DETAILED ACTION
This action is in response to the Applicant Remarks received on August 22, 2025. Claims 1-11 and 23-24 are pending with claims 12-22 canceled, claims 1-5, 7-8, and 10-11 currently amended, and claims 23-24 are newly presented.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 23-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. As summarized in the 2019 Revised Patent Subject Matter Eligibility Guidance, examiners must perform a Two-Part Analysis for Judicial Exceptions.
Step 1
In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture, or composition of matter. The instant invention encompasses a system (i.e., machine) in claims 1-11 and 23-24 for a profile oriented cognitive improvement system. All claims are directed to one of the four statutory categories and meet the requirements of Step 1.
Step 2A, Prong 1
The claimed invention is directed to an abstract idea without significantly more. The instant invention is broadly directed to “a profile oriented cognitive improvement system and method.” (Specification, page 1, para 0002).
Claim 1 (Currently Amended) recites the following (with emphasis added):
A profile oriented cognitive improvement system comprising:
a processor;
an output device arranged to provide a user with a visual or audio output responsive to said processor;
an input device arranged to receive an input from the user; and
a memory, wherein the memory has stored thereon:
data regarding a plurality of diagnostic exercises;
data regarding a plurality of models; and
data regarding a plurality of improvement exercises,
wherein each of said plurality of diagnostic exercises has associated therewith, for each of a plurality of error types, respective parameter limits indicative of said respective error type,
wherein each of said plurality of models is associated with a respective one of a plurality of field profiles,
wherein each of said plurality of field profiles is associated with a respective one of said plurality of error types,
wherein each of a plurality of sets of said plurality of improvement exercises is associated with a respective one of said plurality of field profiles,
wherein said processor is arranged to:
control said output device to output the plurality of diagnostic exercises in a first sequence,
wherein each of the plurality of diagnostic exercises prompts the user to input a response at said input device;
for each of said output diagnostic exercises, receive a respective input at said input device;
for each of said received inputs, determine whether said respective received input is within said parameter limits of said respective diagnostic exercise associated with at least one of said plurality of error types;
for each of said received inputs, identify said at least one of said plurality of error types, wherein said identification is responsive to said determination that said respective received input is within said parameter limits associated with said at least one of said plurality of error types;
for each of said received inputs, store an indication of said identified at least one of said plurality of error types on said memory, wherein said storing is responsive to said identification;
apply a first function to said indications of said identified error types for said received inputs;
compare an output of said applied first function to each of said plurality of models stored on said memory;
responsive to an outcome of said comparison, identify which of said plurality of field profiles describes the user; and
control said output device to output, in a second sequence, a respective portion of said respective set of said plurality of improvement exercises associated with said identified field profile,
wherein each of said plurality of improvement exercises prompts the user to input a response at said input device,
wherein said plurality of error types comprises input error type, output error type and processing error type, and
wherein each of said plurality of error types is determined based at least in part on a speed at which said inputs were received for said output diagnostic exercises.
Claim 1 encompasses the abstract idea, which is also encompassed by the dependent claims 2-11 and 23-24.
Claims 1-11 and 23-24 recite the steps for monitoring users for the purpose of cognitive improvement. The system and method are directed to mental processes and certain methods of organizing human activity. A human – using pen and paper – is capable of monitoring a user, recording data resulting from the analysis, and displaying that data. These limitations, when given their broadest reasonable interpretation, recite collecting, analyzing, and sending data pertaining to improving a user’s cognition. Thus, the steps are directed to mental processes and certain methods of organizing human activity.
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because mere instruction to be implemented on a computer, or merely using a computer as a tool to perform the abstract idea, adding insignificant extra solution activity, and/or generally linking the use of the abstract idea to a technological environment or field is not considered integration into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the present claims include no additional elements other than the abstract idea which include a processor, input device, and output device. The conventional computers over a generic network as presented are directed to the components of a system amount to merely field of use type limitations and/or extra solution activity to implement the mental processes and certain methods of organizing human activity for documenting user cognition analysis.
