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 .
Status of Claims
Claims 1-20 received on 10 October 2024 are currently pending and being considered by Examiner in this Office Action.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 19 December 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner in this Office Action.
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.
The claims recite subject matter within a statutory category as a process (claims 1-13) and a machine (claims 14-20) (Subject Matter Eligibility (SME) Test Step 1: Yes) which recite steps of:
obtaining, by a computing device, a risk status indicator associated with a user, the risk status indicator indicative of a likelihood the user may experience a mental health crisis;
obtaining, by a computing device, first biometric data from at least one wearable device associated with a user, the first biometric data comprising data associated with at least one of sleep, physical activity, and stress;
obtaining, by a computing device, first psychological data from at least one user device, the first psychological data comprising data associated with a mood of the user;
providing the first biometric data and first psychological data to at least one artificial intelligence algorithm, wherein the at least one artificial intelligence algorithm analyzes the first biometric data and first psychological data to establish at least one baseline metric for the user;
obtaining, by a computing device, second biometric data from at least one wearable device associated with a user, the second biometric data comprising data associated with at least one of sleep, physical activity, and stress;
obtaining, by a computing device, second psychological data from at least one user device, the second psychological data comprising data associated with a mood of the user;
providing the second biometric data and second psychological data to at least one artificial intelligence algorithm, wherein the at least one artificial intelligence algorithm analyzes the second biometric data and second psychological to identify patterns, trends, or anomalies indicative of emerging mental health issues, wherein identifying comprises comparing with the at least one baseline metric to determine an amount of change or deviation relative to the at least one baseline metric;
determining, based on the analysis, whether a crisis situation is predicted, the determining comprising determining that the amount of change or deviation exceeds a threshold, the threshold defined based on the risk status indicator; and
initiating, by the computing device, a communication with the user when a crisis situation is predicted, wherein the form of communication initiated is based on at least one of the risk status indicator and the amount of change or deviation.
These steps of obtaining a risk status indicator, obtaining first biometric data from at least one wearable device, obtaining first psychological data from at least one user device, obtaining second biometric data from at least one wearable device, obtaining second psychological data from at least one user device, providing the first and/or second biometric and/or psychological data to an artificial intelligence algorithm, determining whether a crisis situation is predicted, and initiating a communication with the user when the crisis situation is predicted, as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind 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 practically being performed in the mind. For example, but for the obtaining various data, e.g. biometric data and/or psychological data, from one or more devices language, obtaining various data in the context of this claim encompasses a mental process of the user or a doctor collecting patient data from one or more devices, such as a biometric device and/or electronic medical record device. Similarly, the limitation of providing the data to an algorithm, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, such as utilizing generic, well-understood artificial intelligence algorithms being used as a tool. For example, but for the determining whether a crisis situation is predicted language and communicating with the user when a crisis is predicted, determining whether a crisis situation is predicted and communicating with the user when the crisis is predicted in the context of this claim encompasses a mental process of the user or doctor determining whether the data is indicative of the patient/user experiencing a crisis scenario and reaching out to the user/patient. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
These steps of obtaining a risk status indicator, obtaining first biometric data from at least one wearable device, obtaining first psychological data from at least one user device, obtaining second biometric data from at least one wearable device, obtaining second psychological data from at least one user device, providing the first and/or second biometric and/or psychological data to an artificial intelligence algorithm, determining whether a crisis situation is predicted, and initiating a communication with the user when the crisis situation is predicted, as drafted, under the broadest reasonable interpretation, includes methods of organizing human activity. MPEP 2106.04(a)(2)(II) sets forth certain methods of organizing human activity, including fundamental economic principles or practices (including hedging, insurance, mitigating risk), commercial or legal interactions (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations), and managing personal behavior or relationships or interactions between people, (including social activities, teaching, and following rules or instructions). For instance, the steps recited heavily relate to managing personal behavior or relationships or interactions between people, at least by the steps reciting managing a communication with a user/patient based on determinations made by the system. For instance, determining whether to initiate a communication/interaction with a user/patient based on the data received and analyzed effectively manages the communicative interaction between the user and one or more entities. At an even broader level, the steps at least relate to following rules or instructions, such as when to communicate with a user/patient based on rules or instructions established by the electronic system regarding whether a crisis situation is predicted. Accordingly, the claim recites an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 2-13 & 15-20, reciting particular aspects of how obtaining varying data, analyzing varying data, and/or communicating with a user may be performed in the mind but for recitation of generic computer components) (SME Test Step 2A, Prong 1: Yes).
This 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 recitation of a computing device, one or more wearable devices, one or more user devices, at least one artificial intelligence algorithm, a software application amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification [0085] for a computing device; [0091] for one or more wearable devices; [0038] for one or more user devices; [0096] & [0098] for at least one artificial intelligence algorithm; [00124]-[00125] for a software application, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of obtaining a risk status indicator, obtaining first biometric data from a wearable device, obtaining first psychological data from a user device, obtaining second biometric data from a wearable device, obtaining second psychological data from a user device amounts to mere data gathering, providing the first and/or second biometric data and the first and/or second psychological data to an AI algorithm, recitation of specifying the types of psychological data and/or biometric data and/or form of communication initiated amounts to selecting a particular data source or type of data to be manipulated, recitation of applying an artificial intelligence algorithm to analyze the first and/or second biometric data and the first and/or second psychological data, initiating a communication with the user when a crisis situation is predicted amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as specifically linking the steps recited to predicting mental health and/or suicide prevention/intervention, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13 & 15-20, which recite limitations relating to a user device, artificial intelligence language processing algorithms, and/or performing computerized steps via computing device, additional limitations which amount to invoking computers as a tool to perform the abstract idea see applicant’s specification [0038] for one or more user devices; [0096] & [0098] for at least one artificial intelligence algorithm; [00124]-[00125] for a software application/computerized method and [0085] for a computing device, see MPEP 2106.05(f)); claims 3, 5, 8, 11, 16, & 19, which recite limitations relating to obtaining various data, such as mood data at periodic intervals, initiating a communication with the user, sending an alert to one or more users, additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 2-3, & 12, which recite limitations relating to specifying types of data received/obtained, such as specifying the biometric data, the mood data, the crisis details, journal data, etc., additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 4, 6-10, 12-13, 17-18, & 20, which recite limitations relating to analyzing mood data to identify patterns, trends, or anomalies, analyzing journal data, updating the baseline data over time using a multi-day rolling baseline, initiating a communication, identifying user-specific triggers or key factors affecting a user’s mood, determining whether a crisis situation is predicted, recording crisis details, updating an action plan associated with the user when a crisis situation is predicted, additional limitations which amounts to insignificant application; claims 2-13 & 15-20, which recite limitations specifying the steps applied to mental health and suicide prevention, or specifying devices, such as the wearable devices 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 (SME Test Step 2A, Prong 2: No).
