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 September 30, 2025, is acknowledged.
Response to Amendment
Claims 1-6 and 8-13 have been amended. Claims 15-20 have been canceled. Claims 21-26 are new. Claims 1-14 and 21-26 are pending and are provided to be examined upon their merits.
Response to Arguments
Applicant’s arguments with respect to claims 1-14 and 21-26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A response is provided below in bold where appropriate.
The amended paragraphs to the specification fixing minor issues are entered.
Applicant argues 35 USC §101 Rejection, starting pg. 12 of Remarks:
I. Claims 1-14 Are Directed to Patent-Eligible Subject Matter
On page 3 of the Office Action, claims 1-14 are rejected under 35 U.S.C. § 101 as being allegedly directed to non-statutory subject matter. Applicant respectfully requests that the rejections of claims 1-14 be withdrawn for at least the following reasons.
A. Claims 1-14 are Not Directed to a Judicial Exception
Applicant respectfully submits that claims 1-14 are patent eligible because they are not directed to a judicial exception. First, Applicant respectfully submits that claims 1- 14 do not recite an abstract idea. Second, Applicant respectfully submits that even if claims 1-14 do recite an abstract idea, a contention with which Applicant respectfully disagrees, that claims 1-14 would still be patent-eligible because they integrate any alleged judicial exception into a practical application. Therefore, Applicant respectfully requests that the rejections of claims 1-14 under 35 U.S.C. § 101 must be withdrawn.
i. Claims 1-14 Do Not Recite an Abstract Idea
Applicant respectfully submits that claims 1-14 are patent-eligible under Step 2A Prong One because the claims do not recite an abstract idea. On page 4 the Office Action alleges that the recited limitations fall within the group of mental processes. Applicant respectfully disagrees.
Applicant respectfully submits that the claims do not recite a mental process. According to MPEP 2106.04(a)(2)(III), “[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations.” Applicant respectfully submits that the complexity of the combination of “determining a task difficulty level for assessment of a patient having a neurological condition,” “generating an interactive task at the task difficulty level; determining device properties of a mobile device associated with performing the interactive task,” “generating for display on the mobile device a graphical user interface for receiving a task input from the patient attempting to complete the interactive task, the graphical user interface comprising a plurality of interactive elements associated with the interactive task, and a layout of the plurality of interactive elements being based at least in part on the device properties” “until a predetermined condition is met: determining a task outcome based on the received task input[,] generating a modified task difficultly level based on the received task input[,] and dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient,” and “based on the predetermined condition being met, determining a digital biomarker for the patient by analyzing a plurality of received task inputs and determined task outcomes,” would be impractical to be performed using pen-and-paper or as a mental process.
Applicant has amended their claims such that a mental process has been withdrawn as a reason for the claims being abstract. However, the claims remain abstract under Certain Methods of Organizing Human Activity.
For at least these reasons, Applicant respectfully submits that claim 1 fails to recite an abstract idea. Insofar as claim 8 recites elements similar in scope to those of claim 1, Applicant respectfully submits that claim 8 fails to recite an abstract idea for at least similar reasons. In addition, Applicant submits that dependent claims 2-7 and 9-14 do not recite an abstract idea as they depend from claims 1 or 8. This means that the present claims are patent eligible under Prong One of Step 2A. Applicant respectfully requests that the rejections of claims 1-14 be withdrawn.
The claims recite elements that are abstract as they are interacting with a patient, determining a task outcome of a patient, and determining a biomarker, which are steps that are abstract.
ii. Claims 1-14 Recite Additional Elements that Integrate any Alleged Judicial Exception into a Practical Application
Applicant respectfully submits that even if the claims did recite a judicial exception or an abstract idea, an assertion with which Applicant strongly disagrees, claims 1-14 would still be patent-eligible under Step 2A Prong Two because claims 1-14 recite a technical solution to a technical problem. In the discussion of whether a claim is directed to patent-ineligible subject matter under Step 2A, MPEP § 2106.04(d)(1) states that “[a] claim reciting a judicial exception is not directed to the judicial exception if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application. One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field.” (emphasis added). Moreover, as stated in MPEP § 2106.05(a) “[a]n indication that the claimed invention provides an improvement [to the functioning of a computer or to any other technology or technical field] can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art.” Claims 1-14 satisfy this standard.
