DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to arguments and amendments entered on September 8, 2026 for the patent application 19/434,203 filed on December 29, 2025. Claims 1, 3-11, 13, 15, 17 and 20 are amended. Claims 1-20 are pending. The first office action of May 5, 2026 is fully incorporated by reference into this Final Office Action.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – “Statutory Category Identification”
Claim 1 is directed to “a system” (i.e. a machine), claim 15 is directed to “a treatment method” (i.e. a process), and claim 20 is directed to “a method” (i.e. a process), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 1 “Abstract Idea Identification”
However, the claims are drawn to an abstract idea of “rehabilitation and training of a patient,” in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
Per claim 1:
“A) compute a dynamically adjustable virtual environment comprising a character of said patient located within said dynamically adjustable virtual environment;
B) compute a virtual task to be performed by said patient using at least one imaginary movement within said dynamically adjustable virtual environment and present the virtual task…;
C) collect bioelectrical data …from a body of said patient during said at least one imaginary movement, wherein said bioelectrical data comprises at least one of the following:
i) said patient’s electroencephalogram data being a set of activity signals of said patient’s nervous system; said set characterized by a signal registration time and a signal amplitude thereof; and
ii) said patient’s electromyogram data being a set of activity signals of said patient’s nervous system; said set characterized by a signal registration time and a signal amplitude thereof;
D) extract at least one characteristic feature of said bioelectrical data collected during said at least one imaginary movement, wherein the at least one characteristic feature is selected from: a spectral characteristic, a time-series characteristic, a wavelet decomposition characteristic, a spatiotemporal characteristic and any combination thereof;
E) classify said extracted characteristic features into an action pattern using an action pattern classification model configured to recognize imaginary movements based on patterns of actions configured in advance,
wherein the action pattern classification model comprises a two-level committee of local classifiers:
a lower level comprises a combination of at least two classifiers each operating on a different type of extracted characteristic feature, wherein the lower level is designed to summarize classification decisions of the at least two classifiers into a vector, and
an upper level comprises at least one artificial neural network trained on outputs from the lower level classifiers, wherein the upper level receives the vector generated by the lower level and generates said action pattern,
wherein the upper level is trained on a dataset including classification outputs of each local classifier of the lower level,
wherein the action pattern classification model is configured such that, when the processor executes the instructions of C) through I), a time delay between the at least one imaginary movement and the real-time visual feedback presented in response thereto does not exceed 500 milliseconds;
(F) compute a control command for said dynamically adjustable virtual environment based on said action pattern, by calculating parameters of the control command based on corresponding parameters of the action pattern and operation…;
G) present …, the character performing an action in the dynamically adjustable virtual environment based on the control command;
H) evaluate execution performance of the patient’s at least one imaginary movement by calculating correlation values between actions performed by the character based on said control command corresponding to said patient’s at least one imaginary movement and an expected state of the character defining accurate performance; and
I) according to said correlation values, dynamically adapt a real-time visual feedback presented …, for indicating real-time performance of the executed virtual task of said patient within the dynamically adjustable virtual environment according to said patient’s at least one imaginary movement.”
