DETAILED ACTION
This action is responsive to the “RESPONSE TO OFFICE ACTION” filed 20 April 2026. The Examiner acknowledges the amendments to claims 1, 5-7, 9-10, 13-14, and 16, the cancelation of claim 8, and the addition of new claim 21. Claims 1-7 and 9-21.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim(s) 1, 6-7, 10, and 21 is/are objected to because of the following informalities:
Claim 1 should read “exceeds” [each instance in lines 26, 27, 28, 29, 39 (both), 40].
Claim 6 should read “one [[of]] or more” [lines 3-4].
Claim 6 should read “exceeds” [line 7].
Claim 7 should read “exceeds” [line 4].
Claim 10 should read “based on the type of sensor data of the subject” [lines 3-4].
Claim 21 should read “comprises” [line 2].
Claim 21 should read “exceeds” [lines 3, 5].
Appropriate correction is required.
Claim Interpretation
Examiner Notes: currently, NO limitation invokes interpretation under § 112(f).
Claim Rejections - 35 USC § 112
Examiner’s Note Regarding Machine Learning: the previously presented analysis regarding sufficient written description for the claimed machine learning [see p. 5 of Non-Final Rejection dated 20 January 2026] is maintained.
Examiner’s Note Regarding Subjective and Relative Terminology: the previously presented analysis regarding a sufficient standard for measuring a degree of recited subjective and relative terminology [see p. 6 of Non-Final Rejection dated 20 January 2026] is maintained.
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.
Claim(s) 1-7, 9-18 and 21 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more. Each claim has been analyzed to determine whether it is directed to any judicial exceptions.
Representative claim(s) 1 [representing all independent claims] recite(s):
A system comprising:
one or more wearable sensors configured to detect sensor data of a subject substantially continuously or semi-continuously, wherein the one or more wearable sensors comprise at least one sensor configured to detect motion data, at least one sensor configured to detect sound data, at least one sensor configured to detect physiological data, or combinations thereof; and
a control system configured to receive the sensor data of the subject from the one or more wearable sensors; wherein the control system comprises at least one processor and at least one controller, wherein the control system is further configured to determine, for the subject, at least one of target motion data, target sound data, target physiological data, or combinations thereof, wherein the target data is personalized and individually determined for the subject based on the subject's habitual motions, sounds, physiology, or combinations thereof, respectively; wherein the control system is further configured to deliver an audio sound track to the subject, receive sensor data from the one or more wearable sensors during delivery of the audio sound track, determine that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, and associate the audio sound track with the subject,
wherein the at least one processor compares the sensor data to target data; wherein, when at least one of the sensor data are equal to or exceeds exceed the target data, the at least one processor is configured to signal the at least one controller; and wherein the at least one controller delivers an audible sound therapy to the subject; wherein the audible sound therapy comprises, the associated audio sound track that was determined to be effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, wherein the associated audio sound track is characterized by a track rhythm and by a track beat, wherein the associated audio sound track is familiar to the subject, and wherein the associated audio sound track is repeated at least until it is determined that the target data exceeds the sensor data of the subject.
(Emphasis added: abstract idea, additional element)
Step 2A Prong 1
Representative claim(s) 14 recites the following abstract ideas, which may be performed in the mind or by hand with the assistance of pen and paper as indicated:
“determine, for the subject, at least one of target motion data, target sound data, target physiological data, or combinations thereof, wherein the target data is personalized and individually determined for the subject based on the subject's habitual motions, sounds, physiology, or combinations thereof, respectively” – may be performed by merely physically observing a subject and drawing mental conclusions therefrom, or observing known or previously collected data associated with the subject and drawing mental conclusions therefrom [Applicant’s Specification ¶00020 discloses known observable pre-meltdown behaviors, which while disclosed as being “picked up by wearable sensors” are understood to be physically manifestable actions that may be visually or audibly observed]
“deliver an audio sound track to the subject” – may be considered a method of organizing human activity relating to managing personal behavior or relationships or interactions between people, by merely verbally communicating any non-specific sound, or may be considered an instruction for someone to verbally communicate any sound [MPEP § 2106.04(a)(2)(II)]; however for the sake of compact prosecution, the Examiner notes that the identified limitation may instead be interpreted as an additional element and is analyzed at Step 2A Prong 2 and Step 2B below, in the alternative
“receive sensor data from the one or more wearable sensors during delivery of the audio sound track” – may be performed by merely observing known or previously collected data associated with the subject, as the Examiner notes that the limitation as recited fails to positively recite any step of measuring or performing any measuring of data using a sensor, wherein the data being defined as being “from one or more wearable sensors configured to be worn by the subject” merely limits the type of data that may be observed; however for the sake of compact prosecution, the Examiner notes that the identified limitation may instead be interpreted as an additional element and is analyzed at Step 2A Prong 2 and Step 2B below, in the alternative
“determine that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, and associate the audio sound track with the subject” – may be performed by merely observing known or previously collected data and drawing mental conclusions
“compares the sensor data to target data” – may be performed by merely observing known or previously collected data and drawing mental conclusions therefrom
“wherein, when at least one of the sensor data are equal to or exceeds exceed the target data… ” – may be performed by merely observing known or previously collected data and drawing mental conclusions therefrom
“delivers an audible sound therapy to the subject” – may be considered a method of organizing human activity relating to managing personal behavior or relationships or interactions between people, by merely verbally communicating a sound, as the Examiner notes that all sounds may be considered to be defined by a rhythm and beat, or may be considered an instruction for someone to verbally communicate the sound; wherein the recitation of the audio sound track being repeated until a determination is made merely defines performing the abstract idea for a predetermined amount of time; however for the sake of compact prosecution, the Examiner notes that the identified limitation may instead be interpreted as an additional element and is analyzed at Step 2A Prong 2 and Step 2B below, in the alternative
“until it is determined that the target data exceeds the sensor data of the subject” – may be performed by merely observing known or previously collected data and drawing mental conclusions therefrom
If a claim, under BRI, covers performance of the limitations in the mind but for the mere recitation of extra-solutionary activity (and otherwise generic computer elements) then the claim falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea under Step 2A Prong 1 of the Mayo framework as set forth in the 2019 PEG.
