemented method of claim 4. Shields/Fortkort/Zapol may not explicitly teach:
wherein the reward is a scalar function of the context.
Ameko teaches:
wherein the reward is a scalar function of the context. [Ameko teaches at Table 1 Contexts for the proposed contextual multi-armed bandit algorithm a row title Motivation change to reported motivated change feelings on a 1-10 scale. Ameko teaches at pg. 250 contextual multi-armed bandit for emotion regulation that contextual multi-armed bandit (CMAB) is an reinforcement learning algorithm that leverages contextual information to learn a policy that triggers actions based on the context to achieve optimal expected rewards. This teaches that the reward would be a scalar function of the context.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of healthcare, at the time of filing, to modify the computer based tools and techniques for optimizing emergency medical treatment of Shields to the AI augmented photobiomodulation wellness system with a community of users of Fortkort to the systems and methods of producing nitrogen oxide in outpatient conditions of Zapol to the offline contextual multi-armed bandits for mobile health interventions of Ameko with the motivation of delivering treatment recommendations via persuasive electronic devices such as mobile phones that has the potential to be viable and scalable treatment medium for long-term health behavior management (Ameko at the Abstract).
Regarding Claim 13
Due to its similarity to Claim 5, Claim 13 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 5.
Claim(s) 7,15,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0006068 A1 (hereafter Shields) in view of US 2024/0395388 A1 (hereafter Fortkort) in view of Zapol (Systems and methods of producing nitrogen oxide in outpatient conditions) in view of Ameko (Offline Contextual Multi-Armed Bandits for Mobile Health Interventions: A Case Study on Emotion Regulation) in view of Shankar (Noise dependent Super Gaussian Coherence based dual microphone speech enhancement for hearing aid application using smartphone).
Regarding Claim 7
Shields/Fortkort/Zapol teach the computer-implemented method of claim 1. Shields/Fortkort/Zapol may not explicitly teach:
wherein the function is learned using a contextual multi-armed bandit robust with respect to super-gaussian noise.
Ameko teaches the following noted feature:
wherein the function is learned using a contextual multi-armed bandit robust... [Ameko teaches at pg. 250 3 Contextual Multi-Armed Bandit for Emotion Regulation that contextual multi-armed bandit (CMAB) is an reinforcement learning algorithm that leverages contextual information to learn a policy that triggers actions based on the context to achieve optimal expected rewards. Ameko teaches at Equation 7 that the reward signal for each context x and action a is defined as 1 where the condition is satisfied and 0 otherwise. Collectively, the teaching of Ameko are interpreted to be the reward is a scalar function of the context.]
Shankar teaches the following noted feature:
with respect to super-gaussian noise. [Shankar teaches at pg. 1 II. Weighted Combination of Coherence-Based and SGJMAP gain functions Item A. Super gaussian joint maximum a posteriori method, and on the next page teaches the use of the super-gaussian function to reduce/suppress background sound in hearing aids, the background sound is interpreted to result in the noise in the data.]
It would have been prima facie obvious to one of ordinary skill in the art at the time the invention was made to combine the noted features of Ameko with teaching of Shankar since the combination of the two references is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). It can be seen that each element claimed is present in either Ameko or Shankar. The contextual multi-armed bandit function would be applied the same way even with the addition of any treatment of super-gaussian noise in the data. Since the functionalities of the elements in Ameko and Shankar do not interfere with each other, the results of the combination would be predictable.
Regarding Claim 15
Due to its similarity to Claim 7, Claim 15 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Regarding Claim 20
Due to its similarity to Claim 7, Claim 20 is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Response to Arguments
35 U.S.C. 101 Argument Responses
Applicant argues that the amended claims, each considered as a whole, are not abstract. Claim 1 as a whole does not fall within “certain methods of organizing human activity” or “mathematical concept” per se, under Step 2A, Prong One. Like in Example 39 of the 2019 PEG, “While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited” in claim 1.
The Examiner thanks the Applicant for the quotation from the 2019 PEG. The message in this document reinforces the MPEP. Both documents arguably support classifying the description of the parameters of learning a function as a mathematical concepts: …learning a function that relates the context to a reward derived from the medical treatment, the subset of the data having been identified as the context based on an impact the subset of data has on the reward, the reward being at least a pain level of the patient, mood level of the patient, number of steps taken by the patient in a given time period, and alertness of the patient… This is describing the parameters of a learning function and is arguably mathematical concepts regardless if mathematics is literally written into the claim. Note that the MPEP indicates that mathematical concepts can take a variety of forms in the claim. Regardless, it is somewhat of a moot point as the MPEP indicates that the Examiner should indicate if even a single category of judicial exception has been recited. Note that it cautions Examiner’s not to parse the claim and it requires Examiners treat the abstract idea as a single abstract idea even if it falls under multiple subgroupings. Here, where the Examiner has been directly questioned they have answered as required. The Examiner asserts the claim recites a certain methods of organizing human activity at least, regardless of if a mathematical process is recited. The Examiner has just pointed to at least one use of another judicial exception.
Claim 1 also does not recite certain methods of organizing human activity such as “fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); and managing person behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)” as defined in MPEP 2106.04(a)(2)(II). For example, transmission via a wearable medical device, and causing of administering a treatment via the wearable medical device does not fall within “certain methods of organizing human activity” category enumerated in Step 2A.
