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 .
Response to Arguments
Claim Interpretation
Applicant's arguments filed 12/15/2025 have been fully considered.
Applicant argues that the claims do not employ the term “means for” and the use of “module” and “engine” reflects well-understood structures in the software and computer systems arts and does not invoke 112(f).
In response to Applicant’s arguments, while the claims do not recite “means for” in the claims, they recite the generic placeholders, such as, “module” and “engine” in multiple claims. The following is a list of non-structural generic placeholders that may invoke 35 U.S.C. 112(f): "mechanism for," "module for," "device for," "unit for," "component for," "element for," "member for," "apparatus for," "machine for," or "system for." Welker Bearing Co., v. PHD, Inc., 550 F.3d 1090, 1096, 89 USPQ2d 1289, 1293-94 (Fed. Cir. 2008); Mass. Inst. of Tech. v. Abacus Software, 462 F.3d 1344, 1354, 80 USPQ2d 1225, 1228 (Fed. Cir. 2006); Personalized Media, 161 F.3d at 704, 48 USPQ2d at 1886–87; Mas-Hamilton Group v. LaGard, Inc., 156 F.3d 1206, 1214-1215, 48 USPQ2d 1010, 1017 (Fed. Cir. 1998). Note that there is no fixed list of generic placeholders that always result in 35 U.S.C. 112(f) interpretation, and likewise there is no fixed list of words that always avoid 35 U.S.C. 112(f) interpretation. See MPEP 2181 (I)(A). The terms “module” and “engine” are construed to be non-structural generic placeholders that invoke 112(f) similar to those provided in the non-exhaustive list in the MPEP. Thus, 112(f) is invoked. See the interpretations below for further clarification.
Applicant argues that all of the recited module terms should be construed according to their plain and ordinary meaning as software components of the server-side administrative interface. They are not generic placeholders. The specification further provides structural context for each module.
In response to Applicant’s arguments, as discussed above, the modules fall under the list provided as non-structural generic placeholders that invoke 112(f). Thus, the interpretations are maintained.
Applicant argues that the “artificial intelligence engine” should be construed according to its plain and ordinary meaning as a software-implemented AI or machine learning component executed by the server.
In response to Applicant’s arguments, as discussed above, the AI engine is construed to fall under the list provided as a non-structural generic placeholder that invokes 112(f). Thus, the interpretation is maintained. Based on the specification, the engine is construed as a processor, which can execute instructions and steps. See below for further clarification.
Rejection Under 112
Applicant's arguments filed 12/15/2025 have been fully considered.
Applicant argues that regarding the 112(a) rejection, the reading of the specification was too narrow. The specification describes the server-side administrative interface as a web application providing features for system monitoring, user management, data analysis, and AI engine management, which includes database operations and interface-driven actions that constitute manipulating wellness data (e.g., viewing, filtering, querying, organizing, adding, removing, modifying, exporting, and otherwise operating on stored wellness records via administrative/database interfaces).
In response to Applicant’s argument, Applicant’s arguments are persuasive. See the specification at [0065]. The rejection is withdrawn.
Applicant argues that regarding the 112(b) rejection, the specification informs the person of ordinary skill in the art with the scope of the invention and is therefore not indefinite. The system is disclosed to include a wellness database interface that provides data tools. Thus the rejection should be withdrawn.
In response to Applicant’s argument, Applicant’s arguments are persuasive. See the specification at [0065]. The rejection is withdrawn.
Rejection Under 101
Applicant's arguments filed 12/15/2025 have been fully considered.
Applicant argues that the characterization of the claims, as management of personal behavior or interactions (i.e., following rules or instructions), does not reflect what is actually claimed. The pending claims are directed to a specific, concrete wellness treatment system and associated control methodology, which is operated using server-side AI to generate recommendations and to drive treatment parameter adjustments.
In response to Applicant’s argument, as discussed in the rejection below and pointed out by Applicant’s own argument, the claims are directed to providing a treatment recommendation. This falls under the organizing human activity. See MPEP 2106.04(a)(2)(II).
The claims cannot be following rules or instructions or organizing human activity since the claimed subject matter requires physical PBM devices associated with the users, software, and a server equipped with an AI engine.
In response to Applicant’s argument, the limitations at issue are considered additional elements and construed as applying the abstract idea by invoking the use of computers and machinery to carry out the abstract idea. Thus, the abstract idea, but for the computer components, amounts to falling under organizing human activity. See the rejection below for clarification.
The specification makes clear that the recommendations are generated by the AI engine and are device parameters for the PBM devices that the user reviews. Since the claimed invention is rooted in real world technology it is not an abstract mental process or method of organizing human activity.
