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 .
Status of Claims
This office action for the 17/927462 application is in response to the communications filed March 02, 2026.
Claims 1-14 were amended March 02, 2026.
Claims 1-20 are currently pending and considered below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As per claim 1,
Step 1: The claim recites subject matter within a statutory category as a process.
Step 2A is a two-prong inquiry, in which Prong 1 determines whether a claim recites a judicial exception. Prong 2 determines if the additional limitations of the claim integrates the recited judicial exception into a practical application. If the additional elements of the claim fail to integrate the judicial exception into a practical application, claim is directed to the recited judicial exception, see MPEP 2106.04(II)(A).
Step 2A Prong 1: The claim contains subject matter that recites an abstract idea, with the steps of a method of monitoring and providing mental health treatment, comprising: receiving sensor data associated with the user after the dosing session; analyzing the sensor data to generate a set of biomarkers of depression or anxiety; adjusting individual sensor settings based on patient preferences; combining one or more biomarkers of depression or anxiety of the set to generate one or more additional biomarkers of depression or anxiety; monitoring the additional biomarkers of depression or anxiety to identify signals of depression or anxiety, generating a summary and one or more depression or anxiety recommendations based, at least in part, upon the signals and the generated set of depression or anxiety biomarkers and the additional biomarkers of depression or anxiety and providing mental health treatment resources to the user, wherein at least one of the mental health resources comprises mindfulness, guided meditation, or breathing exercises. These steps, as drafted, under the broadest reasonable interpretation are directed to:
certain methods of organizing human activity (e.g., fundamental economic principles or practices including: hedging; insurance; mitigating risk; etc., commercial or legal interactions including: agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations; etc., managing personal behavior or relationships or interactions between people including: social activities; teaching; following rules or instructions; etc.) but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from being directed to certain methods of organizing human activity. The identified abstract idea, law of nature, or natural phenomenon identified above, in the context of this claim, encompasses a certain method of organizing human activity, namely managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. If a claim limitation, under its broadest reasonable interpretation, covers at least the recited methods of organizing human activity above, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. See MPEP 2106.04(a).
Step 2A Prong 2: The claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception, see MPEP 2106.05(f), such as:
“administering therapy to a user in a dosing session” which corresponds to merely reciting the words “apply it” (or an equivalent) with the judicial exception. This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way. Thus, the administration step does not integrate the mental analysis step into a practical application. See MPEP 2106.04(d)(2)(a).
“using artificial intelligence” which corresponds to merely using a computer as a tool to perform an abstract idea. Paragraph [0016] of the as-filed specification describes that the technology the claimed steps are being implemented on are technology substantially equivalent to a generic computer. Implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two or add significantly more in Step 2B, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
add insignificant extra-solution activity to the abstract idea, see MPEP 2106.05(g), such as:
“providing the generated summary and one or more depression or anxiety treatment recommendations for display on a client device.” which corresponds to mere data gathering and/or output.
Accordingly, this claim is directed to an abstract idea.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and/or generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, identified as insignificant extra-solution activity to the abstract idea, amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as:
computer functions that have been identified by the courts as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity, see MPEP 2106.05(d)(II), such as:
“providing the generated summary and one or more depression or anxiety treatment recommendations for display on a client device.” which corresponds to receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 2,
Claim 2 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 1 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the sensor data is analyzed using at least one neural network” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 3,
Claim 3 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 3 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the summary and the one or more recommendations are determined using machine learning” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 4,
Claim 4 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 4 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the sensor data is collected from one or more of: light sensors, global positioning system (GPS) sensors, accelerometers, gyroscopes, magnetometers, barometers, network connectivity sensors, activity state sensors, screen touch events, and data from paired wearables” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to mere data gathering and/or output and receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 5,
Claim 5 depends from claim 1 and inherits all the limitations of the claim from which it depends. Claim 5 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining a score for at least one biomarker; determining that the score falls below a determined threshold; and generating the one or more recommendations based, at least in part, upon the score” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 6,
Claim 6 is substantially similar to claim 1. Accordingly, claim 6 is rejected for the same reasons as claim 1.
As per claim 7,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“combining a plurality of biomarkers, of the set of biomarkers, to generate a new biomarker; and generating the one or more recommendations based, at least in part, upon the generated set of biomarkers and the new biomarker” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 8,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein at least one of the first sensor data and the second sensor data is analyzed using at least one neural network” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 9,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the one or more recommendations are determined using machine learning” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to merely using a computer as a tool to perform an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 10,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“wherein the sensor data is collected from one or more of: light sensors, global positioning system (GPS) sensors, accelerometers, gyroscopes, magnetometers, barometers, network connectivity sensors, activity state sensors, screen touch events, and data from paired wearables” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to mere data gathering and/or output and receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 11,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining a score for at least one biomarker; determining that the score falls below a determined threshold; and generating the one or more recommendations based, at least in part, upon the score” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 12,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining a baseline score for one or more biomarkers of the set; determining that a second score for the one or more biomarkers fall below the determined baseline score; and generating the one or more recommendations based, at least in part, upon the baseline score and the second score” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 13,
Claim 7 depends from claim 6 and inherits all the limitations of the claim from which it depends. Claim 7 merely further defines the abstract idea and/or introduces additional elements that are insufficient to provide a practical application or something significantly more:
“determining a score for at least one biomarker; generating an alert if the score falls below a determined threshold” further describes the abstract idea. This claim limitation is still directed to “Certain Methods of Organizing Human Activity” and therefore continues to recite an abstract idea.
“providing the alert for display on the client device” further defines an additional element that was insufficient to provide a practical application and/or significantly more. The claim with this further defining limitation still corresponds to mere data gathering and/or output and receiving or transmitting data over a network.
Looking at the limitations of the claim as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely recite an abstract idea and/or provide conventional computer implementation which does not impose a meaningful limit to integrate the abstract idea into a practical application and/or amount to no more than limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields.
As per claim 14,
Claim 14 is substantially similar to claim 1. Accordingly, claim 14 is rejected for the same reasons as claim 1.
