DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-15 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.
Step 1 of the Alice/Mayo Test
Claims 1-13 are drawn to a method, which is within the four statutory categories (i.e. process). Claim 15 is drawn to a system, which is within the four statutory categories (i.e. apparatus). Claim 14 is drawn to a computer program in a computer-readable storage medium, which is not within the four statutory categories. the claimed invention is directed to non-statutory subject matter. Claim 14 does not fall within at least one of the four categories of patent eligible subject matter because the claim recites a computer program per se by not having a physical or tangible form. An amendment could bring the claim into compliance and therefore the claim will continue to be analyzed moving forward. See MPEP 2106.03(II).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claims 14, 15) recites:
A content providing method for performing adaptable diagnosis and treatment for mental health on the basis of digital biomarker data through at least one processor of a device, the method comprising:
a step in which first digital biomarker data obtained through a first application installed in a user terminal are received from the user terminal;
a step in which a mental health condition of a user who uses the user terminal is classified through analysis of the first digital biomarker data;
a step in which content corresponding to the mental health condition is transmitted to the user terminal to be provided to the user through a second application installed in the user terminal;
a step in which second digital biomarker data obtained through the second application are received from the user terminal;
a step in which mental health condition variation of the user who uses the user terminal is determined through analysis of the second digital biomarker data; and
a step in which the content is changed and transmitted to the user terminal in accordance with the mental health condition variation.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the processor of a device, user terminal, application on a user terminal, the limitations in the context of this claim encompass an automation of organizing biomarker data to determine mental health conditions of the user and any variations. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-13 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claims 14, 15) recites:
A content providing method for performing adaptable diagnosis and treatment for mental health on the basis of digital biomarker data through at least one processor of a device, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) the method comprising:
a step in which first digital biomarker data obtained through a first application installed in a user terminal are received from the user terminal; (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) and (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g))
a step in which a mental health condition of a user who uses the user terminal is classified through analysis of the first digital biomarker data;
a step in which content corresponding to the mental health condition is transmitted to the user terminal (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) and (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g)) to be provided to the user through a second application installed in the user terminal; (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
a step in which second digital biomarker data obtained through the second application are received from the user terminal; (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
a step in which mental health condition variation of the user who uses the user terminal is determined through analysis of the second digital biomarker data; and
a step in which the content is changed and transmitted to the user terminal (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) and (merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g)) in accordance with the mental health condition variation.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the processor of a device, user terminal, application on a user terminal, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification pgs. 13, 18, 30, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving and transmitting data from and to the user terminal amounts to insignificant extrasolution activity, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13 recite additional limitations that further the abstract idea; claims 2-3, 5-6, 13 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 2-13 recite additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the processor of a device, user terminal, application on a user terminal, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. pgs. 13, 18, 30); using a processor of a device, user terminal, application on a user terminal, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application. The following represent examples that courts have identified as insignificant extrasolution activities (e.g. see MPEP 2106.05(g)): receiving and transmitting data from and to the user terminal, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 8, 11, 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Periyasamy et al. (US 2021/0098110) in view of Hariton (US 2021/0298687).
Regarding claim 1, Periyasamy discloses a content providing method for performing adaptable diagnosis and treatment for mental health on the basis of digital biomarker data through at least one processor of a device, the method comprising:
a step in which first digital biomarker data obtained through a first application installed in a user terminal are received from the user terminal; ([0004] A mental health monitoring device in accordance with the present disclosure seeks to provide a solution to the aforementioned problems with the conventional mental health monitoring devices by using a mobile device, e.g., a smart phone, wearable biomarker tracking devices (e.g., a smart watch, smart glasses, etc.), other user devices (e.g., laptop computers, etc.), and public data to continuously collect, track, screen, monitor, or alert patients real-time. As such, data collection may take place via a mobile health application (e.g., implemented in the mobile device)
a step in which a mental health condition of a user who uses the user terminal is classified through analysis of the first digital biomarker data; ([0004] The tracked and monitored data may be used to predict potential symptoms of mental health conditions (e.g., mental disorders, behavioral illnesses, etc.) and identify the potential mental health conditions at the onset of the symptoms so as to ensure that those mental health conditions are identified for treatment as soon as practicable, and thus do not become exacerbated; [0005] Every interaction involves a semantic segmentation of the conversations to extract meaningful information from the user. From the segmented conversations, regression models are run in the background to interpret the behavior of the user based on his/her mental health condition(s). The mental health conditions may be categorized and captured using neural networks that may be trained on characteristics and understanding of the different mental health conditions or disorders. Chatbot can also extract vital information (including, but not limited to, sleep data, heartrate, calorie intake (e.g., consumed through food intake and activities), and activities) which may be biomarkers for various mental health conditions or disorders.)