In response to Applicant’s argument, the claim interpretation of the AI engine is described below and with that construction the AI engine is considered an additional element for carrying out the abstract idea. Additionally, the recommendation being generated for the user is still considered to fall under organizing human activity. See the rejection below.
The claims do not merely use a computer to implement an abstract idea, they recite an integrated architecture tying together PBM devices, a client installed on the PBM devices… device control (see Remarks pgs. 10-11). The recommendations are also not generic advice but specific treatment parameters that adjust device settings. The claims recite a specific networked PBM device control system and therefore recites a practical application.
In response to Applicant’s argument, the additional elements amount to nothing more than generic computer components since they are recited at a high level of generality (e.g., database, server, processor – based on 112(f) claim interpretation). The recommendation is not an additional element and thus part of the abstract idea. By recited known components for their intended purposes, the additional elements do not amount to a practical application.
Rejection Under 102/103
Applicant's arguments filed 12/15/2025 have been fully considered.
Applicant argues that the Steingold does not disclose the software-client module set , the wellness database of collected user wellness data, and the AI engine operation on the data. Steingold is used to teach the AI engine, which is interpreted as a processor, however the claim requires the server be equipped with the AI engine and further requires the AI engine to operate on the wellness data to generate recommendations for treatment.
The specification at [00180]-[00182] describes the looking at the data from the sensors such as the EEG and determining the treatment recommendations and also their effectiveness using machine learning techniques. As previously taught in the rejection the sensor, EEG, can be connected to a processor. (See [0015]). Therefore, the processor is carrying out the machine learning techniques on the wellness data to provide the treatment recommendations, as construed to be claimed (see the claim interpretation). For further clarification the specification, at multiple locations, discusses how the processor analyzes and processes the data in order to determine the therapy output. See [00146], [00159]. Therefore the rejection is maintained.
Applicant argues that Steingold cannot anticipate claims 2-8 since it depends from claim 1, which as previously argued does not teach the AI engine operating on the wellness data.
In response to Applicant’s argument, the dependent claims are maintained in light of maintaining the rejection of the independent claim. See Response to Argument “A”.
Applicant argues that Steingold does not disclose claim 9 and the steps of operating a wellness database with an AI engine to generate a PBM recommendation.
In response to Applicant’s argument, as discussed above the reference does disclose the limitation and the rejection is maintained. See Response to Argument “A”.
Applicant argues that Steingold cannot anticipate claims 17-20 since it depends from claim 9 which does not cure the deficiencies of the base claim.
In response to Applicant’s argument, the dependent claims are maintained in light of maintaining the rejection of the independent claim. See Response to Argument “A”.
Applicant argues that cited rationale does not establish a proper motivation to combine, and does not address the claims as written. DeJonge is directed to an augmented reality interface for assisting a user to operate an ultrasound device and is therefore rooted in ultrasound imaging workflows, image-derived feature maps, and AR guidance for image acquisition/interpretation, which is different than Steingold’s photobiomodulation treatment system.
In response to Applicant’s argument, the teaching motivation to combine the references was taught in de Jonge which discussed processing multiple pieces of data (i.e., image data) and then refine analysis estimates, in order to analyze sequential data (i.e., wellness data). See the rejection below.
Applicant argues that the rejection does not provide supported explanation for why the substitution of adding the LSTM to Steingold would have been predictable.
In response to Applicant’s argument, the teaching motivation to combine the references was taught in de Jonge as discussed in the rejection below and further explained in Response to Argument “E”. See also MPEP 2143.
Applicant argues that the claims 11-16, which depend from claim 10, recite additional limitations concerning the LSTM…. And since claim 10 is not properly combined the frameworks also fails for the rest of the claims.
In response to Applicant’s argument, see Response to Argument “F”. The rejection are maintained sine the motivation is proper.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier.
Regarding Claim 2 – The claim recites a data management module for manipulating the wellness data... See MPEP 2181. The claim limitation uses the term data management module. The “data management module” is modified by functional language “for manipulating the wellness data….” The data management module is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. The specification at [0065] describes the server-side administrative interface being equipped with a wellness database interface for data visualization and analysis tools. For examination purposes the data management module is construed to be an interface dashboard.
Regarding Claim 2 – The claim recites an Al management module for configuring the operation of the artificial intelligence engine... See MPEP 2181. The claim limitation uses the term Al management module. The “Al management module” is modified by functional language “for configuring the operation of the artificial intelligence engine….” The Al management module is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. at [0066] which describes an AI engine interface for working with the AI engine so parties can train and validate AI models, adjust parameters, test performance, and view AI learning logs. This AI engine interface is construed as AI management module. For examination purposes the Al management module is construed to be an interface dashboard.