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, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, 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 obtaining a risk status indicator, obtaining first biometric data from a wearable device, obtaining first psychological data from a user device, obtaining second biometric data from a wearable device, obtaining second psychological data from a user device, initiating a communication with the user, which is understood to include receiving/transmitting one or more communications over a network, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); applying an artificial intelligence algorithm to analyze the first and/or second biometric data and the first and/or second psychological data, determining whether the crisis situation is predicted, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); maintaining one or more records of the obtained user/patient data, maintaining one or more communication protocols, maintaining one or more risk statuses of a user/patient, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing various obtained data, storing computerized instructions for performing the steps recited, storing a software application to be executed on the computing device, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); obtaining various psychological data from one or more user devices, which could include extraction from one or more documents, analyzing data for identifying one or more aspects, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)).
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. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-13 & 15-20, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 2-3, 5, 8, 11-12, 16, & 19, which recite limitations relating to obtaining various data, such as mood data at periodic intervals, initiating a communication with the user, sending an alert to one or more users, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 4, 6-10, 12-13, 17-18, & 20, which recite limitations relating to analyzing mood data to identify patterns, trends, or anomalies, analyzing journal data, updating the baseline data over time using a multi-day rolling baseline, initiating a communication, identifying user-specific triggers or key factors affecting a user’s mood, determining whether a crisis situation is predicted, recording crisis details, updating an action plan associated with the user when a crisis situation is predicted, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 7, 12-13, & 18 which recite limitations relating to updating the baseline data over time using a multi-day rolling baseline, updating an action plan associated with the user when a crisis situation is predicted, recording e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 2-13 & 15-20, which recite limitations relating to storing various obtained data, storing computerized instructions for performing the steps recited, storing a software application to be executed on the computing device, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 2-3, 5, 12, & 16-17, which recite limitations relating to obtaining various data, such as mood data at periodic intervals, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). 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 (SME Test Step 2B: No).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-10, 12-15, & 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over LaBorde et al. (U.S. Patent Publication No. 2023/0138557), hereinafter “LaBorde”, in view of Patel et al. (U.S. Patent Publication No. 2023/0329630), hereinafter “Patel”.
Claim 1 –
Regarding Claim 1, LaBorde discloses a method for facilitating mental health management and suicide prevention, comprising:
obtaining, by a computing device, a risk status indicator associated with a user, the risk status indicator indicative of a likelihood the user may experience a mental health crisis (See LaBorde Par [0077] which discloses predicting, i.e. obtaining, a suicide risk associated with a patient; See LaBorde Par [0112] which discloses the DCE or server device determining the patient’s risk for suicide based upon data received and utilizing machine learning algorithms techniques, such that the probability that a patient is at risk or will attempt to take their life or changes in the risk profile or “risk signature” over time, i.e. risk status indicator being assigned to a user profile; See LaBorde Par [0015] which discloses generating an information reply including a graphical display indicating, i.e. indicator, of the output value of the suicide prevention readiness score generated by the trained model);
obtaining, by a computing device, first biometric data from at least one wearable device associated with a user, the first biometric data comprising data associated with at least one of sleep, physical activity, and stress (See LaBorde Par [0119] which discloses a patient wearing a patient ID band including an RFID tag, i.e. wearable device; See LaBorde Par [0224]-[0228] which discloses acoustic properties might include a plurality of attributes including stress data, emotionality, sleepiness, i.e. sleep; See LaBorde Par [0081] which discloses tracking activity via hospital information systems that are derived from the RFID data, including patient movements, i.e. physical activity, under BRI without further specifying “physical activity”);
obtaining, by a computing device, first psychological data from at least one user device, the first psychological data comprising data associated with a mood of the user (See LaBorde Par [0224] which discloses acoustic properties including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient);
providing the first biometric data and first psychological data to at least one artificial intelligence algorithm (See LaBorde Par [0112]-[0114] which discloses the system determining the patient's risk for suicide based upon the data enumerated above by utilizing algorithms developed leveraging machine learning techniques analyzing pre-existing or previously collected “training data”, in addition to newly acquired data, to predict an outcome, i.e. the probability that a patient is at risk or will attempt to take their life or changes in the risk profile or “risk signature” over time), wherein
the at least one artificial intelligence algorithm analyzes the first biometric data and first psychological data to establish at least one baseline metric for the user (See LaBorde Par [0112]-[0114] which discloses the system determining the patient's risk for suicide based upon the data enumerated above by utilizing algorithms developed leveraging machine learning techniques analyzing pre-existing or previously collected “training data”, in addition to newly acquired data, to predict an outcome, i.e. the probability that a patient is at risk or will attempt to take their life or changes in the risk profile or “risk signature” over time; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide, i.e. understood to be from a normal or baseline value, based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features);
