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
This action is in reply to the amendment filed on 09/04/26.
Claims 1, 10, 16 have been amended and are hereby entered.
Claim 6 has been canceled.
Claims 1-5, 7-20 are currently pending and have been examined.
This action is made final.
Priority Date
Applicant’s claim to the benefit of and priority to US Provisional Application 63/599,912 is acknowledged. Accordingly, a priority date of 11/16/23 has been given to this application.
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 1-5, 7-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to a judicial exception (an abstract idea) without significantly more.
Step 1
Claims 1-5, 7-9 are drawn to a method, Claims 10-15 are drawn to a non-transitory machine-readable medium and Claims 16-20 is drawn to a system, each of which are within the four statutory categories. Claims 1-5, 7-20 are further directed to an abstract idea on the grounds set out in detail below.
Step 2A Prong 1
Claim 1 recites implementing the steps of:
obtaining a first set of user data, including survey data, cognitive data, creativity data, mindfulness data, and social behavior data;
obtaining a second set of user data;
obtaining a third set of user data;
inputting, the first set of user data, the second set of user data, and the third set of user data into a predictive model to determine a score;
predicting, using the predictive model and based on the score, a mental state status of a user; and
displaying the mental state status of the user.
These steps amount to managing personal behavior or relationships or interactions
between people and therefore recite certain methods of organizing human activity. Obtaining various data sets, using the obtained data to determine a score using a predictive model, predicting a mental state status of a user using the predictive model and score, and displaying the mental status of the user are a personal behaviors that may be performed by a healthcare provider or mental health professional.
In as much as the claims are obtaining data, determining a score using a model, predicting a mental state status, and displaying the mental state status, the claims are obtaining data, analyzing and providing a result. Therefore, the claims are also abstract under the Mental Processes grouping of abstract ideas, as the claims can be performed mentally or with pen and paper.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Independent claims 10 and 16 recites similar limitations and also recites an abstract idea under the same analysis.
The above claims are therefore directed to an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application because the additional
elements within the claims only amount to:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
The independent claims additionally recite:
a processor as implementing the steps of the abstract idea (Claim 1)
a first measurement system as implementing the step of obtaining the first data and displaying the mental state status of the user (Claims 1, 10, 16)
a second measurement system as implementing the step of obtaining the second data (Claims 1, 10, 16)
a third measurement system as implementing the step of obtaining the third data (Claims 1, 10, 16)
a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to implement the steps of the abstract idea (Claim 10)
a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to implement the steps of the abstract idea (Claim 16)
The broad recitation of general purpose computing elements at a high level of generality only amounts to mere instructions to implement the abstract idea using computing components as tools. Regarding the first, second and third measurement systems, the specification does not appear to disclose any particulars of these “measurement systems” nor does it define the measurement systems in terms of hardware. The measurement systems are understood to be implemented via client device computing system which may be a desktop, laptop, PDA, smartphone, mobile telephone or “other suitable device” (para. [0034]). Therefore, recitation of the first, second and third measurement systems only amount to mere instructions to apply the abstract idea of obtaining data on a general purpose computer.
Regarding the non-transitory machine-readable medium, memory, and processor, per paras. [0034] and [0132]-[0133] these are all understood to be general purpose computing components/devices functioning in their ordinary capacities.
These elements are therefore not sufficient to integrate the abstract idea into a practical application. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B
The present claims do not include additional elements that are sufficient to amount to
more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of:
A. Instructions to Implement the Judicial Exception. MPEP 2106.05(f)
As explained above, claims 1, 10 and 16 only recite the aforementioned computing elements as tools for performing the steps of the abstract idea, and mere instructions to perform the abstract idea using a computer is not sufficient to amount to significantly more than the abstract idea. MPEP 2106.05(f).