Regarding Claim 2 – The claim recites a user management module for monitoring user interactions and controlling user access to the system... See MPEP 2181. The claim limitation uses the term user management module. The “user management module” is modified by functional language “monitoring user interactions and controlling user access to the system….” The user management module is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. at [0063] which describes the user management module as part of a dashboard of the interface where data can be added, removed, or modified. The dashboard is also equipped with a feature for managing permissions and tracking user status. For examination purposes the user management module is construed to be an interface dashboard.
Regarding Claim 9 – The claim recites an artificial intelligence engine to generate PBM recommendations... See MPEP 2181. The claim limitation uses the term artificial intelligence engine. The “artificial intelligence engine” is modified by functional language “to generate PBM recommendations….” The artificial intelligence engine is not modified by sufficient structure, material or act for performing the claim. Therefore 112(f) is invoked. See Spec. at [0050] which sufficiently describes the AI engine as using hardware such as a processor to carry out the steps. For examination purposes the AI engine is construed to be hardware such as a processor.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-8 are drawn to a system, which is within the four statutory categories (i.e. apparatus). Claims 9-20 are drawn to a method for, which is within the four statutory categories (i.e. process).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, independent claim 9 (and substantially similar with independent claim 1) recites:
A method for promoting wellness, comprising the steps of:
installing a software client on a plurality of client photobiomodulation (PBM) devices, each of which is associated with one of a plurality of users;
collecting wellness data from said plurality of users to form a wellness database;
receiving, at a server in communication with said plurality of client PBM devices, wellness data and treatment objectives from said plurality of users via said software client; and
operating on said wellness database with an artificial intelligence engine to generate PBM recommendations for each of said plurality of users based on the treatment objectives input for each of said plurality of users.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the PBM devices, wellness database, server, artificial intelligence engine {construed as processor – see claim interpretation}, the limitations of this claim encompass following rules or instructions in order to provide a PBM recommendation for users based on treatment objectives. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behavior or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-8 and 10-20 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, independent claim 9 (and substantially similar with independent claim 1) recites:
A method for promoting wellness, comprising the steps of:
installing a software client on a plurality of client photobiomodulation (PBM) devices, each of which is associated with one of a plurality of users;
collecting wellness data from said plurality of users to form a wellness database;
receiving, at a server in communication with said plurality of client PBM devices, wellness data and treatment objectives from said plurality of users via said software client; and
operating on said wellness database with an artificial intelligence engine to generate PBM recommendations for each of said plurality of users based on the treatment objectives input for each of said plurality of users.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the PBM devices, wellness database, server, artificial intelligence engine {construed as processor – see claim interpretation}, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0043]-[0045], [0048], [0050], [0052], see MPEP 2106.05(f))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2-8 and 10-20 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 2-8 and 10-20 recite additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the PBM devices, wellness database, server, artificial intelligence engine {construed as processor – see claim interpretation}, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0043]-[0045], [0048], [0050], [0052]); using wellness database, server, artificial intelligence engine {construed as processor – see claim interpretation}, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-9, 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Steingold et al. (WO 2022/197937).
Regarding claim 1, Steingold discloses a system for promoting wellness, comprising:
a plurality of photobiomodulation (PBM) devices, each associated with a respective user; ([0008] Transcranial photobiomodulation (“tPBM”) of the brain with near infrared and red light has been shown to be beneficial for treating various psychiatric and neurological conditions such as anxiety, stroke and traumatic brain injury. [0012] Preferred embodiments provide devices and methods in which a head wearable device is configured to be worn by a subject that is operated to deliver illuminating wavelengths of light with sufficient energy that are absorbed by a region of brain tissue during a therapeutic period. Transcranial delivery of illuminating light can be performed with a plurality of light emitting devices mounted to the head wearable device that can also preferably include control and processing circuitry)
a server equipped with an artificial intelligence engine; ([0015] The system can include a networked server to enable communication with remote devices… The EEG electrodes can be integrated with the head wearable device and be connected either directly to a processor thereon {AI engine interpreted as a processor – see the claim interpretation above})