Step 2B
Step 2B in the analysis requires us to determine whether the claims do significantly more than simply describe that abstract method. Mayo, 132 S. Ct. at 1297. We must examine the limitations of the claims to determine whether the claims contain an "inventive concept" to "transform" the claimed abstract idea into patent-eligible subject matter. Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1294, 1298). The transformation of an abstract idea into patent-eligible subject matter "requires ‘more than simply stat[ing] the [abstract idea] while adding the words ‘apply it.’’" Id. (quoting Mayo, 132 S. Ct. at 1294) (alterations in original). "A claim that recites an abstract idea must include ‘additional features’ to ensure ‘that the [claim] is more than a drafting effort designed to monopolize the [abstract idea].’" Id. (quoting Mayo, 132 S. Ct. at 1297) (alterations in original). Those "additional features" must be more than "well-understood, routine, conventional activity." Mayo, 132 S. Ct. at 1298.
The present claims do not include the additional elements that are sufficient to amount to significantly more than the judicial exception. Any potentially technical aspects of the claims are well-known, generic computational components performing conventional functions (e.g., “Processor 30 and memory 40 are illustrated as being co-located within a user work- station, computer or mobile device such as a notebook, notepad or smartphone…” (Specification, [0024])). The present claims have been analyzed both individually and in combination and, the instant claims do not provide any improvement of the functioning of the computer or improvement to computer technology or any other technical field. There do not appear to be any meaningful limitations other than those that are well-understood, routine, and conventional in the field. Thus, the present claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
The claims are generally linked to implement an abstract idea on a generic computer. When looked at individually and as a whole, the claim limitations are determined to be an abstract idea without "significantly more," and thus not patent eligible.
Claim Rejections - 35 USC § 103
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.
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.
Claims 1-11 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Alailima [WO 2018017767 A1].
Regarding claim 1 (Currently Amended), Alailima discloses:
A profile oriented cognitive improvement system comprising:
a processor (Alailima, [0052], “Example processing unit 104 can include, but is not limited to, a microchip, a processor, a microprocessor, a special purpose processor, an application specific integrated circuit, a microcontroller, a field programmable gate array, any other suitable processor, or combinations thereof.”);
an output device arranged to provide a user with a visual or audio output responsive to said processor (Alailima, [0044], “…the instructions may be provided visually (e.g., based on a rendered user interface) or via sound.”);
an input device arranged to receive an input from the user (Alailima, [0040], “As used herein, the term "stimulus," refers to a sensory event configured to evoke a specified functional response from an individual. … In any example herein, the task and/or interference includes a stimulus.”); and
a memory, wherein the memory has stored thereon:
data regarding a plurality of diagnostic exercises (Alailima, [0058], “…the measurement data 112 can include…omission errors…and/or performance threshold…”);
data regarding a plurality of models (Alailima, [0079], “The example systems, methods, and apparatus can be configured to extend accumulation of belief information, modeled using a computational model of human decision-making (such as but not limited to a drift- diffusion model (DDM) and/or a Bayesian model), and decision boundaries that reflect different strategies.”); and
data regarding a plurality of improvement exercises (Alailima, [0004], “…the apparatus also can be configured to adapt the tasks and/or interferences to enhance the individual's cognitive abilities.”);
wherein each of said plurality of diagnostic exercises has associated therewith, for each of a plurality of error types (Alailima, [0058], “…errors…”), respective parameter limits indicative of said respective error type (Alailima, [0058], “…performance threshold…”),
wherein each of said plurality of models is associated with a respective one of a plurality of field profiles (Alailima, [0074], “A non-limiting example characteristic of a type of decision boundary metric that can be computed based on the response profile is the response criterion (a time-point measure)…”),
wherein each of said plurality of field profiles is associated with a respective one of said plurality of error types (Alailima, [0005], “…determine a decision boundary metric from the response profile, the decision boundary metric comprising a quantitative measure of a tendency of the individual to provide at least one type of response of the two or more differing types of responses to the task or the interference…”),
wherein each of a plurality of sets of said plurality of improvement exercises is associated with a respective one of said plurality of field profiles (Alailima, [0005], “…response profiles…”),
wherein said processor is arranged to:
control said output device to output the plurality of diagnostic exercises in a first sequence (Alailima, [0197], “The example systems, methods, and apparatus can be configured to implement sessions that involve differing sequences and combinations of single-tasking and multi-tasking trials.”), wherein each of the plurality of diagnostic exercises prompts the user to input a response at said input device (See citation on “task” and “stimulus” above.);
for each of said output diagnostic exercises, receive a respective input at said input device (Alailima, [0005], “…the user interface being configured to measure data indicative of two or more differing types of responses to the task or to the interference.”);
