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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/18/2026 has been entered.
Response to Arguments
Rejection Under 101
Applicant's arguments filed 06/18/2026 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 amended claims are directed to treatment parameter adjustments operating PBM devices. The claims recite a specific arrangement of technological components to generate treatment protocols. The dependent claims reinforce the technological nature of the invention.
In response to Applicant’s argument, as discussed in the previous office action, the disclosure is directed to providing a treatment protocol recommendation, which is supported by the specification at [0019]. The amended claims now recite generating a personalized treatment protocol for users. As discussed below, the steps to determine the protocol falls under the organizing human activity. See MPEP 2106.04(a)(2)(II). Additionally, the technological 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 claims integrate the exception into a practical application by improving the operation of the PBM systems.
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, etc.). By reciting known components for their intended purposes, the additional elements do not amount to a practical application.
The claims recite significantly more than the abstract idea. The ordered combination requires a specific technological arrangement in which treatment outcome data is accumulated, analyzed, used to generated treatment protocols, and communicated for implementation. The combination is neither routine nor conventional and represents a specific technological application to improving PBM treatments.
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, etc.) and recited for their intended purposes. As discussed below in the rejection, these generic components are well understood, routine and conventional. See the updated rejection for further clarification.
Rejection Under 102/103
Applicant's arguments filed 06/18/2026 have been fully considered.
Applicant argues that the cited prior art does not teach or suggest the amended claims 21-43. Therefore, the rejection should be withdrawn.
In response to Applicant’s argument, the argument appears to be directed to the amendments and is therefore moot in light of the new grounds of rejection. See the updated rejection for further clarification.
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 21-43 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 21-43 are drawn to a system, which is within the four statutory categories (i.e. apparatus).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, independent claim 21 (and substantially similar with independent claim 43) recites:
A system for promoting wellness, comprising:
a software client, an instance of which is installed on each of a plurality of client photobiomodulation (PBM) devices, wherein each client PBM device is associated with one of a plurality of users;
a wellness database containing wellness data collected from the plurality of users and treatment outcome data associated with PBM treatments administered to the plurality of users; and
a server, in communication with the plurality of client PBM devices, configured to receive wellness data and treatment objectives from the plurality of users via the software client;
wherein the server comprises one or more processors configured to
(a) identify correlations between PBM treatment parameters and treatment outcomes represented in the wellness database, wherein the identified correlations are derived from treatment outcome data associated with a plurality of different users,
(b) generate a personalized PBM treatment protocol for a user based on the user's wellness data, the treatment objectives, and the identified correlations, wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with users other than the user receiving the personalized PBM treatment protocol, and
(c) communicate the personalized PBM treatment protocol to a client PBM device associated with the user.
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 system, software client, client PBM devices, wellness database, server, processor, the limitations of this claim encompass following rules or instructions in order to provide a personalized PBM treatment protocol for users based on received wellness data, treatment objectives, and identified correlations. 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 22-42 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, independent claim 21 (and substantially similar with independent claim 43) recites:
A system (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) for promoting wellness, comprising:
a software client, an instance of which is installed on each of a plurality of client photobiomodulation (PBM) devices, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) wherein each client PBM device is associated with one of a plurality of users;
a wellness database (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) containing wellness data collected from the plurality of users and treatment outcome data associated with PBM treatments administered to the plurality of users; and
a server, in communication with the plurality of client PBM devices, (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) configured to receive wellness data and treatment objectives from the plurality of users via the software client; (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
wherein the server comprises one or more processors configured to (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
(a) identify correlations between PBM treatment parameters and treatment outcomes represented in the wellness database, wherein the identified correlations are derived from treatment outcome data associated with a plurality of different users,
(b) generate a personalized PBM treatment protocol for a user based on the user's wellness data, the treatment objectives, and the identified correlations, wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with users other than the user receiving the personalized PBM treatment protocol, and
(c) communicate the personalized PBM treatment protocol to a client PBM device (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) associated with the user.
