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 .
Receipt of Applicant’s Amendment filed June 5, 2025, is acknowledged.
Election/Restrictions
Applicant’s election without traverse of Group I in the reply filed on June 5, 2025, is acknowledged.
Response to Amendment
Claims 15-20 have been withdrawn. Claims 1-14 are pending and are provided to be examined upon their merits.
Specification
The disclosure is objected to because of the following informalities: para. [0080] first sentence should end with a period; para. [0150] has “The digital biomarker may be determined any computing device…” which has a grammar issue.
Appropriate correction is required.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-14 are directed to a method or product, which are statutory categories of invention. (Step 1: YES).
The Examiner has identified method Claim 1 as the claim that represents the claimed invention for analysis and is similar to product Claim 8.
Claim 1 recites the limitations of:
A computer-implemented method of generating a digital biomarker, comprising:
determining a task difficulty level for assessment of a patient having a neurological condition;
generating an interactive task at the task difficulty level;
generating for display on a mobile device a graphical user interface for receiving task input from the patient attempting to complete the task;
determining a task outcome based on the received task input;
generating a modified task difficultly level based on the received task input;
iterating through the previous generating and determining the task outcome steps using the modified task difficulty level until a predetermined condition is met; and
based on the predetermined condition being met, determining a digital biomarker for the patient by analyzing the plurality of received task inputs and determined task outcomes.
These above limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements, in non-bold above, which covers performance of the limitation as managing personal behavior. Determining a task level for assessment of a patient, generating an interactive task, generating a display for receiving task input from the patient, determining a task outcome based on the input, generating a modified task difficulty level based on task input, and determining a digital biomarker for the patient based on analyzing the received task inputs and determined outcomes is managing personal behavior including following rules or instructions. Also, diagnosing or determining a patient’s health status (e.g., determining a digital biomarker for a patient) is managing personal behavior by teaching. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as managing personal behavior, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claim 8 is also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
In as much as the claims can be performed in the mind of a person, with pen and paper, the claims are also abstract as a Mental Process. For example, a health professional can determine a task difficulty level of a patient, generate (create) an interactive task at the task difficulty level, generate (create) with pen and paper a display (drawing/document) for receiving input from a patient, determine the outcome, generate a modified task difficulty level, iterate the process until a condition is met, and determine a digital biomarker by analyzing the task inputs and outcomes. See also MPEP 2106.04(a)(2) III C where using generic computers was shown to be abstract as a mental process.
This judicial exception is not integrated into a practical application. In particular, the claims only recite: computer, mobile device (Claim 1); non-transitory machine-readable medium, processors, mobile device (Claim 8). The computer hardware is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore claims 1 and 8 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Steps such as receiving are considered insignificant extra solution activity and mere instructions to apply the exception using general computer components (see MPEP 2106.05(d), II). Thus claims 1 and 8 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-7 and 9-14 further define the abstract idea that is present in their respective independent claims 1 and 8 and thus correspond to Certain Methods of Organizing Human Activity and Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. The claims themselves are either abstract or further limit abstract ideas. Claims 5 and 7 recite server device which is applied at a high level of generality. Claims 7 and 14 recite Markov Chain Monte Carlo simulations which is abstract as a mathematical concept as these are mathematical algorithms. Therefore, the claims 2-7 and 9-14 are directed to an abstract idea. Thus, the claims 1-14 are not patent-eligible.
Examiner Request
The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 2, 5, 8, 9, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2017/0098385 to Martucci et al. in view of WO 2018/050763 to Baker et al.