a software client installed on each of the PBM devices or on a user device in communication with one of the PBM devices, ([0015] A computing device such as a tablet or laptop computer can be used to control diagnostic and therapeutic operations of the head worn device and other devices used in conjunction with a therapeutic session. Such computing devices can store and manage patient data and generate electronic health or medical records for storage and further use. The computing device can be programmed with software modules [0062] The wearable device 50 may be paired with a user device (e.g., smartphone, smartwatch), which may provide instructions that may determine a frequency of transmitted light, the type of light (e.g., red light or infrared light), the meditations, and/or the linguistic inputs)
the software client including a client-side user interface having an input module for the user to input treatment objectives, ([0063] the computing device 150 includes a visual display device 152 that can display a graphical user interface (GUI) 160. The GUI 160 includes an information display area 162 and user-actuatable controls 164. Optionally, the computing device 150 is also in communication with an external EEG system 120'. Optionally, the computing device 150 is also in communication with an external light sensor array 122'. An operating user can operate the computing device 150 to control operation of the photobiomodulation device 110 including activation of the functions of the photobiomodulation device 110 and mono- or bi directional data transfer between the computing device 150 and the photobiomodulation device 110. [0064] The operating user can change among operational modes of the computing device 150 by interacting with the user-actuatable controls 164 of the GUI 160. Examples of user- actuatable controls include controls to access program control tools, stored data and/or stored data manipulation and visualization tools, audio program tools, assessment tools, and any other suitable control modes or tools known to one of ordinary skill in the art. [0065] In the program control mode, the GUI 160 can display program controls including one or more presets 165. Activation of the preset by the operating user configures the photobiomodulation device 110 to use specific pre-set variables appropriate to light therapy for a particular class of patients or to a specific patient. [00195] interfaces for the user (child 4004 such as a tablet), parents (using a personal computer 4006 to access a website interface))
a data display module to visualize wellness data, ([0064] The operating user can change among operational modes of the computing device 150 by interacting with the user-actuatable controls 164 of the GUI 160. Examples of user- actuatable controls include controls to access program control tools, stored data and/or stored data manipulation and visualization tools, audio program tools, assessment tools, and any other suitable control modes or tools known to one of ordinary skill in the art. [0066] The data can be transmitted and logged before, during, and after a therapy session. Similar data can also be received at the computing device 150 from the external EEG system 120' or the external light sensor array 122' in embodiments that utilize these components. In the stored data manipulation and/or visualization mode, the operating user can review the data logged from these sources and received at the computing device 150.)
a recommendation module to display PBM treatment recommendations, and ([00177] The Personalized Treatment Module (PTM) 3004 leverages the cluster-treatment mapping data from the Machine Learning Module 3018 to create personalized plans for the Neuromodulation Treatment Module (NMT) 3008 and the Cognitive Programming Module (CPM) 3010. This includes physical device treatment duration, intensity, and frequency as well as specific cognitive treatment activity portfolios to be administered to the child. [00178] The Neuromodulation Treatment Module (NMT) 3008 leverages the personalized treatment recommendations of the PTM and provides them across the parent and therapist interfaces for administration [00179] The Cognitive Programming Module (CPM) 3010 leverages the personalized treatment recommendations from the PTM and provides cognitive activity and treatment content to the child via the child interface and/or the parent/therapist interfaces)
a device interaction module to control the PBM device; ([0064] The operating user can change among operational modes of the computing device 150 by interacting with the user-actuatable controls 164 of the GUI 160. Examples of user- actuatable controls include controls to access program control tools, stored data and/or stored data manipulation and visualization tools, audio program tools, assessment tools, and any other suitable control modes or tools known to one of ordinary skill in the art. [0080] FIG. 5 illustrates a schematic layout of the photobiomodulation device 110 of the present invention. The processor board 111 is, for example, a printed circuit board including components to control functions of the photobiomodulation device 110. The processor board 111 can include a central processing unit 112 and a power management module 114 in some embodiments. [0081] The power management module 114 can monitor and control use of particular light emitter panels 115a-115e during a therapy session. In some embodiments, the power management module 114 can take action to control or provide feedback to a patient user related to whether light emitter panels 115a-l 15e are not used, or are only partially used, during a particular therapy session)