No limitations are provided that would force the complexity of any of the identified evaluation steps to be non-performable by pen-and-paper practice.
The dependent claims merely include limitations that either further define the abstract idea [e.g. limitations relating to the data gathered or particular steps which are entirely embodied in the mental process] and amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they are merely incidental or token additions to the claims that do not alter or affect how the process steps are performed.
Thus, these concepts are similar to court decisions of abstract ideas of itself: collecting, displaying, and manipulating data [Int. Ventures v. Cap One Financial], collecting information, analyzing it, and displaying certain results of the collection and analysis [Electric Power Group], collection, storage, and recognition of data [Smart Systems Innovations].
Step 2A Prong 2
The judicial exception is not integrated into a practical application.
Representative claim 14 only recites additional elements of extra-solutionary activity – in particular, extra-solution activity of generic computer function [delivering an audio signal of an audible sound therapy (wherein the Examiner notes that the recitation of the audible sound being “therapy” is not considered to limit the audible sound itself) via a generic speaker], data gathering [receiving/acquiring sensor data from one or more wearable sensors may alternatively be considered to refer to a data gathering step], necessary precursor for mental analysis [delivering an audio sound track to the subject and receiving sensor data during delivery of the audio sound track is considered to recite a necessary precursor for the mental analysis of the determination that the audio sound track is effective] – without further sufficient detail that would tie the abstract portions of the claim into a specific practical application (2019 PEG p. 55 – the instant claim, for example does not tie into a particular machine, a sufficiently particular form of data or signal collection – via the claimed generic computer function, or a sufficiently particular form of display or computing architecture/structure).
Dependent claim(s) 2, 4-7, 10, 12, 15, 18, and 21 merely add detail to the abstract portions of the claim but do not otherwise encompass any additional elements which tie the claim(s) into a particular application/integration [the dependent claim(s) recite generic ‘units’ or ‘steps’ which encompass mere computer instructions to carry out an otherwise wholly abstract idea].
Dependent claim(s) 9 and 13 encounter substantially the same issues as the independent claim(s) from which they depend in that they encompass further generic extra-solutionary activity [generic data gathering] and/or generic computer elements [storage, memory per se].
Accordingly, the claim(s) are not integrated into a practical application under Step 2A Prong 2.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Independent claims 1 and 14 as individual wholes fail to amount to significantly more than the judicial exception at Step 2B. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of extra-solutionary activity [i.e., generic computer function, data gathering, necessary precursor for mental analysis] and generic computer elements cannot amount to significantly more than an abstract idea [MPEP § 2106.05(f)] and is further considered to merely implement an abstract idea on a generic computer [MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality].
For the independent claim portions and dependent claims which provide additional elements of extra-solutionary data gathering, MPEP § 2106.05(g) establishes that mere data gathering for determining a result does not amount to significantly more. The extra-solutionary activity of processor steps processor steps [acquiring, storing, transmitting signals, etc.] as presently recited, cannot provide an inventive concept which amounts to significantly more than the recited abstract idea.
For the independent claims as well as the dependent claims merely reciting generic computer elements and functions [computer elements of a processor, controller, speaker recited at a high level of generality, and functions therein], MPEP § 2106.05(d)(II) establishes computer-based elements which are considered to be well understood, routine, and conventional when recited at a high level of generality.
Accordingly, the generic computer elements and functions therein, as presently limited, cannot provide an inventive concept since they fall under a generic structure and/or function that does not add a meaningful additional feature to the judicial exception(s) of the claim(s).