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 system, software client, client PBM devices, wellness database, server, processor, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0043]-[0048], [0050], [0052], [0242]-[0243], [0266] 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 22-42 recite additional limitations which amount to invoking computers as a tool to perform the abstract idea, and claims 22-42 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 system, software client, client PBM devices, wellness database, server, processor, 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]-[0048], [0050], [0052], [0242]-[0243], [0266]; see also Steingold et al. (WO 2022/197937) which teaches client PBM devices); using the system, software client, wellness database, server, processor, 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 § 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 21-29, 31-33, 35-38, 40, 42-43 are rejected under 35 U.S.C. 103 as being unpatentable over Steingold et al. (WO 2022/197937) in view of Zhang (US 2019/0083809).
Regarding claim 21, Steingold discloses a system for promoting wellness, comprising:
a software client, an instance of which is installed on each of a plurality of client photobiomodulation (PBM) devices, wherein each client PBM device is associated with one of a plurality of users; (Steingold [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 [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. [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 [0066] In some embodiments, the photobiomodulation device 110 can transmit and/or receive data from the computing device 150))
a wellness database containing wellness data collected from the plurality of users (Steingold [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)
a server, in communication with the plurality of client PBM devices, configured to receive wellness data and treatment objectives from the plurality of users via the software client; (Steingold [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))
wherein the server comprises one or more processors configured to (Steingold [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 [00146] In an alternative embodiment, the parameters can be set algorithmically or automatedly. The processor of the computing device can process the patient data (including, for example, age and condition data) to determine the first therapeutic dose level or dose level sequence (step 620). For example, the processor 155 of the remote computing device 150 can analyze and process the patient data. [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))
(a) identify correlations between PBM treatment parameters and treatment outcomes represented in the wellness database, wherein the identified correlations are derived from treatment outcome data associated with a plurality of different users, (Steingold [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. [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)
(b) generate a personalized PBM treatment protocol for a user based on the user's wellness data, the treatment objectives, and the identified correlations, (Steingold [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 [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)
(c) communicate the personalized PBM treatment protocol to a client PBM device associated with the user. (Steingold [00146] the automatically selected illumination and therapy session parameters (as well as other session parameters) can be displayed on the display associated with the computing device (step 622))
Steingold does not appear to explicitly disclose the following, however, Zhang teaches it is old and well known in the art of data processing to teach:
treatment outcome data associated with PBM treatments administered to the plurality of users; and (Zhang [0254] A computing cloud is defined as a set of resources (e.g., processing, storage, or other resources) available through a network that can serve at least some traditional datacenter functions for an enterprise [0238] a database of treatment information aggregated from a plurality of patients)
wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with users other than the user receiving the personalized PBM treatment protocol, and (Zhang [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients [0214] the PBM control module can store and/or transmit to a remote device all information, including data and images from sensors, treatment progress input, patient profile data, as well as the prescribed treatment details in a data warehouse for further analysis with machine learning and artificial intelligence to improve human light therapy knowledge and understanding)
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 Steingold, to incorporate treatment outcome data associated with PBM treatments administered to the plurality of users; and wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with users other than the user receiving the personalized PBM treatment protocol, as taught by Zhang, in order to improve light therapy knowledge and understanding. See Zhang [0214].
Regarding claim 22, Steingold-Zhang teaches the system of claim 21, and Steingold further discloses wherein the treatment outcome data comprises treatment outcome data collected after administration of PBM treatments (Steingold [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)
And Zhang further teaches:
to the plurality of users. (Zhang [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients).
The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 23, Steingold-Zhang teaches the system of claim 21, and Steingold further discloses wherein the one or more processors are configured to update the identified correlations as additional treatment outcome data is received. (Steingold [00191] 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 [00129] A neural network can be used for example to tune the parameters employed for transcranial illumination of a child at a certain age range undergoing treatment for autism [0076] The database(s) 401 may be updated manually or automatically at any suitable time).
Regarding claim 24, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein the one or more processors are configured to identify a subgroup of users exhibiting similar responses to PBM treatments, and wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with the subgroup. (Zhang [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients).
Regarding claim 25, Steingold-Zhang teaches the system of claim 24, and Zhang further teaches wherein the subgroup is identified based on at least one of (a) similarities in wellness data associated with the plurality of users, and (b) similarities in treatment outcome data associated with the plurality of users. (Zhang [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information).