Per claim 15:
“A) compute a dynamically adjustable virtual environment comprising a character of said patient located within said dynamically adjustable virtual environment;
B) compute a virtual task to be performed by said patient using at least one imaginary movement within said dynamically adjustable virtual environment;
C) collect bioelectrical data …from a body of said patient during said at least one imaginary movement, wherein said bioelectrical data comprises at least one of the following:
i) said patient’s electroencephalogram data being a set of activity signals of said patient’s nervous system; said set characterized by a signal registration time and a signal amplitude thereof; and
ii) said patient’s electromyogram data being a set of activity signals of said patient’s nervous system; said set characterized by a signal registration time and a signal amplitude thereof;
D) extract at least one characteristic feature of said bioelectrical data collected during said at least one imaginary movement, wherein the at least one characteristic feature is selected from: a spectral characteristic, a time-series characteristic, a wavelet decomposition characteristic, a spatiotemporal characteristic and any combination thereof;
E) classify said extracted characteristic features into an action pattern using an action pattern classification model configured to recognize imaginary movements based on patterns of actions configured in advance,
wherein the action pattern classification model comprises a two-level committee of local classifiers:
a lower level comprises a combination of at least two classifiers each operating on a different type of extracted characteristic feature, wherein the lower level is designed to summarize classification decisions of the at least two classifiers into a vector, and
an upper level comprises at least one artificial neural network trained on outputs from the lower level classifiers, wherein the upper level receives the vector generated by the lower level and generates said action pattern,
wherein the upper level is trained on a dataset including classification outputs of each local classifier of the lower level,
wherein the action pattern classification model is configured such that, when the processor executes the instructions of C) through I), a time delay between the at least one imaginary movement and the real-time visual feedback presented in response thereto does not exceed 500 milliseconds;
F) compute a control command for said dynamically adjustable virtual environment based on said action pattern, by calculating parameters of the control command based on corresponding parameters of the action pattern and operation…;
G) present …, the character performing an action in the dynamically adjustable virtual environment based on the control command;
H) evaluate execution performance of the patient’s at least one imaginary movement by calculating correlation values between actions performed by the character based on said control command corresponding to said patient’s at least one imaginary movement and an expected state of the character defining accurate performance; and
I) according to said correlation values, dynamically adapt a real-time visual feedback presented …, for indicating real-time performance of the executed virtual task of said patient within the dynamically adjustable virtual environment according to said patient’s at least one imaginary movement.”
Per claim 20:
“A) compute a dynamically adjustable virtual environment comprising a character of said patient located within said dynamically adjustable virtual environment;
B) compute a virtual task to be performed by said patient using at least one imaginary movement within said dynamically adjustable virtual environment;
C) collect bioelectrical data …from a body of said patient during said at least one imaginary movement, wherein said bioelectrical data comprises at least one of the following:
i) said patient’s electroencephalogram data being a set of activity signals of said patient’s nervous system; said set characterized by a signal registration time and a signal amplitude thereof; and
ii) said patient’s electromyogram data being a set of activity signals of said patient’s nervous system; said set characterized by a signal registration time and a signal amplitude thereof;
D) extract at least one characteristic feature of said bioelectrical data collected during said at least one imaginary movement, wherein the at least one characteristic feature is selected from: a spectral characteristic, a time-series characteristic, a wavelet decomposition characteristic, a spatiotemporal characteristic and any combination thereof;
E) classify said extracted characteristic features into an action pattern using an action pattern classification model configured to recognize imaginary movements based on patterns of actions configured in advance,
wherein the action pattern classification model comprises a two-level committee of local classifiers:
a lower level comprises a combination of at least two classifiers each operating on a different type of extracted characteristic feature, wherein the lower level is designed to summarize classification decisions of the at least two classifiers into a vector, and
an upper level comprises at least one artificial neural network trained on outputs from the lower level classifiers, wherein the upper level receives the vector generated by the lower level and generates said action pattern,
wherein the upper level is trained on a dataset including classification outputs of each local classifier of the lower level, wherein the action pattern classification model is configured such that, when the processor executes the instructions of C) through I), a time delay between the at least one imaginary movement and the real-time visual feedback presented in response thereto does not exceed 500 milliseconds;
F) compute a control command for said dynamically adjustable virtual environment based on said action pattern, by calculating parameters of the control command based on corresponding parameters of the action pattern and operation…;
G) present …, the character performing an action in the dynamically adjustable virtual environment based on the control command;
H) evaluate execution performance of the patient’s at least one imaginary movement by calculating correlation values between actions performed by the character based on said control command corresponding to said patient’s at least one imaginary movement and an expected state of the character defining accurate performance; and