Claim 11 recites “a model that is trained with sensor data from the subject and/or with sensor data from at least one additional subject; wherein the model is configured to evaluate the sensor data of the subject with respect to the target data to determine the presence or absence of a pre-meltdown stage for the subject” and claim 16 recites “at least one machine learning model that is trained with sensor data from the subject and/or with data from at least one additional subject, wherein the at least one machine learning model is configured to evaluate the sensor data of the subject with respect to target data… provide, by the control system, the input to the at least one machine learning model; determine, by the control system, an evaluation result”. Such a “model that is trained” and “machine learning model” is considered well-understood, routine, and conventional, as known by at least:
Hu (“Intelligent Sensor Networks”, previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Hu, Page 5)]
Huang (“Kernel Based Algorithms for Mining Huge Data Sets”, previously presented) [In supervised learning, the learner is provided with labeled input data. This data contains a sequence of input/output pairs of the form xi, yi, where xi is a possible input and yi is the correctly labeled output associated with it. The aim of the learner in supervised learning is to learn the mapping from inputs to outputs. The learning program is expected to learn a function f that accounts for the input/output pairs seen so far, f (xi) = yi, for all i. This function f is called a classifier if the output is discrete and a regression function if the output is continuous. The job of the classifier/regression function is to correctly predict the outputs of inputs it has not seen before (Huang, Page 1)]
Mitchell (“The Discipline of Machine Learning”, previously presented) [For example, we now have a variety of algorithms for supervised learning of classification and regression functions; that is, for learning some initially unknown function f : X [Calibri font/0xE0] Y given a set of labeled training examples {xi; yi} of inputs xi and outputs yi = f(xi) (Mitchell, Pages 3-4)]
Claim 14 recites “one or more wearable sensors configured to detect sensor data of a subject substantially continuously or semi-continuously, wherein the one or more wearable sensors comprise at least one sensor configured to detect motion data, at least one sensor configured to detect sound data, at least one sensor configured to detect physiological data, or combinations thereof”, wherein the Examiner notes that while claims 1, 8, and 11 fail to positively recite the “one or more wearable sensors”, however for the sake of compact prosecution, the analysis below is considered to be applicable. Such “one or more sensors” is/are considered well-understood, routine, and conventional, as known by at least:
Applicant’s disclosure is not particular regarding the particular structure of the generically claimed “one or more wearable sensors”, and recites the “one or more wearable sensors” at a high level of generality [In an aspect, the wearable sensor comprises a wearable motion sensor, wherein the wearable motion sensor is configured to acquire motion data of the subject. The wearable motion sensor can comprise a motion sensing unit. The motion sensing unit may comprise a micro-electro- mechanical system (MEMS) based motion sensor, a gyroscope, an accelerometer, a magnetometer, a distance measurement sensor, an absolute position sensor (e.g., a trilateration device), and the like, or combinations thereof. In an aspect, the wearable sensor comprises a wearable sound sensor, wherein the wearable sound sensor is configured to acquire sound data of the subject. The wearable sound sensor can comprise a microphone, and optionally an amplifier. In an aspect, the wearable sensor comprises a wearable physiological sensor, wherein the wearable physiological sensor is configured to acquire physiological data of the subject. The wearable physiological sensor can comprise a pulse oximeter, a piezoelectric pressure sensor, a radio frequency identification (RFID) sensor, and the like, or combinations thereof (Applicant’s Specification ¶¶0022-0024)]. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the field of audio delivery devices. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional element because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) [see Berkheimer memo from April 19, 2018, Page 3, (III)(A)(1), not attached]. Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible [TLI Communications].
Zhao (US-20160113569-A1, previously presented) [As would be understood by one of skill in the art, the “smart” wearable user devices may include processing systems, memory systems, communication systems, sensor systems, and/or any other devices or systems known in the art that allow those wearable user devices to collect the user health data and communicate it as discussed below. For example, the “smart” glasses 202 may collect audio data, video data (i.e., from the point of view of the first user 200, of the first user 200 via an eye or face facing camera, etc.), user movement (e.g., head movement) data, brainwave data, temperature data, breathing data, and/or any other user data known in the art that is collectable by “smart” glasses. Similarly, the “smart” watch 204 may collect user movement (e.g., arm and hand movement) data, pulse data, temperature data, and/or any other user data known in the art that is collectable by “smart” watches, the “smart” ring 206 may collect user movement (e.g., arm, hand, and finger movement) data, pulse data, temperature data, and/or any other user data known in the art that is collectable by “smart” rings, and the “smart” shoes 206 may collect user movement (e.g., foot and leg movement such as walking/running movements) data, pulse data, temperature data, and/or any other user data known in the art that is collectable by “smart” shoes (Zhao ¶0027)]
Claim 17 recites “an audio speaker, earbuds, and/or headphones, wherein the audible sound therapy is delivered via the audio speaker, earbuds, and/or headphones without requiring assistance from a caregiver”. Such an “audio speaker, earbuds, and/or headphones” is considered well-understood, routine, and conventional, as known by at least:
Applicant’s disclosure is not particular regarding the particular structure of the generically claimed “audio speaker, earbuds, and/or headphones”, and recites the “audio speaker, earbuds, and/or headphones” at a high level of generality [the device (e.g., wearable item or phone) can comprise speakers for delivering the audible sound therapy. Furthermore, the device (e.g., wearable item or phone) can be connected (e.g., wired or wireless connection) to headphones, earbuds, a speaker, a smart- speaker, etc. (Applicant’s Specification ¶0027)]. This lack of disclosure is acceptable under 35 U.S.C. 112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the medical technology arts. Thus, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the field of audio delivery devices. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional element because it describes such an additional element in a manner that indicates that the additional element is sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. 112(a) [see Berkheimer memo from April 19, 2018, Page 3, (III)(A)(1), not attached]. Adding hardware that performs “well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible [TLI Communications].