Thus, taken alone, the additional elements do not amount to significantly more than the
above-identified judicial exception. Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Depending Claims
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims:
Claim 2, Claim 11, and Claim 17 recite limitations pertaining to wherein the predictive model includes signal patterns of psychological risks of depression. This only further narrows the scope of the respective parent claim, and is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 3, Claim 12, and Claim 18 recite limitations pertaining to wherein the predictive model includes signal patterns of psychological risks of anxiety. This only further narrows the scope of its parent claim, and is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 4, Claim 13 and Claim 19 recite limitations pertaining to wherein the predictive model includes signal patterns of psychological risks of aggression. This only further narrows the scope of the respective parent claim, and is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 5, Claim 14, and Claim 20 recite limitations pertaining to wherein the predictive model includes signal patterns of psychological risks of attention-deficit/hyperactivity disorder (ADHD). This only further narrows the scope of the respective parent claim, and is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 7 recites limitations pertaining to wherein the second data includes at least one of: school reports; clinician reports; or family reports. This only further narrows the scope of its parent claim, and is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 8 recites limitations pertaining to wherein the third data includes at least one of: user reports; educator reports; or counsellor reports. This only further narrows the scope of its parent claim, and is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Claim 9 and Claim 15 recite limitations pertaining to wherein the predictive model is a deep convolutional neural network model which only amounts to mere instructions to apply the abstract idea on a general purpose computer. MPEP 2106.05(f). No particulars of the DCNN appear to be disclosed by the specification. Therefore this limitation only amounts to using the DCNN and a computer as tools to apply data to a model to generate a result (see MPEP 2106.05(f)(2)). The additional element of a DCNN is not sufficient to integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
Dependent claims 2-5, 7-9, 11-14, 17-20 do not recite any additional elements to integrate the abstract idea into a practical application. As such, these limitations do not integrate the abstract idea into a practical application or amount to significantly more.
The dependent claims have been given the full two-part analysis including analyzing the additional limitations both individually and in combination. The dependent claims, when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101 as they include all of the limitations of claim 1, 10, or claim 16 respectively. The additional recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the additional recited limitations of the dependent claims merely further narrow the abstract idea. Beyond the limitations which recite the abstract idea, the claims recite additional elements consistent with those identified above with respect to the independent claims which encompass adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Dependent claims 2-5, 7-9, 11-15, 17-20 recite additional subject matter which amounts to additional elements consistent with those identified in the analysis of the independent claims above. As discussed above with respect to the independent claims and integration of the abstract idea into a practical application, recitation of these additional elements only amount to invoking computers as a tool to perform the abstract idea. 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.
Dependent claims 2-5, 7-9, 11-15, 17-20, when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
For the reasons stated, Claims 1-5, 7-20 fail the Subject Matter Eligibility Test and are consequently 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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 7-11, 15-17 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tyler et. al. (US Publication US20250182901A1), further in view of Dilip (US Publication 20220148737A1 ), and further in view of Lee et. al. (US Publication 20220027415A1).
Regarding Claim 1, Tyler discloses a computer-implemented method ([0005]), comprising:
obtaining, by a processor, using a first measurement system, a first set of user data ([0017], teaching on user input comprising data received from one or more user devices such as a mobile device, tablet, other personal computing device; the data may include user responses to one or more surveys or questionnaires provided to the user; see Fig. 1; [0134]-[0137] teach on “data sources” 110; the system can retrieve data from one or more data sources 110; the data retrieval module can receive “user input 122” interpreted as “first data”; regarding use of a processor see at least [0044], the system comprises a digital processing device; the digital processing device comprises at least one processor; [0140], the system and its components can be implemented on one or more computing devices; the computing devices can have general purpose processors) including survey data [0017], teaching on user input comprising data received from one or more user devices such as a mobile device, tablet, other personal computing device; the data may include “user responses to one or more surveys or questionnaires provided to the user” which is interpreted as reading on “survey data”, cognitive data ([0144] teaches on an example of a survey question asking a user to rate their ability to maintain focus and attention, which is interpreted as reading on broadest reasonable interpretation of “cognitive data’), or social behavior data ([0017] teaching on user input comprising data received from one or more user devices such as a mobile device, tablet, other personal computing device; the data may include “social interaction” (quality or quantity thereof”).