Regarding claims 1 and 8
(claim 1) A computer-implemented method of generating a digital biomarker, comprising:
determining a task difficulty level for assessment of a patient having a neurological condition;
Martucci et al. teaches:
Assessment (determining) maximal task (task level difficulty) for cognitive (neurological) assessment, where task difficulty levels is part of performance range…
“The present disclosure describes systems and methods for the implementation of personalized cognitive training. As an example, a processor-implemented method is provided for enhancing cognitive abilities of a user by personalizing cognitive training regimens. A cognitive assessment of a user is performed using a set of assessment tasks. A maximal performance of the user related to the set of assessment tasks is estimated. A performance range is determined based at least in part on the maximal performance of the user. The performance range is divided into a plurality of progress gates corresponding to a plurality of task difficulty levels for personalizing cognitive training regimens.” [0004]
Assess condition of specific neural signatures (neurological condition)…
“As yet another example, the subject's cognitive ability is assessed by pre-training and post-training physiological tests that measure internal markers of disease or health such as detection of amyloid beta, cortisol and other stress response markers; and brain imaging studies that assess a condition based on presence of specific neural signatures. For example, the subject suffers from age-related cognitive decline, mild cognitive impairment, Alzheimer's disease, Parkinson's disease, Huntington's disease, depression, schizophrenia, dementia, Pick's disease, cognitive deficit associated with fatigue, multiple sclerosis, post traumatic stress disorder, obsessive-compulsive disorder, brain damage, anxiety, stress, panic, depression, dysphoria, malaise, attention deficit disorder, Autism Spectrum Disorder, chronic neurological illnesses or chronic psychiatric illnesses.” [0017]
generating an interactive task at the task difficulty level;
Generating tasks associated with progress gate (task difficulty level)…
“…Further, the method includes: dividing, using the one or more data processors, the performance range into a plurality of progress gates, the plurality of progress gates corresponding to a plurality of task difficulty levels, data related to the performance range being stored in a data structure in a non-transitory machine-readable storage medium; selecting, using the one or more data processors, a first progress gate within the performance range; and generating, using the one or more data processors, a first set of training tasks associated with the first progress gate…” [0006]
generating for display on a mobile device a graphical user interface for receiving task input from the patient attempting to complete the task;
Pictorial representation (generating display) and user (patient) can select which task (receiving task input)…
“FIG. 5A-FIG. 5F include a pictorial representation of a cognitive training game (e.g., Project: EVO), which uses methods described in the present disclosure to present to an individual a personalized cognitive training experience. FIG. 5A-FIG. 5F show exemplary screenshots from one game session comprising the initiation, assessment and training steps described in detail in FIG. 2. The session begins with a user login screen (501), where new users first set up a user profile and enter demographic information. New and existing users are then greeted with a welcome screen (502), inviting them to tap the screen to initiate a new task challenge. Users can select which task challenge (world′) to undertake in the next step (503).” [0043]
Fig. 5B teaches initiate (generating) task challenge on a display…
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determining a task outcome based on the received task input;
Example of evaluates (determining outcome) on tasks…
“…Project: EVO comprises multiple worlds with progressive task complexity. New users can choose the first world for their initial session. Subsequent worlds are unlocked when users are able to successfully perform at the previous worlds. Once a user selects a world, the system provides an option to initiate an assessment (called a ‘Challenge’ session in the game) or a training session (504). New users may initiate with an assessment, while existing users are provided an option to retake an assessment or to continue with training Project: EVO evaluates and trains individuals on two types of tasks: a perceptual reaction task called Tapping, and a visuomotor task called Navigation. The assessment begins with the Tapping task where users are stimulated with visual targets and their responses collected (505, 506). This is followed by an assessment of the user's ability on the Navigation task performed in isolation (507), and his/her performance on both Tapping and Navigation tasks performed simultaneously (not shown). Once the user's baseline