DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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 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.
3. The following office action is a Final Office Action in response to the communications received on 04/28/2026.
Claims 1, 9 and 17 have been amended; claims 8, 16 and 24 have been canceled. Therefore, claims 1-7, 9-15 and 17-23 are currently pending in this application. Response to Amendment
4. The amendment to claim 9 is sufficient to overcome the rejection set forth in the previous office action under section §112(a). Accordingly, the Office withdraws the above rejection.
Claim Rejections - 35 USC § 101
5. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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 1-7, 9-15 and 17-23 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
Step 2A [Wingdings font/0xE0] Prong One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1, 9 and 17 as representative claims, the following claimed limitations recite an abstract idea:
[collect] MRI image of a user;
analyze the image to generate analysis result;
determine a first target based on the analysis result;
prompt the first target object for recognizing by a user;
receive a feedback response from the user;
determine a correctness of the feedback response by comparing the feedback response with an answer; and
adjust a level or a type of a second target object based on a guideline associated with the correctness of the feedback response, wherein the second target object is provided for recognizing by the user after the first target object is provided.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to certain methods of organizing human activity, and/or mental processes, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)). When considering the abstract idea group, certain methods of organizing human activity, the current claims correspond to the sub-grouping managing personal behavior. For instance, after presenting a user with a first object, the user is prompted to recognize the first object; so that the user provides a response to the prompt; and furthermore, after determining the accuracy of the user’s response to the initial prompt above, the user is presented with a second target, wherein the level/type of the second target is adjusted based on the accuracy of the user’s response to the initial prompt, etc.
Similarly, given the limitations that recite the process of: analyzing MRI image of the user to generate analysis result; determining a first target object based on the analysis result; determining the correctness of the user’s feedback response by comparing the feedback response with a answer; adjusting the level or type of a second target object based on a guideline associated with the correctness of the feedback response, etc., the claims also correspond to a mental process; such as, an evaluation, an observation, and/or a judgment, etc.
Step 2A [Wingdings font/0xE0] Prong Two:
The claims recite additional element(s), wherein a computing device (e.g., a computer that incorporates basic units, including a processor, a memory, etc.) is utilized to facilitated the recited steps/functions regarding: collecting image data (e.g., “receiving a magnetic resonance imaging (MRI) of the user; inputting the magnetic resonance imaging into an image analysis model”); analyzing the image data to generate a result (e.g., “an image analysis result corresponding to the magnetic resonance imaging being generated in real time by the image analysis model; automatically determining a first target object based on the image analysis result by the computing device”); generating/presenting a prompt to a user (e.g., “prompting the first target object for recognizing by a user”); collecting input(s) from the user (e.g., “receiving a feedback response from the user by the computing device”); analyzing, using an algorithm/rule, the collected input(s) in order to determine one or more results (e.g., “determining a correctness of the feedback response by comparing the feedback response with a stored answer by the computing device; and adjusting a level or a type of a second target object based on a guideline associated with the correctness of the feedback response”); presenting pertinent information based on the analysis result (e.g., providing “second target object . . . for recognizing by the user after the first target object is provided”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Thus, when each claim is considered as a whole, the additional element(s) fail to integrate the abstract idea into a practical application since they fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
Step 2B
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions.
Accordingly, when each of the current claims is considered as a whole (e.g., see the discussion under Prong Two above regarding such consideration of the claim as a whole), none of the claims recites an element—or a combination of elements—directed to an inventive concept.
In addition, the utilization of the conventional computer/network technology to facilitate the presentation of one more pertinent content items to a user(s); such as, the process of prompting a user to perform a given task/exercise, including presenting the user with a modified task/exercise based on the evaluation of the user’s response to the first task/exercise, etc., is directed to a well-understood, routine, conventional activity in the art (e.g., see US 2016/0321939; US 2014/0250043, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-7, 10-15 and 18-23). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element(s) utilized to facilitate the abstract idea.