Li (US-20190356989-A1, previously presented) [a speaker (e.g., headphones, internal speakers of the signal processing device). Additionally or alternatively, the system interfaces (e.g., via wireless protocols such as BLUETOOTH or BLUETOOTH LOW ENERGY) with a hearing assistance device to execute Blocks of the method S100. As used herein, a “hearing assistance device” can include a hearing aid, a wearable hearing-related device (a “hearable” device), earphones/headphones in coordination with an integrated microphone, or any other device capable of augmenting incoming sound (Li ¶0011); the system can increase volumes of discrete frequency ranges (e.g., by 10 decibels) in discrete intervals (e.g., every 50 Hz) across the audible spectrum or across the vocal spectrum in a series of soundbites and upload original and modified versions of these soundbites to the hearing assistance device (Li ¶0118)]
Lee (US-20100137739-A1, previously presented) [the controlling unit 100 may retrieve and sequentially output the sound sources of a test sound set from the test sound storage unit 102 through the test sound output unit 104 and the speaker 106 (Lee ¶0063); The auditory threshold is a value which is estimated as a subject's hearable minimum volume within any frequency band (Lee ¶0066); Herein, sound sources included in one test sound set may have the same frequency band, and sequential volume levels with a predetermined difference (Lee ¶0067)]
Ganter (US-20120230501-A1, previously presented) [The audio output means may comprise one or more of: speakers and headphones (Ganter ¶0028); It consists of a set of discrete frequency values measured in Hertz (Hz) and a related set of threshold sensitivity values measured in decibels (dB) (Ganter ¶0083)]
Examiner’s Note Regarding Particular Treatment or Prophylaxis: Claim(s) 1 and 14 recite subject matter regarding “delivers an audible sound therapy to the subject; wherein the audible sound therapy comprises, the associated audio sound track that was determined to be effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, wherein the associated audio sound track is characterized by a track rhythm and by a track beat, wherein the associated audio sound track is familiar to the subject, and wherein the associated audio sound track is repeated at least until it is determined that the target data exceeds the sensor data of the subject”, wherein claim 3 clarifies that the delivery of audible sound therapy to the subject “prevents the onset of a meltdown stage for the subject or decreases the severity of a meltdown stage for the subject” and claim 4 further limits the audio sound track to comprise “a song, a music album, an audiobook chapter, an audio book, a recited poem, a collection of recited poems, or combinations thereof”, which the Examiner notes is considered NOT to be a particular treatment or prophylaxis, as none of the identified claims positively recite or include language that is considered to be a particular treatment or prophylaxis as an additional element to integrate the judicial exception into a practical application or allow the identified claims to amount to significantly more than the judicial exception [MPEP § 2106.04(d)(2)], as the Examiner notes that the identified limitations are considered to refer to an abstract idea [see Step 2A Prong 1 analysis above, wherein delivering an audible sound therapy comprising an audio sound may be considered a method of organizing human activity relating to managing personal behavior or relationships or interactions between people, by merely verbally communicating any non-specific sound].
However, for the sake of compact prosecution, the Examiner has also analyzed the identified limitation regarding delivery of an audible sound therapy under Step 2A Prong 2 and Step 2B, wherein the Examiner notes that the identified limitations are still considered NOT to be a particular treatment or prophylaxis. Regarding the particularity or generality of the treatment or prophylaxis, the Examiner notes that the step of delivering an audio sound track to the subject and determining that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject to associate the audio sound track with the subject for later delivery of the audible sound therapy to the subject [comprising the associated sound track] is not considered to be sufficiently particular and is merely considered to refer to mere instructions to “apply” the exception in a generic way, as the audible sound therapy is merely defined as an audio track characterized by a track rhythm and by a track beat, claim 4 further “limits” the audio track to comprise “a song, a music album, an audiobook chapter, an audio book, a recited poem, a collection of recited poems, or combinations thereof”, which is considered to define the sound track at a high level of generality and generically refer to any song, audiobook, or recited poem [Conversely, consider a claim that recites the same abstract idea and "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way (MPEP § 2106.04(d)(2)(a), wherein the Examiner notes that the audio sound track being associated with the subject as being “effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject” is considered to be similar to the cited portion of the MPEP, which recites a medication recited at a high level of generality that is suitable to treat the patient, as the audio sound track while being considered to be specifically associated with the subject is still in itself recited at a high level of generality (characterized by a track rhythm and a track beat, or further comprise a song, a music album, an audiobook chapter, an audio book, a recited poem, a collection of recited poems, or combinations thereof)]; furthermore, the recitation of claim 3 wherein the audible sound therapy “prevents the onset of a meltdown stage for the subject or decreases the severity of a meltdown stage for the subject” is considered to be an equivalent of “apply it”. Regarding whether the identified limitations of claims 1, 3-4, and 14 are merely extra-solution activity or a field of use, the Examiner notes that the step of “delivering audible sound therapy to the subject… wherein the audio sound track is repeated until it is determined, in any sequence, at least one of the following: that the target motion data exceed the motion data of the subject; that the target sound data exceed the sound data of the subject; and that the target physiological data exceed the physiological data of the subject” [emphasis applied] is considered to define a pre-solution activity, as the delivery of the audible sound therapy is performed in order to gather data for a mental analysis step [see Step 2A Prong 1 analysis above], and is a necessary precursor for all uses of the recited exception [this administration is performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application (MPEP § 2106.04(d)(2)(c)].