As per claim 15,
Claim 15 is substantially similar to claim 7. Accordingly, claim 15 is rejected for the same reasons as claim 7.
As per claim 16,
Claim 16 is substantially similar to claim 9. Accordingly, claim 16 is rejected for the same reasons as claim 9.
As per claim 17,
Claim 17 is substantially similar to claim 10. Accordingly, claim 17 is rejected for the same reasons as claim 10.
As per claim 18,
Claim 18 is substantially similar to claim 11. Accordingly, claim 18 is rejected for the same reasons as claim 11.
As per claim 19,
Claim 19 is substantially similar to claim 12. Accordingly, claim 19 is rejected for the same reasons as claim 12.
As per claim 20,
Claim 20 is substantially similar to claim 13. Accordingly, claim 20 is rejected for the same reasons as claim 13.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tran (US 2021/0212606) in view of Etkin et al. (US 2021/0353224; herein referred to as Etkin) in further view of Aimone et al. (US 2014/0223462; herein referred to as Aimone).
As per claim 1,
Tran teaches a method of monitoring mental health treatment, comprising: administering therapy to a user in a dosing session, receiving sensor data associated with the user after the dosing session and analyzing the sensor data using artificial intelligence to generate a set of biomarkers:
(Paragraphs [0004], [0006] and [0023] of Tran. The teaching describes a system which includes a processor; and at least two wearable sensors including accelerometer, heart rate sensor, bioimpedance sensor. The bioimpedance sensor measures high and low frequency signals from the body, while the glucose sensor uses a plurality of LEDs, each individually activated so as to emit corresponding wavelengths in sequence. The accelerometer indicates the sensor orientation and movement and is used by the signal processor in determining valid plethysmographs. In a further aspect, a treatment method includes: providing a non-invasive sensor to monitor a biomarker, calibrating the non-invasive sensor with a medical grade sensor; detecting an oral intake and estimating a biomarker level from the oral intake; performing a physical activity to keep the biomarker level at a range. The system enables convenient CGM management, without the invasive aspects of existing CGM. The data available through CGM permits significantly more fine-tuned adjustments in insulin dosing and other therapies than spot testing from self-monitoring of blood glucose (SMBG) can provide. The system can be used for Closed loop control (CLC). Also known as an “artificial” or “bionic” pancreas, this technology will link CGM with automatically controlled insulin delivery, using non-living components made of silicon, plastic, and metal.)
(Paragraphs [0277] and [0289] of Tran. The teaching describes that in another embodiment, the bioimpedance system can be used with electro-encephalograph (EEG) or ERP. Since this embodiment collects signals related to blood flow in the brain, collection can be concentrated in those regions of the brain surface corresponding to blood vessels of interest. A headcap with additional electrodes placed in proximity to regions of the brain surface fed by a blood vessel of interest, such as the medial cerebral artery enables targeted information from the regions of interest to be collected. The headcap can cover the region of the brain surface that is fed by the medial cerebral artery. Other embodiments of the headcap can concentrate electrodes on other regions of the brain surface, such as the region associated with the somatosensory motor cortex. In alternative embodiments, the headcap can cover the skull more completely. Further, such a headcap can include electrodes throughout the cap while concentrating electrodes in a region of interest. Depending upon the particular application, arrays of 1-16 head electrodes may be used, as compared to the International 10/20 system of 19-21 head electrodes generally used in an EEG instrument. A neural network is used to recognize each pattern as the neural network is quite robust at recognizing user habits or patterns. Once the treatment features have been characterized, the neural network then compares the input user information with stored templates of treatment vocabulary known by the neural network recognizer, among others. The recognition models can include a Hidden Markov Model (HMM), a dynamic programming model, a neural network, a fuzzy logic, or a template matcher, among others.)
(Paragraphs [0252] and [0333] of Tran. The teaching describes that the EEG probes allow the system to determine cognitive status of the patient to determine whether a stroke had just occurred, the EKG and the BI probes provide information on the stroke to enable timely treatment to minimize loss of functionality to the patient if treatment is delayed. Aphasia is a cognitive disorder marked by an impaired ability to comprehend (receptive aphasia) or express (expressive aphasia) language. Exemplary embodiments are disclosed for detecting receptive aphasia by displaying text or playing verbal instructions to the user, followed by measuring the correctness and/or time delay of the response from the user.)
Tran further teaches adjusting individual sensor settings based on patient preferences:
(Paragraph [0321] of Tran. The teaching describes EMG/EEG signal can be used for man-machine interfaces by directly connecting a person to a computer via the human electrical nervous system. Based on EMG and EEG signals, the system applies pattern recognition system to interpret these signals as computer control commands. The system can also be used for Mime Speech Recognition which recognizes speech by observing the muscle associated with speech and is not based on voice signals but EMG. The MSR realizes unvoiced communication and because voice signals are not used, MSR can be applied in noisy environments; it can support people without vocal cords and aphasics. In another embodiment, EMG and/or electroencephalogram (EEG) features are used for predicting behavioral alertness levels. EMG and EEG features were derived from temporal, frequency spectral, and statistical analyses. Behavioral alertness levels were quantified by correct rates of performance on an auditory and a visual vigilance task, separately. A subset of three EEG features, the relative spectral amplitudes in the alpha (alpha %, 8-13 Hz) and theta (theta %, 4-8 Hz) bands, and the mean frequency of the EEG spectrum (MF) can be used for predicting the auditory alertness level.)