Accordingly, the findings above demonstrate that none of the claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
► Applicant’s arguments directed to section §101 have been fully considered (the arguments filed on 04/28/2026). However, the arguments are not persuasive at least for the following reasons:
Firstly, regarding Prong Two of Step 2A, Applicant argues, “the image analysis model is used for identifying and analysing the input magnetic resonance imaging of the user to generate the corresponding image analysis result which represents the cognitive level of the user, and the first target object is determined based on the image analysis result. Therefore, the integration of the magnetic resonance imaging of the user enables the method to automatically, effectively and objectively provide the cognitive level of the user. Moreover, the claimed method includes the step of automatically determining (such as mapping) the initial target object (i.e., the first target object) for recognizing and the next target object (i.e., the second target object) for recognizing, where the level or the type of the second target object may be adjusted on the basis of the correctness of the feedback response corresponding to the first target object” (emphasis added).
However, Applicant appears to fail to address the core issue concerning the inquiry under Prong Two of Step 2A. In particular, Applicant fails to demonstrate whether any of the current claims, when considered as a whole, is implementing an element—or a combination of elements—that provides a technological improvement over the relevant existing technology, regardless of whether the technology being considered is the existing medical-imaging technology or the existing computer/network technology. Instead, Applicant is demonstrating the purpose of one or more of the claimed features. For instance, the claimed method/system implements an image analysis model in order to analyze the MRI image of the user; and such implementation enables the method/system to: (a) provide the cognitive level of the user “automatically, effectively and objectively”, and (b) automatically determine each of the initial target and the subsequent target that the user is required to recognize, wherein the level/type of the subsequent target may be adjusted based on the accuracy of the user’s response to the initial target.
However, none of the above features, whether considered individually or in any ordered combination, signifies any technological improvement. For instance, the existing technology already implements one or more algorithms (e.g., image analysis algorithm, etc.) to automatically analyze an image (e.g., MRI images, CT images, etc.) and determine one or more relevant results. Similarly, it is also part of the existing technology to automatically present one or more pertinent exercises to the user, based on the result of the image analysis above, including modifying one or more subsequent exercises based on the evaluation of the user’s response to one or more of the initial exercises, etc. In fact, Applicant’s summary above is confirming the Office’s findings presented under Prong Two. This is because Applicant is effectively demonstrating that the claimed/disclosed computer system is being used—merely as a tool—to facilitate the abstract idea; namely, presenting a first piece of information to the user based on the analysis of the user’s medical image; and this is followed by the presentation of a subsequent piece of information to the user, wherein the type/level of the second piece of information is adjusted based on the accuracy of the user’s response to the first piece of information. Accordingly, given the lack of technological improvement per the currently claimed—and the originally disclosed—method/system, none of the current claims, when considered as a whole, integrates the abstract idea into a patent-eligible practical application. Consequently, Applicant’s arguments are not persuasive.
Applicant further asserts, “claim 1 enables cognitive training to be conducted in an automated and systematic manner, rather than relying on the physical therapist's real-time man-made judgment and adjustments during the cognitive training. As a result, the claimed method addresses the staffing burdens and resource allocation constraints associated with traditional cognitive training, which requires physical therapists to participate in the rehabilitation process on a one-on-one basis with participant. By improving the implementation and efficiency of cognitive training processes, the claimed method provides practical improvements to the field of rehabilitation . . . the amended claim 1 of the present application is not directed to an abstract idea and it has been integrated into a practical application . . . the amended independent claims 9 and 17 of the present application have same above technical features as the amended claim 1. Accordingly, the amended independent claims 9 and 17 of the present application are eligible as well” (emphasis added).