obtaining, by a computing device, second biometric data from at least one wearable device associated with a user, the second biometric data comprising data associated with at least one of sleep, physical activity, and stress (See LaBorde Par [0119] which discloses a patient wearing a patient ID band including an RFID tag, i.e. wearable device; See LaBorde Par [0224]-[0228] which discloses acoustic properties might include a plurality of attributes including stress data, emotionality, sleepiness, i.e. sleep; See LaBorde Par [0081] which discloses tracking activity via hospital information systems that are derived from the RFID data, including patient movements, i.e. physical activity, under BRI without further specifying “physical activity”; See LaBorde Par [0055] which discloses using various data inputs and changes therein over time, such that it is understood that a first dataset at an earlier time would be received relative to the real-time/changed data later on; See LaBorde Par [0113] which discloses the system being configured to evolve and consume new data types/sets as inputs to its predictive technologies, such that the system’s models are capable of ongoing learning over time and is thereby capable of learning from new data in real time allowing for maintenance of an potential improvement in predictive capabilities, particularly as behaviors of the patient evolve and new data sources are introduced);
obtaining, by a computing device, second psychological data from at least one user device, the second psychological data comprising data associated with a mood of the user (See LaBorde Par [0224] which discloses acoustic properties including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient);
providing the second biometric data and second psychological data to at least one artificial intelligence algorithm (See LaBorde Par [0088] which discloses the use of machine learning based predictive analytics, i.e. species of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time based on input attributes provided to the algorithm), wherein
the at least one artificial intelligence algorithm analyzes the second biometric data and second psychological data to identify patterns, trends, or anomalies indicative of emerging mental health issues (See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide, i.e. understood to be from a normal or baseline value, based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features), wherein
identifying comprises comparing with the at least one baseline metric to determine an amount of change or deviation relative to the at least one baseline metric (See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features);
determining, based on the analysis, whether a crisis situation is predicted, the determining comprising determining that the amount of change or deviation exceeds a threshold, the threshold defined based on the risk status indicator (See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time, and further discloses providing alerts via any number of communication mediums when certain events have been predicted to occur with a probability greater than the configured threshold level; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide; See LaBorde Par [0089], [0151], & [0188] which discloses predicting an output, for example an interval of time from the current time, at which the risk of a crisis situation, e.g. a fatal suicide event, is anticipated to exceed a certain threshold, e.g. probability, time, etc. or reference range); and
initiating, by the computing device, a communication with the user when a crisis situation is predicted (See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken or automated routing of notifications from the inventive system to a call center for an outreach call/check to a patient with specific training in suicide prevention), wherein
the form of communication initiated is based on at least one of the risk status indicator and the amount of change or deviation (See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken, i.e. which communication mediums to be used/initiated as in LaBorde Par [0089]-[0093], or automated routing of notifications from the inventive system to a call center for an outreach call/check to a patient with specific training in suicide prevention; See LaBorde Par [0188] which discloses comparing the predicted risk for suicide with the threshold criteria the system has been configured with to determine whether notification or escalation is required, i.e. the type of communication that needs to be initiated).
While LaBorde generally discloses using various data inputs and changes therein over time, such that it is understood by Examiner that a dataset at an earlier time, i.e. first, would be received relative to the real-time/changed, i.e. second, dataset later on and therefore constitute first and second instances of data as claimed in the limitations found below, this is not explicitly recited by LaBorde. Therefore, for purposes of clarity and advancing prosecution, an additional reference will be relied upon hereinafter that explicitly recites first and second instances of datasets/vectors being received, such as for determining patient’s baselines and changes from said baseline, respectively.
Therefore, Patel discloses first and second instances of datasets/vectors being received (See Patel Par [0150] which discloses a baseline being determined based on a plurality of initially received feature sets, such that a baseline that is multi-reference may comprise a single representative feature set that is based on multiple feature sets from multiple time intervals (e.g., comprising an average or composite of feature set values from different time periods or instances); See Patel Par [0151]-[0152] which discloses comparison of a currently received feature vector to said baseline vector previously received in Patel Par [0150] to indicate how a user’s condition or state compares to a known condition or state, i.e. patient’s baseline, or if the user’s condition or state has changed or not). The disclosure of Patel is directly applicable to the disclosure of LaBorde because the disclosures share limitations and capabilities, such as being directed towards monitoring one or more patients to determine patient health status/condition.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of LaBorde which already discloses using various data inputs and changes therein over time, to explicitly include first and second instances of datasets/vectors being received, such as for determining patient’s baselines and changes from said baseline, as disclosed by Patel, because this allows for indication of how a user’s condition or state compares to a known condition or state, i.e. patient’s baseline, or if the user’s condition or state has changed or not (See Patel Par [0151]-[0152]).
Claim 2 –
Regarding Claim 2, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, wherein:
the biometric data comprises at least one of heart rate variability (HRV), cortisol levels, electroencephalography (EEG), galvanic skin response (GSR), pupil dilation, sleep patterns, physical activity levels, voice analysis, eye movement, blood pressure, and heart rate (It is understood by Examiner that the claim reciting “at least one of” only requires one or more of the elected biometric data to be disclosed in order for the claim/limitations to be met, therefore see LaBorde Par [0081] which discloses tracking patient activity via hospital information systems that are derived from the RFID data, including patient movements, facility visits or admissions, etc.; See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient, and could constitute both biometric data and/or psychological data under BRI).
Claim 3 –
Regarding Claim 3, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, wherein:
the data associated with a mood of the user is obtained from one or more questions presented to the user at periodic intervals (See LaBorde Par [0197] & [0206] which discloses input attributes related to free text and/or responses entered in a form or standardized questionnaire, such as a “self-check quiz”, i.e. one or more questions presented to the user at various intervals).