The independent claim involves recited limitations that are collecting, analyzing information from a patient and then deciding what treatment to deliver to the patient. As such the claims represent following rules or instruction to collect patient information and treat a patient. Additionally, the claims represent commercial or legal interactions (including business relations-the collection of patient information, analysis of patient information and then treatment of a medical patient is a direct example of business relations).
Applicant argues further, under Step 2A, Prong Two, the amended claim 1, which recites, inter alia,:
“Receiving data associated with a patient undergoing a medical treatment, the data pertaining to a use of the medical treatment and a state of the patient, the medical treatment having been administered via a wearable medical device of the patient,”
Line by line explanations have been provided because the Examiner has been questioned specifically about the limitation. Note that the additional elements were considered alone and in combination. Because a judicial exception alone is not eligible subject matter, if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. Here, Applicant has merely recited another aspect of the judicial exception. In the updated subject matter eligibility rejection, this limitation was treated as an additional element and was determined to be extra solution activity because it amounted to mere data gathering. MPEP 2106.05(g) Insignificant Extra-Solution Activity indicates that mere data gathering is insignificant extra solution activity. Moreover, this same section indicates mere data gathering includes performing clinical tests on individuals to obtain input for an equation and obtaining information about a transaction using the Internet to verify credit card transactions are examples of mere data gathering.
The claim further recites the additional element of receiving data associated with a patient undergoing medical treatment… Receiving data associated with a patient undergoing medical treatment is recited with high level of generality (i.e., as a general means of transmitting data) and amounts to the mere transmission of data, even if it recites further the type of data, and as such the limitation is a form of extra-solution activity. MPEP 2106.04(d)(I) indicates that extra-solution data gathering activity cannot provide a practical application. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application or provide significantly more.
“transmitting the type of treatment that is approved to the wearable medical device of the patient, responsive to an approval, causing the wearable medical device to automatically adjust the type of treatment to the patient,” in context with the rest of the recited features of claim 1, incorporates a practical application.
Prior to amendment, the independent claims recited …causing the wearable medical device to automatically adjust the type of treatment to the patient. MPEP 2106.05(f) indicates that a consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words “apply it” (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. The identified additional element from the independent claims is no more than mere recitation of the words “apply it” (or an equivalent) and/or are instructions to implement an abstract idea or other exception on a computer and therefore cannot provide a practical application or provide significantly more. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Currently, the amendment to the independent claim recites: “…wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information.” This is part of the abstract idea. It cannot integrate the judicial exception into a practical application of the exception because it is the exception itself. Even as an additional element, alone or in combination with other additional elements, it does not provide a practical application or significantly more. MPEP 2106.04(d)(2) particular treatment and prophylaxis in step 2A prong two indicates A claim reciting a judicial exception is not directed to the judicial exception if it also recites additional element(s) demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the additional elements apply or use the recited judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition.
The following factors are relevant when determining whether a claim applies or uses a recite judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition:
The particularity or generality of the treatment or prophylaxis
Whether the limitation(s) have more than a nominal or insignificant relationship to the exceptions
Whether the limitations are merely extra-solution activity or field of use
Here, the actual recited part of the claim that is relevant to the treatment has a degree of generality. For example, the reader has no idea what type of treatments will be delivered from the broadest reasonable interpretation of the claim. The treatment could span across different fields as currently recited. The treatment limitation represents mere instructions to apply the exception in a generic way, the MPEP indicates this is not sufficient to integrate the judicial exception into a practical application of the exception. Regardless, as recited, this limitation was part of the abstract idea.
Applicant argues that amended claim 1 recites, inter alia, “The state of the patient being automatically received via at least one sensor of the wearable medical device, wherein changes to the state of the patient are automatically received via the least one sensor of the wearable medical device,” and “wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information.” Such recitations do not fall within enumerated categories of Step 2A, Prong 1. Further the recitations provide an improvement to medical treatment technology, where “changes to the state of the patient are automatically received via the least one sensor of the wearable medical device” and where “different treatment are delivered to the patient in non-stationary environment with non-stationary information,” e.g., based on the current situation of the patient, and e.g., not necessarily within the confines of a medical facility. The specification on paragraph [0014] refers to such improvement, e.g., “…in this way, for example, effective medical treatment can be automatically determined and/or delivered even while the patient is away from the clinic.”
See explanation above which addresses this topic and argument.
35 U.S.C. 103 Argument Responses
The cited references do not appear to disclose or suggest amended claims 1,8 and 16, in particular “wherein different treatments are delivered to the patient in non-stationary environment with non-stationary information.”
See the updated 35 U.S.C 103 rejection. New art has been applied.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Z. Afkir, H. Guermah, M. Nassar and S. Ebersold, "Machine Learning Based Approach for Context Aware System," 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Napoli, Italy, 2019, pp. 43-48. Afkir teaches context aware systems with machine learning with applications relevant to E-health.
US 2014/0039923 A1 (hereafter Schaefer) teaches at the Abstract receiving data representative of a medical treatment to be administered to a patient (medical data).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/T.I.E./Examiner, Art Unit 3683
/CHRISTOPHER L GILLIGAN/Primary Examiner, Art Unit 3683
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