However, here also Applicant fails to address the core issue or inquiry under Prong Two of Step 2A. This is again because Applicant fails to demonstrate whether any of the claims is implementing an element—or a combination of elements—that provides a technological improvement over the relevant existing technology. Instead, Applicant is demonstrating the advantages that the computer system has over a manual process—namely, a human (a medical professional or therapist) who is attempting to perform the process manually. Of course, when compared with such a manual process, the computer system has many advantages, including: speed, accuracy, overall efficiency, etc. In fact, such advantages are the reason why the existing technology is utilized—as a tool—to facilitate various tasks in many fields (e.g., the education field, the medical field, the entertainment field, etc.). Accordingly, Applicant’s attempt to portray an alleged technological improvement, while comparing the claimed computer-based process to a human/manual process, is not persuasive. This is because such comparison does not necessarily demonstrate a technological improvement. In contrast, a technological improvement (if any) is demonstrated by comparing two technologies—such as, the relevant existing technology and Applicant’s claimed (or disclosed) technology. Consequently, Applicant’s conclusory assertions, including the alleged improvement in the “implementation and efficiency of cognitive training processes”, and/or the alleged “practical improvements to the field of rehabilitation”, are all not persuasive.
It is also worth noting—per the inquiry under Step 2B—that the current claimed (and the originally disclosed) technology is directed to the conventional and generic arrangement of the additional elements.
Thus, at least for the reasons discussed above, the Office concludes that none of the current claims, when considered as a whole, implements an inventive concept that amounts to “significantly more” than an abstract idea.
Claim Rejections - 35 USC § 112
6. The following is a quotation of 35 U.S.C.112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C.112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
● Claims 1-7, 9-15 and 17-23 are rejected under 35 U.S.C.112(b), or second paragraph (pre-AIA ), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Each of claims 1, 9 and 17 recites, “receiving a magnetic resonance imaging (MRI) of the user” (emphasis added).
However, the term “the user”, as recited per each of the claims above, lacks sufficient antecedent basis. Accordingly, appropriate correction is required (e.g., amending the above term, --a user--).
Applicant is further advised to evaluate each of the current claims and make appropriate correction if additional discrepancies are discovered.
Claim Rejections - 35 USC § 103
7. 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Note that the one or more citations (paragraphs or columns) presented in this office action regarding the teaching of a cited reference(s) are exemplary only. Accordingly, such citation(s) are not intended to limit/restrict the teaching of the reference(s) to the cited portion(s) only. Applicant is required to evaluate the entire disclosure of each reference; such as additional portions that teach or suggest the claimed limitations.
● Claims 1-7, 9-15 and 17-23 are currently rejected under 35 U.S.C.103 as being unpatentable over Anantha 2016/0321939 in view of Gao 2020/0121236.
Regarding claim 1, Anantha teaches the following claimed limitations: a method for providing cognitive training by using a computing device, comprising: prompting a first target object for recognizing by a user; receiving a feedback response from the user by the computing device ([0003]; [0091]; [0092]: e.g., a computer-based system/method that provides cognitive training; wherein the system prompts the user to perform one or more tasks—such as, a task related to picture naming, wherein the user is prompted to name the picture being displayed to the user, etc., and accordingly, the user provides a corresponding response/feedback using one or more input devices; such as, a microphone, a keyboard, etc.); determining a correctness of the feedback response by comparing the feedback response with a stored answer by the computing device; and adjusting a level or a type of a second target object based on a guideline associated with the correctness of the feedback response, wherein the second target object is provided for recognizing by the user after the first target object is provided ([0061]; [0086]; [0088]; [0093]: e.g., after receiving the user’s response, the system determines whether the user has performed the task correctly. This indicates—at least implicitly—that the system compares the user’s response with a prestored correct answer in its memory/database. Thus, if the user has correctly performed the task, or if the user’s performance on the task is above a performance threshold, the system provides the user with a new task—such as, a more difficult task. If the user has failed to correctly perform the task, or if the user’s performance on the task is below a threshold, the system provides the user with a different task—such as, a less difficult task. The teaching above indicates that the system already implements a guideline associated with the correctness of the feedback response; thus, once it determines the correctness of the user’s response/feedback, it adjusts the type of the second target based on the guideline above; and wherein, the second target object is presented to the user after the first object is presented to the user).
Although Anantha evaluates the user’s context data—such as, the user’s medical information, in order to select the task/content (i.e., target) appropriate to the user ([0066]; [0067]), Anantha does not expressly describe the medical information above includes analysis results based on MRI of the user—i.e., receiving MRI of the user; inputting the MRI into an image analysis model; generating, via the image analysis model, image analysis result corresponding to the MRI in real time; and automatically determining a first target object based on the image analysis result.