Tran further teaches combining one or more biomarkers of the set using artificial intelligence to generate one or more additional biomarkers:
(Paragraph [0104] of Tran. The teaching describes embodiments of the glucose sensor can also monitor biomarkers of glycemic control. Hemoglobin A1c (A1C) is the best biomarker indicator of glycemic control over the past 2-3 months due to strong data predicting complications. Hemoglobin A1c refers to the non-enzymatic addition of glucose to the N-terminal valine of the hemoglobin beta chain. Assays are based upon charge and structural differences between hemoglobin molecules. Therefore, variants in hemoglobin molecules may lead to analytic interferences. It should be noted that some homozygous hemoglobin variants (HbC or HbD, or sickle cell disease) also alter erythrocyte life span and therefore, even if the assay does not show analytic interference, other methods of monitoring glycemia should be utilized, as HbA1c will be falsely low. Fructosamine refers to a family of glycated serum proteins and this family is comprised primarily of albumen and to a lesser extent, globulins, and to an even lesser extent, other circulating serum proteins. No product exists for home use that measures serum fructosamine. The largest constituent of fructosamine is glycated albumin. Several investigators and companies are developing portable assays for glycated albumin to assess overall control during periods of rapidly changing glucose levels. In these situations, an A1C test may change too slowly to capture a sudden increase or decrease in mean glycemia. The aforementioned biomarkers for measuring glycemic control, (A1C, fructosamine, and glycated albumin) only reflect mean levels of glycemia. This glycemic biomarker is a combined biomarker created from a plurality of biomarkers.)
(Paragraphs [0277], [0278] and [0289] of Tran. The teaching describes that in another embodiment, the bioimpedance system can be used with electro-encephalograph (EEG) or ERP. Since this embodiment collects signals related to blood flow in the brain, collection can be concentrated in those regions of the brain surface corresponding to blood vessels of interest. A headcap with additional electrodes placed in proximity to regions of the brain surface fed by a blood vessel of interest, such as the medial cerebral artery enables targeted information from the regions of interest to be collected. The headcap can cover the region of the brain surface that is fed by the medial cerebral artery. Other embodiments of the headcap can concentrate electrodes on other regions of the brain surface, such as the region associated with the somatosensory motor cortex. In alternative embodiments, the headcap can cover the skull more completely. Further, such a headcap can include electrodes throughout the cap while concentrating electrodes in a region of interest. Depending upon the particular application, arrays of 1-16 head electrodes may be used, as compared to the International 10/20 system of 19-21 head electrodes generally used in an EEG instrument. A neural network is used to recognize each pattern as the neural network is quite robust at recognizing user habits or patterns. Once the treatment features have been characterized, the neural network then compares the input user information with stored templates of treatment vocabulary known by the neural network recognizer, among others. The recognition models can include a Hidden Markov Model (HMM), a dynamic programming model, a neural network, a fuzzy logic, or a template matcher, among others. In one implementation, each amplifier for each EEG channel is a high quality analog amplifier device. Full bandwidth and ultra-low noise amplification are obtained for each electrode. Low pass, high pass, hum notch filters, gain, un-block, calibration and electrode impedance check facilities are included in each amplifier. All 8 channels in one EEG amplifier unit have the same filter, gain, etc. settings. Noise figures of less than 0.1 uV r.m.s. are achieved at the input and optical coupling stages. These figures, coupled with good isolation/common mode rejection result in signal clarity. Nine high pass filter ranges include 0.01 Hz for readiness potential measurement, and 30 Hz for EMG measurement. This means that the at least one biomarker is being combined using artificial intelligence to generate an amplified additional biomarker.)
Tran further teaches monitoring the additional biomarkers using artificial intelligence to identify signals of mental health deterioration:
(Paragraphs [0277], [0278] and [0289] of Tran. The teaching describes that in another embodiment, the bioimpedance system can be used with electro-encephalograph (EEG) or ERP. Since this embodiment collects signals related to blood flow in the brain, collection can be concentrated in those regions of the brain surface corresponding to blood vessels of interest. A headcap with additional electrodes placed in proximity to regions of the brain surface fed by a blood vessel of interest, such as the medial cerebral artery enables targeted information from the regions of interest to be collected. The headcap can cover the region of the brain surface that is fed by the medial cerebral artery. Other embodiments of the headcap can concentrate electrodes on other regions of the brain surface, such as the region associated with the somatosensory motor cortex. In alternative embodiments, the headcap can cover the skull more completely. Further, such a headcap can include electrodes throughout the cap while concentrating electrodes in a region of interest. Depending upon the particular application, arrays of 1-16 head electrodes may be used, as compared to the International 10/20 system of 19-21 head electrodes generally used in an EEG instrument. A neural network is used to recognize each pattern as the neural network is quite robust at recognizing user habits or patterns. Once the treatment features have been characterized, the neural network then compares the input user information with stored templates of treatment vocabulary known by the neural network recognizer, among others. The recognition models can include a Hidden Markov Model (HMM), a dynamic programming model, a neural network, a fuzzy logic, or a template matcher, among others. In one implementation, each amplifier for each EEG channel is a high quality analog amplifier device. Full bandwidth and ultra-low noise amplification are obtained for each electrode. Low pass, high pass, hum notch filters, gain, un-block, calibration and electrode impedance check facilities are included in each amplifier. All 8 channels in one EEG amplifier unit have the same filter, gain, etc. settings. Noise figures of less than 0.1 uV r.m.s. are achieved at the input and optical coupling stages. These figures, coupled with good isolation/common mode rejection result in signal clarity. Nine high pass filter ranges include 0.01 Hz for readiness potential measurement, and 30 Hz for EMG measurement. This means that the at least one biomarker is being combined using artificial intelligence to generate an amplified additional biomarker.)
(Paragraphs [0252] and [0333] of Tran. The teaching describes that the EEG probes allow the system to determine cognitive status of the patient to determine whether a stroke had just occurred, the EKG and the BI probes provide information on the stroke to enable timely treatment to minimize loss of functionality to the patient if treatment is delayed. Aphasia is a cognitive disorder marked by an impaired ability to comprehend (receptive aphasia) or express (expressive aphasia) language. Exemplary embodiments are disclosed for detecting receptive aphasia by displaying text or playing verbal instructions to the user, followed by measuring the correctness and/or time delay of the response from the user.)