Accordingly, the claim(s) as whole(s) fail amount to significantly more than the judicial exception under Step 2B.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-7, 9-10, 14-15, 17-18, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ganesh (US-10231664-B2, previously presented) in view of Brimmer (US-20190126002-A1).
Regarding claim 1, Ganesh teaches
A method comprising:
(a) determining, for a subject, at least one of target motion data, target sound data, target physiological data, or combinations thereof, wherein the target data is personalized and individually determined for the subject based on the subject's habitual motions, sounds, physiology, or combinations thereof, respectively [The signals of pre-autistic meltdown may be gathered through at least two sets of sensor systems:… Category B: Sensors 103 that detect physiological stress symptoms, comprising accelerometers that may detect restlessness, galvanic skin response sensors that may detect perspiration levels, flex resistors that may detect muscle tension, pulse oximetry sensors that detect various types of breathing patterns, including hypoventilation, when the patient is breathing room air, microphone that detects patient's audible frequency and vocal patterns (Ganesh Col 5:20-36); The threshold values indicate the normal non-meltdown range of the sensors specific to each individual patient (Ganesh Col 6:28-30); These threshold values may be used as the initial or default values by the configure sensor thresholds module 515. FIG. 9 shows a screenshot of one example embodiment of the caregiver mobile device that provides the ability for the caregiver to set the threshold values for the sensors 634-645 and the therapy and alert response 647 (Ganesh Col 7:17-22); A casual assessment of the relation between antecedent triggers of the stress and the resulting behavior can be mapped by analyzing the chronological data from the sensors. This analysis may aid with the efforts to diagnose the symptoms of conditions such as Autism Spectrum Disorder (ASD) and epilepsy, to understand the factors contributing to the stress, and fine tune the appropriate thresholds of the sensor parameters and therapeutic calming response unique to each patient (Ganesh Col 7:40-48)];
(c) acquiring sensor data substantially continuously or semi-continuously from the one or more wearable sensors in the absence of any associated audio sound track, wherein the one or more wearable sensors are integrated with a control system, wherein the control system comprises at least one processor and at least one controller, wherein the control system receives the sensor data from the one or more wearable sensors, wherein the sensor data comprise motion data, sound data, physiological data, or combinations thereof of the subject [Ganesh Col 5:20-36; This integrated system continuously monitors environmental triggers and physiological stress indicative parameters of a patient diagnosed with autistic spectrum disorder, or other emotional or physical disorders (Ganesh Abstract); The controller 101 includes a microprocessor/microcontroller 410 that may be interfaced with wearable device communication interface 411, storage module 406, and real time clock 407. The microprocessor 410 interfaces with environmental sensor system 102, physiological sensor system 103, and therapy device 104 (Ganesh Col 5:9-15, Fig. 7), wherein monitoring the subject prior to delivering any form of audible sound therapy (see Ganesh Col 6:42-48) is considered to read on the acquiring sensor data in the absence of an associated audio sound track];
(d) responsive to step (c), comparing, by the at least one processor, the sensor data of the subject with target data, wherein the motion data, the sound data, the physiological data, or combinations thereof of the subject are compared with target motion data, target sound data, target physiological data, or combinations thereof, respectively [Periodically, the readings from the environmental sensor system 102 and the physiological sensor system 103, are monitored by the microprocessor 410 and compared with the sensors' corresponding threshold values 515… The threshold values indicate the normal non-meltdown range of the sensors specific to each individual patient (Ganesh Col 6:11-15, 27-30)];
(e) responsive to step (d) determining, in any sequence, at least one of the following: that the motion data of the subject is equal to or exceed the target motion data; that the sound data of the subject is equal to or exceed the target sound data; and that the physiological data of the subject is equal to or exceed the target physiological data, wherein, when at least one of the sensor data is equal to or exceed the target data, the at least one processor signals the at least one controller [When the readings from the sensors cross the thresholds, the response initiated by microprocessor 410 comprises of the following: Calming response to the patient is implemented through the calming response module 104 (Ganesh Col 6:30-34); The actions that are dynamically controlled by the calming response module 104 include (example embodiments are shown in FIG. 2, FIG. 3, and FIG. 4)… playing favorite music, playing a discrete gentle audible alert (Ganesh Col 6:42-48)]; and
(f) responsive to step (e), delivering, by the at least one controller, audible sound therapy to the subject, wherein the audible sound therapy comprises an associated audio sound track that was determined to be effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, wherein the associated audio sound track is characterized by a track rhythm and by a track beat, wherein the associated audio sound track is familiar to the subject [Ganesh Col 6:42-48], and wherein the associated audio sound track is repeated at least until it is determined, in any sequence, at least one of the following: that the target motion data exceed the motion data of the subject; that the target sound data exceed the sound data of the subject; and that the target physiological data exceed the physiological data of the subject [The therapeutic calming response characteristics of the calming response module 104, such as duration and intensity of the responses, can be controlled by the ‘configure the calming response module 517’ that is located on the caregivers' mobile device 521 (Ganesh Col 6:51-55); After the first iteration 702 of polling and recording the sensor, location and real time clock information into the storage module, the process is repeated periodically until any sensor value exceeds its corresponding threshold 703. If the latter occurs,… Independent of an active connection between the wearable module and the caregiver's mobile device, if the wearable device has been pre-configured to deliver therapy 706, then corresponding therapy is delivered to the patient… This sequence of steps repeat going back to the collection of the sensor, location, and real time clock information 702 (Ganesh Col 8:26-41, Fig. 10), wherein the loop depicted in Fig. 10 of Ganesh is considered to maintain the audible sound therapy so long as the sensor thresholds (target data) are exceeded, such that when the sensor thresholds are not exceeded, the therapy is not activated in the next iteration of the loop].