However, Gao teaches a computer-based system that acquires MRI data related to a user—such as, a set of MRI images related to the brain of the user ([0169]; [0172]; [0173]); wherein the system implements one or more machine-learning models, which are utilized to analyze the MRI data above and classify the user based on the analysis result; and furthermore, it utilizes classification result above to generate one or more treatments, including a treatment in the form of software/computer-based cognitive therapy ([0174] to [0177]).
Note the above process of implementing one or more machine-learning models to analyze the MRI data, including classifying the user based on the analysis results, etc., already indicates the system already implements an image analysis model that generals—in real-time—an image analysis result.
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Anantha in view of Gao; for example, by expanding the medical information of the user to include one or more MRI images that relate to the user’s brain; wherein the system incorporates a portable MRI scanner to acquire the images, and/or the system electronically receives the user’s MRI images from a clinical database; and one or more machine-learning models are also incorporated into the system, so that the system analyzes the user’s MRI images and classifies the user according to one or more diagnoses; and furthermore, the system utilizes—as part of the user’s context data—one or more of the diagnoses above when determining one or more cognitive tasks to the user; so that the suitability of the task(s) being presented to the user would be more pertinent to the user’s specific condition, etc.
Regarding claim 9, Anantha teaches the following claimed limitations: a computing device for providing cognitive training, comprising: a processing module; a storage module configured to couple with the processing module, wherein a code being stored in the storage module ([0003]; [0158] to [0165]: a computer-based system that provides cognitive training; wherein the computer-based system comprises a computer that incorporates various computer-components, including: a processor, a memory, and also input/output devices in the form of a keyboard, a display, etc., and furthermore, the computer communicates with one or more devices via a communication network. Thus, the system above already comprises a processing module; a storage module configured to couple with the processing module; and wherein a code is stored in the storage module), and after the processing module executing the code stored in the storage module, the computing device being able to execute steps as described below: prompting a first target object for recognizing by a user; receiving a feedback response from the user by the computing device ([0091]; [0092]: e.g., the system prompts the user to perform one or more tasks—such as, a task related to picture naming; wherein the user is prompted to name the picture being displayed to the user, etc., and accordingly, the user provides a corresponding response/feedback using one or more input devices; such as, a microphone, a keyboard, etc.); determining a correctness of the feedback response by comparing the feedback response with a stored answer by the computing device; and adjusting a level or a type of a second target object based on a guideline associated with the correctness of the feedback response, wherein the second target object is provided for recognizing by the user after the first target object is provided ([0061]; [0086]; [0088]; [0093]: e.g., after receiving the user’s response, the system determines whether the user has performed the task correctly. This indicates—at least implicitly—that the system compares the user’s response with a prestored correct answer in its memory/database. Thus, if the user has correctly performed the task, or if the user’s performance on the task is above a performance threshold, the system provides the user with a new task—such as, a more difficult task. If the user has failed to correctly perform the task, or if the user’s performance on the task is below a threshold, the system provides the user with a different task—such as, a less difficult task. The teaching above indicates that the system already implements a guideline associated with the correctness of the feedback response; and thus, once it determines the correctness of the user’s response/feedback, it adjusts the type of the second target based on the guideline above; and wherein, the second target object is presented to the user after the first object is presented to the user).
Although Anantha evaluates the user’s context data—such as, the user’s medical information, in order to select the task/content (i.e., target) s appropriate to the user ([0066]; [0067]), Anantha does not expressly describe the medical information above includes analysis results based on MRI of the user—i.e., receiving MRI of the user; inputting the MRI into an image analysis model; generating, via the image analysis model, image analysis result corresponding to the MRI in real time; and automatically determining a first target object based on the image analysis result.
However, Gao teaches a computer-based system that acquires MRI data related to a user—such as, a set of MRI images related to the brain of the user ([0169]; [0172]; [0173]); wherein the system implements one or more machine-learning models, which are utilized to analyze the MRI data above and classify the user based on the analysis result; and furthermore, it utilizes classification result above to generate one or more treatments, including a treatment in the form of software/computer-based cognitive therapy ([0174] to [0177]).