Tran further teaches generating a summary and one or more recommendations based, at least in part, upon the generated set of biomarkers and the additional biomarkers; and providing the generated summary and one or more recommendations for display on a client device:
(Paragraphs [0048] and [0236] of Tran. The teaching describes that the user can take his/her weight, blood pressure, and cholesterol measurement daily, and the data is sent from a health base station to a monitoring service at his doctor's office. Periodically, the user gets an automated health summary generated by a service at his doctor's office as well as information to help him maintain a healthy lifestyle. A summary “dashboard” of readings from all Patients assigned to the Administrator is displayed upon log in to the Portal by the Administrator. Readings may be color coded to visually distinguish normal vs. readings that have generated an alert, along with description of the alert generated. The Administrator may drill down into the details for each Patient to further examine the readings data, view charts etc. in a manner similar to the Patient's own use of the system. The Administrator may also view a summary of all the appliances registered to all assigned Patients, including but not limited to all appliance identification information.)
(Paragraph [0110] of Tran. The teaching describes that the sensor and insulin supply can be combined to form an artificial pancreas. This can be user operated or computer controlled. For user control of the CGM and insulin injection, one embodiment provides tiered recommendations that are based upon the meter glucose and sensor trend where patients increase or decrease the meal+correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms. Other methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend. Another method adjusts boluses pre-meal and at least 4 hours post-meal in 0.5 unit increments based upon the trend arrow and the patient's sensitivity. A computer-controlled embodiment provides an artificial pancreas with 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a radio to send the glucose level to the body surface for continuous display onto a monitor. The neural network predictive algorithm enhances sensor accuracy and reduces issues such as lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness. The system improves glucose control without increasing the complexity of decision-making on the part of the patient. The system can work with open-source software, such as Open Artificial Pancreas System, and Loop, for example.)
Tran does not explicitly teach that the biomarkers, summary and recommendations provided are based on depression or anxiety biomarkers.
However, Etkin teaches the use of depression or anxiety biomarkers when using an EEG:
(Paragraphs [0012] and [0099] of Etkin. The teaching describes that the first data includes an electroencephalogram (EEG) data, a transcranial magnetic stimulation electroencephalogram (TMS-EEG) data, a magnetoencephalography (MEG) data, a functional magnetic resonance imaging (fMRI) data, and/or a functional near-infrared spectroscopy (fNIRS) data. This establishes a plurality of biomarkers that can be derived from patient monitoring data. We designed a latent-space machine learning algorithm tailored for resting-state electroencephalography (rsEEG) and applied it to data from a large placebo-controlled antidepressant (sertraline) treatment prediction study in depression. Symptom change was robustly predicted in a manner both specific for sertraline (versus placebo) and generalizable across different study sites and EEG equipment. Our sertraline-predictive EEG signature furthermore generalized to a second depression sample, wherein we found reduced EEG-predicted symptom improvement using the sertraline-defined model for historically treatment-resistant patients compared to those that had previously shown a partial response to an antidepressant. Using a third independent depression data set, we then tested for two properties of the predictive signature: convergent validation and neurobiological significance. We calculated in the third sample outcome predictions derived from the rsEEG model we trained in our first sample, as well as predictions from a task-based fMRI classifier we also developed from our first sample in a prior analysis. These two predictions were found to correlate in the third sample, providing convergent multi-modal evidence for a treatment-response phenotype within the broader clinical diagnosis of depression. We also found that the rsEEG-derived outcome predictions in the third sample indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation (TMS) during EEG, thereby elucidating neurobiological significance. Finally, in a fourth depression treatment data set we found that the smaller the rsEEG-predicted symptom improvement with sertraline, the better the response to 1 Hz repetitive TMS treatment over the right dorsolateral prefrontal cortex with concurrent psychotherapy. Our findings thus advance the neurobiological understanding of depression and antidepressant treatment through an EEG-tailored computational model, as well as provide a clinically applicable avenue for personalized treatment approaches in psychiatry with the possibility of differential treatment prediction. This establishes a definitive link between depression biomarkers and the EEG modalities of data collection.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the EEG data collection and analysis of the patient monitor of Tran, the EEG-derived depression or anxiety biomarkers of Etkin. Paragraph [0099] of Etkin teaches that the methods used in EEG data collection and analysis lead to improved patient outcome. One of ordinary skill in the art in possession of Tran would have looked to Etkin’s EEG advantages to incorporate them into its own EEG analysis. Accordingly, the summary and recommendations provided by Tran would have had more information that is based on depression or anxiety data collected by Etkin. One of ordinary skill in the art would have added to the teaching of Tran, the teaching of Etkin based on this incentive without yielding unexpected results.
The combined teaching of Tran and Etkin does not explicitly teach providing mental health resources to the user, wherein at least one of the mental health resources comprises mindfulness, guided meditation, or breathing exercises.
However, Aimone teaches a machine learning model that detects biomarkers for depression or anxiety, generates an output and then provides mental health resources to the user based on identifying biomarkers for depression or anxiety, wherein at least one of the mental health resources comprises mindfulness, guided meditation, or breathing exercises:
(Paragraphs [0157] and [0326]-[0331] of Aimone. The teaching describes a health management application in which goals may include: monitoring across a number of variables; diagnosis (physiological state estimation); asynchronous feedback based on statistics (suggestion to change a behaviour); recommend neurofeedback based therapy; and administer neurofeedback based therapy. An application may include managing/treating a psychological disorder. In this case, a user's brain state may be tracked. Treatment may include the user engaging with exercises that can improve the user's brain state. Performance in the treatment is monitored by the rules engine and may recommend exercises that engages the user. Neurofeedback may be provided for depression or anxiety. A non-limiting list of types of rules may include: discrete relations (e.g. decision trees; lookup tables; if/then); control laws/relationships (e.g. PID controller, threshold based events with hysteresis); method (e.g. reward based neurofeedback paradigms on normative user data); training systems (e.g. neurofeedback mindfulness protocols; state change methods including progressive stimulus with neurofeedback, and iterative relaxation/excitation state hopping); and stimulus based active state estimation (e.g. ERP based cognitive load monitoring; and ERSP based attention monitoring).)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the combined teaching of Tran and Etkin, the psychological treatment teachings of Aimone. Paragraph [0331] of Aimone teaches that the methods disclosed improve the subjects brain state through treatment. One of ordinary skill in the art in possession of combined teaching of Tran and Etkin would have looked to Aimone to improve the mental state of their subject in kind. One of ordinary skill in the art would have added to combined teaching of Tran and Etkin, the teaching of Aimone based on this incentive without yielding unexpected results.