performance levels have been determined in the assessment and the personalized performance range and difficulty progression for the training session calculated, users are directed to initiate a training session (508). During training, users have to perform the Tapping and Navigation tasks simultaneously, and their performance on both tasks (i.e. their multitasking performance) is recorded (509). When users perform at a difficulty level corresponding to a progress gate, they are presented with a reward in the form of a star (510). At the end of the training session, users are reported their overall progress in training (511). Users are also presented with other rewards that may be tied to performance or other metrics such as number of assessment or training sessions completed (512). The session ends, and users are redirected to screen 503 to continue assessment and training in the same world or progress to the next world.” [0043]
Example of improvement (task outcome) in single task (received task)…
“The individual shows improvement in his general ability on the Navigation task (indicated by increase in performance in the single task condition in the later assessment) as well as in his multitasking ability on both Tapping and Navigation tasks (indicated by the reduced interference cost in the later assessments) as a result of training.” [0047]
generating a modified task difficultly level based on the received task input;
One example of once baseline performance levels determined, initiate (generating) a training session (modified task difficulty) and another example of progress to the next world…
“…Project: EVO comprises multiple worlds with progressive task complexity. New users can choose the first world for their initial session. Subsequent worlds are unlocked when users are able to successfully perform at the previous worlds. Once a user selects a world, the system provides an option to initiate an assessment (called a ‘Challenge’ session in the game) or a training session (504). New users may initiate with an assessment, while existing users are provided an option to retake an assessment or to continue with training Project: EVO evaluates and trains individuals on two types of tasks: a perceptual reaction task called Tapping, and a visuomotor task called Navigation. The assessment begins with the Tapping task where users are stimulated with visual targets and their responses collected (505, 506). This is followed by an assessment of the user's ability on the Navigation task performed in isolation (507), and his/her performance on both Tapping and Navigation tasks performed simultaneously (not shown). Once the user's baseline performance levels have been determined in the assessment and the personalized performance range and difficulty progression for the training session calculated, users are directed to initiate a training session (508). During training, users have to perform the Tapping and Navigation tasks simultaneously, and their performance on both tasks (i.e. their multitasking performance) is recorded (509). When users perform at a difficulty level corresponding to a progress gate, they are presented with a reward in the form of a star (510). At the end of the training session, users are reported their overall progress in training (511). Users are also presented with other rewards that may be tied to performance or other metrics such as number of assessment or training sessions completed (512). The session ends, and users are redirected to screen 503 to continue assessment and training in the same world or progress to the next world.” [0043]
iterating through the previous generating and determining the task outcome steps using the modified task difficulty level until a predetermined condition is met; and
“…Project: EVO comprises multiple worlds with progressive task complexity. New users can choose the first world for their initial session. Subsequent worlds are unlocked when users are able to successfully perform at the previous worlds. Once a user selects a world, the system provides an option to initiate an assessment (called a ‘Challenge’ session in the game) or a training session (504). New users may initiate with an assessment, while existing users are provided an option to retake an assessment or to continue with training Project: EVO evaluates and trains individuals on two types of tasks: a perceptual reaction task called Tapping, and a visuomotor task called Navigation. The assessment begins with the Tapping task where users are stimulated with visual targets and their responses collected (505, 506). This is followed by an assessment of the user's ability on the Navigation task performed in isolation (507), and his/her performance on both Tapping and Navigation tasks performed simultaneously (not shown). Once the user's baseline performance levels have been determined in the assessment and the personalized performance range and difficulty progression for the training session calculated, users are directed to initiate a training session (508). During training, users have to perform the Tapping and Navigation tasks simultaneously, and their performance on both tasks (i.e. their multitasking performance) is recorded (509). When users perform at a difficulty level corresponding to a progress gate, they are presented with a reward in the form of a star (510). At the end of the training session, users are reported their overall progress in training (511). Users are also presented with other rewards that may be tied to performance or other metrics such as number of assessment or training sessions completed (512). The session ends, and users are redirected to screen 503 to continue assessment and training in the same world or progress to the next world.” [0043]