I) according to said correlation values, dynamically adapt a real-time visual feedback presented …for indicating real-time performance of the executed virtual task of said patient within the dynamically adjustable virtual environment according to said patient’s at least one imaginary movement, thereby providing at least one of rehabilitation and training of said patient based on the at least one imaginary movement.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 2 – “Practical Application”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “a processor,” “a display,” “electrodes,” “a non-transitory computer readable storage medium,” and “a graphical user interface (GUI),” are claimed, as these are merely claimed to generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “rehabilitation and training of a patient,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Step 2B – “Significantly More”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “a processor,” “a display,” “electrodes,” “a non-transitory computer readable storage medium,” and “a graphical user interface (GUI),” are claimed, these are generic, well-known, and conventional elements. As evidence that these are generic, well-known, and a conventional elements (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known, the Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a). As such, this satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Moreover, the elements of “a processor,” “a display,” “a non-transitory computer readable storage medium,” and “a graphical user interface (GUI),” are best described on page 55, lines 9-30 as follows:
“Fig. 18 is an example of general-purpose computing system: personal computer or server 20withcentral processing unit 21, system memory 22 and system bus 23, which contains various system components, including memory connected with central processing unit 21. System bus 23 is implemented as any bus structure known in the prior art, which in its turn contains bus memory or a bus memory controller, a peripheral bus and a local bus, which can interact with any other bus architecture. System memory contains read-only memory (ROM)24, random access memory(RAM)25.Basic input/output system (BIOS)26, contains the main procedures, which provide information transfer between the elements of personal computer 20, for example, at the moment of operating system loading using ROM24.In its turn, personal computer 20 contains hard disk drive 27 to read and write data, disk drive 28 to read and write data to/from removable disks 29 and optical drive 30 to read and write data to/from optical disks 31, such as CD-ROM, DVD-ROM and other optical data storage means (device)s. Hard disk drive 27,disk drive 28,optical drive 30 are connected with system bus 23though hard disk interface disk interface 33 and optical drive interface 34, correspondingly. The drives and corresponding computer data storage means (device)s are nonvolatile storage means (device)s for computer instructions, data structures, software modules and other data from personal computer 20.
The given description discloses the implementation of the system, which uses hard disk drive removable disk 29 and removable optical disk 31, but it will be appreciated that other types of computer data storages 56 can be used, which can store data in the form, accessible for reading by computer (solid-state drives, flash memory cards, digital disks, random access memory(RAM etc.), which are connected to system bus 23 with controller 55.”
These elements are reasonably interpreted as generic computer components as part of a generic computer which provides no details of anything beyond ubiquitous standard off-the-shelf equipment.
Finally, the element of “electrodes,” mentioned only three (3) times in the written description of the specification as originally filed, are best described on page 15, lines 13-16 as follows:
“For example, to collect data on brain's activity (electroencephalogram, EEG) of operator 100, a set of sensors (electrodes), attached to the head of operator 100, or located at a small distance from the head of operator 100, can be used (for example, a set of sensor fixed into a head-piece).”
This element is reasonably interpreted as an input device as part of a generic computer which also provides no details of anything beyond ubiquitous standard off-the-shelf equipment.
Therefore, the Applicant’s own specification discloses ubiquitous standard equipment that is (1) generic, routine, conventional, and/or commercially available; and (2) does not provide anything significantly more. Thus, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-14 and 16-19 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-14 and 16-19 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to claim 1 or 15. Therefore, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
The Applicant’s remarks filed on September 8, 2026 related to claims 1-20 are fully considered, but are not persuasive.
Drawings
The Applicant respectfully argues “The drawings were objected to under 37 CFR 1.84(a)(1) (Figures 1-9 and 11-18) and 37 CFR 1.84(b)(1) (Figure 10). Applicant submits herewith replacement sheets for all eighteen sheets of drawings, as described under "Amendments to the Drawings" above, which render all lines and lettering in solid black, present Figure 10 as a line drawing, re-letter the axes of Figures 11-13 at legible size and delete the extraneous publication markings from Figure 12. The content of the figures is otherwise unchanged and no new matter is introduced. Withdrawal of the objections to the drawings is respectfully requested. The content of the replacement drawings includes no new matter.”
The Examiner respectfully agrees. As such, the argument is persuasive. Therefore, the drawing objections are withdrawn.
Claim Objections
The Applicant respectfully argues “Claims 1-20 are objected to because of the following informalities: typographical errors. Applicant amended claims to correct typographical errors.
The Office Action indicates that Claims 1, 15, 17 and 20 also recite the combination of conjunctions "and/or." Applicant amended the claims, by cancelling "and/or", and introducing "at least one of'.”
The Examiner respectfully agrees. As such, the argument is persuasive. Therefore, the claim objections are withdrawn.