As per claim 2,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 1.
Tran further teaches wherein the sensor data is analyzed using at least one neural network:
(Paragraph [0006] of Tran. The teaching describes that a neural network is used to match glucose related composite parameters. Clinical data collection compares invasive blood draw measurements to noninvasive sensor measurements of the same person. Neural networks or statistical analyzers predict composite parameters CPs derived noninvasively from sensor data. The clinical data collection derives an invasive blood panel that generates a myriad of blood constituents such as blood urea nitrogen (BUN), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total hemoglobin (THB), creatine (CRE) to name just a few. In various embodiments, the parameters are trained with invasively-measured glucose over a general population of interest; the highest correlation with invasively-measured glucose over a specific population matching a patient of interest; or the lowest error in the measurement of glucose, for example. The neural network derives glucose estimates based on the average glucose across a population of individuals according to the measured parameters. The population-based glucose estimate can also be refined by receiving an individually-calibrated glucose from a blood glucose sensor and noninvasive sensor estimate.)
As per claim 3,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 1.
Tran further teaches wherein the summary and the one or more recommendations are determined using machine learning:
(Paragraph [0006] of Tran. The teaching describes that a neural network is used to match glucose related composite parameters. Clinical data collection compares invasive blood draw measurements to noninvasive sensor measurements of the same person. Neural networks or statistical analyzers predict composite parameters CPs derived noninvasively from sensor data. The clinical data collection derives an invasive blood panel that generates a myriad of blood constituents such as blood urea nitrogen (BUN), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total hemoglobin (THB), creatine (CRE) to name just a few. In various embodiments, the parameters are trained with invasively-measured glucose over a general population of interest; the highest correlation with invasively-measured glucose over a specific population matching a patient of interest; or the lowest error in the measurement of glucose, for example. The neural network derives glucose estimates based on the average glucose across a population of individuals according to the measured parameters. The population-based glucose estimate can also be refined by receiving an individually-calibrated glucose from a blood glucose sensor and noninvasive sensor estimate.)
(Paragraphs [0048] and [0236] of Tran. The teaching describes that the user can take his/her weight, blood pressure, and cholesterol measurement daily, and the data is sent from a health base station to a monitoring service at his doctor's office. Periodically, the user gets an automated health summary generated by a service at his doctor's office as well as information to help him maintain a healthy lifestyle. A summary “dashboard” of readings from all Patients assigned to the Administrator is displayed upon log in to the Portal by the Administrator. Readings may be color coded to visually distinguish normal vs. readings that have generated an alert, along with description of the alert generated. The Administrator may drill down into the details for each Patient to further examine the readings data, view charts etc. in a manner similar to the Patient's own use of the system. The Administrator may also view a summary of all the appliances registered to all assigned Patients, including but not limited to all appliance identification information.)
(Paragraph [0110] of Tran. The teaching describes that the sensor and insulin supply can be combined to form an artificial pancreas. This can be user operated or computer controlled. For user control of the CGM and insulin injection, one embodiment provides tiered recommendations that are based upon the meter glucose and sensor trend where patients increase or decrease the meal+correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms. Other methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend. Another method adjusts boluses pre-meal and at least 4 hours post-meal in 0.5 unit increments based upon the trend arrow and the patient's sensitivity. A computer-controlled embodiment provides an artificial pancreas with 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a radio to send the glucose level to the body surface for continuous display onto a monitor. The neural network predictive algorithm enhances sensor accuracy and reduces issues such as lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness. The system improves glucose control without increasing the complexity of decision-making on the part of the patient. The system can work with open-source software, such as Open Artificial Pancreas System, and Loop, for example.)
As per claim 4,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 1.
Tran further teaches wherein the sensor data is collected from one or more of: light sensors, global positioning system (GPS) sensors, accelerometers, gyroscopes, magnetometers, barometers, network connectivity sensors, activity state sensors, screen touch events, and data from paired wearables:
(Paragraph [0095] of Tran. The teaching describes a partially-invasive glucose sensor such as a continuous glucose monitoring (CGM) [paired wearable] system from IF sampling (for example a Dexcom 4) can be used. The CGM unit needs to be calibrated with a validated starting glucose value. The blood sugar level measured directly from blood with the glucose meter is provided to calibrate the CGM system. The Dexcom CGM utilizes a glucose oxidase sensor at the tip of a wire that is implanted in the subcutaneous space. The G4 sensor is inserted via a dedicated applicator by the user or clinician just under the skin where it is held in place by an adhesive to the skin. The transmitter is snapped into a platform located on top of the sensor. The data are transmitted wirelessly and are displayed on a separate receiver. This device is FDA approved to provide glucose readings for 168 hours or 7 days. The device is calibrated every 12 hours.)
As per claim 5,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 1.
Tran further teaches determining a score for at least one biomarker; determining that the score falls below a determined threshold; and generating the one or more recommendations based, at least in part, upon the score:
(Paragraph [0004] of Tran. The system provides real time (RT-CGM) which not only display the current glucose every few minutes, but may also alert the patient for impending (projected alert) or actual (threshold alert) hyperglycemia or hypoglycemia or rate of change in glucose. Current and recent glucose levels, trend information, and a visual alarm are all presented so that a patient can predict future low or high glucose excursions. Using this information will allow the patient to take actions to spend more time in the euglycemic range and less time in the hypoglycemic or hyperglycemic ranges. Personalized treatments thanks to online measurements of the actual amount of glucose in the blood, as well as another step towards an artificial pancreas are added benefits of this method. This also facilitates continuous patient monitoring in the intensive care unit because it eliminates the need for staff to check blood sugar levels intermittently several times a day. Continuous blood glucose monitoring allows many conclusions about the patient's metabolism. The temperature, water content or the analysis of body fluids on the skin surface—such as sweat—are also measurement parameters that play an important role in endurance sports. The system can predict the glycemic index of a mixed food assimilated by a healthy human and can be used for developing various devices and systems for automatic monitoring of carbohydrate content of human food. Moreover, the system can provide virtual medication to treat diabetes without chemicals and drugs when it applies a learning system to adjust user behavior (such as spatial and temporal timing of selected diet and exercise) to keep glucose in a predetermined range.)