a server-side administrative interface for monitoring and managing the system; ([00100] Detected electrical signals from the sensors can be routed to the controller board and stored in local memory and can also be transmitted via wireless transmission to the external tablet device so that a user or clinician can monitor the therapeutic session and control changes to the operating parameters of the system during use. [00145] Thus, an operating module of the software can be programmed to retrieve fields of data or data files from a patient data entry module that can include patient information and other initial observations of parents or clinicians regarding a child's age, condition, medical history including medications that may impact a further diagnostic or therapeutic program. FIG. 16 illustrates a process flow diagram for a method 600 of selecting and optimizing parameters over multiple therapeutic sessions including manual and automated selection tracks. Initially, patient data related to a child or adult patient (such as age or condition) can be entered by a user into a memory of a computing device (step 602). For example, data can be entered by a user through the GUI 160 of the remote computing device 150 (such as a tablet computing device) and stored in the memory 156 as described previously in relation to FIG. 4. The method 600 can then follow one of two tracks. In one embodiment, the user can manually select illumination or therapy session parameters for a first therapeutic dose level or dose level sequence based upon the patient data (step 604). For example, the user can manually select parameters from menu or other displays on the GUI 160 of the remote computing device 150. [00195] interfaces for the user… therapist interface 4008 and the software system 4010 for personalized treatment needs, assessment, recommendation, and progress monitoring)
and a wellness database that contains the wellness data collected from the plurality of users, ([0015] during or after therapeutic sessions to generate diagnostic data for the patient [0076] The remote computing device 150 may also interact with one or more computer storage devices or databases 401, such as a hard-drive, CD-ROM, or other computer readable media, for storing data and computer-readable instructions and/or software that implement exemplary embodiments of the present disclosure (e.g., applications). For example, exemplary storage device 401 can include modules to execute aspects of the GUI 160 or control presets, audio programs, activity data, or assessment data. The database(s) 401 may be updated manually or automatically at any suitable time to add, delete, and/or update one or more data items in the databases. The remote computing device 150 can send data to or receive data from the database 401 including, for example, patient data, program data, or computer-executable instructions)
wherein the artificial intelligence engine operates on the wellness data to generate the PBM recommendations. ([00180] The Sensor and Quantitative Data Feedback Module (SQD) 3012 captures data from physical sensors and devices such as EEG, heart rate and pulse wearables, and other devices alongside with performance data of the child on the cognitive programming module (CPM) as well as parental and therapist feedback to measure the impact of the treatments on the NDA metrics of the child. [00181] The Performance Progress Module (PPM) 3014 compares the individual data from the SQD 3012 with expected progress thresholds established for the selected cluster within the RPM 3016 and provides effectiveness scores for administered treatments. [00182] The Machine Learning Module (MLM) 3018 uses an embedding-based vectorization methodology to create user profile vectors that are then mapped into different profile-treatment clusters which match an individual profile background to treatments that have the highest effectiveness scores for individuals with similar user profile vectors.)
Regarding claim 2, Steingold discloses the system of claim 1, and Steingold further discloses further discloses wherein the server-side administrative interface includes:
a data management module for manipulating the wellness data; ([0015] Such computing devices can store and manage patient data and generate electronic health or medical records for storage and further use. The computing device can be programmed with software modules such as a patient data entry module, a system operating module that can include diagnostic and therapeutic submodules, and an electronic medical records module)
an Al management module for configuring the operation of the artificial intelligence engine; and ([0015] Such computing devices can store and manage patient data and generate electronic health or medical records for storage and further use. The computing device can be programmed with software modules such as a patient data entry module, a system operating module that can include diagnostic and therapeutic submodules, and an electronic medical records module)
a user management module for monitoring user interactions and controlling user access to the system. ([0064] The operating user can change among operational modes of the computing device 150 by interacting with the user-actuatable controls 164 of the GUI 160. Examples of user- actuatable controls include controls to access program control tools).
Regarding claim 3, Steingold discloses the system of claim 1, and Steingold further discloses wherein the artificial intelligence engine is of a type selected from the group consisting of Deep Belief Networks, Convolutional Neural Networks, Recurrent Neural Networks, Feedforward Neural Networks, Radial Basis Function Neural Networks, and Self-Organizing Maps. ([00191] The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established).
Regarding claim 4, Steingold discloses the system of claim 1, and Steingold further discloses wherein the recommendation module in the client- side user interface is further configured to provide explanations of each treatment recommendation, and wherein the explanations include expected benefits and potential side effects. ([00202] A 'total improvement score' was computed by combining the categories that are related to socialization (e.g. eye contact), language (new words), and responsiveness to create the total Benefit Index score. [00203] FIG. 35A illustrates values of the benefit index for individual subjects and the average for all subjects including error bars. The results show a statistically significant difference in the Benefit score between the Active and Placebo kids. Separately, the categories that relate to “over-excitement” such as hyperactivity, headaches, wakefulness, and others can be combined to create the Side Effect Index score. FIG. 35B illustrates values of the side effect index for individual subjects and the average for all subjects including error bars. The results also show a non-statistically significant difference in Side Effects between Active and Placebo children).