Various examples of performing at specific difficulty levels (iterating through using task levels) and earn a star when performs at a difficulty level (predetermined condition)…
“FIG. 6 is a pictorial representation of the rewards presented to the user in the cognitive game Project: EVO, to motivate user engagement and compliance. Three exemplary rewards are shown, which are tied to the user's performance and personalized difficulty progression in the game, in accordance with one embodiment in the present disclosure. 601 is a screenshot of the wrap-up screen presented to the user after an assessment session, which reports the number of ‘supercoins’ earned by the user during the assessment. Supercoins represent rewards offered to the user for performing at specific difficulty levels during an assessment, and are intended to motivate the user to perform at his/her maximal current ability during the assessment. 602 is a screenshot from the game reporting the user's star level. Stars represent rewards tied to the user's personalized performance range and progress gates for training A user earns a star each time he/she successfully performs at a difficulty level corresponding to a progress gate. In Project: EVO, a user's performance range for training is divided into 5 progress gates, allowing the user to earn up to 5 stars in a training session. After earning 5 stars, the user is presented with a re-assessment to evaluate his/her new baseline performance levels and reset the performance range for subsequent training sessions. In Project: EVO, a user undergoes multiple re-assessments and training cycles and has to earn 15 stars before he/she is allowed to progress to the next world. 603 is a screenshot of the multiple worlds in Project: EVO. When a user successfully completes training in one world, he/she is rewarded with access to subsequent worlds which comprise tasks with greater complexity than the recently completed world.” [0044]
Another example of a repeating cycle (iterative steps)…
“Accordingly, it is an aspect of the present disclosure that multiple assessments are made throughout a cognitive training regimen, each assessment re-setting the difficulty progression and performance range for the subsequent cognitive training phase. Thus, it is envisioned that an efficient cognitive training experience entails a repeating cycle where assessment informs the difficulty progression levels in training, and frequently or infrequently a re-assessment is made, the re-assessment results then being used to set training difficulty range and progression levels, and so forth. The process may be carried out for as many times as necessary to reach an end-goal for the individual, such as a certain cognitive function ability attained or a certain time spent on a cognitive training regimen. A final assessment at the end of such cycles may be useful in determining the overall progress from the beginning of cognitive training through the end of a cognitive training regimen, as measured by assessment phases.” [0083]
based on the predetermined condition being met, determining a digital biomarker for the patient by analyzing the plurality of received task inputs and determined task outcomes.
Measure (digital) internal markers of disease and assess specific neural signatures (biomarker)…
“As yet another example, the subject's cognitive ability is assessed by pre-training and post-training physiological tests that measure internal markers of disease or health such as detection of amyloid beta, cortisol and other stress response markers; and brain imaging studies that assess a condition based on presence of specific neural signatures. For example, the subject suffers from age-related cognitive decline, mild cognitive impairment, Alzheimer's disease, Parkinson's disease, Huntington's disease, depression, schizophrenia, dementia, Pick's disease, cognitive deficit associated with fatigue, multiple sclerosis, post traumatic stress disorder, obsessive-compulsive disorder, brain damage, anxiety, stress, panic, depression, dysphoria, malaise, attention deficit disorder, Autism Spectrum Disorder, chronic neurological illnesses or chronic psychiatric illnesses.” [0017]
Digital Biomarker
Martucci et al. teaches cognition and skills. They also teach neural signatures. They do not literally teach digital biomarker.