35 U.S.C. § 101 Rejections
Step 2A Prong One
The Applicant respectfully argues “Thus, in considering whether a claim recites a mental process such as a concept performed in the human mind, the Office focuses on the practicality of performing the claimed subject matter in the human mind. In light of the above clarifications to the guidelines, it is obvious that the claimed invention of amended Claims 1, 15, and 20 includes limitations that encompass AI in a way that cannot be practically performed in the human mind or using pen and paper. In particular, amended Claims 1, 15, and 20 recite wherein the action pattern classification model comprises a two-level committee of local classifiers: a lower level comprises a combination of at least two classifiers each operating on a different type of extracted characteristic feature, wherein the lower level is designed to summarize classification decisions of the at least two classifiers into a vector, and an upper level comprises at least one artificial neural network trained on outputs from the lower level classifiers, wherein the upper level receives the vector generated by the lower level and generates said action pattern, wherein the upper level is trained on a dataset including classification outputs of each local classifier of the lower level, wherein the action pattern classification model is configured such that, when the processor executes the instructions of C) through I), a time delay between the at least one imaginary movement and the real-time visual feedback presented in response thereto does not exceed 500 milliseconds. Applicant respectfully submits that the aforementioned claimed features cannot be performed by a human mind as such processing requires use of processing hardware having capabilities that are beyond human faculties. For example, a human cannot perform computations designed to be performed by the action pattern classification model within 500 milliseconds.
Thus, under Step 2A Prong One, the claim is not directed to a judicial exception, and the rejection must be withdrawn and the claim should be allowed.”
The Examiner respectfully disagrees. With respect to mental processes, actual mental performance of the abstract idea is not required. Further, the MPEP § 2106.04(a)(2)(III)(C) states that “claims can recite a mental process even if they are claimed as being performed on a computer” and that “examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite “a mental process.” In the present case, the claim limitations perform steps that are performed on a generic computer and/or computer environment; and merely uses a computer as a tool to perform the concept. As such, the argument is not persuasive.
Step 2A Prong Two
The Applicant respectfully argues “Applicant submits that the claimed subject matter includes an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field. Amended independent Claims 1, 15, and 20 recite wherein the action pattern classification model comprises a two-level committee of local classifiers: a lower level comprises a combination of at least two classifiers each operating on a different type of extracted characteristic feature, wherein the lower level is designed to summarize classification decisions of the at least two classifiers into a vector, and an upper level comprises at least one artificial neural network trained on outputs from the lower level classifiers, wherein the upper level receives the vector generated by the lower level and generates said action pattern, wherein the upper level is trained on a dataset including classification outputs of each local classifier of the lower level, wherein the action pattern classification model is configured such that, when the processor executes the instructions of C) through I), a time delay between the at least one imaginary movement and the real-time visual feedback presented in response thereto does not exceed 500 milliseconds.
Specifically, because artificial neural network models commonly used in data processing require a high level of computing resources to be consumed for a high level of performance, it is often difficult to use such models in environments where real-time performance is needed.
According to the claimed subject matter, real-time visual feedback generated and presented to a user with a time delay not exceeding 500 milliseconds is obtained by the claimed architecture of the action pattern classification model. The claimed invention addresses the technical difficulties of providing a user with real-time visual feedback within 500 milliseconds.
Applicant submits that amended Claims 1, 15, and 20, when considered as a whole, integrates the alleged abstract idea into a practical application. Amended Claim 1 recites a specific architecture of a classifier which provides the practical application of analyzing EEG data and updating a presentation on a display within 500 milliseconds, which provides real-time feedback. This is a specific improvement to how the classification model itself operates, and is not merely the application of a generic model.”
The Examiner respectfully disagrees. The Applicant’s argument is misguided as to the proper analysis of a “Practical Application” as required under Step 2A, Prong 2. Specifically, the Applicant’s argument appears to describe claimed utility, which is not the test. Instead, the Applicant’s claims are not considered a “Practical Application,” because the claims do not provide any of the following:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
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Furthermore, there are also several factors that reasonably explain that the Applicant’s claims are not indicative of integration into a practical application, which include:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
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Here, the Applicant’s claims are not providing any technological advancement as described in the first five bulleted factors and, as described above in the rejection, the Applicant’s claims are merely claimed to use a computer as a tool to perform an abstract idea and to generally link the use of a judicial exception to a particular technological environment or field of use. As such, the argument is not persuasive.