(Paragraph [0110] of Tran. The teaching describes that the sensor and insulin supply can be combined to form an artificial pancreas. This can be user operated or computer controlled. For user control of the CGM and insulin injection, one embodiment provides tiered recommendations that are based upon the meter glucose and sensor trend where patients increase or decrease the meal+correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms. Other methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend. Another method adjusts boluses pre-meal and at least 4 hours post-meal in 0.5 unit increments based upon the trend arrow and the patient's sensitivity. A computer-controlled embodiment provides an artificial pancreas with 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a radio to send the glucose level to the body surface for continuous display onto a monitor. The neural network predictive algorithm enhances sensor accuracy and reduces issues such as lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness. The system improves glucose control without increasing the complexity of decision-making on the part of the patient. The system can work with open-source software, such as Open Artificial Pancreas System, and Loop, for example.)
As per claim 6,
Claim 6 is substantially similar to claim 1. Accordingly, claim 6 is rejected for the same reasons as claim 1.
As per claim 7,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 6.
Tran further teaches combining a plurality of biomarkers, of the set of biomarkers, to generate a new biomarker; and generating the one or more recommendations based, at least in part, upon the generated set of biomarkers and the new biomarker:
(Paragraph [0104] of Tran. The teaching describes embodiments of the glucose sensor can also monitor biomarkers of glycemic control. Hemoglobin A1c (A1C) is the best biomarker indicator of glycemic control over the past 2-3 months due to strong data predicting complications. Hemoglobin A1c refers to the non-enzymatic addition of glucose to the N-terminal valine of the hemoglobin beta chain. Assays are based upon charge and structural differences between hemoglobin molecules. Therefore, variants in hemoglobin molecules may lead to analytic interferences. It should be noted that some homozygous hemoglobin variants (HbC or HbD, or sickle cell disease) also alter erythrocyte life span and therefore, even if the assay does not show analytic interference, other methods of monitoring glycemia should be utilized, as HbA1c will be falsely low. Fructosamine refers to a family of glycated serum proteins and this family is comprised primarily of albumen and to a lesser extent, globulins, and to an even lesser extent, other circulating serum proteins. No product exists for home use that measures serum fructosamine. The largest constituent of fructosamine is glycated albumin. Several investigators and companies are developing portable assays for glycated albumin to assess overall control during periods of rapidly changing glucose levels. In these situations, an A1C test may change too slowly to capture a sudden increase or decrease in mean glycemia. The aforementioned biomarkers for measuring glycemic control, (A1C, fructosamine, and glycated albumin) only reflect mean levels of glycemia. This glycemic biomarker is a combined biomarker created from a plurality of biomarkers.)
As per claim 8,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 6.
Tran further teaches wherein at least one of the first sensor data and the second sensor data is analyzed using at least one neural network:
(Paragraph [0006] of Tran. The teaching describes that a neural network is used to match glucose related composite parameters. Clinical data collection compares invasive blood draw measurements to noninvasive sensor measurements of the same person. Neural networks or statistical analyzers predict composite parameters CPs derived noninvasively from sensor data. The clinical data collection derives an invasive blood panel that generates a myriad of blood constituents such as blood urea nitrogen (BUN), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total hemoglobin (THB), creatine (CRE) to name just a few. In various embodiments, the parameters are trained with invasively-measured glucose over a general population of interest; the highest correlation with invasively-measured glucose over a specific population matching a patient of interest; or the lowest error in the measurement of glucose, for example. The neural network derives glucose estimates based on the average glucose across a population of individuals according to the measured parameters. The population-based glucose estimate can also be refined by receiving an individually-calibrated glucose from a blood glucose sensor and noninvasive sensor estimate.)
As per claim 9,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 6.
Tran further teaches wherein the one or more recommendations are determined using machine learning:
(Paragraph [0006] of Tran. The teaching describes that a neural network is used to match glucose related composite parameters. Clinical data collection compares invasive blood draw measurements to noninvasive sensor measurements of the same person. Neural networks or statistical analyzers predict composite parameters CPs derived noninvasively from sensor data. The clinical data collection derives an invasive blood panel that generates a myriad of blood constituents such as blood urea nitrogen (BUN), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total hemoglobin (THB), creatine (CRE) to name just a few. In various embodiments, the parameters are trained with invasively-measured glucose over a general population of interest; the highest correlation with invasively-measured glucose over a specific population matching a patient of interest; or the lowest error in the measurement of glucose, for example. The neural network derives glucose estimates based on the average glucose across a population of individuals according to the measured parameters. The population-based glucose estimate can also be refined by receiving an individually-calibrated glucose from a blood glucose sensor and noninvasive sensor estimate.)
(Paragraph [0110] of Tran. The teaching describes that the sensor and insulin supply can be combined to form an artificial pancreas. This can be user operated or computer controlled. For user control of the CGM and insulin injection, one embodiment provides tiered recommendations that are based upon the meter glucose and sensor trend where patients increase or decrease the meal+correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms. Other methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend. Another method adjusts boluses pre-meal and at least 4 hours post-meal in 0.5 unit increments based upon the trend arrow and the patient's sensitivity. A computer-controlled embodiment provides an artificial pancreas with 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a radio to send the glucose level to the body surface for continuous display onto a monitor. The neural network predictive algorithm enhances sensor accuracy and reduces issues such as lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness. The system improves glucose control without increasing the complexity of decision-making on the part of the patient. The system can work with open-source software, such as Open Artificial Pancreas System, and Loop, for example.)
As per claim 10,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 6.