Note the above process of implementing one or more machine-learning models to analyze the MRI data, including classifying the user based on the analysis results, etc., already indicates the system already implements an image analysis model that generals—in real-time—an image analysis result.
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Anantha in view of Gao; for example, by expanding the medical information of the user to include one or more MRI images that relate to the user’s brain; wherein the system incorporates a portable MRI scanner to acquire the images, and/or the system electronically receives the user’s MRI images from a clinical database; and one or more machine-learning models are also incorporated into the system, so that the system analyzes the user’s MRI images and classifies the user according to one or more diagnoses; and furthermore, the system utilizes—as part of the user’s context data—one or more of the diagnoses above when determining one or more cognitive tasks to the user; so that the suitability of the task(s) being presented to the user would be more pertinent to the user’s specific condition, etc.
Regarding claim 17, Anantha teaches the following claimed limitations: a non-transitory computer-readable recording medium capable of providing cognitive training, after a computing device loading and executing a code stored in the non-transitory computer-readable recording medium ([0003]; [0158] to [0165]: a computer-based system that provides cognitive training; wherein the computer-based system comprises a computer that incorporates various computer-components, including: a processor, a memory, etc., and wherein the memory stores computer-executable instructions/code. Thus, the above already indicates a non-transitory computer-readable recording medium capable of providing cognitive training, after a computing device loading and executing a code stored in the non-transitory computer-readable recording medium), the non-transitory computer-readable recording medium being able to complete steps as described below: prompting a first target object for recognizing by a user; receiving a feedback response from the user by the computing device ([0091]; [0092]: e.g., the system prompts the user to perform one or more tasks—such as, a task related to picture naming, wherein the user is prompted to name the picture being displayed to the user, etc., and wherein the user provides a response/feedback using one or more input devices; such as, a microphone, a keyboard, etc.); determining a correctness of the feedback response by comparing the feedback response with a stored answer by the computing device; and adjusting a level or a type of a second target object based on a guideline associated with the correctness of the feedback response, wherein the second target object is provided for recognizing by the user after the first target object is provided ([0061]; [0086]; [0088]; [0093]: e.g., after receiving the user’s response, the system determines whether the user has performed the task correctly. This indicates—at least implicitly—that the system compares the user’s response with a prestored correct answer in its memory/database. Accordingly, if the user has correctly performed the task, or if the user’s performance on the task is above a performance threshold, the system provides the user with a new task—such as, a more difficult task. If the user has failed to correctly perform the task, or if the user’s performance on the task is below a threshold, the system provides the user with a different task—such as, a less difficult task. The teaching above indicates that the system already implements a guideline associated with the correctness of the feedback response; thus, once it determines the correctness of the user’s response/feedback, it adjusts the type of the second target based on the guideline above; and wherein, the second target object is presented to the user after the first object is presented to the user).
Although Anantha evaluates the user’s context data—such as, the user’s medical information, in order to select the task/content (i.e., target) appropriate to the user ([0066]; [0067]), Anantha does not expressly describe the medical information above includes analysis results based on MRI of the user—i.e., receiving MRI of the user; inputting the MRI into an image analysis model; generating, via the image analysis model, image analysis result corresponding to the MRI in real time; and automatically determining a first target object based on the image analysis result.
However, Gao teaches a computer-based system that acquires MRI data related to a user—such as, a set of MRI images related to the brain of the user ([0169]; [0172]; [0173]); wherein the system implements one or more machine-learning models, which are utilized to analyze the MRI data above and classify the user based on the analysis result; and furthermore, it utilizes classification result above to generate one or more treatments, including a treatment in the form of software/computer-based cognitive therapy ([0174] to [0177]).
Note the above process of implementing one or more machine-learning models to analyze the MRI data, including classifying the user based on the analysis results, etc., already indicates the system already implements an image analysis model that generals—in real-time—an image analysis result.