Regarding claim 26, Steingold-Zhang teaches the system of claim 24, and Zhang further teaches wherein the subgroup comprises users exhibiting similar responses to at least one of a particular PBM wavelength or a particular PBM pulse frequency. (Zhang [0070] As used herein, “low level light therapy” (“LLLT”) is the use of light comprising a plurality of application parameters, such as emitted wavelength(s), pulse frequencies, duty cycle/pulse width, intensity, individual treatment duration, total treatment duration, number of individual treatments during a total treatment regimen, time between individual treatments in a total treatment regimen, first LLLT administration time from an event (such as a surgery), time-course dosage, maintenance treatments, with the goal of optimal treatment, management, and/or cure of wounds or delay/reverse tissue degeneration at one or more locations on a patient in need of such treatment, management, or cure [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients).
Regarding claim 27, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein the identified correlations comprise a statistical relationship between treatment outcome and at least one parameter selected from the group consisting of wavelength, pulse frequency and duration. (Zhang [0070] As used herein, “low level light therapy” (“LLLT”) is the use of light comprising a plurality of application parameters, such as emitted wavelength(s), pulse frequencies, duty cycle/pulse width, intensity, individual treatment duration, total treatment duration, number of individual treatments during a total treatment regimen, time between individual treatments in a total treatment regimen, first LLLT administration time from an event (such as a surgery), time-course dosage, maintenance treatments, with the goal of optimal treatment, management, and/or cure of wounds or delay/reverse tissue degeneration at one or more locations on a patient in need of such treatment, management, or cure).
Regarding claim 28, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein treatment outcome data obtained after administration of a personalized PBM treatment protocol is incorporated into the wellness database. (Zhang [0214] the PBM control module can store and/or transmit to a remote device all information, including data and images from sensors, treatment progress input, patient profile data, as well as the prescribed treatment details in a data warehouse for further analysis with machine learning and artificial intelligence to improve human light therapy knowledge and understanding).
Regarding claim 29, Steingold-Zhang teaches the system of claim 28, and Steingold further discloses wherein the one or more processors are configured to update the identified correlations using the incorporated treatment outcome data. (Steingold [00191] 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 [00129] A neural network can be used for example to tune the parameters employed for transcranial illumination of a child at a certain age range undergoing treatment for autism [0076] The database(s) 401 may be updated manually or automatically at any suitable time).
Regarding claim 31, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein:(a) the personalized PBM treatment protocol specifies at least one treatment parameter selected from the group consisting of wavelength, treatment duration, treatment intensity, pulse frequency, fluence, irradiance, repetition rate, and treatment location; (Zhang [0070] As used herein, “low level light therapy” (“LLLT”) is the use of light comprising a plurality of application parameters, such as emitted wavelength(s), pulse frequencies, duty cycle/pulse width, intensity, individual treatment duration, total treatment duration, number of individual treatments during a total treatment regimen, time between individual treatments in a total treatment regimen, first LLLT administration time from an event (such as a surgery), time-course dosage, maintenance treatments, with the goal of optimal treatment, management, and/or cure of wounds or delay/reverse tissue degeneration at one or more locations on a patient in need of such treatment, management, or cure [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients)
And Steingold further discloses:
and(b) the software client is configured to automatically adjust one or more operating parameters of the client PBM device according to the personalized PBM treatment protocol. (Steingold [00191] 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 [00129] A neural network can be used for example to tune the parameters employed for transcranial illumination of a child at a certain age range undergoing treatment for autism).
The motivation to combine the references is discussed above and incorporated herein.
Regarding claim 32, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein the personalized PBM treatment protocol for the user is generated using treatment outcome data associated with at least one different user represented in the wellness database, and wherein the at least one different user exhibits wellness characteristics similar to those of the user. (Zhang [0070] As used herein, “low level light therapy” (“LLLT”) is the use of light comprising a plurality of application parameters, such as emitted wavelength(s), pulse frequencies, duty cycle/pulse width, intensity, individual treatment duration, total treatment duration, number of individual treatments during a total treatment regimen, time between individual treatments in a total treatment regimen, first LLLT administration time from an event (such as a surgery), time-course dosage, maintenance treatments, with the goal of optimal treatment, management, and/or cure of wounds or delay/reverse tissue degeneration at one or more locations on a patient in need of such treatment, management, or cure).
Regarding claim 33, Steingold-Zhang teaches the system of claim 32, and Steingold further discloses wherein the wellness characteristics comprise at least one characteristic selected from the group consisting of age, sex, wellness objectives, physiological measurements, medical history, and prior PBM treatment response. (Steingold [00191] 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 [00129] A neural network can be used for example to tune the parameters employed for transcranial illumination of a child at a certain age range undergoing treatment for autism).