obtaining, by the processor, using a second measurement system, a second set of user data ([0018] teaches on “third party input” comprising “data” from various parties including the user’s doctor(s), family, teacher, etc.; the third party input (interpreted as “second data”) may include doctor input such as observations after a user-doctor visit; the doctor’s means of providing data is understood to be different computing device than the user’s system for providing first data, per [0138]- a “third party device” for assessing the user’s well-being and as such is interpreted that the data is obtained using a “second measurement system”; see Fig. 1; [0134]-[0137] teach on “data sources” 110; the system can retrieve data from one or more data sources 110; the data retrieval module can receive “third party input 124” which is interpreted as “second data” from doctor, family or teachers; per [0153] “third party data” is understood to include input data from a variety of roles and may be from a combination of data sources, e.g., the system can receive data from the user’s family regarding the user’s recent life activities and if the user has shown any signs of increased or decreased well-being; regarding use of a processor see at least [0044], the system comprises a digital processing device; the digital processing device comprises at least one processor; [0140], the system and its components can be implemented on one or more computing devices; the computing devices can have general purpose processors);
obtaining, by the processor, using a third measurement system, a third set of user data ([0018] teaches on “third party input” comprising “data” from various parties including the user’s therapists and counselors; Examiner interprets input from therapists/counselors to be “third data”; the third party’s (therapist/counselor’s) means of providing data is understood to be different computing device than the user’s system for providing first data, per [0138]- a “third party device” for assessing the user’s well-being and as such is interpreted that the data is obtained using a “third measurement system”; see Fig. 1; [0134]-[0137] teach on “data sources” 110; the system can retrieve data from one or more data sources 110; the data retrieval module can receive “third party input 124” which is interpreted as “third data” from therapists/counselors; per [0153] “third party data” is understood to include input data from a variety of roles and may be from a combination of data sources, e.g., the system can receive data from the user’s therapist regarding the user’s mental state, including the user’s current level of anxiety, depression, stress, coping, mood, sleep, attention, quality of life); regarding use of a processor see at least [0044], the system comprises a digital processing device; the digital processing device comprises at least one processor; [0140], the system and its components can be implemented on one or more computing devices; the computing devices can have general purpose processors);
inputting, by the processor, the first set of user data, the second set of user data, and the third set of user data into a predictive model to determine a score ([0137] teaches on data processing module comprising one or more data processing algorithms for processing the data received from the one or more sources (interpreted as the first, second and third data above); the data processing algorithms can comprise as non-limiting examples, feature extraction algorithms, machine learning algorithms, artificial intelligence algorithms, Bayesian algorithms (e.g., Bayesian assimilation, Bayesian estimation), and/or statistical analysis algorithms; modeling module 150 can initiate one or more models such as AI models, machine learning models; the output modules generates an output to a user of the system that is associated with affecting or improving the user’s well-being; per [0025], the generated output may be a score which may be a qualitative score, quantitative score or combination; the score may be descriptive (positive vs. negative) or on a numerical score from 0-100; Examiner interprets any of the aforementioned models as being “predictive” models as they use the first, second and third data to output a score pertaining to the user’s well-being; regarding use of a processor see at least [0044], the system comprises a digital processing device; the digital processing device comprises at least one processor; [0140], the system and its components can be implemented on one or more computing devices; the computing devices can have general purpose processors));
predicting, by the processor, using the predictive model and based on the score, a mental state status of a user ([0137] teaches on data processing module comprising one or more data processing algorithms for processing the data received from the one or more sources (interpreted as the first, second and third data above) and using a model to generate an output pertaining to the user’s well-being, where the generated output may be a score per [0025]; [0177] further teaches on the output being a score where the score may be positive, negative or neutral or various degrees thereof (e.g., extremely, very, moderately or slightly positive); the word “positive” can be exchanged with “happy, elated, excited, alert, content, relaxed, calm or sleepy; the word “negative” can be replaced with nervous, tense, stressed, frustrated, sad, depressed, bored, fatigued – any of the degrees of positive/negative or specific descriptor words (content or frustrated) are interpreted as being the “mental state status” of the user; regarding use of a processor see at least [0044], the system comprises a digital processing device; the digital processing device comprises at least one processor; [0140], the system and its components can be implemented on one or more computing devices; the computing devices can have general purpose processors)); and
displaying, by the processor, by the first measurement system, the mental state status of the user ([0139] teaches on a GUI showing graphical elements that allow a user to monitor generated scores and a report regarding the user’s well-being state which are interpreted as the “mental state status” of the user; [0177] teaches on outputting the score where the score may be qualitative, e.g., positive or negative and any of a variety of associated words, e.g., happy, relaxed, frustrated, depressed, etc. which is interpreted as mental state status; regarding use of a processor see at least [0044], the system comprises a digital processing device; the digital processing device comprises at least one processor; [0140], the system and its components can be implemented on one or more computing devices; the computing devices can have general purpose processors)).