for each of said received inputs, determine whether said respective received input is within said parameter limits of said respective diagnostic exercise associated with at least one of said plurality of error types (See citation on “decision boundary metric” and “performance threshold” above.);
for each of said received inputs, identify said at least one of said plurality of error types, wherein said identification is responsive to said determination that said respective received input is within said parameter limits associated with said at least one of said plurality of error types (Alailima, [0098], “The trained classifier can be applied to measures of the responses of the individual to the tasks and/or interference to classify the individual as to a population label (e.g., cognitive disorder, executive function disorder, disease or other cognitive condition).”);
for each of said received inputs store an indication of said identified at least one of said plurality of error types on said memory, wherein said storing is responsive to said identification (Alailima, [0052], “As shown in FIG. 1, the memory 102 also can be used to store data 110, such as but not limited to measurement data 112.”);
apply a first function to said indications of said identified error types for said received inputs (See citation on “the trained classifier” above.);
compare an output of said applied first function to each of said plurality of models stored on said memory (See citation on “the trained classifier…to classify the individual as to a population label” above.);
responsive to an outcome of said comparison, identify which of said plurality of field profiles describes the user (See citation on “response profile” above.); and
control said output device to output, in a second sequence, a respective portion of said respective set of said plurality of improvement exercises (See citation on “differing sequences and combinations” above.) associated with said identified field profile (See citation on “response profile” above.), wherein each of said plurality of improvement exercises prompts the user to input a response at said input device (See citation on “task” and “stimulus” above.),
wherein said plurality of error types comprises input error type, output error type, and processing error type (Alailima, [0058], “…the measurement data 112 can include reaction time, response variance, correct hits, omission errors, number of false alarms (such as but not limited to a response to a non-target), learning rate, spatial deviance, subjective ratings, and/or performance threshold, or data from an analysis, including percent accuracy, hits, and/or misses in the latest completed trial or session. Other non-limiting examples of measurement data 112 include response time, task completion time, number of tasks completed in a set amount of time, preparation time for task, accuracy of responses, accuracy of responses under set conditions (e.g., stimulus difficulty or magnitude level and association of multiple stimuli), number of responses a participant can register in a set time limit number of responses a participant can make with no time limit, number of attempts at a task needed to complete a task, movement stability, accelerometer and gyroscope data, and/or self-rating.”), and
wherein each of said plurality of error types is determined based at least in part on a speed at which said inputs were received for said output diagnostic exercises (Alailima, [0058], See citation directly above).
Regarding claim 2 (Currently Amended), the claim is rejected by virtue of its dependency on claim 1.
Regarding claim 3 (Currently Amended), Alailima discloses:
The system of claim 1, wherein said processor is further arranged to:
for each of said error types, determine the number of said received inputs having said respective error type (Alailima, [0098], “Using a cluster analysis, similarity metric of each subset and the separation between different subsets can be computed, and these similarity metrics may be applied to data indicative of an individual's responses to a task and/or interference to classify that individual to a subset.”);
for each of said error types, compare said determined number to a respective error threshold value (Alailima, [0094], “In a non-limiting example, a metric can be derived characterizing a degree of impulsiveness of the individual's response strategy based on the area of this decision boundary compared with the area of the "ideal" decision boundary (the response deadline times the full width of the belief axis).”); and
for each of said error types, determine whether the user exhibits said respective error type, wherein said determination is responsive to an output of said comparison to said respective error threshold value, wherein said applied first function comprises said determination of said error type exhibition of the user (See citation on “the trained classifier” above.), and
wherein each of said plurality of models comprises a respective combination of said error types (Alailima, In Figs 6-8D, the measurement data (wherein the error types are included) can be seen to create a continuous spectrum of possibilities relative to the criterion.).
Regarding claim 4 (Currently Amended), Alailima discloses:
The system of claim 1, wherein said processor is further arranged to:
responsive to said determinations whether said respective received inputs are within said parameter limits, determine an accuracy value of a percentage of correct inputs associated with said output diagnostic exercises (Alailima, [0215], “…based on the response data from the individual's interaction with the task and/or interference, the processing unit can be configured to compute at least one response profile representative of the performance of the individual and determines a decision boundary metric (such as but not limited to the response criterion) from the response profile.”); and
responsive to said determined accuracy value, determine a level of difficulty of said portion of said output improvement exercises (Alailima, [0085], “…comparison of the drift rates can provide a relative measure of task difficulty.”).