Regarding claim 35, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein the one or more processors are configured to identify PBM treatment parameters associated with improved treatment outcomes across the plurality of users represented in the wellness database and to generate the personalized PBM treatment protocol using the identified PBM treatment parameters. (Zhang [0070] As used herein, “low level light therapy” (“LLLT”) is the use of light comprising a plurality of application parameters, such as emitted wavelength(s), pulse frequencies, duty cycle/pulse width, intensity, individual treatment duration, total treatment duration, number of individual treatments during a total treatment regimen, time between individual treatments in a total treatment regimen, first LLLT administration time from an event (such as a surgery), time-course dosage, maintenance treatments, with the goal of optimal treatment, management, and/or cure of wounds or delay/reverse tissue degeneration at one or more locations on a patient in need of such treatment, management, or cure [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients [0313] light to be delivered from PBM control module 2130, where that light source is in operational and optical communication with light guide 2520 and (optionally) light guides 2525 and 2565, which can be used to deliver additional energy or light at a different wavelength than light delivered by light guide 2520, or to transmit light beam 2555 from light therapy delivery liner 505 as beam 560 back to sensors [not shown] associated with PBM control module 2130 for analysis to determine, for example, tissue condition, treatment progress, compliance and dose-response [0214] the PBM control module can store and/or transmit to a remote device all information, including data and images from sensors, treatment progress input, patient profile data, as well as the prescribed treatment details in a data warehouse for further analysis with machine learning and artificial intelligence to improve human light therapy knowledge and understanding).
Regarding claim 36, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with users having prior PBM treatment responses similar to those of the user, wherein the personalized PBM treatment protocol for the user is generated using treatment outcome data associated with at least one different user represented in the wellness database.(Zhang [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients).
Regarding claim 37, Steingold-Zhang teaches the system of claim 36, and Zhang further teaches wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with users having the same wellness objective. (Zhang [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients).
Regarding claim 38, Steingold-Zhang teaches the system of claim 21, and Steingold further discloses wherein the one or more processors are configured to identify correlations between wellness objectives and treatment outcomes.(Steingold [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) [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 40, Steingold-Zhang teaches the system of claim 35, and Steingold further discloses wherein the identified PBM treatment parameters are associated with treatment outcomes exceeding a threshold outcome metric. (Steingold [0034] FIG. 15 illustrates a table with exemplary parameters having variable ranges between upper and lower thresholds used for transcranial illumination of a patient in accordance with preferred embodiments [00144] These parameters typically fall within a range of values that the system can use that extend between a minimum threshold and a maximum threshold).
Regarding claim 42, Steingold-Zhang teaches the system of claim 21, and Zhang further teaches wherein the personalized PBM treatment protocol is generated using treatment outcome data associated with users having a wellness objective matching the treatment objective of the user. (Zhang [0086] Yet further, the devices and methods of the present invention can provide enhanced information about the course and effects of a LLLT treatment program in a single patient, and among a plurality of patients, thereby improving the ability to manage LLLT in a single patient or in a population of patients by use of data associated with medical indication, dosage, treatment compliance, progress, outcome, physiological conditions, among other information. Therefore, in some aspects, the inventions herein relate to the generation of information associated with an applied LLLT treatment or program from the patient who is undergoing treatment for one or more medical indications suitable for treatment with LLLT, and incorporation of at least some of that patient generated information into a subsequent treatment for that patient, for another patient or for a group of patients {objective taught above}).
Regarding claim 43, the claim recites substantially similar limitations as those already recited in the rejections of claims 21 and 24, and, as such, is rejected for similar reasons as given above.
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Steingold-Zhang in view of Kehr et al. (US 2003/0036683).
Regarding claim 30, Steingold-Zhang teaches the system of claim 21, but does not appear to disclose the following, however, Kehr teaches it is old and well known in the art of healthcare data processing wherein the wellness data comprises physiological data obtained from one or more sensors associated with the user, and wherein the physiological data comprises one or more items selected from the group consisting of heart rate, heart rate variability, blood pressure, blood glucose level, blood oxygen level, respiration rate, sleep metrics, and body temperature. (Kehr [0228] The medical monitors capture data on medication compliance, health status, quality-of-life, physiologic status (e.g. blood pressure, EKG, pO2, pulse rate, weight, pulmonary function, etc.), and various measures of blood, serum, urine, and other laboratory tests. The medical monitors are linked to a database that updates and synchronizes the information presented to all of the monitors, and captures information from all of the monitors. Individual patient medical treatment plans may be remotely created, modified, or viewed in the database… for each device to record whether the patient is following the treatment plan).