Baker et al. also in the business of cognition and skills teaches:
Digital biomarker…
“Advantageously, it has been found in the studies underlying the present invention that fine motoric activity parameters, optionally together with other performance parameters of motoric and cognitive capabilities, obtained from datasets measured during certain activities of patients suspect to or suffering from a cognition and movement disease or disorder can be used as digital biomarkers for assessing, e.g., identifying or monitoring, those patients which suffer from the said disorder or disease. The said datasets can be acquired from the patients in a convenient manner by using mobile devices such as the omnipresent smart phones, portable multimedia devices or tablet computers. The datasets thereby acquired can be subsequently evaluated by the method of the invention for the at least one cognition or fine motoric activity parameter suitable as digital biomarker. Said evaluation can be carried out on the same mobile device or it can be carried out on a separate remote device. Moreover, by using such mobile devices, recommendations on life style or therapy can be provided to the patients directly, i.e. without the consultation of a medical practitioner in a doctor's office or hospital ambulance. Thanks to the present invention, the life conditions of patients can be adjusted more precisely to the actual disease status due to the use of actual determined parameters by the method of the invention. Thereby, drug treatments can be selected that are more efficient or dosage regimens can be adapted to the current status of the patient. It is to be understood that the method of the invention is, typically, a data evaluation method which requires an existing dataset of dataset of cognition or fine motoric activity measurements from a subject. Within this dataset, the method determines at least one cognition or fine motoric activity parameter which can be used for assessing a cognition and movement disease or disorder, i.e. which can be used as a digital biomarker for said disease or disorder.” (pg. 46, lines 9-30)
“In yet an embodiment of the method of the present invention, said cognition and movement disease or disorder is selected from the group consisting of: multiple sclerosis, stroke, a cerebellar disorder, cerebellar ataxia, spastic paraplegia, essential tremor, myasthemia or other forms of neuromuscular disorders, muscular dystrophy, myositis or other muscular disorders, a peripheral neuropathy, cerebal palsy, extrapyramidal syndromes, Alzheimers disease, other forms of dementia, leukodystrophies, autism spectrum disorders, attention- deficit disorders (ADD/ ADHD), intellectual disabilities as defined by DSM-5, impairment of cognitive performances and reserve related to aging, Parkinson.sup.'s disease, Huntigton.sup.'s disease, a polyneuropathy, and amyotrophic lateral sclerosis.” (pg. 47, lines 15-23)
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use digital biomarkers as taught by Baker et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Baker et al. who teaches the benefits of using digital biomarkers for cognition analysis.
Regarding claims 2 and 9
(claim 2) The computer-implemented method of claim 1, wherein the predetermined condition comprises the patient completing the task at a predetermined difficulty level.
Martucci et al. teaches:
Example of determined (predetermined) task difficulty levels and task completed…
“Once the system has determined the performance range, task difficulty levels and reward levels for the participating subject, the subject is initialized into the training at step 209. Training for a new subject is initiated at a difficulty level corresponding to the subject's starting progress gate. Training for an existing subject is initiated at a difficulty level corresponding to the highest progress gate the subject successfully performed at in a previous training session. Upon the start of the cognitive training process at step 209, the training may continue for the length of the predetermined duration of the session at step 210. After the desired session length is reached, the training session ends at step 216. If the current duration time is less than the desired duration time, the system continues to the present to the subject suitable stimuli related to the task(s) to be completed for training, and collects the subject's responses at step 211.” [0037]
Regarding claims 5 and 12
(claim 5) The computer-implemented method of claim 1, wherein determining the digital biomarker is performed by a server device configured to receive task input and task outcomes.
Martucci et al. teaches:
Client device-server relationship…
“The computer components, software modules, functions, data stores and data structures described herein may be connected directly or indirectly to each other in order to allow the flow of data needed for their operations. It is also noted that a module or processor includes but is not limited to a unit of code that performs a software operation, and can be implemented for example as a subroutine unit of code, or as a software function unit of code, or as an object (as in an object-oriented paradigm), or as an applet, or in a computer script language, or as another type of computer code. The software components and/or functionality may be located on a single computer or distributed across multiple computers depending upon the situation at hand. The computing system can include client devices and servers. A client device and server are generally remote from each other and typically interact through a communication network. The relationship of client device and server arises by virtue of computer programs running on the respective computers and having a client device-server relationship to each other.” [0236]
Claims 3, 7, 10, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (7) above in further view of Pub. No. US 2016/0262680 to Martucci et al. (hereinafter referred to as Martucci1)
Regarding claims 3 and 10
(claim 3) The computer-implemented method of claim 1, wherein the predetermined condition comprises the patient making at least a predetermined number of errors while attempting to complete any task.
The combined references teach task. They do not teach error.