Regarding claim 5 (Currently Amended), Alailima discloses:
The system of claim 4, wherein said data regarding each of said plurality of improvement exercises comprises respective parameter limits indicative of said respective error type (Alailima, [0215], “…based on the response data from the individual's interaction with the task and/or interference, the processing unit can be configured to compute at least one response profile representative of the performance of the individual and determines a decision boundary metric (such as but not limited to the response criterion) from the response profile.”), and
wherein said processor is further arranged to:
for each of said output improvement exercises, receive a respective input at said input device (Alailima, [0059], “…the user response to tasks, such as but not limited to targeting and/or navigation and/or facial expression recognition or object recognition task(s), can be recorded using an input device of the cognitive platform.”);
for each of said received inputs, determine whether said respective received input is within said respective parameter limits associated with any of said plurality of error types (Alailima, [0052], “As shown in FIG. 1, the memory 102 also can be used to store data 110, such as but not limited to measurement data 112.” and see citation in claim 1 regarding “performance threshold”);
responsive to said determinations whether said respective received inputs are within said parameter limits of said plurality of error types, determine said accuracy value of a percentage of correct inputs associated with said output improvement exercises (Alailima, [0215], “…based on the response data from the individual's interaction with the task and/or interference, the processing unit can be configured to compute at least one response profile representative of the performance of the individual and determines a decision boundary metric (such as but not limited to the response criterion) from the response profile.”);
compare an outcome of said accuracy value of said percentage of correct inputs associated with said output improvement exercises with said outcome of said accuracy value of said percentage of correct inputs associated with said output diagnostic exercises (Alailima, [0198], “The performance can be further analyzed to compare the effects of two different types of interference (e.g. distraction or interruptor) on the performances of the various tasks. Some comparisons can include performance without interference…”); and
adjust a difficulty level of said output improvement exercises responsive to an outcome of said accuracy value outcome comparison (Alailima, [0198], “The cost of each type of interference (e.g. distraction cost and interruptor/multi-tasking cost) on the performance level of a task is analyzed and reported to the individual.” and Alailima, [0063], “For example, the computing device can be configured to cause the platform product or cognitive platform to provide smaller or larger reaction time window for a user to provide a response to the tasks as an example way of adjusting the difficulty level.”).
Regarding claim 6 (Original), Alailima discloses:
The system of claim 4, wherein, responsive to said determined accuracy value of said percentage of correct input associated with said output diagnostic exercises, said processor is further arranged to control a pace of said output improvement exercises (Alailima, [0137], “…the processing unit can be configured to control the user interface to modify a time-varying characteristics of an aspect of the task or the interference rendered to the user interface.”).
Regarding claim 7 (Currently Amended), Alailima discloses:
The system of claim 1, wherein said processor is further arranged to:
for each of said output diagnostic exercises, determine the amount of time which passed from said respective output to said respective received input (Alailima, [0058], “…measurement data 112 include response time, task completion time, number of tasks completed in a set amount of time, preparation time for task…”);
determine a time function to said determined amounts of time (See citation regarding “measurement data” directly above.); and
control a pace of said output improvement exercises responsive to said determined time function (Alailima, [0137], “…the processing unit can be configured to control the user interface to modify a time-varying characteristics of an aspect of the task or the interference rendered to the user interface.”).
Regarding claim 8 (Currently Amended), the claim recites similar limitations to claims 1 and 5 with the minute modification of swapping the term “diagnostic exercises” with the term “improvement exercises” (See citation regarding Alailima’s apparatus “configured to adapt the tasks”.); therefore, see claims 1 and 5 for citation on rejection of claim 8.
Regarding claim 9 (Original), Alailima discloses:
The system of claim 8, wherein said processor is further arranged to output an outcome of said comparison of said identified field profiles (Alailima, [0005], “…generate a classifier output indicative of the cognitive response capabilities of the individual.”).
Regarding claim 10 (Currently Amended), Alailima discloses:
The system of claim 1, wherein said plurality of diagnostic exercises comprises a plurality of groups of diagnostic exercises, each of said plurality of groups associated with a respective one of a plurality of cognitive fields, wherein said field profile is identified separately for each of the plurality of cognitive fields, wherein said plurality of sets of improvement exercises comprises a plurality of groups of sets of improvement exercises, each of said plurality of groups associated with a respective one of the plurality of cognitive fields, and wherein said output of said improvement exercises is performed for each of the plurality of cognitive fields (Alailima, [0245], “…various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.”).