However, while Ganesh discloses delivery of an audible sound therapy that is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject and is associated with the subject based on measurable target motion, sound, and/or physiological data [Ganesh Col 6:42-48, wherein a subject’s “favorite” music is considered to be associated with the subject] and discloses personalized comforting sounds to calm ASD individuals [Music therapy has been documented as useful for calming some ASD individuals. For such individuals, specific comforting sounds could bring down their stress level (Ganesh Col 2:32-35)], Ganesh fails to explicitly disclose (b) delivering an audio sound track to the subject; receiving sensor data from one or more wearable sensors configured to be worn by the subject, wherein the sensor data comprise motion data, sound data, physiological data, or combinations thereof of the subject that is indicative of an activity of the subject during delivery of the audio sound track; determining that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject; and associating the audio sound track with the subject; wherein the acquired sensor data is in the absence of the associated audio sound track, and such that the delivered audible sound therapy is the associated audio sound track that was determined to be effective.
Brimmer discloses systems and methods for preventing onset of patient agitation or decrease severity of patient agitation using personalized audible sound therapy [The audio playback device 15, through attachment of the headphones 20, may be engaged to provide patient with soothing, personalized, and selected music from the playlist 25A at regular prescribed intervals, when needed (that is, according to PRN), such as when the patient becomes agitated (initiated manually and/or automatically), preventatively in anticipation of a disruptive event, and/or upon request of the patient, patient's family, or a caregiver (Brimmer ¶0021); As the agitated patient hears the personalized music playlist 25 that was relevant to him or her, the agitated patient typically may be calmed in an efficient and effective manner (Brimmer ¶0056)], wherein Brimmer discloses delivering an audio sound track to the subject; receiving sensor data from one or more wearable sensors configured to be worn by the subject, wherein the sensor data comprise motion data, sound data, physiological data, or combinations thereof of the subject that is indicative of an activity of the subject during delivery of the audio sound track [The system 10 may include an agitation sensor 30A (motion, voice-activated, and/or the like) and/or electronic controller 35 operationally connected to the audio playback device 15 that initiates the device 15 to skip playback of a song that is associated with increasing agitation and/or override playback to initiate performance of a song weighted for its calming effect (Brimmer ¶0020)]; determining that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject; and associating the audio sound track with the subject [Machine learning algorithms, as described above, and/or manual feedback may be employed to better correlate combinations of sensor signals 710 to a specific patient state 700 that, once identified, may be paired with specifically assembled playlists 25, as well as to automatically edit and reefing the respective playlists 25 to delete music that is correlated with negative efficacy (i.e., music that increases undesired behaviors or that fails to increase desired behaviors) and add music that is identified as likely to have the desired effect on behavior. Likewise, the controller 35 may correlate those songs most effective in treating the patient and assign so-identified songs high priority and/or frequent repeat appearances in a respective playlist 25 (Brimmer ¶0064)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Ganesh to employ (b) delivering an audio sound track to the subject; receiving sensor data from one or more wearable sensors configured to be worn by the subject, wherein the sensor data comprise motion data, sound data, physiological data, or combinations thereof of the subject that is indicative of an activity of the subject during delivery of the audio sound track; determining that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject; and associating the audio sound track with the subject; wherein the acquired sensor data is in the absence of the associated audio sound track, and such that the delivered audible sound therapy is the associated audio sound track that was determined to be effective, so as to allow for identification of particular audible sound therapy known to be effective to prevent onset of agitation for the subject or decrease severity of agitation of the subject, and to allow for personalization of audible sound therapy to each subject.
Regarding claim 2, Ganesh in view of Brimmer teaches
The method of claim 1, wherein the subject has a neurodevelopmental disorder comprising autism spectrum disorder (ASD), sensory processing disorder, or combinations thereof [Ganesh Abstract]; wherein, when the subject has ASD, the sensor data being equal to or exceeding target data correlates with the onset of a pre-meltdown stage for the subject [When the sensor values exceed the pre-configured threshold values, the system determines that the patient has reached the pre-meltdown phase, also called antecedent to the meltdown phase (Ganesh Col 3:9-12)].