a step in which content corresponding to the mental health condition is transmitted to the user terminal to be provided to the user through a second application installed in the user terminal; ([0006] alert the user based on the severity of the current or potential mental health conditions)
a step in which second digital biomarker data obtained through the second application are received from the user terminal; ([0006] A root cause may include a contributing factor, and the root cause to the potential mental health condition may be identified by continuously monitoring the data collected from the tracked user data and user inputs. Chatbot may provide one-on-one interactive therapy and counseling sessions to provide self-help to manage the identified symptom and/or potential mental health conditions, e.g., digital therapeutics. [0062] Therefore, the mental health monitoring device 10 in accordance with the present disclosure not only provides accurate and reliable analysis and determination of a symptom(s) and potential mental health condition based on the comprehensive and real-time user data continuously collected from all available user device tracking the user's biomarkers and/or mental health condition)
Periyasamy does not appear to explicitly disclose the following, however, Hariton teaches it is old and well known in the art of healthcare data processing to have:
a step in which mental health condition variation of the user who uses the user terminal is determined through analysis of the second digital biomarker data; and a step in which the content is changed and transmitted to the user terminal in accordance with the mental health condition variation. (Hariton [0166] The method 400 may be used to monitor a condition of a patient. A patient may have been previously diagnosed with a condition. The method 400 may be used to monitor the progress of the condition. The method 400 may be used to monitor and/or alter a treatment plan for the condition. For example the method 400 may be used to monitor the effectiveness of a medication being used to treat the condition. The retinal signal data may be collected before, during, and/or after the patient is undergoing treatment for the condition)
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Periyasamy to incorporate a step in which mental health condition variation of the user who uses the user terminal is determined through analysis of the second digital biomarker data; and a step in which the content is changed and transmitted to the user terminal in accordance with the mental health condition variation, as taught by Hariton, in order to alter treatment plans to make sure to maximize effectiveness while undergoing treatment. See Hariton [0166].
Regarding claim 3, Periyasamy-Hariton teaches the method of claim 1, and Periyasamy further discloses further comprising: a step in which standard test information is transmitted to the user terminal in accordance with a preset first period such that a standard test for classifying the mental health condition is provided through the first application installed in the user terminal; and a step in which answer data to the standard test are obtained through the first application and received from the user terminal, wherein the step in which the mental health condition of the user who uses the user terminal is classified is a step in which the answer data are analyzed together with the first digital biomarker data, whereby the mental health condition of the user who uses the user terminal is classified. (Periyasamy [0047] The AI based mental health and wellbeing question engine 14 allows the a chatbot to ask appropriate questions to the user based on the collected data, identified symptoms, and determined mental health condition of the user…. These AI based engines and models may utilize a cognitive behavioral therapy information and/or training modules, a list of diagnoses based on diagnostic and statistical manual of mental disorders, mental health condition treatment and preventive measure training modules, so as to enable the chatbot to engage in a meaningful, effective, reliable, accurate, timely, and humanized interactions with the user at all time, especially, the one-on-one interactive therapy and counseling session with the user).
Regarding claim 8, Periyasamy-Hariton teaches the method of claim 1, wherein the first digital biomarker data include at least one or more items of information of Heart Rate Variability (HRV) information of the user, gaze information of the user, voice-related information of the user, and response time information of the user. (Periyasamy [0005] Every interaction involves a semantic segmentation of the conversations to extract meaningful information from the user. From the segmented conversations, regression models are run in the background to interpret the behavior of the user based on his/her mental health condition(s). The mental health conditions may be categorized and captured using neural networks that may be trained on characteristics and understanding of the different mental health conditions or disorders. Chatbot can also extract vital information (including, but not limited to, sleep data, heartrate, calorie intake (e.g., consumed through food intake and activities), and activities) which may be biomarkers for various mental health conditions or disorders).