Regarding claim 11 (Currently Amended), Alailima discloses:
The system of claim 10, wherein said processor is further arranged to identify, responsive to said identified field profile of each of the plurality of cognitive fields, a common profile which describes the user, wherein said identified common profile is responsive to a common profile function of said identified field profiles of the plurality of cognitive fields (Alailima, [0005], “…generate a classifier output indicative of the cognitive response capabilities of the individual.”).
Regarding claims 12-20, the claims have been canceled by the Applicant after the First Action on the Merits and before the current Action.
Regarding claims 21-22, the Applicant has canceled the claims prior to initial examination.
Regarding claim 23 (New), the claim is rejected by virtue of its dependency on claim 2.
Regarding claim 24 (New), Alailima discloses similar features to claim 1 with the citations provided from the prior art covering the additional features disclosed in claim 24. See the citations within claim 1 for further details on rejection.
Response to Arguments
Applicant’s arguments, see page 10, filed August 22, 2025, with respect to the claim objections of claims 1 and 12, acknowledged in the first paragraph under the section titled, “35 U.S.C. 112”, have been fully considered and are persuasive. The claim objections of claims 1 and 12 have been withdrawn.
Applicant’s arguments, see pages 10-11, filed August 22, 2025, with respect to the rejections under 35 U.S.C. 112(b) of claims 1-20, have been fully considered and are persuasive. The claim rejections of claims 1-20 have been withdrawn.
Applicant's arguments, see pages 11-12, filed August 22, 2025, with respect to the rejection under 35 U.S.C. 101 of claims 1-20, have been fully considered, but they are not persuasive.
On page 11 of the remarks, Applicant argues the following:
As described, each improvement exercise prompts the user to input a response at the input device. These limitations are not one of the certain methods of organizing human activity, which include: fundamental economic principles or practices; commercial or legal interactions; and managing personal behavior or relationships or interactions between people. As cited in the preamble of claim 1, the system is designed to treat a user and provide cognitive improvement.
The Examiner respectfully submits that the claims, as drafted, under their broadest reasonable interpretation, encompass providing a set of instructions on how to orchestrate an exam to a person, which can be accomplished in the mind, but for generic computer components (a processor, an output device, an input device, and a memory – see claim 1).
Due to the claim limitations being directed to a set of instructions on how to orchestrate an exam to a person, the claims are directed to the abstract idea of Certain Methods of Organizing Human Activity as they are managing personal behavior or relationships or interactions between people. Specifically, the claims walkthrough how a user may provide exercises to another user, receive answers from the another user regarding the exercises, then cross reference the answers received from the another user with pre-logged data to identify error types and field profile characteristics.
Due to the human mind being capable of performing the claim limitations, the claims are directed to the abstract idea of Mental Processes. Specifically, a user could utilize a pen (i.e., output device), paper (i.e., input device), and their mind (i.e., processor and memory) to replace the generic computer components.
For example, a user could utilize: a pen to provide a user with an output in response to data from their mind, a paper to receive an input from another user and record various models/profiles, and their mind to store data regarding various exercises and their corresponding error types and parameter limits. To further the example, the human mind (either directly or with the assistance of pen and paper) is arranged to:
control said pen to write (i.e., output) exercises in a specific sequence, wherein each of the exercises prompts another user to provide input (e.g., a teacher providing a paper exam to a student for the student to complete the exam);
for each output exercise, receive a respective input on paper;
for each received input from the another user, determine whether the respective received input is within certain parameter limits associated with other exercises and/or error types;
for each of the received inputs, identify an error type, wherein the identification is responsive to the determination that the respective received input is within the parameter limits associated with the at least one of the pluralities of error types;
for each of the received inputs, store (either through memory in the human mind or paper) an indication of the identified error types for the received inputs;
apply a first function to the indications of the identified error types for the received inputs;
compare an output of the applied first function to each of the plurality of models stored in memory;
responsive to an outcome of the comparison, identify which of the plurality of field profiles describes the another user; and
control the pen to output, in a second sequence, a respective portion of the respective set of the plurality of exercises associated with the identified field profile, wherein each of the plurality of exercises prompts the another user to input a response on paper, wherein the plurality of error types comprises input error type, output error type, and processing error type, and wherein each of the plurality of error types is determined based at least in part on a speed at which the inputs were received for the output exercises (which can be accomplished in the human mind via counting).