Claim 4 –
Regarding Claim 4, LaBorde and Patel disclose the method of Claim 3 in its entirety. LaBorde further discloses a method, wherein:
analyzing the mood data in conjunction with the biometric data to identify patterns, trends, or anomalies indicative of emerging mental health issues (See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient, and could constitute both biometric data and/or psychological data under BRI; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features).
Claim 7 –
Regarding Claim 7, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde does not further disclose, but Patel discloses a method, further comprising:
updating the baseline data over time using a multi-day rolling baseline (See Patel Par [0150]-[0152] which discloses determinations of a user/patient’s baseline for a particular state to indicate how a user’s condition or state compares to a known condition or state, and further describes utilizing a multiday or multi-reference baseline that may be rolling or fixed, allowing for performing a comparison of recent feature vector/data against this baseline to determine whether a user’s condition has changed, and whether the user is sick or well, i.e. getting worse or better if difference measurements are larger or smaller).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of LaBorde, which discloses determining a patient’s baseline data, to further specifically include updating said determined baseline data, such as over time using a multi-day rolling baseline, as disclosed by Patel, because this allows for performing a comparison of recent feature vector/data against this baseline to determine whether a user’s condition has changed, and whether the user is becoming more sick or becoming better, i.e. getting worse or better if baseline difference measurements between a recent feature vector and an earlier feature vector are larger or smaller (see Patel Par [0152]).
Claim 8 –
Regarding Claim 8, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, wherein:
initiating a communication with the user comprises at least one of generating a prompt on a user device, sending a text message to the user, and establishing audio and/or video communication with the user (It is understood by Examiner that the claim reciting “at least one of” only requires one or more of the elected communications, therefore see LaBorde Par See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken, i.e. which communication mediums to be used/initiated as in LaBorde Par [0089]-[0093], or automated routing of notifications from the inventive system to a call center for an outreach call/check, i.e. audio communication, to a patient with specific training in suicide prevention; See LaBorde Par [0188] which discloses comparing the predicted risk for suicide with the threshold criteria the system has been configured with to determine whether notification or escalation is required, i.e. the type of communication that needs to be initiated; See LaBorde Par [0091] which discloses various forms of communication modalities potentially being used such as phone calls, i.e. audio communication, email, SMS messages, i.e. text message, push notification, i.e. visual prompt on user device, visual alerts, etc.).
Claim 9 –
Regarding Claim 9, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, wherein:
the at least one artificial intelligence algorithm is operable to identify user-specific triggers or key factors affecting a mood of the individual (See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient, and could constitute both biometric data and/or psychological data under BRI; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time, i.e. key factors affecting a mood of the individual; See LaBorde Par [0106]-[0107] which discloses the system being configured to maximize, weight, or priories certain factors and/or input parameters in making match recommendations including attributes, i.e. “key factors” under BRI; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features; See LaBorde Par [0244] which further discloses contexts being extracted from certain properties, such as audio properties from an audio signal to determine context and location and preferences and/or activities the device holder may be participating in and contributing to said device holder’s risk, i.e. also constituting “key factors” under BRI).
Claim 10 –
Regarding Claim 10, LaBorde and Patel disclose the method of Claim 9 in its entirety. LaBorde further discloses a method, wherein:
determining whether a crisis situation is predicted comprises evaluating the user-specific triggers or key factors affecting a mood of the individual (See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient, and could constitute both biometric data and/or psychological data under BRI; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time, i.e. key factors affecting a mood of the individual; See LaBorde Par [0106]-[0107] which discloses the system being configured to maximize, weight, or priories certain factors and/or input parameters in making match recommendations including attributes, i.e. “key factors” under BRI; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features; See LaBorde Par [0244] which further discloses contexts being extracted from certain properties, such as audio properties from an audio signal to determine context and location and preferences and/or activities the device holder may be participating in and contributing to said device holder’s risk, i.e. also constituting “key factors” under BRI).
Claim 12 –
Regarding Claim 12, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, further comprising:
recording crisis details when a crisis situation is predicted, wherein the crisis details comprise user data associated with the crisis and interaction information with the user (See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken, i.e. which communication mediums to be used/initiated as in LaBorde Par [0089]-[0093], or automated routing of notifications from the inventive system to a call center for an outreach call/check to a patient with specific training in suicide prevention; See LaBorde Par [0188] which discloses comparing the predicted risk for suicide with the threshold criteria the system has been configured with to determine whether notification or escalation is required, i.e. the type of communication that needs to be initiated; See LaBorde Par [0092], [0106], & [0169] which discloses data collected being used as an input to the system’s predictive analytics (i.e. the aggregate patterns over time therein, particularly from antecedent cases with known outcomes—suicide/no suicide, i.e. interaction information under BRI) and further helps the system better predict outcomes, i.e. recording various known outcomes associated with one or more antecedent cases such that services can be provided to a patient in need that is similar to said antecedent cases).
Claim 13 –
Regarding Claim 13, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, further comprising:
updating an action plan associated with the user when a crisis situation is predicted, wherein the action plan comprises recommendations for improving or maintaining mental health status (See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken, i.e. which communication mediums to be used/initiated as in LaBorde Par [0089]-[0093], or automated routing of notifications from the inventive system to a call center for an outreach call/check to a patient with specific training in suicide prevention; See LaBorde Par [0188] which discloses comparing the predicted risk for suicide with the threshold criteria the system has been configured with to determine whether notification or escalation is required, i.e. the type of communication that needs to be initiated; See LaBorde [0266]-[0267] & [0273] which discloses output of the server device to a patient includes an output that is issued to lower incidence of suicidal ideation, preparatory behavior, attempts, and/or death, i.e. improving or maintaining mental health; See LaBorde Par [0285] & [0287] which discloses the server determining whether deployment of any given available resource is likely to mitigate the predicted suicide risk for a given patient event, such that the given resource allocation recommendation is made based on the server predicting the probability of a suicide occurring being potentially reduced, i.e. improved mental health status under BRI).