Regarding claim 11, Periyasamy-Hariton teaches the method of claim 1, wherein the content is any one of treatment content for treating a mental disorder of the user and a training content for training a cognitive function of the user. (Periyasamy [0050] based on the monitored items, the user may view a sample recommendation plan 600 as shown in FIG. 6, add to a “To Do” list or utilize a journal entry section 420. However, the user may only access some cognitive behavioral therapy (CBT) files (e.g., self-help, supportive therapy, relaxation techniques and music) while all other files may be locked in a wellbeing screen. Also, the user may not be able to designate CBT files as favorite. Additionally, the user may not send feedback through a support menu or access group chat facilities. While the user may chat with the chatbot by, e.g., text with limited access, the user may not access a live one-on-one interactive video and/or audio therapy and counseling session with the chatbot).
Regarding claim 14, recites substantially similar limitations as those addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 15, recites substantially similar limitations as those addressed in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Claims 2, 4-6, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Periyasamy-Hariton in view of Vaughan (US 2019/0043610).
Regarding claim 2, Periyasamy-Hariton teaches the method of claim 1, but does not appear to explicitly teach the following, however Vaughan teaches it is old and well known in the art of healthcare data processing wherein: further comprising a step in which when homeostasis, which is property of a human body trying to maintain an internal environment at a constant level becomes a critical level or higher, it is determined that there is mental health condition variation and customized test information excluding items having constant homeostasis in standard test information transmitted to the user terminal in accordance with a preset first period is created and transmitted to the user terminal in accordance with a second period set shorter than the first period. (Vaughan [0130] features may comprise a plurality of questions presented to a subject, observations of the subject, or tasks assigned to the subject [0131] The prediction module may output determined risk for each of the one or more behavioral, neurological or mental health disorders in the assessment model. If the prediction module cannot fit the data to any specific developmental disorder within a confidence interval at or exceeding the designated threshold value, the prediction module may determine, in step 830, whether there are any additional features that can be queried. If the new data comprises a previously-collected, complete dataset, and the subject cannot be queried for any additional feature values, “no diagnosis” may be output as the predicted classification, as shown in step 840. If the new data comprises data collected in real time from the subject or caretaker during the prediction process, such that the dataset is updated with each new input data value provided to the prediction module and each updated dataset is fitted to the assessment model, the prediction module may be able to query the subject for additional feature values…. The updated dataset including the additional input data may then be fitted to the assessment model again (step 815), and the loop may continue until the prediction module can generate an output).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Periyasamy-Hariton, as modified above, to incorporate a step in which when homeostasis, which is property of a human body trying to maintain an internal environment at a constant level becomes a critical level or higher, it is determined that there is mental health condition variation and customized test information excluding items having constant homeostasis in standard test information transmitted to the user terminal in accordance with a preset first period is created and transmitted to the user terminal in accordance with a second period set shorter than the first period, as taught by Vaugh, in order to accurately and efficiently diagnose and treat mental health disorders without burdening the patient and providers with lengthy tests. See Vaughan [0005]-[0006].
Regarding claim 4, Periyasamy-Hariton teaches the method of claim 3, but does not appear to explicitly teach the following, however Vaughan teaches it is old and well known in the art of healthcare data processing wherein: the step in which mental health condition variation is determined is a step of determining that there is the mental health condition variation when homeostasis becomes a critical level or higher by analyzing the second digital biomarker data. (Vaughan [0131] The prediction module may output determined risk for each of the one or more behavioral, neurological or mental health disorders in the assessment model. If the prediction module cannot fit the data to any specific developmental disorder within a confidence interval at or exceeding the designated threshold value, the prediction module may determine, in step 830, whether there are any additional features that can be queried. If the new data comprises a previously-collected, complete dataset, and the subject cannot be queried for any additional feature values, “no diagnosis” may be output as the predicted classification, as shown in step 840. If the new data comprises data collected in real time from the subject or caretaker during the prediction process, such that the dataset is updated with each new input data value provided to the prediction module and each updated dataset is fitted to the assessment model, the prediction module may be able to query the subject for additional feature values…. The updated dataset including the additional input data may then be fitted to the assessment model again (step 815), and the loop may continue until the prediction module can generate an output). The motivation to combine the references was discussed above and incorporated herein.