The Applicant respectfully argues “Amended Claim 1 is not simply directed to "use AI" or "apply machine learning" generically. Rather, amended Claim 1 recites a specific structural and functional arrangement of multiple heterogeneous classifiers that achieves a concrete technical improvement-reducing processing time to enable real-time mapping of EEG signals to visual feedback presented on a display.”
The Examiner respectfully disagrees. The Applicant is merely applying a new dataset to existing technology. No new advancement in computing hardware or new form of artificial intelligence software is claimed. As such, the argument is not persuasive.
The Applicant respectfully argues “Applicant submits that amended Claim 1 is analogous to the claims of Appeal No. 2025-002654, where the Patent Trial and Appeal Board (PTAB) reversed the 101 rejection because Claim 1 "identifie[d] a specific way of light weighting the neural network to achieve the improvements identified in the Specification that are reflected in the specific limitations." (Decision at 6.) Applicant argued that "Thus, the light-weighted artificial neural network model may be used in a mobile device that may have relatively limited computing resources.".”
The Examiner respectfully disagrees. Specifically, the Applicant has presented a PTAB case (i.e. Appeal No. 2025-002654). However, decisions originating from the Patent Trial and Appeals Board are classified as non-precedential and are not fully considered as binding precedent on the USPTO. Only Supreme Court decisions and Federal Circuit decisions are considered precedential and are fully considered as binding precedent on the USPTO. As such, decisions originating from the Patent Trial and Appeals Board contain non-binding precedent and do not super cede the Examiner’s subject-matter eligibility analysis using precedential Supreme Court decisions and Federal Circuit decisions. Therefore, the argument is not persuasive.
The Applicant respectfully argues “The Board found this was "essentially the same" patent eligibility issue addressed by the precedential ARP decision in Ex parte Desjardins, No. 2024-000567, 2025 WL 3095778 (PTAB Sept. 26, 2025), where the Director held that "the improvement to how the [ML] model itself operates" conferred patent eligibility under Enfish, LLC V. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016). (Decision at 5-6.)
Moreover, in the December 5, 2025 MEMORANDUM it is explained that "The following paragraph is added to the end of MPEP § 2106.04(d), subsection III: In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of "catastrophic forgetting" encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task" reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO)."
In an analogous manner, feature E of amended Claim 1 recites a specific architecture for the action pattern classification model, which is designed to provide the technical improvement of enabling the processor to execute features C) through I), including the action pattern classification model, within 500 milliseconds – provides the advantages of reduced system complexity and streamlining - which are listed above with respect to Desjardins. Accordingly, the same conclusion is reached with respect to amended Claim 1 as reached with respect to Desjardins: "the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO)"..”
The Examiner respectfully disagrees. In Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks.
In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were deemed to be outside any specific, enumerated judicial exception (Step 2A: NO).
The courts have not provided an explicit test for this consideration, but have instead illustrated how it is evaluated in numerous decisions. These decisions, and a detailed explanation of how examiners should evaluate this consideration are provided in MPEP § 2106.05(a). In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but only in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine that the claim improves technology or a technical field.
In the present case, it is the latter. The claimed term “artificial neural network” is described in paras. [0074]-[0075], [0190] and [0194] with regard to a basic understanding as to how an artificial neural network works and not towards advancing technology.
See paras. [0074]-[0075], [0190] and [0194] as follows:
“[0074] The term “Artificial neural network hereinafter refers to a set of neurons, united into a network by connecting neuron inputs of one layer with neuron outputs of another layer; at that, the neuron inputs of the first layer are the inputs of the whole neural network, and the neuron outputs of the last layer are the outputs of the whole neural network.”
“[0075] From the point of view of machine learning, the use of neural networks is a special case of pattern recognition methods, discriminatory analysis, classification methods etc.”
“[0190] In one of the embodiments, a pattern classification model is an artificial neural network and is preliminarily generated with machine learning methods.”
“[0194] The above mentioned artificial neural networks (ANNs) are based on the principles of distributional, non-linear and parallel data processing with learning. In the above-mentioned example, ANNs are implemented in the form of a multi-layer perceptron, consisting of three layers: two discrete layers and one output layer. A sigmoid function is used as a function to activate in discrete layers where α—a slope parameter of the sigmoid function, and a linear function in the output layer.”