Tyler does not explicitly disclose the following types of data as the data included in the first set of user data. But Dilip, which is directed to a system and method for evaluating wellness of a user, teaches:
mindfulness data ([0034] teaches on providing a user with a “mindfulness questionnaire” which includes multiple questions to determine a mindfulness score, and provides examples of the types of questions on mindfulness questionnaire).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify Tyler with these teachings of Dilip, to include mindfulness data as taught by Dilip as part of the first set of user data of Tyler, because mindfulness is a pillar of a user’s wellness (Dilip [0004], [0007]).
Tyler/Dilip do not teach the following but Lee, which his directed to a state-aware interface for determining a user state based on received information, teaches:
creativity data ([0032]-[0033] teach on receiving information which may include user inputs including “creativity”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to further modify Tyler/Dilip with these teachings of Lee, to include creativity data as taught by Lee within the first set of user data of Tyler/Dilip, with the motivation of exploring possibilities, alternatives and new ideas pertaining to the user).
Regarding Claim 2, Tyler/Dilip/Lee teach the limitations of Claim 1. Tyler further discloses wherein the predictive model includes signal patterns of psychological risks of depression ([0137] teaches on using a model to generate an output pertaining to the user’s well-being, where the generated output may be a score per [0025]; [0177] further teaches on the output being a score where the score may be positive, negative or neutral; the word “negative” can be replaced with various words including “depressed” which is interpreted as the model signaling a pattern of risk of depression if user is determined to be “depressed”).
Regarding Claim 7, Tyler/Dilip/Lee teach the limitations of Claim 1. Tyler further discloses wherein the second data includes at least one of: school reports; clinician reports; or family reports ([0018] teaches on “third party input” comprising “data” from the user’s doctor(s), family, teachers/professors; for example, a doctor may provide observations after a doctor-user visit regarding the user’s well-being, which is interpreted as a “clinician report”; third part input may also comprise input (observations) from the user’s family (interpreted as “family reports”) and teachers or professors (interpreted as “school reports”).
Regarding Claim 8, Tyler/Dilip/Lee teach the limitations of Claim 1. Tyler further discloses wherein the third data includes at least one of: user reports; educator reports; or counsellor reports ([0018] teaches on “third party input” comprising “data” from various parties including the user’s therapists and counselors; per [0153] “third party data” may include data received from the user’s therapist regarding the user’s mental state, including the user’s current level of anxiety, depression, stress, coping, mood, sleep, attention, quality of life – interpreted as a “counsellor report” as Examiner equates therapist and counsellor as being synonymous with each other).
Regarding Claim 9, Tyler/Dilip/Lee teach the limitations of Claim 1. Tyler further discloses wherein the predictive model is a deep convolutional neural network model ([0137] teaches on data processing module comprising one or more data processing algorithms for processing the data received from the one or more sources (interpreted as the first, second and third data above); the data processing algorithms can comprise as non-limiting examples, feature extraction algorithms, machine learning algorithms, artificial intelligence algorithms, Bayesian algorithms (e.g., Bayesian assimilation, Bayesian estimation), and/or statistical analysis algorithms; modeling module 150 can initiate one or more models such as AI models, machine learning models; para. [0169] teaches on the machine learning models comprising a neural network such as a deep neural network or convolutional neural network; [0196] teaches on using a “deep convolutional neural network” for classifying, clustering or recognizing patterns or relationships amongst brain activity and patient reported outcomes; while this is a different embodiment than the machine learning models/neural networks taught in [0137], [0169], it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a DCNN as taught in para. [0196] as the prediction model of [0137] and [0169] since it is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the DCNN of the para. [0196] for any of a variety of machine learning models/neural network models of [0137]/[0169]). Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding Claim 10, Tyler/Dilip/Lee teach the limitations of Claim 1. Claim 10 recites limitations that are the same or substantially similar to Claim 1 and the discussion above with respect to Claim 1 is equally applicable to Claim 10. Claim 10 recites the following which are also taught by Tyler: non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations ([0072]).