On page 11 of the remarks, Applicant argues, “…outputting improvement exercises prompting the user to input a response at the input device is not mere display of information.”
The Examiner respectfully agrees that the fully disclosed process is not mere display of information but rather a compilation of abstract ideas as described above.
Regarding amended claim 1, on page 11 of the remarks, Applicant argues, “Furthermore, amended claim 1 cites that each of the error types is determined based on a speed at which inputs are received, which is not a mental process.”
The Examiner respectfully submits that, in context, the referenced speed being calculated is the speed at which an answer is received for a given exercise. As one of ordinary skill would recognize, the human mind is capable of counting to measure time; therefore, the recitation of speed is a mental process.
Regarding amended claim 2, on page 11 of the remarks, Applicant argues the following:
Claim 2 is further deemed integrated into a practical application for citing that the error types are determined by comparing the time from when the first portion of each exercise is output to when the first portion user input is received and the time from when the second portion of the exercise to when a second portion user input is received. This is not a mental process, and is thus integrated into a practical application.
The Examiner respectfully submits that comparing data is a mental process. As stated by MPEP 2106.04(a)(2)(III)(A), “Examples of claims that recite mental processes include: … a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011)”.
Regarding amended claim 5, on page 11 of the remarks, Applicant argues the following:
Amended claim 5 is further deemed integrated into a practical application for citing that a difficulty level of the improvement exercises is adjusted based on accuracy values of the responses to the diagnostic exercises and improvement exercises.
The Examiner respectfully submits that adjusting difficulty level of exercises based on the performance level of a user is a common mental process amongst educators. For example, an educator commonly provides exercises to students to identify the student’s progress in learning material to determine whether:
(1) the students fail to understand foundational material (which would result in the educator reviewing previous material – i.e., difficulty decreasing),
(2) the students understand foundational material but fail to understand the current material (which would result in the educator reviewing current material – i.e., difficulty remaining constant), and
(3) the students understand all material presented (which would result in the educator moving onto elevated material – i.e., difficulty increasing).
Regarding amended claim 8, on page 12 of the remarks, Applicant argues the following:
Amended claim 8 is further deemed integrated into a practical application for citing that the user inputs from the improvement exercises are utilized for determining a change in the field profile of the user, and updating the improvement exercises accordingly.
The Examiner respectfully submits that adjusting content based on user inputs is not a practical application as stated in the response to the argument of amended claim 5 above. Although the claims recited different outcomes, both claims recited adjusting content based on user inputs which can be accomplished in the human mind with or without a physical aid.
Regarding newly presented claim 23, on page 12 of the remarks, Applicant argues the following:
New claim 23 is further deemed integrated into a practical application for citing that the second portion of each diagnostic exercise is output responsive to the first portion input.
The Examiner respectfully submits that the claim invokes computers or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)). The process of waiting to output information until receiving input is an existing process; therefore, newly presented claim 23 does not integrate the claimed invention into a practical application.
Regarding newly presented claim 24, on page 12 of the remarks, Applicant argues the following:
New claim 24 is further deemed integrated into a practical application for citing that steps a - h are repeated, and the pace and difficulty of the improvement exercises are adjusted based on analysis of a change in the field profile from the first iteration of improvement exercises.
The Examiner respectfully submits that this argument mirrors the arguments made above and directs the Applicant to those responses for further information.
Applicant's arguments, see pages 12-15, filed August 22, 2025, with respect to the rejection under 35 U.S.C. 102 of claims 1-20, have been fully considered, but they are not persuasive.
Regarding the Applicant’s argument of the purpose of the claimed system and method, the Applicant states:
It is noted that the present claims are addressed to a system and method which is relevant to anyone who wishes to improve their learning and thinking skills, by adapting learning processes to their personal learning profile as revealed through diagnostic assessment. This includes individuals with learning disabilities, people on the autism spectrum (high functioning), and elderly individuals.
The Examiner respectfully submits a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
Furthermore, on pages 13 and 14, the Applicant argues certain terms of Alailima do not equate to certain terms of the instant application. The Examiner respectfully submits that the Applicant misunderstood the Examiner’s mapping of the prior art. The Examiner has presented a term mapping below for the convenience of the Applicant.