Claim 14 –
Regarding Claim 14, LaBorde discloses a system for facilitating mental health management and suicide prevention, comprising:
a computing device (See LaBorde Par [0010] & [0058] which discloses one or more computing devices);
at least one wearable device associated with a user (See LaBorde Par [0119] which discloses a patient wearing a patient ID band including an RFID tag, i.e. wearable device); and
a software application executing on the computing device (See LaBorde Par [0010] & [0065] which discloses the system providing client applications for managing patients identified to be at high risk for suicide and performing embodiments throughout LaBorde), the software application configured to:
obtain, by a computing device, a risk status indicator associated with a user, the risk status indicator indicative of a likelihood the user may experience a mental health crisis (See LaBorde Par [0077] which discloses predicting, i.e. obtaining, a suicide risk associated with a patient; See LaBorde Par [0112] which discloses the DCE or server device determining the patient’s risk for suicide based upon data received and utilizing machine learning algorithms techniques, such that the probability that a patient is at risk or will attempt to take their life or changes in the risk profile or “risk signature” over time, i.e. risk status indicator being assigned to a user profile; See LaBorde Par [0015] which discloses generating an information reply including a graphical display indicating, i.e. indicator, of the output value of the suicide prevention readiness score generated by the trained model);
obtain, by a computing device, first biometric data from at least one wearable device associated with a user, the first biometric data comprising data associated with at least one of sleep, physical activity, and stress (See LaBorde Par [0119] which discloses a patient wearing a patient ID band including an RFID tag, i.e. wearable device; See LaBorde Par [0224]-[0228] which discloses acoustic properties might include a plurality of attributes including stress data, emotionality, sleepiness, i.e. sleep; See LaBorde Par [0081] which discloses tracking activity via hospital information systems that are derived from the RFID data, including patient movements, i.e. physical activity, under BRI without further specifying “physical activity”);
obtain, by a computing device, first psychological data from at least one user device, the first psychological data comprising data associated with a mood of the user (See LaBorde Par [0224] which discloses acoustic properties including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient);
provide the first biometric data and first psychological data to at least one artificial intelligence algorithm (See LaBorde Par [0112]-[0114] which discloses the system determining the patient's risk for suicide based upon the data enumerated above by utilizing algorithms developed leveraging machine learning techniques analyzing pre-existing or previously collected “training data”, in addition to newly acquired data, to predict an outcome, i.e. the probability that a patient is at risk or will attempt to take their life or changes in the risk profile or “risk signature” over time), wherein
the at least one artificial intelligence algorithm analyzes the first biometric data and first psychological data to establish at least one baseline metric for the user (See LaBorde Par [0112]-[0114] which discloses the system determining the patient's risk for suicide based upon the data enumerated above by utilizing algorithms developed leveraging machine learning techniques analyzing pre-existing or previously collected “training data”, in addition to newly acquired data, to predict an outcome, i.e. the probability that a patient is at risk or will attempt to take their life or changes in the risk profile or “risk signature” over time; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide, i.e. understood to be from a normal or baseline value, based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features);
obtain, by a computing device, second biometric data from at least one wearable device associated with a user, the second biometric data comprising data associated with at least one of sleep, physical activity, and stress (See LaBorde Par [0119] which discloses a patient wearing a patient ID band including an RFID tag, i.e. wearable device; See LaBorde Par [0224]-[0228] which discloses acoustic properties might include a plurality of attributes including stress data, emotionality, sleepiness, i.e. sleep; See LaBorde Par [0081] which discloses tracking activity via hospital information systems that are derived from the RFID data, including patient movements, i.e. physical activity, under BRI without further specifying “physical activity”; See LaBorde Par [0055] which discloses using various data inputs and changes therein over time, such that it is understood that a first dataset at an earlier time would be received relative to the real-time/changed data later on; See LaBorde Par [0113] which discloses the system being configured to evolve and consume new data types/sets as inputs to its predictive technologies, such that the system’s models are capable of ongoing learning over time and is thereby capable of learning from new data in real time allowing for maintenance of an potential improvement in predictive capabilities, particularly as behaviors of the patient evolve and new data sources are introduced);
obtain, by a computing device, second psychological data from at least one user device, the second psychological data comprising data associated with a mood of the user (See LaBorde Par [0224] which discloses acoustic properties including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient);
provide the second biometric data and second psychological data to at least one artificial intelligence algorithm (See LaBorde Par [0088] which discloses the use of machine learning based predictive analytics, i.e. species of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time based on input attributes provided to the algorithm), wherein
the at least one artificial intelligence algorithm analyzes the second biometric data and second psychological to identify patterns, trends, or anomalies indicative of emerging mental health issues (See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide, i.e. understood to be from a normal or baseline value, based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features), wherein
identifying comprises comparing with the at least one baseline metric to determine an amount of change or deviation relative to the at least one baseline metric (See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features);
determine, based on the analysis, whether a crisis situation is predicted, the determining comprising determining that the amount of change or deviation exceeds a threshold, the threshold defined based on the risk status indicator (See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time, and further discloses providing alerts via any number of communication mediums when certain events have been predicted to occur with a probability greater than the configured threshold level; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide; See LaBorde Par [0089], [0151], & [0188] which discloses predicting an output, for example an interval of time from the current time, at which the risk of a crisis situation, e.g. a fatal suicide event, is anticipated to exceed a certain threshold, e.g. probability, time, etc. or reference range); and
initiate, by the computing device, a communication with the user when a crisis situation is predicted (See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken or automated routing of notifications from the inventive system to a call center for an outreach call/check to a patient with specific training in suicide prevention), wherein
the form of communication initiated is based on at least one of the risk status indicator and the amount of change or deviation (See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken, i.e. which communication mediums to be used/initiated as in LaBorde Par [0089]-[0093], or automated routing of notifications from the inventive system to a call center for an outreach call/check to a patient with specific training in suicide prevention; See LaBorde Par [0188] which discloses comparing the predicted risk for suicide with the threshold criteria the system has been configured with to determine whether notification or escalation is required, i.e. the type of communication that needs to be initiated).