Applicant is arguing they are improving computer technology. However, there is no teaching in the disclosure of new/improved computer technology.
Here, Applicant’s Specification discusses how the claimed invention is a technical solution to a technical problem. Specifically, Applicant’s Specification at paragraph [0003] and [0081] recite:
[0003] Autism spectrum disorders (ASD) include a variety of neurodevelopmental conditions, including autism and Asperger syndrome. Individuals with ASD may have trouble with social communication and interaction and/or exhibit restricted and/or repetitive patterns of behavior. Those in the mild range of ASD may function independently, while those in the moderate to severe range may require substantial support in their daily lives. Those with ASD may experience deficits in social communication, have repetitive and limited interests, and/or exhibit sensory sensitivities. A central executive function tightly related with cognitive performance of patients with ASD is spatial working memory, i.e., the ability to hold and transform spatial information on demand. However, known methods of measuring spatial working memory require special equipment and/or specially trained personnel to perform each assessment, thereby raising costs for assessment and treatment.
[0081] To evaluate visual and/or spatial working memory, existing techniques are mostly restricted to change detection tests (in which patient are asked to identified aspects of a static scene that change from one presentation to the other) to measure the precision in matching paradigms (in which patients have to reproduce a feature of a visual scene) and to evaluate the influence of distractors in memory encoding. These tests do not evaluate the dynamics of spatial working memory, i.e., how items are stored, retrieved and removed from memory as the complexity of visual scenes increase, is not well understood and there is no prior art suggesting how to measure the spatial working memory capacity when patients are required to store, delete and update items in memory.
To solve this problem, Applicant has claimed various solutions for improving subject testing and subject analysis and measurement of spatial working memory by providing a “an interactive game specifically optimized for mobile devices ... [where the] evaluation of the interactive game utilizes a novel computational approach based on the discrete slot model of spatial working memory that accurately captures the behavior of the users.” See Applicant’s Specification at ¶ [0083].
Respectfully, the above is not improving computer technology, rather using computer technology for an abstract idea. Applying computer technology to a judicial exception has been found to be abstract.
In view of the foregoing, Appellant respectfully submits that the claimed embodiments provide solutions for improving detection and measurement of spatial working memory. Claim 1, for example, recites:
determining a task difficulty level for assessment of a patient having a neurological condition;
generating an interactive task at the task difficulty level;
determining device properties of a mobile device associated with performing the interactive task;
generating for display on the mobile device a graphical user interface for receiving a task input from the patient attempting to complete the interactive task, the graphical user interface comprising a plurality of interactive elements associated with the interactive task, and a layout of the plurality of interactive elements being based at least in part on the device properties;
until a predetermined condition is met:
determining a task outcome based on the received task input:
generating a modified task difficultly level based on the received task input; and
dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient; and
based on the predetermined condition being met, determining a digital biomarker for the patient by analyzing a plurality of received task inputs and determined task outcomes.
Accordingly, Applicant respectfully submits that the claimed invention is a technical solution to a technical problem. Indeed, by “generating ... [a] graphical user interface comprising a plurality of interactive elements associated with the interactive task ... [where the] layout of the plurality of interactive elements [is] based at least in part on the device properties” and “dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient,” the analysis and measurement of spatial working memory in patients is improved.
Respectfully, the above working memory is improved is an intended result that may or may not happen and is directed to a patient. Further, the above is not a technical improvement of computer technology.
Because Applicant is claiming a technical solution to a technical problem, claims 1-14 are not abstract under Step 2A Prong Two. Accordingly, Applicant respectfully requests that the rejections of claims 1-14 under 35 U.S.C. § 101 be withdrawn.
The rejection is respectfully maintained based on the above response.
B. Claims 1-14 Amount to Significantly More than Any Alleged Judicial Exception
Even if the pending claims were directed to an abstract idea — an assertion or contention with which Applicant strongly disagrees — they still amount to significantly more than an abstract idea because the claims contain an inventive concept.
The Office Action (p. 5) alleges that the “judicial exception is not integrated into a practical application.” Applicant respectfully disagrees.