Martucci1 et al. also in the business of task teaches:
Threshold based on ratio of correct responses to incorrect responses and in addition, quantity above/below a threshold, therefore, predetermined quantity of incorrect tasks (error)…
“In one embodiment, the psychophysics metric determined from user inputs may be based on performance threshold. This threshold may be defined as the maximum stimulus magnitude (such as speed in a visuomotor navigation task) of a task for which a user can achieve a specified ratio of correct responses to incorrect responses in an adaptive task over time. For instance, the threshold may be defined as the maximum stimulus magnitude of a task for which a user can correctly perform the task about 1%, about 10%, about 50% of the time, about 70% of the time, about 80% of the time, or between 90-100% of the time. The threshold may also be defined as the maximum stimulus magnitude of a task for which a user achieves a specified ratio of correct responses to incorrect responses when the stimulus magnitude is increased incrementally. In addition, the threshold may be characterized by the quantity or percent of stimuli that are responded to correctly above or below the threshold level in an adaptive task. In a preferred embodiment, the performance threshold may the reaction time window at which the user can to continuously achieve 80% correct responses to a perceptual reaction task…” [0119] Inherent with threshold based on correct quantity and ratio of based on correct to incorrect responses is a quantity of incorrect responses.
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to count incorrect responses as taught by Martucci1 since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Martucci1 who teaches the benefits of keeping track of correct and incorrect responses as further way to measure cognitive ability.
Regarding claims 7 and 14
(claim 7) The computer-implemented method of claim 1, wherein generating the modified task difficulty level comprises using an iterative procedure selected from the group consisting of Markov Chain Monte Carlo simulations, grid search, and Bayesian estimation.
The combined references teach task level. They also teach iterative procedure. They do not teach Bayesian.
Martucci1 also in the business of task level teaches:
Bayesian analysis for correct and incorrect response…
“In some embodiments, the statistical summary measurement taken may be created from Bayesian statistical methods. For example, the Bayesian analysis can include but is not limited to the probability of a correct response given an incorrect response and the probability of an incorrect response given a correct response.” [0111]
Example of repeat (iterative)…
“Participants in the study were given an evaluation in different EVO worlds within the game. The participants then participated in the Project: EVO cognitive training program, which includes taking the evaluation at least two more times within each world. This process was repeated for at least 3 worlds. Participants were given the worlds in a random order. The participants played at most 7 rounds of the Project: EVO training or assessment per day for 28 days. The initial evaluation was done in the lab setting under the supervision of the researcher. All the remaining sessions were played at home with no guidance or interference from the research team.” [0170]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use Bayesian analysis as taught by Martucci1 since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Martucci1 who teaches the benefits of determining probability of correct and incorrect responses and this would help in determining thresholds for task performance and improve cognitive testing by measure tasks performance relative to thresholds.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (7) above in further view of Pub. No. US 2017/0333796 to Do et al.
Regarding claims 4 and 11
(claim 4) The computer-implemented method of claim 1, wherein the interactive task comprises hiding an object on a game board, and wherein each difficulty is associated with a different number of interactive elements on the game board.
The combined references teach tasks. They do not teach hidden.