While LaBorde generally discloses using various data inputs and changes therein over time, such that it is understood by Examiner that a dataset at an earlier time, i.e. first, would be received relative to the real-time/changed, i.e. second, dataset later on and therefore constitute first and second instances of data as claimed in the limitations found below, this is not explicitly recited by LaBorde. Therefore, for purposes of clarity and advancing prosecution, an additional reference will be relied upon hereinafter that explicitly recites first and second instances of datasets/vectors being received, such as for determining patient’s baselines and changes from said baseline, respectively.
Therefore, Patel discloses first and second instances of datasets/vectors being received (See Patel Par [0150] which discloses a baseline being determined based on a plurality of initially received feature sets, such that a baseline that is multi-reference may comprise a single representative feature set that is based on multiple feature sets from multiple time intervals (e.g., comprising an average or composite of feature set values from different time periods or instances); See Patel Par [0151]-[0152] which discloses comparison of a currently received feature vector to said baseline vector previously received in Patel Par [0150] to indicate how a user’s condition or state compares to a known condition or state, i.e. patient’s baseline, or if the user’s condition or state has changed or not).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of LaBorde which already discloses using various data inputs and changes therein over time, to explicitly include first and second instances of datasets/vectors being received, such as for determining patient’s baselines and changes from said baseline, as disclosed by Patel, because this allows for indication of how a user’s condition or state compares to a known condition or state, i.e. patient’s baseline, or if the user’s condition or state has changed or not (See Patel Par [0151]-[0152]).
Claim 15 –
Regarding Claim 15, LaBorde and Patel disclose the system of claim 14 in its entirety. LaBorde further discloses a system, wherein:
the at least one wearable device comprises at least one of a smartwatch and a smartphone (See LaBorde Par [0066] which discloses the use of a smartphone as the client device for determining/receiving varying inputs from the patient, such as RFID data/communications; See LaBorde Par [0081] which discloses tracking activity via hospital information systems that are derived from the RFID data, including patient movements).
Claim 18 –
Regarding Claim 18, LaBorde and Patel disclose the system of claim 14 in its entirety. LaBorde and Patel further disclose a system, wherein:
the software application is further configured to update the baseline over time using a multi-day rolling baseline (See LaBorde Par [0010] & [0065] which discloses the system providing client applications for managing patients identified to be at high risk for suicide and performing embodiments throughout LaBorde; See Patel Par [0150]-[0152] which discloses determinations of a user/patient’s baseline for a particular state to indicate how a user’s condition or state compares to a known condition or state, and further describes utilizing a multiday or multi-reference baseline that may be rolling or fixed, allowing for performing a comparison of recent feature vector/data against this baseline to determine whether a user’s condition has changed, and whether the user is sick or well, i.e. getting worse or better if difference measurements are larger or smaller).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of LaBorde and Patel, which discloses determining a patient’s baseline data, to further specifically include updating said determined baseline data, such as over time using a multi-day rolling baseline, as disclosed by Patel, because this allows for performing a comparison of recent feature vector/data against this baseline to determine whether a user’s condition has changed, and whether the user is becoming more sick or becoming better, i.e. getting worse or better if baseline difference measurements between a recent feature vector and an earlier feature vector are larger or smaller (see Patel Par [0152]).
Claim 19 –
Regarding Claim 19, LaBorde and Patel disclose the system of claim 14 in its entirety. LaBorde further discloses a system, wherein:
the software application is further configured to initiate a communication with the user by at least one of generating a prompt on a user device, sending a text message to the user, and establishing audio or video communication with the user (It is understood by Examiner that the claim reciting “at least one of” only requires one or more of the elected communications, therefore see LaBorde Par which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken, i.e. which communication mediums to be used/initiated as in LaBorde Par [0089]-[0093], or automated routing of notifications from the inventive system to a call center for an outreach call/check, i.e. audio communication, to a patient with specific training in suicide prevention; See LaBorde Par [0188] which discloses comparing the predicted risk for suicide with the threshold criteria the system has been configured with to determine whether notification or escalation is required, i.e. the type of communication that needs to be initiated; See LaBorde Par [0091] which discloses various forms of communication modalities potentially being used such as phone calls, i.e. audio communication, email, SMS messages, i.e. text message, push notification, i.e. visual prompt on user device, visual alerts, etc.; See LaBorde Par [0010] & [0065] which discloses the system providing client applications for managing patients identified to be at high risk for suicide and performing embodiments throughout LaBorde).
Claim 20 –
Regarding Claim 20, LaBorde and Patel disclose the system of claim 14 in its entirety. LaBorde further discloses a system, wherein:
the at least one artificial intelligence algorithm is operable to identify user-specific triggers or key factors affecting a mood of the individual (See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient, and could constitute both biometric data and/or psychological data under BRI; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time, i.e. key factors affecting a mood of the individual; See LaBorde Par [0106]-[0107] which discloses the system being configured to maximize, weight, or priories certain factors and/or input parameters in making match recommendations including attributes, i.e. “key factors” under BRI; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features; See LaBorde Par [0244] which further discloses contexts being extracted from certain properties, such as audio properties from an audio signal to determine context and location and preferences and/or activities the device holder may be participating in and contributing to said device holder’s risk, i.e. also constituting “key factors” under BRI), and wherein
determining whether a crisis situation is predicted comprises evaluating the user-specific triggers or key factors affecting a mood of the individual (See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient, and could constitute both biometric data and/or psychological data under BRI; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time, i.e. key factors affecting a mood of the individual; See LaBorde Par [0106]-[0107] which discloses the system being configured to maximize, weight, or priories certain factors and/or input parameters in making match recommendations including attributes, i.e. “key factors” under BRI; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features; See LaBorde Par [0244] which further discloses contexts being extracted from certain properties, such as audio properties from an audio signal to determine context and location and preferences and/or activities the device holder may be participating in and contributing to said device holder’s risk, i.e. also constituting “key factors” under BRI).