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 Steingold-Zhang, as modified above, to incorporate wherein the wellness data comprises physiological data obtained from one or more sensors associated with the user, and wherein the physiological data comprises one or more items selected from the group consisting of heart rate, heart rate variability, blood pressure, blood glucose level, blood oxygen level, respiration rate, sleep metrics, and body temperature, as taught by Kehr, in order to modify the treatment plans over time based on relevant wellness data so as to improve patient adherence to their treatment plan. See Kehr [0205], [0251].
Claim 34, 39 are rejected under 35 U.S.C. 103 as being unpatentable over Steingold-Zhang in view of Tran et al. (US 2019/0361917).
Regarding claim 34, Steingold-Zhang teaches the system of claim 21, but does not appear to disclose the following, however, Tran teaches it is old and well known in the art of healthcare data processing wherein the one or more processors are configured to predict a wellness outcome associated with each of a plurality of candidate PBM treatment protocols, and wherein the personalized PBM treatment protocol is selected from the plurality of candidate PBM treatment protocols based on the predicted wellness outcome. (Tran [0285] Patient A is in registration at a particular hospital. The PPLT is used to identify Patient A as belonging to a particular plan. The smart contracts in the blockchain automatically updates Patient A's care plan. The blockchain adds a recommendation to put Patient A by looking at the complete history of treatments by all providers and optimizes treat. For example, the system can recommend the patient be enrolled in a weight loss program after noticing that the patient was treated for sedentary lifestyle, had history of hypertension, and the family history indicates a potential heart problem. The blockchain data can be used for predictive analytics, allowing patients to learn from their family histories, past care and conditions to better prepare for healthcare needs in the future).
Therefore, it would have been obvious to one of ordinary skill in the art of data processing, before the effective filing date of the claimed invention, to modify Steingold-Zhang, as modified above, to incorporate wherein the one or more processors are configured to predict a wellness outcome associated with each of a plurality of candidate PBM treatment protocols, and wherein the personalized PBM treatment protocol is selected from the plurality of candidate PBM treatment protocols based on the predicted wellness outcome, as taught by Tran, in order to have optimize treatment protocols and predict future healthcare needs. See Tran [0285].
Regarding claim 39, Steingold-Zhang-Tran teaches the system of claim 34, and Zhang further teaches wherein the predicted wellness outcome comprises a predicted improvement in a user-selected wellness objective. (Zhang [0214] the PBM control module can store and/or transmit to a remote device all information, including data and images from sensors, treatment progress input, patient profile data, as well as the prescribed treatment details in a data warehouse for further analysis with machine learning and artificial intelligence to improve human light therapy knowledge and understanding).
Claim 41 is rejected under 35 U.S.C. 103 as being unpatentable over Steingold-Zhang in view of Kutzko et al. (US 2020/0273578).
Regarding claim 41, Steingold-Zhang teaches the system of claim 35, but does not appear to disclose the following, however, Kutzko teaches it is old and well known in the art of healthcare data processing wherein the identified PBM treatment parameters are ranked according to associated treatment outcomes observed across the plurality of users. (Kutzko [0131] an artificial intelligence module 152 may determine two possible therapies 127, such as a first therapy and a second therapy, and the artificial intelligence module 152 may rank the first and second possible therapies 127 by probability of the first and second possible therapies 127 being a successful treatment, wherein the probability of success is based on factors including patient compliance, the patient's existing condition or existing conditions, treatment cost, and historical success rate of the treatments as determined from information stored on the blockchain database. As a further example, the artificial intelligence module 152 may rank the second possible therapy 127).
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 Steingold-Zhang, as modified above, to incorporate wherein the identified PBM treatment parameters are ranked according to associated treatment outcomes observed across the plurality of users, as taught by Kutzko, in order to best determine treatment compliance with the most successful treatment plan. See Kutzko [0131].
Conclusion
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
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
Prosecution Insights