Regarding claim 5, Periyasamy-Hariton-Vaughan teaches the method of claim 4, and Vaughan further teaches further comprising a step in which when it is determined that there is the mental health condition variation, customized test information excluding items having constant homeostasis in the standard test information is created and transmitted to the user terminal. (Vaughan [0131] obtain additional input data values from the subject, for example by presenting additional questions to the subject [0132] FIG. 9 is an exemplary operational flow 900 of a feature recommendation module 625 as described herein by way of a non-limiting example. The prediction module may comprise a feature recommendation module 625, configured to identify, select or recommend the next most predictive or relevant feature to be evaluated in the subject, based on previously provided feature values for the subject. For example, the feature recommendation module can be a question recommendation module, wherein the module can select the most predictive next question to be presented to a subject or caretaker, based on the answers to previously presented questions. The feature recommendation module can be configured to recommend one or more next questions or features having the highest predictive utility in classifying a particular subject's developmental disorder. The feature recommendation module can thus help to dynamically tailor the assessment procedure to the subject, so as to enable the prediction module to produce a prediction with a reduced length of assessment and improved sensitivity and accuracy). The motivation to combine the references was discussed above and incorporated herein.
Regarding claim 6, Periyasamy-Hariton-Vaughan teaches the method of claim 5, and Vaughan further teaches further comprising a step in which when it is determined that the mental health condition variation exists, the customized test information excluding items having constant homeostasis is repeatedly created and transmitted to the user terminal at every second period set shorter than the first period. (Vaughan [0132] FIG. 9 is an exemplary operational flow 900 of a feature recommendation module 625 as described herein by way of a non-limiting example. The prediction module may comprise a feature recommendation module 625, configured to identify, select or recommend the next most predictive or relevant feature to be evaluated in the subject, based on previously provided feature values for the subject. For example, the feature recommendation module can be a question recommendation module, wherein the module can select the most predictive next question to be presented to a subject or caretaker, based on the answers to previously presented questions. The feature recommendation module can be configured to recommend one or more next questions or features having the highest predictive utility in classifying a particular subject's developmental disorder. The feature recommendation module can thus help to dynamically tailor the assessment procedure to the subject, so as to enable the prediction module to produce a prediction with a reduced length of assessment and improved sensitivity and accuracy). The motivation to combine the references was discussed above and incorporated herein.
Regarding claim 13, Periyasamy-Hariton teaches the method of claim 1, but does not appear to explicitly teach the following, however Vaughan teaches it is old and well known in the art of healthcare data processing wherein: further comprising: receiving log data of each of the first application and the second application; determining whether any one application of the first application and the second application has reached any one value of a preset number of times of use and a preset use time with reference to the log data; and creating alarm information about use of an application that has not reached any one value of the preset number of times and the preset use time and transmitting the alarm information to the user terminal. (Vaughan [0067] The mobile devices as describe herein may comprise sensors to collect data of the subject that can be used as part of the feedback loop so as to improve outcomes and decrease reliance on user input. The mobile device may comprise passive or active sensors as described herein to collect data of the subject subsequent to treatment. The same mobile device or a second mobile device, such as an iPad™ or iPhone™ or similar device, may comprise a software application that interacts with the user to tell the user what to do in improve treatment on a regular basis, e.g. day by day, hour by hour, etc. The user mobile device can be configured to send notifications to the user in response to treatment progress). The motivations to combine the references is discussed above and incorporated herein.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Periyasamy-Hariton-Vaughan in view of Harase et al. (WO 2021/132284).