Accordingly, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Anantha in view of Gao; for example, by expanding the medical information of the user to include one or more MRI images that relate to the user’s brain; wherein the system incorporates a portable MRI scanner to acquire the images, and/or the system electronically receives the user’s MRI images from a clinical database; and one or more machine-learning models are also incorporated into the system, so that the system analyzes the user’s MRI images and classifies the user according to one or more diagnoses; and furthermore, the system utilizes—as part of the user’s context data—one or more of the diagnoses above when determining one or more cognitive tasks to the user; so that the suitability of the task(s) being presented to the user would be more pertinent to the user’s specific condition, etc.
Anantha in view of Gao teaches the claimed limitations as discussed above per each of the independent claims. Anantha further teaches:
Regarding claims 2, 10 and 18, wherein the first target object comprises at least one of a target image, a target text, and a target voice ([0091]: e.g., the exemplary task above that relates to picture naming—such as, prompting the user to name the picture being displayed, already indicates that the target object comprises at least a target image);
Regarding claims 3, 11 and 19, the correctness of the feedback response comprises at least one of a match result that the feedback response exactly matches with the stored answer or not and a match ratio that the feedback response is close to the stored answer ([0093]: e.g., the process of presenting the user with a new task when the user has correctly performed the task, etc., already indicates that the correctness of the feedback response comprises at least a match result that the user’s feedback response exactly matches the prestored correct answer or not. Note that the claim requires at least one of the two alternatives, but not both);
Regarding claims 4, 12 and 20, the guideline is that the level of the second target object is upgraded when the match result is that the feedback response exactly matches with the stored answer or when the match ratio is greater than or equal to a threshold, and the level of the second target object is downgraded when the match result is that the feedback response does not exactly match with the stored answer or when the match ratio is less than the threshold ([0088]; [0093]: e.g., as already discussed per claim 1, when the system determines, based on the user’s response, that the user has correctly performed the task—or the user’s performance on the task is above a performance threshold—, then the system subsequently provides the user with a more difficult task. This indicates the process of upgrading—per guideline—the level of the second target object when: (i) the match result indicates that the feedback response exactly matches with the stored answer, or (ii) the match ratio is greater than or equal to a threshold. Similarly, when the user has failed to correctly perform the task—or if the user’s performance is below a threshold—, then the system provides the user with a less difficult task. This indicates the process of downgrading the second target object when: (i) the match result indicates that the feedback response does not exactly match with the stored answer, or (ii) the match ratio is less than the threshold);
Regarding claims 5, 13 and 21, wherein the guideline is that the type of the second target object which is more difficult than the first target object is determined when the match result is that the feedback response exactly matches with the stored answer or when the match ratio is greater than or equal to a threshold, and the type of the second target object which is easier than the first target object is determined when the match result is that the feedback response does not exactly match with the stored answer or when the match ratio is less than the threshold ([0088]; [0093]: e.g., here also once the system determines, based on the user’s feedback/response, that the user has correctly performed the task—or the user’s performance on the task is above a performance threshold—, then the system subsequently provides the user with a more difficult task. This indicates the process of determining a type of second target object that is more difficult than the first object when: (i) the match result indicates that the feedback response exactly matches with the stored answer, or (ii) the match ratio is greater than or equal to a threshold. Similarly, when the user has failed to correctly perform the task—or if the user’s performance is below a threshold—then, the system provides the user with a less difficult task. This indicates the process of determining a type of second target object that is easier than the first object when: (i) the match result indicates that the feedback response does not exactly match with the stored answer, or (ii) the match ratio is less than the threshold);
Regarding claims 6, 14 and 22, wherein the stored answer comprises at least one of a primary answer and a secondary answer ([0088]; [0093]: e.g., the system determines, after analyzing the user’s response, whether the user has correctly performed the task, or whether the user’s performance is above or below one or more thresholds. The above indicates the prestored answer comprise a primary answer, and/or a secondary answer. It is also worth to note, per the claim language above, that the claim requires just one of the two alternatives, but not necessarily both. Nevertheless, the teaching above is still relevant to address the claim);
Regarding claims 7, 15 and 23, the prompting of the first object for recognizing by the user is performed by the computing device ([0091]: e.g., the computer generates the prompt using one or more formats—such as, by displaying text, image and/or playing audio. Thus, the computing device is the one performing the prompting process).