Do et al. also in the business of tasks teaches:
Example of performance level (difficulty) required…
“Referring to FIG. 6, the process for preparing benchmarking tables may go through all the players who have played the various games to create benchmark table. The benchmark table can help determine the scores and/or performance level required to be at the 99.sup.th percentile, 98.sup.th percentile, etc. As shown in FIG. 6, the Ability Area can be Logic, Math, Music, Attention, Focus, etc. The filters may provide the ability to look at all players or select the comparison set based on (among other possibilities): gender, age, clinical diagnosis, etc. In an embodiment, a batch process can be initiated periodically, for example, hourly, every x hours, or daily. The periods for the batch process may be predetermined.” [0139]
Games for spatial processing, visual memory, focus, engagement and memory…
“FIG. 20 illustrates examples of repurposed games according to an embodiment. As shown in FIG. 20, the repurposed games may assess abilities such as logic, spatial processing, visual memory, math, and linguistics. The repurposed games for logic may include: Parking Lot, Seesaw Logic, Rainbow Mechanic, and Christmas Tree Light-up. The repurposed games for spatial processing may include: Spot the Difference, Share Inlay, Count the Cubes, and Count the Sheep. The repurposed games for visual memory may include: Pattern Memory, and Memory III. The repurposed games for math may include: Bus Driver Math, and Quick Calculate. The repurposed games for linguistics may include a Word Search. Each repurpose game may also assess a number of Executive Functions, for example, focus, engagement, initiation and stop, memory manipulation, prioritization, time sensitivity, etc.” [0168]
Easter Egg Hunt with number of hidden eggs…
“The game data for Easter Egg Hunt collected and passed to the API when a level ends may include:
Date/time stamp
Level
Successful (yes or no)
score
amount of time available
amount of time used
percent of available time used
no of hidden eggs
no of eggs found
no of wrong clicks
no of times the hint is used
no of times the game is extended” [0216] – [0252]
Example of different levels with different faces (elements)….
“Level 2 and beyond can work the same way as Level 1, but the system can randomly select from images with 2 or more faces. Levels:
Level 1: 1 face
Level 2: 2 faces
Level 3: 3 faces
Level 4: 4 faces
Level 5: 5 faces
Level 6: 6 faces
Level 7: 7 faces
Level 8: 8 or more faces” [0525] – [0534]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use hidden objects and different number of elements as taught by Do et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Do et al. who teaches the benefits of different games for improving cognitive skills.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (7) above in further view of Pub. No. US 2019/0243944 to Jain et al.
Regarding claims 6 and 13
(claim 6) The computer-implemented method of claim 1, wherein:
the patient is selected from a population that has been administered a treatment; and
Martucci et al. teaches:
“Individuals that can use the methods and tools of the present disclosure can be any person, especially those interested in enhancing cognitive abilities. For any of the target populations described below, diagnostics to assess one's cognitive ability (e.g. impairment or susceptibility to interference) and training are particularly useful applications of the methods of the present disclosure. It is recognized in the cognitive field that interference in cognitive function severely impacts cognitive performance across a range of functions, including perception, attention, and memory. Accordingly, there are many potential populations that would benefit from a new training method that specifically aims to enhance the ability to deal with interference.” [0119]
the method further comprises:
obtaining a historical digital biomarker for each patient in the in the population;
See Biomarker below.
generating a new digital biomarker, using the method of claim 1, for each patient in the population; and
See Biomarker below.
determining, based on a comparison of the historical digital biomarker and the new digital biomarker for each patient, a treatment effectiveness for the administered treatment.
See Biomarker below.
Biomarker
The combined references teach digital biomarker. They do not teach historical and effectiveness.
Jain et al. also in the business of digital biomarker teaches:
Digital therapeutics and patient’s history…
“The systems described in this document can create dynamic care plans using techniques highly suited to the complex needs of cancer patients and survivors. The system provides tools and templates that can be used to create an initial care plan as a combination of various different digital therapeutics programs. From this initial state, the systems dynamically and automatically vary the nature of the care plan according to analysis of the patient's status and history. The system is able to leverage input from a variety of data sources, reflecting the patient's medical history, historical behavior, current physical state, environment, mood, symptoms, medication, and more. This allows the system to provide information, experiences, interventions, and other content that is contextually relevant and adapted to each individual user's needs.” [0014]
Digital biomarkers with activities and lifestyle and risks…