In Applicant’s case, no new technological advancement in “an artificial neural network,” is provided. Therefore, the specification explicitly sets forth only a bare assertion of an improvement by using the technology with a desired dataset without the detail necessary to be apparent to a person of ordinary skill in the art that the claims actually improve technology or a technical field.
Therefore, it is the opinion of the Examiner that the Applicant’s claims are (1) directed to an abstract idea by implementing the abstract idea using conventional techniques; (2) the claims are not directed to a technological solution to a technological problem; and (3) the claimed elements are generic, well-known, and a conventional data gathering computing elements (or an equivalent term), which are part of a commercially available product, that indicates that the additional elements are sufficiently well-known; and are thereby deemed not subject-matter eligible.
The Applicant respectfully argues “Independent Claim 15 - a particular treatment of a particular medical condition. Independent Claim 15 is eligible for a further and independent reason. Claim 15 recites a treatment method comprising administering a treatment to a patient identified as having brain damage, traumatic brain injury, or a condition resulting in decreased or disturbed physical activity, including prolonged loss of mobility following surgery, injury or accident (and, per dependent Claims 16-19, stroke, prolonged post-surgical immobility, or loss of mobility following an accident or musculoskeletal injury), the treatment consisting of the recited closed-loop steps: presenting a virtual task to be performed by imagined movement; collecting the patient's electroencephalogram or electromyogram data during the imagined movement; extracting characteristic features; classifying them with the recited two-level committee of classifiers; driving the patient's character in the virtual environment from the resulting control command; evaluating execution performance; and adapting real-time visual feedback within 500 milliseconds of the imagined movement.
Under MPEP § 2106.04(d)(2) and Vanda Pharmaceuticals Inc. V. West-Ward Pharmaceuticals Int'l Ltd., 887 F.3d 1117 (Fed. Cir. 2018), a claim that applies a judicial exception to effect a particular treatment or prophylaxis for a particular disease or medical condition integrates the exception into a practical application. Claim 15 does not merely instruct a practitioner to "apply" an abstract idea; it recites a specific neurorehabilitation protocol - a specific patient population, a specific stimulus (an imagined motor action within a virtual task), specific measurements, a specific classification architecture, and a specific real-time feedback regime - and that protocol is the treatment. The specification describes the system as an after-care system in which neurofeedback stimulates brain flexibility and restorative processes in the patient's central nervous system, visualizes the improvement of motor function where performance of real movements is impossible for the patient, and permits training where active therapeutic physical training is not yet permitted (specification, page 42, last paragraph, continuing to page 43; see also Claim 11).
The Office Action does not address Claim 15 under § 2106.04(d)(2); it analyzes Claim 15 identically to system Claim 1. Applicant respectfully submits that, independently of the arguments above, Claim 15 and dependent Claims 16-19 integrate any recited judicial exception into a practical application as a particular treatment of a particular medical condition, and requests that Claims 15-19 be separately considered on that basis.”
The Examiner respectfully disagrees. The Vanda Memo relates to Pathway B (i.e. the claim is not directed to a judicial exception). Here, Pathway B does not apply since the broadly claimed abstract idea of “rehabilitation and training of a patient,” does not provide any specificity as to what type of “rehabilitation and training” is required to effectively treat the unclaimed conditions of the patient. Furthermore, the claims in Vanda recited a specific method of treating a patient having schizophrenia with iloperidone, a drug known to cause QTc prolongation (a disruption of the heart’s normal rhythm that can lead to serious health problems) in patients having a particular genotype associated with poor drug metabolism, which is not on point with the Applicant’s claims. As such, the argument is not persuasive.
Step 2B
The Applicant respectfully argues “The Step 2B analysis "is the search for an inventive concept, or some element or combination of elements sufficient to ensure that the claim in practice amounts to significantly more than a patent on an ineligible concept." DDR Holdings, LLC V. Hotels.com, L.P., 773 F.3d 1245, 1261 (Fed. Cir. 2014) (internal quotation marks omitted). Even if, arguendo, the instant claims were directed to an abstract idea under Step 2A (which Applicant refutes), the claims nevertheless recite significantly more than an abstract idea, thus satisfying Step 2B.