Claims 5-6 & 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over LaBorde, in view of Patel, further in view of Tkach et al. (U.S. Patent Publication No. 2021/0110924), hereinafter “Tkach”.
Claim 5 –
Regarding Claim 5, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, wherein:
the data associated with a mood of the user comprises obtaining journal data from the user via a user device (While not “journal data” per se, LaBorde Par [0198] describes performing NLP of data from text-based narratives and/or audio and/or video content from which text is extracted from the text/audio signal, and further discloses input attributes including emotions expressed via words and combinations of patterns thereof, including text analysis and linguistic analyses of language from said text parsing), wherein the journal data comprises at least one of written data, audio data, and voice-to-text data (While not “journal data” per se, LaBorde Par [0198] describes performing NLP of data from text-based narratives and/or audio and/or video content from which text is extracted from the text/audio signal, and further discloses input attributes including emotions expressed via words and combinations of patterns thereof, including text analysis, audio signal analysis, acoustic properties of speech, linguistic analyses of language from said text/audio parsing; See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient).
While LaBorde and Patel generally disclose performing NLP of data from text-based narratives and/or audio and/or video content from which text is extracted from the text/audio signal, such that it is understood by Examiner that the text-based narratives and/or audio and/or video content could constitute “journal data” under BRI, this is not explicitly recited by LaBorde and Patel. Therefore, for purposes of clarity and advancing prosecution, an additional reference will be relied upon hereinafter for specifically electing the alerted personnel to be a therapist in order to meet the entirety of claim 5.
Therefore, Tkach discloses obtaining journal data from the user via a user device (See Tkach Par [0087] which discloses mitigating suicidal ideation in a patient in order to prevent suicide; See Tkach Par [0088] which discloses alerting emergency services or other local support depending on questions answered by a user, such as in a psychological questionnaire; See Tkach Par [0098]-[0100] which discloses specifically receiving journal entries as text input data to an electronic platform for determining risk of a patient, such as risk of a suicide event). The disclosure of Tkach is directly applicable to the disclosure of LaBorde and Patel because the disclosures share limitations and capabilities, such as being directed towards alerting/notification of emergency personnel when a patient is experiencing a medical emergency/crisis, such as suicide.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify that the text-based narratives and/or audio and/or video content of LaBorde and Patel would constitute journal data especially in view of Applicant’s claimed definition of “journal data”, as specifically disclosed by Tkach, because this allows for comparing and contrasting baseline personal, i.e. journal, input data to assess a user’s risk to determine potential suicidal ideation in a patient and/or to mitigate a risk of a suicide event (See Tkach Par [0087] & [0098]-[0100]).
Claim 6 –
Regarding Claim 6, LaBorde, Patel, and Tkach discloses the method of Claim 5 in its entirety. LaBorde and Tkach further disclose a method, further comprising:
analyzing the journal data using artificial intelligence language processing algorithms to identify risk indicators (While not “journal data” per se, LaBorde Par [0198] describes performing NLP of data from text-based narratives and/or audio and/or video content from which text is extracted from the text/audio signal, and further discloses input attributes including emotions expressed via words and combinations of patterns thereof, including text analysis, audio signal analysis, acoustic properties of speech, linguistic analyses of language from said text/audio parsing; See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient; See Tkach Par [0087] which discloses mitigating suicidal ideation in a patient in order to prevent suicide; See Tkach Par [0088] which discloses alerting emergency services or other local support depending on questions answered by a user, such as in a psychological questionnaire; See Tkach Par [0098]-[0100] which discloses specifically receiving journal entries as text input data to an electronic platform for determining risk of a patient, such as risk of a suicide event).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify that the text-based narratives and/or audio and/or video content of LaBorde, Patel, and Tkach would constitute journal data especially in view of Applicant’s claimed definition of “journal data”, as specifically disclosed by Tkach, because this allows for comparing and contrasting baseline personal, i.e. journal, input data to assess a user’s risk to determine potential suicidal ideation in a patient and/or to mitigate a risk of a suicide event (See Tkach Par [0087] & [0098]-[0100]).
Claim 16 –
Regarding Claim 16, LaBorde and Patel disclose the system of claim 14 in its entirety. LaBorde and Tkach further disclose a system, wherein:
the software application is further configured to obtain mood data and journal data from the user via a user device (While not “journal data” per se, LaBorde Par [0198] describes performing NLP of data from text-based narratives and/or audio and/or video content from which text is extracted from the text/audio signal, and further discloses input attributes including emotions expressed via words and combinations of patterns thereof, including text analysis, audio signal analysis, acoustic properties of speech, linguistic analyses of language from said text/audio parsing; See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient; See Tkach Par [0087] which discloses mitigating suicidal ideation in a patient in order to prevent suicide; See Tkach Par [0088] which discloses alerting emergency services or other local support depending on questions answered by a user, such as in a psychological questionnaire; See Tkach Par [0098]-[0100] which discloses specifically receiving journal entries as text input data to an electronic platform for determining risk of a patient, such as risk of a suicide event).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify that the text-based narratives and/or audio and/or video content of LaBorde and Patel would constitute journal data especially in view of Applicant’s claimed definition of “journal data”, as specifically disclosed by Tkach, because this allows for comparing and contrasting baseline personal, i.e. journal, input data to assess a user’s risk to determine potential suicidal ideation in a patient and/or to mitigate a risk of a suicide event (See Tkach Par [0087] & [0098]-[0100]).