Applicant respectfully submits that claims 1-14 amount to significantly more than the alleged abstract idea because the claims contain an inventive concept of a non- conventional arrangement of elements that addresses problems in spatial working memory detection and measurement. For example, claim 1 recites, in part, “generating for display on the mobile device a graphical user interface for receiving a task input from the patient attempting to complete the interactive task, the graphical user interface comprising a plurality of interactive elements associated with the interactive task, and a layout of the plurality of interactive elements being based at least in part on the device properties” and “until a predetermined condition is met: determining a task outcome based on the received task input[,] generating a modified task difficultly level based on the received task input[,] and dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient.” Indeed, the claimed embodiments solve problems of traditional detection techniques. As a result, the claims recite a specific, technical solution to a technical problem. Thus, considered as an ordered combination, the claims recite an inventive concept. Since the claims contain an inventive concept, the claims amount to significantly more than an abstract idea.
Applicant has amended their claims to add features related to a graphical user interface (determining device properties, generating a layout based in part on device properties, and dynamically updating the graphical user interface based on device properties). However, the claimed steps are recited at a high level of generality and there is no teaching as to how the various steps are accomplished nor an indication of an improvement to computer technology in the specification.
For at least these reasons, Applicant respectfully submits that, while claims 1-14 are not directed to an abstract idea as previously discussed, they would alternatively amount to significantly more than an abstract idea. Applicant therefore respectfully requests that the rejections of claims 1-14 be withdrawn.
Based on the above response, the rejection is respectfully modified for the claim amendments but maintained.
Applicant argues 35 USC §103 Rejection, starting pg. 18 of Remarks:
ll. Claims 1, 2, 5, 8, 9, and 12 Are Patentable over Martucci, et al. and Baker, et al.
On page 8 of the Office Action, claims 1, 2, 5, 8, 9, and 12 have been rejected under 35 U.S.C. § 103(a) as allegedly being unpatentable over Martucci, et al. (US Pub. 2017/0098385, hereinafter Martucci) in view of Baker, et al. (WO 2018050763, hereinafter Baker). |n order to establish a prima facie case of obviousness under 35 U.S.C. § 103, the Examiner must provide a factual basis to support the legal conclusion of obviousness by showing that each and every element of the claim is described or suggested by the prior art or would have been obvious in view of the prior art. See /n re Fine, 837 F.2d 1071, 1073-74 (Fed. Cir. 1988); Ex Parte Wada and Murphy, Appeal 2007-3733 (B.P.A.I. 2008); see also, KSR Int'l v. Teleflex, Inc., 550 U.S. 398, 411 (2007) (claim deemed obvious to one of ordinary skill where all claim elements were disclosed in the cited prior art references). In addition, “[rjejections on obviousness grounds cannot be sustained by mere conclusory statements; instead, there must be some articulated reasoning with some rational underpinning to support the legal conclusion of obviousness.” KSR Int’, 550 U.S. at 418 (quoting /n re Kahn, 441 F.3d 977, 988 (Fed. Cir. 2006)). Applicant respectfully requests that the rejections of claims 1, 2, 5, 8, 9, and 12 be withdrawn for at least the following reasons.
To begin, Applicant respectfully submits that the combination of cited references fails to show or suggest each and every element of amended claim 1. Claim 1, as amended, recites:
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;
generating an interactive task at the task difficulty level;
determining device properties of a mobile device associated with performing the interactive task,
generating for display on the mobile device a graphical user interface for receiving a task input from the patient attempting to complete the interactive task, the graphical user interface comprising a plurality of interactive elements associated with the interactive task, and a layout of the plurality of interactive elements being based at least in part on the device properties,
until a predetermined condition is met:
determining a task outcome based on the received task input:
generating a modified task difficultly level based on the received task input; and
dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient, and based on the predetermined condition being met, determining a digital biomarker for the patient by analyzing a plurality of received task inputs and determined task outcomes.
(Emphasis added). Applicant notes that the above-emphasized elements of claim 1 are newly added. Support for the newly added elements can be found in at least paragraphs [0102], [0104], [0111], [0133], and [0134] of the published Specification. Applicant respectfully submits that the combination of cited references fails to show or suggest at least the above emphasized elements of claim 1.