Tran further teaches wherein the sensor data is collected from one or more of: light sensors, global positioning system (GPS) sensors, accelerometers, gyroscopes, magnetometers, barometers, network connectivity sensors, activity state sensors, screen touch events, and data from paired wearables:
(Paragraph [0095] of Tran. The teaching describes a partially-invasive glucose sensor such as a continuous glucose monitoring (CGM) [paired wearable] system from IF sampling (for example a Dexcom 4) can be used. The CGM unit needs to be calibrated with a validated starting glucose value. The blood sugar level measured directly from blood with the glucose meter is provided to calibrate the CGM system. The Dexcom CGM utilizes a glucose oxidase sensor at the tip of a wire that is implanted in the subcutaneous space. The G4 sensor is inserted via a dedicated applicator by the user or clinician just under the skin where it is held in place by an adhesive to the skin. The transmitter is snapped into a platform located on top of the sensor. The data are transmitted wirelessly and are displayed on a separate receiver. This device is FDA approved to provide glucose readings for 168 hours or 7 days. The device is calibrated every 12 hours.)
As per claim 11,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 6.
Tran further teaches further comprising: determining a score for at least one biomarker; determining that the score falls below a determined threshold; and generating the one or more recommendations based, at least in part, upon the score:
(Paragraph [0004] of Tran. The system provides real time (RT-CGM) which not only display the current glucose every few minutes, but may also alert the patient for impending (projected alert) or actual (threshold alert) hyperglycemia or hypoglycemia or rate of change in glucose. Current and recent glucose levels, trend information, and a visual alarm are all presented so that a patient can predict future low or high glucose excursions. Using this information will allow the patient to take actions to spend more time in the euglycemic range and less time in the hypoglycemic or hyperglycemic ranges. Personalized treatments thanks to online measurements of the actual amount of glucose in the blood, as well as another step towards an artificial pancreas are added benefits of this method. This also facilitates continuous patient monitoring in the intensive care unit because it eliminates the need for staff to check blood sugar levels intermittently several times a day. Continuous blood glucose monitoring allows many conclusions about the patient's metabolism. The temperature, water content or the analysis of body fluids on the skin surface—such as sweat—are also measurement parameters that play an important role in endurance sports. The system can predict the glycemic index of a mixed food assimilated by a healthy human and can be used for developing various devices and systems for automatic monitoring of carbohydrate content of human food. Moreover, the system can provide virtual medication to treat diabetes without chemicals and drugs when it applies a learning system to adjust user behavior (such as spatial and temporal timing of selected diet and exercise) to keep glucose in a predetermined range.)
(Paragraph [0110] of Tran. The teaching describes that the sensor and insulin supply can be combined to form an artificial pancreas. This can be user operated or computer controlled. For user control of the CGM and insulin injection, one embodiment provides tiered recommendations that are based upon the meter glucose and sensor trend where patients increase or decrease the meal+correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms. Other methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend. Another method adjusts boluses pre-meal and at least 4 hours post-meal in 0.5 unit increments based upon the trend arrow and the patient's sensitivity. A computer-controlled embodiment provides an artificial pancreas with 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a radio to send the glucose level to the body surface for continuous display onto a monitor. The neural network predictive algorithm enhances sensor accuracy and reduces issues such as lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness. The system improves glucose control without increasing the complexity of decision-making on the part of the patient. The system can work with open-source software, such as Open Artificial Pancreas System, and Loop, for example.)
As per claim 12,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 6.
Tran further teaches determining a baseline score for one or more biomarkers of the set; determining that a second score for the one or more biomarkers fall below the determined baseline score; and generating the one or more recommendations based, at least in part, upon the baseline score and the second score:
(Paragraph [0004] of Tran. The system provides real time (RT-CGM) which not only display the current glucose every few minutes, but may also alert the patient for impending (projected alert) or actual (threshold alert) hyperglycemia or hypoglycemia or rate of change in glucose. Current and recent glucose levels, trend information, and a visual alarm are all presented so that a patient can predict future low or high glucose excursions. Using this information will allow the patient to take actions to spend more time in the euglycemic range and less time in the hypoglycemic or hyperglycemic ranges. Personalized treatments thanks to online measurements of the actual amount of glucose in the blood, as well as another step towards an artificial pancreas are added benefits of this method. This also facilitates continuous patient monitoring in the intensive care unit because it eliminates the need for staff to check blood sugar levels intermittently several times a day. Continuous blood glucose monitoring allows many conclusions about the patient's metabolism. The temperature, water content or the analysis of body fluids on the skin surface—such as sweat—are also measurement parameters that play an important role in endurance sports. The system can predict the glycemic index of a mixed food assimilated by a healthy human and can be used for developing various devices and systems for automatic monitoring of carbohydrate content of human food. Moreover, the system can provide virtual medication to treat diabetes without chemicals and drugs when it applies a learning system to adjust user behavior (such as spatial and temporal timing of selected diet and exercise) to keep glucose in a predetermined range.)
(Paragraph [0110] of Tran. The teaching describes that the sensor and insulin supply can be combined to form an artificial pancreas. This can be user operated or computer controlled. For user control of the CGM and insulin injection, one embodiment provides tiered recommendations that are based upon the meter glucose and sensor trend where patients increase or decrease the meal+correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms. Other methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend. Another method adjusts boluses pre-meal and at least 4 hours post-meal in 0.5 unit increments based upon the trend arrow and the patient's sensitivity. A computer-controlled embodiment provides an artificial pancreas with 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a radio to send the glucose level to the body surface for continuous display onto a monitor. The neural network predictive algorithm enhances sensor accuracy and reduces issues such as lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness. The system improves glucose control without increasing the complexity of decision-making on the part of the patient. The system can work with open-source software, such as Open Artificial Pancreas System, and Loop, for example.)
As per claim 13,
The combined teaching of Tran, Etkin and Aimone teaches the limitations of claim 6.