“…In addition, the server system 110 can identify digital biomarkers and use them as indicators for selecting certain digital therapeutics. Certain combinations of data about a patient's activities and lifestyle can indicate health status and health risks of a user, just as the user's blood chemistry, genetic profile, and other observable physical traits may indicate health status and health risks. Similarly, data that the server system 110 collects about a user's activities and preferences, in combination with information about physical traits, may serve as digital biomarkers that provide more accurate predictive information than the physical traits alone.” [0084]
System may be used cognitive behavioral therapy techniques…
“While the techniques discussed herein are well-suited to serving cancer patients and cancer survivors, the same techniques can also be applied to provide digital therapeutics and improve wellness in other people also. For example, people who have a chronic physical condition, such as arthritis, diabetes, hepatitis, heart disease, COPD, etc., can also benefit from the application of various digital therapeutics programs and the analysis and adjustment in programs that the system provides. Similarly, the system may be used to treat and support users with psychological conditions such as depression, anxiety disorder, attention-deficit/hyperactivity disorder, bipolar disorder, etc. To assists these users, and any of the other types of users, the system may use cognitive behavioral therapy techniques to assist the users in adjusting behaviors, mood, etc…” [0122]
Groups or population-level data can be used (selected) to determine combinations of programs and interventions appropriate (treatment effectiveness, therefore, based on prior and new interventions)…
“As discussed above, the states of different programs can be dynamically adjusted, based on current information about a user, historical information about the user, and based on the states of other programs. In addition, the types of interconnections between programs, e.g., the rules that define transitions between program states can also dynamically updated based on various factors. For example, the system can use information about the progress and symptoms of users over time can be used to identify conditions or triggers that should cause state transitions. Groups of users that have certain commonalities can be identified and their progress assessed to determine these conditions and triggers, and which actions to perform, e.g., which programs to activate or deactivate, and which levels are most effective. In addition to or instead of using data about users of the system, population-level data can be used in a similar manner to determine which combinations of programs and interventions are appropriate for different users. The population-level data may represent information about a population of a city, county, state or province, country, continent, or the world. Combining information from the data sets of users of the system with population-level data can provide increased accuracy of predictions, better enabling the system to identify predicted interactions and interventions that will address the patient's current or expected needs.” [0123] Inherent with population and effectiveness of programs/interventions are comparing to historical and new biomarkers.
Example of risks (digital biomarkers) for individual and groups of people…
“….A person's lifestyle, exposure to environmental factors, genetic profile, family medical history, and many other factors result in unique risk levels for individual patients. Because the server system 110 collects and stores information for these factors, the server system 110 can calculate the individualized risks based on the data set compiled for the user. To aid in generating these risk levels, the server system 110 may store and access clinical data sets representing outcomes and statistics representing many different groups of people. From clinical data and statistical analysis of the data sets, the server system 110 can determine a baseline risk level as well as a large set of factors that increase and decrease risk. The server system 110 identifies which of the many factors are applicable given a user's current profile and historical data and adjusts the baseline risk accordingly. In this manner, risk levels can be generated for many different conditions, e.g., pain, depression, recurrence of cancer, reduced sensory ability, etc.” [0090]
It would have been obvious to one of ordinary skill in the art before the effective filing date to include in the method and system of the combined references the ability to use and determine historical data and effectiveness of treatment hidden objects and different number of elements as taught by Jan et al. since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Further motivation is provided by Jan et al. who teaches the benefits of using historical data and determining the effectiveness of interventions for patients based on population/group data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following prior art teaches at least cognitive analysis:
CN-110022768-B; CN-111557033-A; WO-2016145372-A1; AU-2015264260-A1; WO-2018027080-A1; JP-2005137629-A; JP-2011083403-A; JP-2015180933-A; JP-2016071897-A; WO-2016069611-A1; WO-2019161050-A1; WO-2018132483-A1; US-20190216392-A1; US-20170365101-A1; US-20200114115-A1; US-20200126645-A1; US-20200174557-A1; US-20110207099-A1; US-20120191425-A1; US-20150112899-A1; US-20200294652-A1; US-20220310247-A1; US-20120258436-A1; US-20110065077-A1; US-20130090562-A1; US-20130095459-A1; US-20220157466-A1; US-20250079019-A1; US-20210085180-A1; US-20090024050-A1; US-9308446-B1; US-9302179-B1; US-12205725-B2; US-11122998-B2; WO-2018039610-A1
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