Regarding claim 5, Steingold discloses the system of claim 1, and Steingold further discloses wherein the data display module in the client-side user interface is configured to provide graphical visualizations of the user's wellness data over time. ([0075] A user may interact with the remote computing device 150 through a visual display device 152, such as a computer monitor, which may display one or more graphical user interfaces 160 [0066] In some embodiments, the photobiomodulation device 110 can transmit and/or receive data from the computing device 150. For example, the photobiomodulation device 110 can transmit data to log information about a therapy session for a patient. Such data can include, for example, illumination patterns, total length of time, time spent in different phases of a therapy program, electroencephalogram (EEG) readings, and power levels used. The data can be transmitted and logged before, during, and after a therapy session. Similar data can also be received at the computing device 150 from the external EEG system 120' or the external light sensor array 122' in embodiments that utilize these components. In the stored data manipulation and/or visualization mode, the operating user can review the data logged from these sources and received at the computing device 150. [0098] preset or manually entered parameters 452 can be entered by touch actuation on the tablet touchscreen so that the system controller can actuate the illumination sequence. [0068] In the assessment mode, a user can input or review data related to patient assessment such as task identity and scoring. For example, FIG. 6 illustrates a particular assessment test displayed in the information display area 162 of the GUI 160).
Regarding claim 6, Steingold discloses the system of claim 1, and Steingold further discloses wherein the input module in the client-side user interface includes a questionnaire form for collecting the user's wellness and treatment objectives. ([00176] The neuro-developmental assessment module (NDA) 3006 uses the user profile together with questionnaire data to assess the baseline and continuous performance of the child along attachment, playing, communication and language, and other behavioral factors using a range of metrics and scores the child's current state for each of the measures).
Regarding claim 7, Steingold discloses the system of claim 1, and Steingold further discloses wherein the device interaction module in the client-side user interface allows the user to control the operation of their photobiomodulation device, including starting and stopping treatment sessions, adjusting treatment parameters, and scheduling future sessions. ([0064] The operating user can change among operational modes of the computing device 150 by interacting with the user-actuatable controls 164 of the GUI 160. Examples of user- actuatable controls include controls to access program control tools, stored data and/or stored data manipulation and visualization tools, audio program tools, assessment tools, and any other suitable control modes or tools known to one of ordinary skill in the art. [0065] In the program control mode, the GUI 160 can display program controls including one or more presets 165. Activation of the preset by the operating user configures the photobiomodulation device 110 to use specific pre-set variables appropriate to light therapy for a particular class of patients or to a specific patient [0066] In some embodiments, the photobiomodulation device 110 can transmit and/or receive data from the computing device 150. For example, the photobiomodulation device 110 can transmit data to log information about a therapy session for a patient. Such data can include, for example, illumination patterns, total length of time, time spent in different phases of a therapy program, electroencephalogram (EEG) readings, and power levels used. The data can be transmitted and logged before, during, and after a therapy session. Similar data can also be received at the computing device 150 from the external EEG system 120' or the external light sensor array 122' in embodiments that utilize these components. In the stored data manipulation and/or visualization mode, the operating user can review the data logged from these sources and received at the computing device 150. [0098] preset or manually entered parameters 452 can be entered by touch actuation on the tablet touchscreen so that the system controller can actuate the illumination sequence).
Regarding claim 8, Steingold discloses the system of claim 1, and Steingold further discloses wherein the user device is selected from the group consisting of desktop PCs, laptop PCs, and mobile technology platforms. ([0075] A user may interact with the remote computing device 150 through a visual display device 152, such as a computer monitor, which may display one or more graphical user interfaces 160).
Regarding claim 9, recites substantially similar limitations as those recited in the rejection of claim 1, and, as such, is rejected for similar reasons as given above.
Regarding claim 17, Steingold discloses the method of claim 9, and Steingold further discloses further comprising the step of utilizing a Convolutional Neural Network (CNN) within said artificial intelligence engine to analyze image-based wellness data from said plurality of users. ([00191] The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established [00171] Brain imaging studies have specifically identified these areas (frontal lobe, corpus callosum, hippocampus and cerebellum) as being most likely to be affected by FACS. Other imaging studies showed that FACS results in poor communication between various brain areas (i.e., poor brain connectivity). Children affected by FACS usually have smaller brains. In addition, children affected by FACS may develop physical characteristics like microcephaly, growth retardation, dislocated limbs, certain facial features (e.g., thinner upper lip) and cardiological problems).
Regarding claim 18, Steingold discloses the method of claim 17, and Steingold further discloses wherein the image-based wellness data includes images of a user's skin, hair, eyes, or other physical attributes. ([00148] FIG. 17 illustrates a process flow diagram for a method 700 for administering a therapeutic session to a patient in accordance with various embodiments described herein. As an optional first step, patient data can be input by a user to a computing device and stored in data fields in a patient data entry module resident in the computing device or a server device (step 702). Relevant patient data entered in this step can include patient age, weight, physical or mental condition, medication history or regimen, and a data map of cranial thickness or density as a function of location on the patient's cranium).