Regarding claim 7, Periyasamy-Hariton-Vaughan teaches the method of claim 5, wherein the standard test information and the customized test information is any one of mental disorder question information that is provided to analyze a mental disorder of the user and cognitive function test information that is provided to classify dementia of the user. (Periyasamy [0047] The AI based mental health and wellbeing question engine 14 allows the a chatbot to ask appropriate questions to the user based on the collected data, identified symptoms, and determined mental health condition of the user…. These AI based engines and models may utilize a cognitive behavioral therapy information and/or training modules, a list of diagnoses based on diagnostic and statistical manual of mental disorders, mental health condition treatment and preventive measure training modules, so as to enable the chatbot to engage in a meaningful, effective, reliable, accurate, timely, and humanized interactions with the user at all time, especially, the one-on-one interactive therapy and counseling session with the user)
Periyasamy-Hariton-Vaughan does not appear to explicitly teach the following, however, Harase teaches it is old and well known in the art of healthcare data processing to classify:
a stage of dementia of the user (Harase Pg. 10 para. 2 FIG. 3B is an image diagram showing that the intensity of one acoustic feature differs for each disease. Subjects show the highest score for disease A. Therefore, the predicted value for the disease A of the subject is calculated higher than that of other disease groups. Further, for example, by setting the intensity 50 as a threshold value, it can be classified into a group of disease A, disease D, and disease E, and a group of disease B and disease C; Pg. 13 para. 5 FIG. 6 shows the determination results of 34 persons performed in the same manner. The correct answer rate by the estimation program was 85.3%. When classified into a dementia group including Levy body dementia, Alzheimer's disease, and Parkinson's disease, and a mood disorder group including major depression, bipolar disorder, and atypical depression, the program will be classified. It has been well demonstrated that it is possible to estimate whether a user is in a healthy, dementia group, or mood disorder group).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Periyasamy-Hariton-Vaughan, as modified above, to incorporate a stage of dementia of the user, as taught by Harase, in order to group patients into different disease stage classifications for more accurate disease detection and treatment. See Harase pgs. 1-2.
Claims 9-10, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Periyasamy-Hariton in view of Shriberg et al. (US 2021/0110895).
Regarding claim 9, Periyasamy-Hariton teaches the method of claim 8, but does not appear to explicitly teach the following, however Shriberg teaches it is old and well known in the art of healthcare data processing wherein the heart rate variability information of the user is extracted in a non- contact type by estimating a heart response by analyzing a color change from a real-time face image. (Shriberg [0361] Although not addressed in any of the Figures, it is entirely within the scope of embodiments of this disclosure that additional models are employed to provide classification regarding the client's 260a-n health state using alternate data sources. For example, it has been discussed that the client devices may be capable of collecting biometric data (temperature, skin chemistry data, pulse rate, movement data, etc.) from the individual during the interaction. Models focused upon these inputs may be leveraged by the runtime model server(s) 2010 to arrive at determinations based upon this data. The disclosed systems may identify chemical markers in the skin (cortisol for example), perspiration, temperature shifts (e.g. flushing), and changes in heart rate, etc. for diagnostic purposes).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify Periyasamy-Hariton, as modified above, to incorporate wherein the heart rate variability information of the user is extracted in a non- contact type by estimating a heart response by analyzing a color change from a real-time face image, as taught by Shriberg, in order to assess their mental health condition accurately by looking at other factors that would reduce any dishonesty in response. See Shriberg [0004].
Regarding claim 10, Periyasamy-Hariton teaches the method of claim 8, but does not appear to explicitly teach the following, however Shriberg teaches it is old and well known in the art of healthcare data processing wherein the gaze information of the user is extracted by analyzing variation of a user's gaze from an image including user's eyes taken when the user's gaze follows a moving object. (Shriberg [0416] Eye/gaze model 4304 assesses a patient's depression from observed and recognized eye movements in the video of the patient's speech [0354] A gaze tracker 2613 is particularly useful in determining where the user is looking, and when (in response to what stimulus) the person's gaze shifts). The motivations to combine the references is discussed above and incorporated herein.
Regarding claim 12, Periyasamy-Hariton teaches the method of claim 1, but does not appear to explicitly teach the following, however Shriberg teaches it is old and well known in the art of healthcare data processing wherein the second digital biomarker data is any one of voice-related information of the user and response information about at least one test of an arithmetic ability test, a memory test, and a gaze test. (Shriberg [0288] Runtime model server logic 504 (FIG. 18) includes visual model 1810, which infers various health states of the patient from face, gaze and pose behaviors. Visual model 1810 may include facial cue modeling, eye/gaze modeling, pose tracking and modeling, etc. [0290] Descriptive features or descriptive analytics are interpretable descriptions that may be computed based on features in the speech, language, video, and metadata that convey information about a speaker's speech patterns in a way in which a stakeholder may understand. For example, descriptive features may include a speaker sounding nervous or anxious, having a shrill or deep voice, or speaking quickly or slowly). The motivations to combine the references is discussed above and incorporated herein.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA R COVINGTON whose telephone number is (303)297-4604. The examiner can normally be reached Monday - Friday, 10 - 5 MT.
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686