Instant Application
Alailima (Prior Art)
Plurality of error types
Errors
Data regarding a plurality of exercises
Measurement Data
Parameter limits
Performance Threshold
Field Profiles
Response Profiles
The Examiner acknowledges the list of terms above is not all encompassing and intends the list to be used to assist the Applicant in understanding the Examiner’s mapping of the prior art on the instant application.
In response to Applicant's argument that, “…in amended claim 1, the steps are not based on statistical measurement of responses, but on revealing thinking patterns and errors and on educational-rehabilitative intervention aimed at changing the underlying cognitive strategies” (Remarks, Page 14), the fact that the inventor has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985).
In response to Applicant’s argument that, “Alailima does not have stored improvement exercises to be used, and just adjusts the diagnostic exercises…”, the Examiner respectfully submits that Alailima discloses storing Data 110 in Memory 102 (Alailima, Fig 1), which would include exercises of all types.
In response to Applicant’s argument that, “This adjustment [of diagnostic exercises] is based on the first decision boundary metric, which is based on a single response profile, and not based on the classified cognitive capabilities. Attempting to equate to the terms of amended claim 1, the adjustment is based on an error type, and not on the field profile”, the Examiner respectfully submits that the disclosure cited by the Applicant in Alailima continues by stating, “The processing unit is configured to analyze collected data indicative of the first response and the second response from the second session, to compute a second response profile and a second decision boundary metric representative of a second performance of the individual. The processing unit is configured to, based at least in part on the first decision boundary metric and second decision boundary metric, generate an output to the user interface…” (Alailima, [0009]). This portion of [0009] describes how the disclosure utilizes multiple response profiles which are based on classified cognitive abilities.
In response to Applicant’s argument that, “Amended claim 2 is further deemed patentable for citing that each diagnostic exercises comprises a first portion and a section portion, and the error types are based on the time differences between the first and second portions” (Remarks, Page 15), the Examiner respectfully submits the citations in the above sections where the limitations, under broadest reasonable interpretation, are represented within the prior art.
In response to Applicant’s argument that, “Claim 23, depending on amended claim 2, is further deemed patentable for citing that the second portion of the exercise is output responsive to receiving the first portion output” (Remarks, Page 15), the Examiner respectfully submits the citations in the above sections where the limitations, under broadest reasonable interpretation, are represented within the prior art.
On page 15 of the remarks, the Applicant argues that:
Claim 3 is further deemed patentable for citing that an error type of the user is dependent on the number of times the error type appears in the user inputs. If an attempt is made to equate the error types of amended claim 1 to the measurement data of Alailima and the field profile of amended claim 1 to the response profile of Alailima, this would not meet the definition of amended claim 3. Particularly, the field profile of Alailima (impulsive or conservative) is not the same as the described measurement data. In fact, nowhere does Alailima even discuss a connection between the measurement data and the field profile.
The Examiner respectfully submits the citations in the above sections where the limitations are represented within the prior art. Furthermore, the Examiner does not intend to equate error types of the instant application with measurement data of Alailima. For a list of features mapped from the prior art to the instant application, see the table above.
In response to Applicant’s argument that, “Claim 5 is further deemed patentable for citing that a difficulty level of the improvement exercises is adjusted based on the accuracy of the responses to the improvement exercises” (Remarks, Page 15), the Examiner respectfully submits the citations in the above sections where the limitations, under broadest reasonable interpretation, are represented within the prior art.
In response to Applicant’s argument that, “Claims 8 and 24 are each further deemed patentable for identifying an improvement in the field profile and adjusting the improvement exercises accordingly” (Remarks, page 15), the Examiner respectfully submits the citations in the above sections where the limitations, under broadest reasonable interpretation, are represented within the prior art.
In response to Applicant’s argument that, “Amended claim 11 is further deemed patentable for citing that a common profile is identified from a field profile of each of a plurality of cognitive fields. The examiner points to the classifier of Alailima, however nowhere does Alailima teach a profile of a profile from a plurality of cognitive fields, where each cognitive field has its own group of improvement exercises” (Remarks, Page 15), the Examiner respectfully submits the citations in the above sections where the limitations, under broadest reasonable interpretation, are represented within the prior art.
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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/Z.J.P./Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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