Response to Arguments.
8. Applicant’s arguments have been fully considered (the arguments filed on 04/28/2026).
Note that the previous analysis under section §102 is withdrawn due to the current claim amendment; and therefore, Applicant’s arguments directed to the previous analysis under section §102 are now moot.
Applicant’s argument directed to section §103, namely the argument directed to the teaching of Gao, has also been considered. However, the argument is not persuasive. In particular, while quoting two of the paragraphs (i.e., [0176] and [0177]), Applicant is asserting that “Gao fails to disclose ‘automatically determining a first target object based on the image analysis result by the computing device’ recited in the amended claim 1, in which the first target object is used to be recognized by the user to provide a series of the cognitive rehabilitation . . . none of Anantha and Gao, individually or in combination, discloses or teaches ‘automatically determining a first target object based on the image analysis result by the computing device’ recited in the amended independent claim 1 . . . the claimed method of the present application automatically determines the first target object based on the image analysis result corresponding to the magnetic resonance imaging (MRI) of the user, so as to provide the user with a customized cognitive training” (emphasis added).
However, Applicant fails to properly construe the combined teaching of Anantha and Gao. Firstly, it is worth noting that the claimed “first target object” is itself a form of software-based content item (e.g., content item generated via a computer), which the claimed method/system is identifying to the user, based on the analysis of the user’s medical data—namely, the user’s MRI. Of course, even Applicant’s original specification confirms that the “first target object” is indeed such content item that the computer is providing to the user (see [0009]; [0015]).
In this regard, the primary reference—i.e., Anantha—already teaches that the system considers various types of content items/tasks to be presented to the user (see [0058]); and the system automatically identifies, based on the analysis of the user’s medical data, a content item/task that is relevant to the user ([0066]; [0067]).
It is quite evident—at least to one skilled in the art—that Anantha does teach the crux of Applicant’s claimed method/system, except for the use of the user’s MRI as part of the medical data. Accordingly, the secondary reference—i.e., Gao—is only required to supplement the above missing element; namely, analyzing a patient’s MRI in order to determine the patient’s condition and/or a relevant treatment.
In this regard, Gao already collects and analyzes, using a machine-learning model, the patient’s MRI data; and further classifies the patient according to the medical condition that the patient is experiencing (see [0174] to [0176]); and the above classification is used not only to “diagnose a mental health disorder” that the patient has, but also to “recommend treatment”; and such treatment includes “cognitive behavioral therapy including software based versions of the therapy or other suitable therapies” ([0177]).
It is again readily apparent—at least to one having ordinary skill in the art—that such “software based versions of the therapy” above implies one or more computer generated content items/tasks that the system is presenting to the user ([0152]).
It is evident from the observation above that Gao’s teaching is more than sufficient to supplement the missing element from Anantha. Accordingly, as already indicated per the Office’s obviousness analysis, PHOSITA would readily be motivated to modify Anantha in view of Gao; for example, by expanding the medical information of the user to include one or more MRI images that relate to the user’s brain (e.g., utilizing a portable MRI scanner to acquire the MRI images; and/or electronically receiving the user’s MRI images from a clinical database, etc.); and one or more machine-learning models are also incorporated into the system, so that the system analyzes the user’s MRI images and classifies the user according to one or more diagnoses; and furthermore, the system utilizes—as part of the user’s context data—one or more of the diagnoses above when determining one or more cognitive tasks to the user; so that the suitability of the task(s) being presented to the user would be more pertinent to the user’s specific condition, etc.
So far, Applicant fails to properly consider the combined teaching of the two references above. Instead, while applying a piecemeal analysis (e.g. considering merely Gao while ignoring Anantha), Applicant is attempting to challenge the Office’s obviousness analysis. Consequently, Applicant’s arguments are not persuasive.
Thus, at least for the reasons above, the Office concludes that the current claims are obvious over the prior art.
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this final 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 filled within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached from 7:00 AM - 3:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER VASAT can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
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