Regarding Claim 11, Tyler/Dilip/Lee teach the limitations of Claim 2. Claim 11 recites limitations that are the same or substantially similar to Claim 2 and the discussion above with respect to Claim 2 is equally applicable to Claim 11.
Regarding Claim 15, Tyler/Dilip/Lee teach the limitations of Claim 9. Claim 15 recites limitations that are the same or substantially similar to Claim 9 and the discussion above with respect to Claim 9 is equally applicable to Claim 15.
Regarding Claim 16, Tyler/Dilip/Lee teach the limitations of Claim 1. Claim 16 recites limitations that are the same or substantially similar to Claim 1 and the discussion above with respect to Claim 1 is equally applicable to Claim 16. Claim 16 recites the following which are also taught by Tyler: a processor; and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations ([0073]).
Regarding Claim 17, Tyler/Dilip/Lee teach the limitations of Claim 2. Claim 17 recites limitations that are the same or substantially similar to Claim 2 and the discussion above with respect to Claim 2 is equally applicable to Claim 17.
Claim(s) 3, 12, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tyler et. al. (US Publication 20250182901A1), further in view of Dilip (US Publication 20220148737A1 ), and further in view of Lee et. al. (US Publication 20220027415A1).
as applied to Claims 1, 10 and 16 above respectively, further in view of Laszlo et. al. (US Publication 20200205741 A1).
Regarding Claim 3, Claim 12 and Claim 18, Tyler/Dilip/Lee teach the limitations of Claims 1, 10 and 16, respectively, but do not teach the following. Laszlo, which is directed to predicting anxiety, teaches: wherein the predictive model includes signal patterns of psychological risks of anxiety ([0028] teaches on a process for predicting a patient’s likelihood of experiencing anxiety in the future; [0032] teaches on using an anxiety prediction module to analyze signals to identify stimulus response patterns that are indicative of a future risk of anxiety; the anxiety prediction module can use a trained machine learning module to predict a patient’s future likelihood of experiencing anxiety).
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 teachings of Tyler/Dilip/Lee with these teachings of Laszlo, to use a predictive model to identify patterns of risks for anxiety, because early detection of a patient’s likelihood to experience clinical anxiety can permit doctors to provide necessary treatment before serious symptoms occur (Laszlo, [0003]).
Claim(s) 4, 13, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tyler et. al. (US Publication 20250182901A1), further in view of Dilip (US Publication 20220148737A1 ), and further in view of Lee et. al. (US Publication 20220027415A1).
as applied to Claims 1, 10 and 16 above respectively, further in view of Buerkle et. al. (US Publication 20210397858A1).
Regarding Claim 4, Claim 13, and Claim 19, Tyler/Dilip/Lee teach the limitations of Claims 1, 10 and 16, respectively, but do not teach the following, but Buerkle, which is directed to detection and mitigation of inappropriate behaviors, teaches: wherein the predictive model includes signal patterns of psychological risks of aggression ([0100] teaches on predicting when the emotional state of a person is “potential aggression”).
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 teachings of Tyler with these teachings of Buerkle, to use the predictive model of Tyler to identify patterns of risks for aggression, with the motivation of early detection of aggression so that it may be prevented (Buerkle [0100]) and preventing violence from occurring and deescalating a situation before it becomes violent (Buerkle [0057], [0062]).
Claim(s) 5, 14, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tyler et. al. (US Publication 20250182901A1), further in view of Dilip (US Publication 20220148737A1 ), and further in view of Lee et. al. (US Publication 20220027415A1).
as applied to Claims 1, 10 and 16 above respectively, further in view of Eleftheriou et. al. (US Publication 20220061726 A1).
Regarding Claim 5, Claim 14, and Claim 20, Tyler/Dilip/Lee teach the limitations of Claims 1, 10 and 16, respectively, but do not teach the following, but Eleftheriou which is directed to a method for detecting, characterizing and monitoring ADHD teaches: wherein the predictive model includes signal patterns of psychological risks of attention-deficit/hyperactivity disorder (ADHD) ([0034] teaches on using a model to leverage a user’s results from cognitive evaluation and biosignal data to estimate a confidence score for the user representing the probability of an ADHD diagnosis; if the confidence score exceeds a threshold confidence, the system interprets an ADHD diagnosis for the user).