Regarding claim 3, Ganesh in view of Brimmer teaches
The method of claim 2, wherein the step of delivering audible sound therapy to the subject prevents the onset of a meltdown stage for the subject or decreases the severity of a meltdown stage for the subject [When the system and method detect that the patient has entered the pre-meltdown phase, the system triggers a set of activities. These activities include providing multiple options to deliver a therapeutic calming response to the patient to prevent further escalation of the patient's stress levels (Ganesh Col 3:13-18)].
Regarding claim 4, Ganesh in view of Brimmer teaches
The method of claim 1, wherein the audio sound track comprises a song [Ganesh Col 6:42-48], a music album, an audio book chapter, an audio book, a recited poem, a collection of recited poems, or combinations thereof.
Regarding claim 5, Ganesh in view of Brimmer teaches
The method of claim 1, wherein the motion data comprises motion frequency and/or motion intensity; wherein the target motion data comprise target motion frequency and/or target motion intensity, respectively [Ganesh Col 5:20-36, 6:11-15, 27-30]; and wherein, when the motion data of the subject is equal to or exceed the target motion data, the audible sound therapy is delivered to the subject at least until the target motion frequency and/or target motion intensity exceed the motion frequency and/or motion intensity, respectively, of the subject [Ganesh Col 8:26-41, Fig. 10, see Examiner’s analysis above].
Regarding claim 6, Ganesh in view of Brimmer teaches
The method of claim 1, wherein the physiological data comprises heart rate, blood pressure, respiration rate, breathing pattern, oxygen saturation rate, muscle tension level, temperature, one or more electrocardiogram (ECG) features, one of more electromyogram (EMG) features, or combinations thereof [Ganesh Col 5:20-36, 6:11-15, 27-30], and wherein, when the physiological data of the subject is equal to or exceed the target physiological data, the audible sound therapy is delivered to the subject at least until the target physiological data exceeds the physiological data of the subject [Ganesh Col 8:26-41, Fig. 10, see Examiner’s analysis above].
Regarding claim 7, Ganesh in view of Brimmer teaches
The method of claim 1, wherein the sound data comprises vocal sounds produced by the subject [Ganesh Col 5:20-36, 6:11-15, 27-30]; and wherein, when the sound data of the subject is equal to or exceeds the target sound data , the audible sound therapy is delivered to the subject at least until the target sound data exceed the sound data of the subject [Ganesh Col 8:26-41, Fig. 10, see Examiner’s analysis above].
Regarding claim 9, Ganesh in view of Brimmer teaches
The method of claim 1, wherein the control system provides for real-time delivery of the audible sound therapy to the subject [Ganesh Col 8:26-41, Fig. 10, wherein the polling of real time information to determine whether to deliver therapy is considered to read on real-time delivery].
Regarding claim 10, Ganesh in view of Brimmer teaches
The method of claim 1, wherein the control system selects the audio sound track that is familiar to the subject from a library of audio sound tracks that are familiar to the subject [Ganesh Col 6:42-48; a mobile computing and/or communication system 1700 within which a set of instructions when executed and/or processing logic when activated may cause the machine to perform any one or more of the methodologies described and/or claimed herein (Ganesh Col 8:59-63), wherein the use of an electronic device comprising a memory being configured to execute instructions to play favorite music or play a gentle audible alert is considered to read on selecting an audio sound track from audio sound tracks stored in a memory (library)]; and wherein said selection is based on the type of sensor data of the subject that is equal to or exceeds the target data [Ganesh Col 6:30-34, wherein the selection of any music based on any threshold being exceeded is considered to read on the claimed limitation] and/or a magnitude of difference between the sensor data of the subject and the target data.
Regarding claim 14, Ganesh teaches
A system comprising:
one or more wearable sensors configured to detect sensor data of a subject substantially continuously or semi-continuously, wherein the one or more wearable sensors comprise at least one sensor configured to detect motion data, at least one sensor configured to detect sound data, at least one sensor configured to detect physiological data, or combinations thereof [This integrated system continuously monitors environmental triggers and physiological stress indicative parameters of a patient diagnosed with autistic spectrum disorder, or other emotional or physical disorders (Ganesh Abstract); The environmental sensor system 102, the physiological sensor system 103, and therapy device 104 can be distributed and worn at different parts of the body (Ganesh Col 4:48-50); The signals of pre-autistic meltdown may be gathered through at least two sets of sensor systems:… Category B: Sensors 103 that detect physiological stress symptoms, comprising accelerometers that may detect restlessness, galvanic skin response sensors that may detect perspiration levels, flex resistors that may detect muscle tension, pulse oximetry sensors that detect various types of breathing patterns, including hypoventilation, when the patient is breathing room air, microphone that detects patient's audible frequency and vocal patterns (Ganesh Col 5:20-36)]; and