In particular, even if the pending claims are erroneously viewed as being directed to an abstract idea, the elements of these claims amount to significantly more than the alleged abstract idea itself to transform the nature of the claims into a patent-eligible invention. The Supreme Court stated: "At Mayo step two, we must examine the elements of the claim to determine whether it contains an "inventive concept" sufficient to "transform" the claimed abstract idea into a patent-eligible application." Alice at 2357. Applicant submits that each of the pending claims recites an "inventive concept" and is accordingly eligible for patenting.
As required by MPEP § 2106.05(e): "it is particularly critical to address the combination of additional elements, because while individually-viewed elements may not appear to add significantly more, those additional elements when viewed in combination may amount to significantly more than the exception by meaningfully limiting the judicial exception." [emphasis added] Applicant asserts that the combination of elements for each claim, taken individually and as a whole, amount to significantly more than an abstract idea.”
The Examiner respectfully disagrees. The elements of “a processor,” “a display,” “electrodes,” “a non-transitory computer readable storage medium,” and “a graphical user interface (GUI),” as claimed, are generic, well-known, and conventional elements, as evidenced above in the rejection by citing the Applicant’s own written description of the specification as originally filed. As such, the argument is not persuasive.
The Applicant respectfully argues “Applicant respectfully submits that the Office Action's rejection under Step 2B is facially deficient with regard to the requirements set forth under the Berkheimer Memo. Accordingly, Applicant respectfully requests one of the above-referenced four types of evidence that must be provided in order to maintain this rejection.
Applicant contends that the additional elements in amended independent Claims 1, 15, and 20 are not well-understood, routine or conventional. The Office alleges that "a processor," "a display," "electrodes," "a non-transitory computer readable storage medium," and "a graphical user interface (GUI)," are generic, well-known, and conventional elements. However, amended independent Claims 1, 15, and 20 recite "wherein the action pattern classification model comprises a two-level committee of local classifiers: a lower level comprises a combination of at least two classifiers each operating on a different type of extracted characteristic feature, wherein the lower level is designed to summarize classification decisions of the al least two classifiers into a vector, and an upper level comprises at least one artificial neural network trained on outputs from the lower level classifiers, wherein the upper level receives the vector generated by the lower level and generates said action pattern, wherein the upper level is trained on a dataset including classification outputs of each local classifier of the lower level, wherein the action pattern classification model is configured such that, when the processor executes the instructions of C) through I), a time delay between the at least one imaginary movement and the real-time visual feedback presented in response thereto does not exceed 500 milliseconds". The Office Action has not provided any evidence that the claimed architecture of the action pattern classification model designed for a processor to execute C-I such that a time delay between the at least one imaginary movement and the real-time visual feedback presented in response thereto does not exceed 500 milliseconds, is well-understood, routine or conventional.
Moreover, the Office Action's own findings foreclose the conclusion that the claimed combination is well-understood, routine and conventional. The Office Action states that the closest prior art of record, Mackellar, does not teach elements D) through I) of the independent claims - the feature extraction, the action-pattern classification, the computation of a control command from the classified action pattern, the presentation of the character's action, the evaluation of execution performance, and the adaptation of real-time visual feedback - and that Claims 1-20 contain allowable subject matter. The well-understood, routine and conventional inquiry sets a higher bar than mere disclosure in the prior art (Berkheimer, 881 F.3d at 1369; MPEP § 2106.05(d)(I)); a combination that the Office has found absent from the closest art of record cannot, a fortiori, be well-understood, routine and conventional. The Step 2B analysis in the Office Action addresses only the generic hardware - "a processor," "a display," "electrodes," a "GUI" - and never evaluates the claimed combination of elements D) through I), as now amended, as an ordered combination. An inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces. BASCOM Global Internet Servs., Inc. V. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016).
As a conclusion of the above arguments, Applicant respectfully requests that the rejection be withdrawn and the claims be allowed. Applicant respectfully submits that the dependent claims are allowable as well, at least as being dependent from allowed amended independent Claims 1, 15, and 20.”