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 teachings of Tyler/Dilip/Lee with these teachings of Eleftheriou, to use the predictive model of Tyler to identify patterns of risks for ADHD, with the motivation of identifying intervention needs and providing intervention activities to the user (Eleftheriou [0014]).
Response to Applicant’s Remarks/Arguments
Please note: When referencing page numbers of Applicant’s response, references are to page numbers as printed.
35 USC 101 Rejections
Applicant’s remarks have been fully considered but are not persuasive.
Regarding Applicant’s remarks at page 18 (“None of these steps can be performed "in the human mind" as they are all specifically tied to concrete technologies. Further, the processing performed is explicitly performed by a computing device and thus there are indeed multiple elements of the claim that "preclude the [claim] from being performed in the human mind”, the Examiner respectfully disagrees. Recitation of the computing device/processor only amounts to mere instructions to apply the abstract idea on a computer. Regarding “tied to concrete technologies”, this does not preclude the steps from being directed to an abstract idea. This is not persuasive.
Regarding remarks at page 18 pertaining to Step 2A Prong 1 and mental processes, Examiner respectfully disagrees with Applicant’s position. Even as amended, the claims only recite the various computing components (e.g., a processor) at a high level of generality to implement the various steps of the abstract idea. Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, analyzing, and presenting the mental status information. Regarding remarks at page 18 pertaining to “proper inquiry”, Examiner submits that the processor and measurement systems only amount to general purpose computing elements used to implement the steps of the abstract idea. The 5 categories of information only narrows the scope of the abstract idea by labeling the specific types of data (e.g., survey data, cognitive data). Examiner submits that but for recitation of measurement systems/processor, the steps outlined at top of page 19 could practically be performed in the mind or with calculation aid such as pen and paper. Using a predictive model to determine a score and predict a mental-state status is a mental process that could be performed by a healthcare provider as it amounts to using a model and predicting a result. Please see MPEP 2106.04(a)(2)(A), which states:
…claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:[AltContent: rect]
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
Examiner respectfully submits that to the extent the “obtaining” first, second and third sets of user data is recited, the Claims are analogous to EPG in that they are directed to collecting information, analyzing it, and displaying results of the collection and analysis. Regarding remarks at bottom of page 19 pertaining a “processor based predictive model operation”, Examiner submits that this has been addressed by the above response. This argument is not persuasive.
Regarding remarks at page 20 pertaining to characterization of the abstract idea as certain methods of organizing human activity, specifically managing personal behavior or relationships between people, Examiner respectfully disagrees with Applicant’s position. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of personal behaviors that a person or persons, with or without the aid of a computer, would follow to obtain user data, determining a score using the obtained data in a predictive model, predicting a mental state status of the user based on the score, and providing the mental status to the user. As discussed above with respect to the “mental process” grouping of abstract ideas, the processor/computer are recited at a high level of generality and only amount to using a computing device functioning in its ordinary capacity to implement the steps of the abstract idea. Because the claim elements fall under a series of steps that a person or persons would follow to determine and provide a mental state status of a user to the user, the claimed invention is directed to an abstract idea. This argument is not persuasive.
Regarding remarks directed to Step 2A Prong 2 beginning at page 20, the Examiner respectfully disagrees that the abstract idea is integrated into a practical application. Regarding “ordered combination” remarks, Applicant has not provided evidence, nor can Examiner find evidence, that any arrangement of additional elements amounts to an unconventional arrangement that provides an improvement or solution to a problem disclosed by the specification. Recitation of computing components, as previously discussed, only amount to using general purpose computing components in their ordinary capacities to implement the steps of the abstract idea. Regarded bulleted list of claim limitations at page 21 and subsequent remarks (“the processor and measurement systems… participate in an expressly recited data-acquisition, model-processing, prediction and output arrangement”), these elements only amount to using computing components to apply the abstract idea. Regarding remark “the operation of one element supplies the data required by another”, Examiner is uncertain as to which elements Applicant refers; as currently presented, the first, second and third measurement systems obtain a first, second and third set of data, respectively. Using a predictive model only amounts to using a computer as a tool to arrive at the solution (mental health state prediction). This argument is not persuasive.