Martucci relates to “the implementation of personalized cognitive training.” Martucci at Abstract. Martucci discusses “pictorial representations of a cognitive training game.” Martucci at ¶ [0043]. Baker relates to “accessing a cognition and movement disease or disorder in a subject.” Baker at Abstract. However, Applicant respectfully submits that neither Martucci nor Baker, either alone or in combination, show or suggest at least “determining device properties of a mobile device associated with performing the interactive task,” “generating... [a] graphical user interface comprising a plurality of interactive elements associated with the interactive task ... [where] a layout of the plurality of interactive elements being based at least in part on the device properties,” or “dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient,” as recited in amended claim 1.
Applicant has amended their claims requiring further prior art.
For at least these reasons, Applicant respectfully submits that the combination of cited references fails to show or suggest each and every element of amended claim 1. Accordingly, Applicant respectfully requests that the rejection of claim 1 be withdrawn. In addition, insofar as claim 8 recites elements similar in scope to those of claim 1, Applicant respectfully requests that the rejection of claim 8 be withdrawn for at least similar reasons, to the extent applicable. Moreover, Applicant respectfully requests that the rejections of claims 2, 5, 9, and 12 be withdrawn for at least the reason that claims 2, 5, 9, and 12 depend from claims 1 or 8, respectively.
The rejection is respectfully maintained but modified for the claim amendments.
lll. Claims 3, 7, 10 and 14 Are Patentable over Martucci, et al., Baker, et al. and Martucci1
On page 16 of the Office Action, claims 3, 7, 10, and 14 have been rejected under 35 U.S.C. § 103(a) as allegedly being unpatentable over Martucci, et al. in view of Baker, and further in view of Martucci, et al. (US Pub. 2016/0262680, hereinafter Martucci7). Applicant notes that claims 3, 7, 10, and 14 depend from claims 1 or 8, respectively. Applicant further submits that the addition of Martucci fails to overcome the deficiencies of Martucci and Baker with respect to the rejections of claims 1 and 8, as discussed above. Accordingly, Applicant respectfully requests that the rejections of claims 3, 7, 10, and 14 be withdrawn for at least the reason that claims 3, 7, 10, and 14 depend from claims 1 or 8, respectively.
The rejection is respectfully maintained but modified for the claim amendments.
IV. Claims 4 and 11 Are Patentable over Martucci, et al., Baker, et al. and Do, et al.
On page 18 of the Office Action, claims 4 and 11 have been rejected under 35 U.S.C. § 103(a) as allegedly being unpatentable over Martucci, et al. in view of Baker, and further in view of Do, et a/. (US Pub. 2017/0333796, hereinafter Do). Applicant notes that claims 4 and 11 depend from claims 1 or 8, respectively. Applicant further submits that the addition of Do fails to overcome the deficiencies of Martucci and Baker with respect to the rejections of claims 1 and 8, as discussed above. Accordingly, Applicant respectfully requests that the rejections of claims 4 and 11 be withdrawn for at least the reason that claims 4 and 11 depend from claims 1 or 8, respectively.
The rejection is respectfully maintained but modified for the claim amendments.
V. Claims 6 and 13 Are Patentable over Martucci, et al., Baker, et al. and Jain, et al.
On page 20 of the Office Action, claims 6 and 13 have been rejected under 35 U.S.C. § 103(a) as allegedly being unpatentable over Martucci, et al. in view of Baker, and further in view of Jain, et al. (US Pub. 2019/0243944, hereinafter Jain). Applicant notes that claims 6 and 13 depend from claims 1 or 8, respectively. Applicant further submits that the addition of Jain fails to overcome the deficiencies of Martucci and Baker with respect to the rejections of claims 1 and 8, as discussed above. Accordingly, Applicant respectfully requests that the rejections of claims 6 and 13 be withdrawn for at least the reason that claims 6 and 13 depend from claims 1 or 8, respectively.
The rejection is respectfully maintained but modified for the claim amendments.
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 and 21-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-14 and 21-26 are directed to a method, product, or system, 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 and system claim 21.