The Examiner respectfully disagrees. In a step 2B analysis, an additional element (or combination of elements) is not well-understood, routine or conventional unless the examiner finds, and expressly supports a rejection in writing with, one or more of four options:
Option 1 – Statement(s) by Applicant
Option 2 – Court Decisions in MPEP § 2106.05(d)(II)
Option 3 – Publication(s)
Option 4 – Official Notice
Here, the Examiner has chosen to apply Option 1 – Statement(s) by Applicant as support of the rejection at step 2B of the analysis.
Statement(s) by the Applicant is/are defined as the following:
(1) An explanation based on an express statement in the specification (e.g., citation to a relevant portion of the specification) that demonstrates the well-understood, routine, conventional nature of the additional element(s). A specification demonstrates the well-understood, routine, conventional nature of additional elements when it describes the additional element(s) as conventional (or an equivalent term); as a commercially available product; or, in a way that shows the element is widely prevalent or in common use; or
(2) A statement made by an applicant during prosecution, that demonstrates the well-understood, routine, conventional nature of the additional element(s).
Here, the Examiner has chosen to incorporate the first definition (i.e.(1)) within the subject-matter eligibility analysis as provided above in the rejection. Specifically, the Examiner has identified that the claims do not include additional elements that either alone or in combination are sufficient to amount to “significantly more” than the judicial exception because to the extent that, e.g. “a processor,” “a display,” “a non-transitory computer readable storage medium,” and “a graphical user interface (GUI),” are claimed these are generic, well-known, and conventional data gather computing elements.
The Examiner, provides evidence of this by citing an express statement in the specification (e.g., citation to a relevant portion of the specification) that demonstrates the well-understood, routine, conventional nature of the additional element(s). This requirement is achieved by citing the Applicant’s own written description of the specification as originally filed on page 55, lines 9-30 as follows:
“Fig. 18 is an example of general-purpose computing system: personal computer or server 20withcentral processing unit 21, system memory 22 and system bus 23, which contains various system components, including memory connected with central processing unit 21. System bus 23 is implemented as any bus structure known in the prior art, which in its turn contains bus memory or a bus memory controller, a peripheral bus and a local bus, which can interact with any other bus architecture. System memory contains read-only memory (ROM)24, random access memory(RAM)25.Basic input/output system (BIOS)26, contains the main procedures, which provide information transfer between the elements of personal computer 20, for example, at the moment of operating system loading using ROM24.In its turn, personal computer 20 contains hard disk drive 27 to read and write data, disk drive 28 to read and write data to/from removable disks 29 and optical drive 30 to read and write data to/from optical disks 31, such as CD-ROM, DVD-ROM and other optical data storage means (device)s. Hard disk drive 27,disk drive 28,optical drive 30 are connected with system bus 23though hard disk interface disk interface 33 and optical drive interface 34, correspondingly. The drives and corresponding computer data storage means (device)s are nonvolatile storage means (device)s for computer instructions, data structures, software modules and other data from personal computer 20.
The given description discloses the implementation of the system, which uses hard disk drive removable disk 29 and removable optical disk 31, but it will be appreciated that other types of computer data storages 56 can be used, which can store data in the form, accessible for reading by computer (solid-state drives, flash memory cards, digital disks, random access memory(RAM etc.), which are connected to system bus 23 with controller 55.”
That being said, “a processor,” “a display,” “a non-transitory computer readable storage medium,” and “a graphical user interface (GUI),” are clearly and reasonably understood to be generic computer components as part of a generic commercially available computer product that is widely prevalent and in common use. Therefore, it is the express statement in the specification on page 55, lines 9-30 that demonstrates the well-understood, routine, conventional nature of the additional element(s) (i.e. computer) that satisfies the Examiner’s burden of providing evidence under the required Berkheimer analysis at step 2B and ultimately overcoming the Applicant’s inaccurate argument. As such, the argument is not persuasive.
Therefore, for the reasons stated here and above, the rejection of claims 1-20 under 35 U.S.C. §101 are not withdrawn.
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 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 ROBERT P BULLINGTON whose telephone number is (313)446-4841. The examiner can normally be reached Mon.-Fri. 8:00-4:00.
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/Robert P Bullington, Esq./
Primary Examiner, Art Unit 3715