Regarding “ordered sequence” presented at top of page 22 and subsequent remarks, the Examiner respectfully disagrees that the claimed arrangement imposes meaningful limits on the abstract idea. Using a computer functioning in its ordinary capacity to perform the steps of the abstract idea and merely outputting a determination of user’s mental state do not meaningfully limit the abstract idea. Regarding remarks pertaining to the various data types, as previously stated, this only further limits the scope of the abstract idea by specifying the data types collected. Examiner respectfully submits that “multi-category, multi-source data processing” can fall within the scope of the abstract idea, e.g., a person can perform analysis of data from different sources and different categories. This argument is not persuasive.
Regarding remarks at top of page 23 pertaining to expressly numerated categories forming part of the combined input and subsequent bullets regarding functional relationship, Examiner submits that the cited limitations fall within the scope of the abstract idea; recitation of the processor/measurement system only amount to mere instructions to apply the abstract idea on a computer as previously discussed. Examiner submits that the analysis above and in non-final action indeed evaluates each actual limitation. Applicant has not cited to, nor can Examiner find, evidence of any ordered combination recited by the claims that would lead to subject matter eligibility. This is not persuasive.
Regarding remarks at bottom of page 23 pertaining to how the result is produced, Examiner submits that all of the steps cited by Applicant are within the scope of the abstract idea but for the processor/measurement systems which only apply to mere instructions to apply the abstract idea. Examiner submits that the claims merely amount to obtaining data, analyzing it, and outputting a result, e.g., a mental health status of a user. This is not persuasive.
Regarding remarks at page 24 pertaining to Step 2B, Examiner respectfully disagrees. Examiner maintains the position that the computing components are general purpose computing elements functioning in their ordinary capacity to implement the steps of the abstract idea. Applicant has not cited to, nor can Examiner find evidence, of the computing components being anything more. Further, Examiner submits that the “ordered combination” applies to additional elements, see MPEP 2106, Pathway C, and MPEP 2106.05(I)(B). Applicant has not cited to, nor can Examiner find evidence in specification, as to how the additional elements amount to significantly more than the abstract idea. Examiner submits that the steps cited at bottom of page 25 only amount to mere instructions to apply the abstract idea on a computer and do not provide significantly more. Using general purpose computing devices functioning in their ordinary capacity to obtain and analyze data and output a result does not provide significantly more. This is not persuasive,.
For all of the above reasons, Applicant’s remarks are not persuasive and the rejections of Claims 1-5, 7-20 under 35 USC 101 are maintained.
35 USC 102/103 Prior Art Rejections
Regarding remarks directed to Claim 1 (representative), Applicant’s remarks have been considered but are not persuasive. Applicant’s remarks appear to be directed to Claim 1 (representative) as amended. As shown, Tyler teaches on the amended portions pertaining to the first set of user data including survey data, cognitive data, and social behavior data; new citations have been added to relevant portions of Tyler. The Dilip and Lee references have been added to teach on the amended limitations pertaining to mindfulness and creativity data, respectfully. New grounds of rejection have been necessitated by Applicant’s amendments.
Regarding the rejection of dependent Claims 2-5, 7-9, 11-14, 17-20, the Applicant has not offered any arguments with respect to these claims other than to reiterate the argument(s) present for the claims from which they depend. As such, the rejection of these claims is also maintained.
The rejections of Claims 1-5, 7-20 under 35 USC 103 are maintained.
Conclusion
Examiner respectfully requests that Applicant provides citations to relevant paragraphs of specification for support for amendments in future correspondence.
The following relevant prior art not cited is made of record:
US Publication 20170188976 A1, teaching on addressing psychological conditions of a patient through guided meditation which includes administering a post-meditation survey to a user
US Publication 20230169882 A1, teaching on a system and method for a virtual mental health system infrastructure
US Publication 20230309883A1, teaching on a system and method for conducting mental health assessment and evaluation, matching needs, and predicting content and experiences for improving mental health of a user
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/ANNE-MARIE K ALDERSON/Primary Examiner, Art Unit 3682
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