Regarding claim 19, Steingold discloses the method of claim 17, and Steingold further discloses further comprising the step of training the CNN to identify conditions or changes in the image-based wellness data that might influence treatment recommendations. ([0098] The data can be used for further analysis such as by application of a machine learning program to provide training data. [00171] Brain imaging studies have specifically identified these areas (frontal lobe, corpus callosum, hippocampus and cerebellum) as being most likely to be affected by FACS. Other imaging studies showed that FACS results in poor communication between various brain areas (i.e., poor brain connectivity). Children affected by FACS usually have smaller brains. In addition, children affected by FACS may develop physical characteristics like microcephaly, growth retardation, dislocated limbs, certain facial features (e.g., thinner upper lip) and cardiological problems. [00172] tPBM (stimulation of the brain with near-infra red light) has been shown in animal and human studies (in vivo and in vitro) to increase blood oxygenation, cerebral blood flow, and mitochondrial ATP production. In addition, EEG and NIRS data has shown that tPBM improves brain connectivity. Therefore, blood brings more oxygen and nutrition to the brain. In addition, increased ATP production results in more neurogenesis and synaptogenesis. Furthermore, functional brain connectivity has been shown to improve after one session. tPBM has been shown to be beneficial for traumatic brain injury, depression, ischemic stroke, and Parkinson's disorder. In addition, it has been shown to be effective for Down syndrome, autism and ADHD. Similarly, tPBM can be effective for the neurological symptoms of FACS by increasing the amount of oxygen and nutrients delivered to the brain, improving functional brain connectivity, and increasing neurogenesis and synaptogenesis. Specifically, the effect may be most pronounced in cortical structures (frontal lobes), which improves organization, focus, and decision making. The effect on memory and motor functions may be less pronounced since sub-cortical structures are implicated (e.g., hippocampus and cerebellum). However, due to neuroplasticity, the beneficial effect of tPBM may be most pronounced when treatment is administered to young children).
Regarding claim 20, Steingold discloses the method of claim 19, and Steingold further discloses wherein the conditions or changes identified by the CNN include symptoms of skin conditions, hair loss, eye diseases, or other physical conditions relevant to PBM treatment. ([00143] To further improve personalization features, the system can be programmed to adjust for skin color based on the timing and strength of dosage. Darker skin pigments absorb light more than lighter skin, therefore fewer photons are likely to reach the brain. In the clinical study, children with darker skin showed improvement later than children with lighter skin, thereby indicating a need to adjust dosage based on skin absorption. Therefore, they might need longer usage of the device at a given dosage to detect improvements. The control software for the device can be programmed for such personal characteristics as race and ethnicity).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 10-16 are rejected under 35 U.S.C. 103 as being unpatentable over Steingold et al. (WO 2022/197937) in view of de Jonge et al. (US 2022/0167945).
Regarding claim 10, Steingold discloses the method of claim 9, but does not appear to disclose the following, however, de Jonge teaches it is old and well known in the art of healthcare data processing wherein further comprising the step of utilizing a Long Short-Term Memory (LSTM) network within said artificial intelligence engine, wherein said LSTM network analyzes sequential wellness data from said plurality of users. (de Jonge [0299] In sequential processing of image sequences, the inputs into the LSTM consist of the feature maps computed from a convolutional neural network).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare data processing, before the effective filing date of the claimed invention, to modify , as modified above, to incorporate further comprising the step of utilizing a Long Short-Term Memory (LSTM) network within said artificial intelligence engine, wherein said LSTM network analyzes sequential wellness data from said plurality of users, as taught by de Jonge, in order to process several images and produce updates with refined estimates. See [0293].
Regarding claim 11, Steingold-de Jonge teaches the method of claim 10 and the LSTM network, and Steingold further discloses further comprising the step of training the network to identify patterns in wellness data over extended treatment periods. ([00180] The Sensor and Quantitative Data Feedback Module (SQD) 3012 captures data from physical sensors and devices such as EEG, heart rate and pulse wearables, and other devices alongside with performance data of the child on the cognitive programming module (CPM) as well as parental and therapist feedback to measure the impact of the treatments on the NDA metrics of the child. [00181] The Performance Progress Module (PPM) 3014 compares the individual data from the SQD 3012 with expected progress thresholds established for the selected cluster within the RPM 3016 and provides effectiveness scores for administered treatments. [00182] The Machine Learning Module (MLM) 3018 uses an embedding-based vectorization methodology to create user profile vectors that are then mapped into different profile-treatment clusters which match an individual profile background to treatments that have the highest effectiveness scores for individuals with similar user profile vectors. [00191] The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established).