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;
determining device properties of a mobile device associated with performing the interactive task;
generating for display on the mobile device a graphical user interface for receiving a task input from the patient attempting to complete the interactive task, the graphical user interface comprising a plurality of interactive elements associated with the interactive task, and a layout of the plurality of interactive elements being based at least in part on the device properties;
until a predetermined condition is met:
determining a task outcome based on the received task input;
generating a modified task difficultly level based on the received task input; and
dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient; and
based on the predetermined condition being met, determining a digital biomarker for the patient by analyzing a 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. Claims 8 and 21 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract)
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); computing device, processor, memory, mobile device (Claim 21). 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. The steps of “determining device properties of a mobile device…;” “generating for display on the mobile device a graphical user interface…and a layout of the plurality of interactive elements being based at least in part on the device properties;” and “dynamically updating the graphical user interface based at least in part on the device properties” are claimed and taught at a level (see also para’s [0133] and p0134] of the specification). There is no teaching of how determining properties of a mobile device, generating a layout based on device properties, or updating the graphical user interface based on device properties are implemented and the steps are recited at a high level of generality. 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, 8, and 21 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, 8, and 21 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Dependent claims 2-7, 9-14, and 22-26 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 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, 12, and 25 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, 9-14, and 22-26 are directed to an abstract idea. Thus, the claims 1-14 and 21-26 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, 12, 21, 22 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Pub. No. US 2017/0098385 to Martucci et al. in view of Pub. No. JP 2011083403 to Murakami et al. and in view of WO 2018/050763 to Baker et al.
Regarding claims 1, 8, and 21
(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]
determining device properties of a mobile device associated with performing the interactive task;
Mobile devices…
“401 illustrates a suitable mobile computing environment, for example, a tablet personal computer or a mobile telephone or smart phone on which certain embodiments in the present disclosure can be deployed. In a basic configuration, mobile computing device is a handheld computer having both input elements and output elements. Input elements may include touch screen display 409 and input buttons (not shown) that allow the user to enter information into the mobile computing device. The screen display 409 provides visual prompting, stimuli and feedback to the user during execution of the computer program. The output elements comprise the inbuilt speaker (not shown) that in some embodiments may provide auditory prompting, stimuli and feedback to the user during execution of the computer program. In alternative embodiments, the mobile computing device may incorporate additional input or output elements such as a physical keypad to enter alphanumeric information or a headphone jack (not shown). Additionally, the mobile computing device may incorporate a vibration module (not shown) which causes mobile computing device to vibrate to provide stimulus or feedback to a user during execution of the computer program.” [0042]
See Device Properties below.
generating for display on the mobile device a graphical user interface for receiving a task input from the patient attempting to complete the interactive task, the graphical user interface comprising a plurality of interactive elements associated with the interactive task, and a layout of the plurality of interactive elements being based at least in part on the device properties;
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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See Device Properties below.
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]
until a predetermined condition is met:
Example of predetermined duration (condition)…
“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]
See Device Properties below.
generating a modified task difficultly level based on the received task input; and
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]
dynamically updating the graphical user interface based at least in part on the device properties and one or more characteristics of the patient; 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]
See Device Properties below.
based on the predetermined condition being met, determining a digital biomarker for the patient by analyzing a 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]
Device Properties
Martucci et al. teaches cognition and skills. They also teach mobile device. They do not teach device properties.