Tran further teaches determining a score for at least one biomarker; generating an alert if the score falls below a determined threshold; and providing the alert for display on the client device:
(Paragraph [0004] of Tran. The system provides real time (RT-CGM) which not only display the current glucose every few minutes, but may also alert the patient for impending (projected alert) or actual (threshold alert) hyperglycemia or hypoglycemia or rate of change in glucose. Current and recent glucose levels, trend information, and a visual alarm are all presented so that a patient can predict future low or high glucose excursions. Using this information will allow the patient to take actions to spend more time in the euglycemic range and less time in the hypoglycemic or hyperglycemic ranges. Personalized treatments thanks to online measurements of the actual amount of glucose in the blood, as well as another step towards an artificial pancreas are added benefits of this method. This also facilitates continuous patient monitoring in the intensive care unit because it eliminates the need for staff to check blood sugar levels intermittently several times a day. Continuous blood glucose monitoring allows many conclusions about the patient's metabolism. The temperature, water content or the analysis of body fluids on the skin surface—such as sweat—are also measurement parameters that play an important role in endurance sports. The system can predict the glycemic index of a mixed food assimilated by a healthy human and can be used for developing various devices and systems for automatic monitoring of carbohydrate content of human food. Moreover, the system can provide virtual medication to treat diabetes without chemicals and drugs when it applies a learning system to adjust user behavior (such as spatial and temporal timing of selected diet and exercise) to keep glucose in a predetermined range.)
(Paragraph [0110] of Tran. The teaching describes that the sensor and insulin supply can be combined to form an artificial pancreas. This can be user operated or computer controlled. For user control of the CGM and insulin injection, one embodiment provides tiered recommendations that are based upon the meter glucose and sensor trend where patients increase or decrease the meal+correction bolus by 10-20% based upon the rate of change and provided specific instructions for responding to alarms. Other methods recommended adjustment of only the correction insulin dose by the amount needed to cover a glucose level that is incrementally higher or lower than the current glucose, based upon the trend. Another method adjusts boluses pre-meal and at least 4 hours post-meal in 0.5 unit increments based upon the trend arrow and the patient's sensitivity. A computer-controlled embodiment provides an artificial pancreas with 1) an automatic and continuous glucose monitor; 2) an implanted continuous insulin delivery system; 3) a control processor to link the insulin delivery rate to the glucose level; and 4) a radio to send the glucose level to the body surface for continuous display onto a monitor. The neural network predictive algorithm enhances sensor accuracy and reduces issues such as lag time, inadequate onset and offset of currently available rapid acting insulin analogs, meal challenges, and changes in insulin sensitivity due to circadian rhythms, exercise, menstrual cycles, and intercurrent illness. The system improves glucose control without increasing the complexity of decision-making on the part of the patient. The system can work with open-source software, such as Open Artificial Pancreas System, and Loop, for example.)
As per claim 14,
Claim 14 is substantially similar to claim 1. Accordingly, claim 14 is rejected for the same reasons as claim 1.
As per claim 15,
Claim 15 is substantially similar to claim 7. Accordingly, claim 15 is rejected for the same reasons as claim 7.
As per claim 16,
Claim 16 is substantially similar to claim 9. Accordingly, claim 16 is rejected for the same reasons as claim 9.
As per claim 17,
Claim 17 is substantially similar to claim 10. Accordingly, claim 17 is rejected for the same reasons as claim 10.
As per claim 18,
Claim 18 is substantially similar to claim 11. Accordingly, claim 18 is rejected for the same reasons as claim 11.
As per claim 19,
Claim 19 is substantially similar to claim 12. Accordingly, claim 19 is rejected for the same reasons as claim 12.
As per claim 20,
Claim 20 is substantially similar to claim 13. Accordingly, claim 20 is rejected for the same reasons as claim 13.
Response to Arguments
Applicant's arguments filed March 02, 2026 have been fully considered.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 101 are not persuasive.
The Applicant argues that the subject matter eligibility rejection in the most recent Office Action issued is improper when seen in light of the October 2019 Guidance and 2024 Guidance.
The Examiner respectfully disagrees. The rejection made is in full compliance with all relevant Office Guidance including the October 2019 Guidance and 2024 Guidance.
The Applicant further argues that the Office Action has improperly identified the claims as a method of organizing human activity because the claims do not fall within one of the enumerated sub groupings.
The Examiner respectfully disagrees. The rejection had made it explicitly clear that the pending claims contained limitations that were analogous to managing personal behavior or relationships or interactions between people. This is because each of the limitations of the abstract idea recites a list of rules or instructions that a human person can follow in the course of their personal behavior. This sub groping falls under “Certain Methods of Organizing Human Activity”.
The Applicant further argues that even if the pending claims do fall within an enumerated sub grouping, they are integrated into a practical application.
The Examiner respectfully disagrees. The Applicant has provided no reasoning to support this argument. In contrast, the Examiner has provided detailed explanations as to why the additional elements to the identified abstract idea fail to integrate the pending claims into a practical application.
The Applicant further argues that the pending claims are integrated into a practical application because they provide a specific treatment of monitoring and providing mental health resources to a user.
The Examiner respectfully disagrees. Providing mental health treatment resources to a user is not the same as providing mental health treatment to a user. To effect the treatment resources claimed, factors completely outside of the claim are solely responsible. A mindfulness practice does not actualize until the user undergoes the process themselves. This does not occur in the claims. The same can be said of guided meditation or breathing exercises. At best, this step of the claim amounts to nothing more than presenting information about these practices on a display. Presenting information, even specific information, on a display does not inherently provide a practical application. The limitations as claimed provide nothing more than insignificant extra-solution activity or a continuation of the abstract idea.
The Applicant further argues that claim 1 recites a set of steps that improves the technical field of mental health treatment. Paragraph [0002] of the as-filed specification identifies the problem being difficulty correlating and properly utilizing patient biomarker data when communicating with patients that is solved by at least the technical solution of paragraphs [0013] and [0027] ibid.
The Examiner respectfully disagrees. The Applicant has failed to identify a technical problem to improve. Unless a technical problem is properly identified, there is no basis to assert an improvement to technology. The cited problem of “difficulty correlating and properly utilizing patient biomarker data when communicating with patients” is not a technical problem at all. It is a problem resultant from the mismanagement of data on part of the user. There has been no technical deficit identified by the Applicant. Accordingly, the Examiner remains unconvinced that the pending claims improve technology.
Applicant’s arguments pertaining to rejections made under 35 U.S.C. 103 are rendered moot in light of the new combination of references used in the current rejection.
Applicant’s remaining arguments are rendered moot in light of the foregoing.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3686