Claim 17 –
Regarding Claim 17, LaBorde and Patel disclose the system of claim 14 in its entirety. LaBorde and Tkach further disclose a system, wherein:
the software application is further configured to analyze the mood data and journal data in conjunction with the biometric data to identify patterns, trends, or anomalies indicative of emerging mental health issues (See LaBorde Par [0010] & [0065] which discloses the system providing client applications for managing patients identified to be at high risk for suicide and performing embodiments throughout LaBorde; See LaBorde Par [0216], [0224], & [0235] which discloses acoustic properties, e.g. voice signal/analysis, including a plurality of attributes being collected, including determination of moods and emotions experienced by the user/patient, and could constitute both biometric data and/or psychological data under BRI; See LaBorde Par [0088]-[0089] which discloses the use of machine learning based predictive analytics, i.e. specifies of artificial intelligence, for assessing probability that a given patient will commit suicide, such that the system can provide real time analytics about patients that have been identified to be at risk and changes in their status of attributes of their medical care and services over time; See LaBorde Par [0114] which discloses the server device utilizing machine learning to predict events related to risk of suicide or changes therein, such that the model can be used to determine whether a patient has been flagged or has had a change/deviation in the predicted risk of suicide based on input attributes received; See LaBorde Par [0110] which discloses data visualization technologies to enable users to observe trends, easily assess current status of processes or workflows versus targets/thresholds/reference ranges, etc., such that the dashboard makes current trends available and provides inputs and controls that enable drill down/roll up and slice and dice features; See Tkach Par [0087] which discloses mitigating suicidal ideation in a patient in order to prevent suicide; See Tkach Par [0088] which discloses alerting emergency services or other local support depending on questions answered by a user, such as in a psychological questionnaire; See Tkach Par [0098]-[0100] which discloses specifically receiving journal entries as text input data to an electronic platform for determining risk of a patient, such as risk of a suicide event).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to identify that the text-based narratives and/or audio and/or video content of LaBorde and Patel would constitute journal data especially in view of Applicant’s claimed definition of “journal data”, as specifically disclosed by Tkach, because this allows for comparing and contrasting baseline personal, i.e. journal, input data to assess a user’s risk to determine potential suicidal ideation in a patient and/or to mitigate a risk of a suicide event (See Tkach Par [0087] & [0098]-[0100]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over LaBorde, in view of Patel, further in view of Shah et al. (U.S. Patent Publication No. 2022/0375627), hereinafter “Shah”.
Claim 11 –
Regarding Claim 11, LaBorde and Patel disclose the method of Claim 1 in its entirety. LaBorde further discloses a method, further comprising:
alerting at least one of a therapist and a peer support counselor associated with the user when a crisis situation is predicted (See LaBorde Par [0111] which discloses a social worker or other healthcare worker employed by the entity for fielding such alerts and determining what action needs to be taken, i.e. which communication mediums to be used/initiated as in LaBorde Par [0089]-[0093], or automated routing of notifications from the inventive system to a call center for an outreach call/check to a patient with specific training in suicide prevention; See LaBorde Par [0188] which discloses comparing the predicted risk for suicide with the threshold criteria the system has been configured with to determine whether notification or escalation is required, i.e. the type of communication that needs to be initiated; See LaBorde Par [0104] which discloses a user interface alerting suicide prevention coordinators and other healthcare workers to manage, monitor, and assess both clinical and administrative aspects of the patient’s care; See LaBorde Par [0208] & [0214] which discloses/elects one or more humans, e.g. healthcare personnel or peer support specialists, or machines for initiating communications to the patient;
While it is understood by Examiner that LaBorde and Patel most likely effectively discloses peer support specialists being alerted/communicated with upon a crisis situation being predicted per LaBorde Par [0104], [0208], [0214], this is not explicitly mentioned by LaBorde and Patel. Therefore, for purposes of clarity and advancing prosecution, an additional reference will be relied upon hereinafter for specifically electing the alerted personnel to be a therapist in order to meet the entirety of claim 11.
Therefore, Shah discloses alerting at least one of a therapist and a peer support counselor associated with the user when a crisis situation is predicted (See Shah Par [0020], [0025], [0033], & [0050] which discloses automatically detecting that a participant in a digital platform should be escalated to a higher level of care, such as to care involving a clinician, and specifically elects therapists as personnel to be involved to improve patient outcomes and/or prevent the increase and/or exacerbation of negative outcomes (e.g. increased thoughts of suicidal ideation, worsening depression, etc.)). The disclosure of Shah is directly applicable to the disclosure of LaBorde and Patel, because the disclosures share limitations and capabilities, such as being directed towards automatic alerting/notification of emergency personnel when a patient is experiencing a medical emergency/crisis, such as suicide.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the disclosure of LaBorde and Patel which discloses a user interface alerting suicide prevention coordinators and other healthcare workers to manage, monitor, and assess both clinical and administrative aspects of the patient’s care to further specifically elect the healthcare workers/providers to be a therapist, as disclosed by Shah, because this allows for performance of crisis intervention by trained personnel and preventing the increase and/or exacerbation of negative outcomes (e.g., increased thoughts of suicidal ideation, worsening depression, etc.) (See Shah Par [0025] & [0050]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Feuerstein et al. (U.S. Patent Publication No. 2022/0148707) discloses a system for time-sensitive adaptive treatment of mental health conditions, including determinations of whether a patient is at increased risk of suicide, or would benefit from interacting with support personnel/contacts/clinicians;
Campi, JR et al. (U.S. Patent Publication No. 2022/0415500) discloses a system for allowing a user to perform check-ins for determinations of mental health and wellness and automated means for determining whether said user should be contacted based on queried responses.
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/H.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684