Regarding claim 12, Steingold-de Jonge teaches the method of claim 11 and the LSTM network, and Steingold further discloses wherein the wellness data processed by the network includes changes in vital signs or symptom ratings over time. ([00174] The User Profile Module (UPM) 3002 receives all data related to the child's profile, including demographic data, neuro-developmental assessment data, health data, ongoing device data, treatment history, progress indicators, parental assessments, and behavioral data. The user profile is continuously updated with treatment and progress data and contains both baseline and longitudinal data [00180] The Sensor and Quantitative Data Feedback Module (SQD) 3012 captures data from physical sensors and devices such as EEG, heart rate and pulse wearables, and other devices alongside with performance data of the child on the cognitive programming module (CPM) as well as parental and therapist feedback to measure the impact of the treatments on the NDA metrics of the child. [00181] The Performance Progress Module (PPM) 3014 compares the individual data from the SQD 3012 with expected progress thresholds established for the selected cluster within the RPM 3016 and provides effectiveness scores for administered treatments. [00182] The Machine Learning Module (MLM) 3018 uses an embedding-based vectorization methodology to create user profile vectors that are then mapped into different profile-treatment clusters which match an individual profile background to treatments that have the highest effectiveness scores for individuals with similar user profile vectors. [00191] The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established)
Regarding claim 13, Steingold-de Jonge teaches the method of claim 12, and Steingold further discloses further comprising the step of processing the wellness data in real-time to provide timely PBM treatment recommendations. ([00174] The User Profile Module (UPM) 3002 receives all data related to the child's profile, including demographic data, neuro-developmental assessment data, health data, ongoing device data, treatment history, progress indicators, parental assessments, and behavioral data. The user profile is continuously updated with treatment and progress data and contains both baseline and longitudinal data).
Regarding claim 14, Steingold-de Jonge teaches the method of claim 13 and the LSTM network, and Steingold further discloses further comprising the step of identifying patterns in wellness data with the network to influence PBM treatment recommendations. ([00182] The Machine Learning Module (MLM) 3018 uses an embedding-based vectorization methodology to create user profile vectors that are then mapped into different profile-treatment clusters which match an individual profile background to treatments that have the highest effectiveness scores for individuals with similar user profile vectors. [00191] The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established)
Regarding claim 15, Steingold-de Jonge teaches the method of claim 14 and the LSTM network, and Steingold further discloses further comprising the step of periodically retraining or updating the network based on new sequential wellness data. ([00174] The User Profile Module (UPM) 3002 receives all data related to the child's profile, including demographic data, neuro-developmental assessment data, health data, ongoing device data, treatment history, progress indicators, parental assessments, and behavioral data. The user profile is continuously updated with treatment and progress data and contains both baseline and longitudinal data [00182] The Machine Learning Module (MLM) 3018 uses an embedding-based vectorization methodology to create user profile vectors that are then mapped into different profile-treatment clusters which match an individual profile background to treatments that have the highest effectiveness scores for individuals with similar user profile vectors. [00191] The architecture of the Deep Neural Network (DNN) contains convolutional neural network (CNN) layers to extract frequency domain features and recurrent neural network (RNN) layers to capture the temporal structure. Thus, the DNN generates quantitative frequency domain and time domain data that are used to characterize the results of the photobiomodulation therapy and can be used to guide modifications of the therapeutic plan for the patient and serve to train the network to treat subsequent patients that are within the same class in the PPM so that the appropriate thresholds are established)
Regarding claim 16, Steingold-de Jonge teaches the method of claim 15 and the LSTM network, and Steingold further discloses wherein the wellness data processed by the network comprises a time-series dataset, and the method further includes predicting future wellness outcomes based on historical data. ([0098] The system than communicates the recorded data 460 for the therapeutic session for storage in the electronic medical record of the patient. The data can be used for further analysis such as by application of a machine learning program to provide training data. [00189] As the patient is treated with photobiomodulation therapy, changes in the detected events can be quantified and stored over time in relation to the course of treatment. Note that x denotes input EEG signals while E(x) denotes a “true” or measured event; there are also “default” events used to train the neural network. An event label / can have a zero-value or a selected non- zero value such as 1.)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA R COVINGTON whose telephone number is (303)297-4604. The examiner can normally be reached Monday - Friday, 10 - 5 MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jason B. Dunham can be reached at (571) 272-8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/JASON B DUNHAM/Supervisory Patent Examiner, Art Unit 3686