Murakami et al. also in the business of testing cognition and skills teaches:
Example of recorded (determining) screen display size (properties) of device…
“As for the size and display position of each object, the screen display size of the display device is recorded (about 14.3 cm × 19 cm if the LCD display size is 10.4 inches). After recording the size as apple (1.6 cm x 1.6 cm), banana (length 2.1 cm, thickness 0.5 cm), etc., and adjusting the objects so that they do not overlap, An assignment display screen can be generated and displayed.” (pg. 15, para. 6; marked as “A”)
Example of display screen and apples counted (interactive elements)…
“In the first task display screen 101, the subject content is that the number of displayed “apples” is counted and temporarily stored until the task on the next page is processed. Confirm the position of the “apple”, assign a number to each “apple”, count the number of “apples”, verbalize the number, and count the result (first count result). Temporarily store it in its working memory.” (pg. 10, para. 6; marked as “B”)
Adjusting objects so they don’t overlap based on screen display size of the device (layout based on device properties)…
“As for the size and display position of each object, the screen display size of the display device is recorded (about 14.3 cm × 19 cm if the LCD display size is 10.4 inches). After recording the size as apple (1.6 cm x 1.6 cm), banana (length 2.1 cm, thickness 0.5 cm), etc., and adjusting the objects so that they do not overlap, An assignment display screen can be generated and displayed.” (pg. 15, para. 6; marked as “A”)
Example of disturbing screen (dynamically updating interface) based on user’s working memory…
“In addition, after executing each task presentation process by each task presentation processing means and before executing the next task presentation process, on the screen of the display means so as to disturb the subject's memory, By providing a disturbing screen display means for displaying the disturbing screen, or by changing the display time of the disturbing screen (providing a delay time) in the disturbing screen display means, the storage capacity of the subject's working memory can be used up.・ Discovery or recognition of cognitive dysfunction by erasing information temporarily stored in the subject's working memory due to disturbing stimuli, overwriting with disturbing information, or requiring longer memory retention time It becomes possible to evaluate the function more effectively.” (pg. 4, para. 3; marked as “C”)
Where the device includes timing control (therefore, timing as another device property)
“The display unit 30 performs display memory access control, display data transfer to the display device 32, and timing control, and is generally composed of a display control LSI, a display memory, or the like. It may be realized by a chip set, a RAM 24 and a program.”
(pg. 5, para. 8; marked as “D”)
The screen is a blank screen, therefore change in layout…
“As a method for adjusting the time, as shown in FIG. 5, “disturbing screen type A” 70A is inserted for a predetermined time. The delay time is set to 1 second in the effect experiment described later, but can be set to a different value. This time adjustment process is executed by the process of step S10 in FIG. In addition to the example shown in FIG. 5, the disturbing screen type A may be a screen that displays nothing (blank screen).” (pg. 11, para. last; marked as “E”)
Presentation constructed (layout) in a user interface format that can be understood by the user (characteristics of patient)…
“The assignment generation unit 12 generates an assignment test after acquiring the test pattern information (see FIG. 10) corresponding to the selected assignment from the test pattern information 23 of the storage unit, and the assignment presentation unit 13 Is constructed in a GUI format (graphical user interface format) as a task composed of text and figures that can be intuitively understood by the user, and displayed on the display device 32 via the display unit 30 (see FIGS. 4 to 9).” (pg. 7, para. 2; marked as “F”)
Required to count (predetermined condition) apples and bananas…
“Specifically, since the tasks are counted in the order of apple .fwdarw. banana .fwdarw. apple for each screen, 6 apples and 3 bananas are displayed on the task display screen 601. First, it is required to count the number of apples (6). In the next task display screen 602, four apples and five bananas are displayed. The number of bananas (5) is counted, and the number of apples (6) on the first screen 601 of the task is determined by the subject. It is required to read from the working memory and add. As a result, 6 + 5 = 11 are calculated and required to be temporarily stored in the working memory of the subject.”
(pp. 13-14, para. bottom; marked as “G”)
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 Martucci et al. the ability to determine device properties as taught by Murakami 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 Murakami et al. who teaches the need to determine screen size for presenting objects (elements) to avoid problems such as overlap and Martucci benefits as they teach using various devices. It would be obvious not to present images that overlap on a screen or have other problems due to various device screen sizes, certainly when testing cognitive function.
Digital Biomarker
The combined references teach 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, 9, and 22
(claim 2) The computer-implemented method of claim 1, wherein the predetermined condition comprises the patient completing the interactive 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, 12, and 25
(claim 5) The computer-implemented method of claim 1, wherein determining the digital biomarker is performed by a server device configured to receive the task inputs 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, 14, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (6) above in further view of Pub. No. US 2016/0262680 to Martucci et al. (hereinafter referred to as Martucci1)
Regarding claims 3, 10, and 23
(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 interactive 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, 11, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (6) above in further view of Pub. No. US 2017/0333796 to Do et al.
Regarding claims 4, 11, and 24
(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 the plurality 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, 13, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over the combined references in section (6) above in further view of Pub. No. US 2019/0243944 to Jain et al.
Regarding claims 6, 13, and 26
(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 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KENNETH BARTLEY/Primary Examiner, Art Unit 3684
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