a control system configured to receive the sensor data of the subject from the one or more wearable sensors; wherein the control system comprises at least one processor and at least one controller [The controller 101 includes a microprocessor/microcontroller 410 that may be interfaced with wearable device communication interface 411, storage module 406, and real time clock 407. The microprocessor 410 interfaces with environmental sensor system 102, physiological sensor system 103, and therapy device 104 (Ganesh Col 5:9-15, Fig. 7)], wherein the control system is further configured to determine, for the subject, at least one of target motion data, target sound data, target physiological data, or combinations thereof, wherein the target data is personalized and individually determined for the subject based on the subject's habitual motions, sounds, physiology, or combinations thereof, respectively [The signals of pre-autistic meltdown may be gathered through at least two sets of sensor systems:… Category B: Sensors 103 that detect physiological stress symptoms, comprising accelerometers that may detect restlessness, galvanic skin response sensors that may detect perspiration levels, flex resistors that may detect muscle tension, pulse oximetry sensors that detect various types of breathing patterns, including hypoventilation, when the patient is breathing room air, microphone that detects patient's audible frequency and vocal patterns (Ganesh Col 5:20-36); The threshold values indicate the normal non-meltdown range of the sensors specific to each individual patient (Ganesh Col 6:28-30); These threshold values may be used as the initial or default values by the configure sensor thresholds module 515. FIG. 9 shows a screenshot of one example embodiment of the caregiver mobile device that provides the ability for the caregiver to set the threshold values for the sensors 634-645 and the therapy and alert response 647 (Ganesh Col 7:17-22); A casual assessment of the relation between antecedent triggers of the stress and the resulting behavior can be mapped by analyzing the chronological data from the sensors. This analysis may aid with the efforts to diagnose the symptoms of conditions such as Autism Spectrum Disorder (ASD) and epilepsy, to understand the factors contributing to the stress, and fine tune the appropriate thresholds of the sensor parameters and therapeutic calming response unique to each patient (Ganesh Col 7:40-48)];
wherein the at least one processor compares the sensor data to target data [Periodically, the readings from the environmental sensor system 102 and the physiological sensor system 103, are monitored by the microprocessor 410 and compared with the sensors' corresponding threshold values 515… The threshold values indicate the normal non-meltdown range of the sensors specific to each individual patient (Ganesh Col 6:11-15, 27-30)]; wherein, when at least one of the sensor data are equal to or exceeds exceed the target data, the at least one processor is configured to signal the at least one controller; and wherein the at least one controller delivers an audible sound therapy to the subject; wherein the audible sound therapy comprises, an associated audio sound track that was determined to be effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, wherein the associated audio sound track is characterized by a track rhythm and by a track beat, wherein the associated audio sound track is familiar to the subject [When the readings from the sensors cross the thresholds, the response initiated by microprocessor 410 comprises of the following: Calming response to the patient is implemented through the calming response module 104 (Ganesh Col 6:30-34); The actions that are dynamically controlled by the calming response module 104 include (example embodiments are shown in FIG. 2, FIG. 3, and FIG. 4)… playing favorite music, playing a discrete gentle audible alert (Ganesh Col 6:42-48)], and wherein the associated audio sound track is repeated at least until it is determined that the target data exceeds the sensor data of the subject [The therapeutic calming response characteristics of the calming response module 104, such as duration and intensity of the responses, can be controlled by the ‘configure the calming response module 517’ that is located on the caregivers' mobile device 521 (Ganesh Col 6:51-55); After the first iteration 702 of polling and recording the sensor, location and real time clock information into the storage module, the process is repeated periodically until any sensor value exceeds its corresponding threshold 703. If the latter occurs,… Independent of an active connection between the wearable module and the caregiver's mobile device, if the wearable device has been pre-configured to deliver therapy 706, then corresponding therapy is delivered to the patient… This sequence of steps repeat going back to the collection of the sensor, location, and real time clock information 702 (Ganesh Col 8:26-41, Fig. 10), wherein the loop depicted in Fig. 10 of Ganesh is considered to maintain the audible sound therapy so long as the sensor thresholds (target data) are exceeded, such that when the sensor thresholds are not exceeded, the therapy is not activated in the next iteration of the loop].
However, while Ganesh discloses delivery of an audible sound therapy that is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject and is associated with the subject based on measurable target motion, sound, and/or physiological data [Ganesh Col 6:42-48, wherein a subject’s “favorite” music is considered to be associated with the subject] and discloses personalized comforting sounds to calm ASD individuals [Music therapy has been documented as useful for calming some ASD individuals. For such individuals, specific comforting sounds could bring down their stress level (Ganesh Col 2:32-35)], Ganesh fails to explicitly disclose wherein the control system is further configured to deliver an audio sound track to the subject, receive sensor data from the one or more wearable sensors during delivery of the audio sound track, determine that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, and associate the audio sound track with the subject; and wherein the audible sound therapy comprises, the associated audio sound track that was determined to be effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject.
Brimmer discloses systems and methods for preventing onset of patient agitation or decrease severity of patient agitation using personalized audible sound therapy [The audio playback device 15, through attachment of the headphones 20, may be engaged to provide patient with soothing, personalized, and selected music from the playlist 25A at regular prescribed intervals, when needed (that is, according to PRN), such as when the patient becomes agitated (initiated manually and/or automatically), preventatively in anticipation of a disruptive event, and/or upon request of the patient, patient's family, or a caregiver (Brimmer ¶0021); As the agitated patient hears the personalized music playlist 25 that was relevant to him or her, the agitated patient typically may be calmed in an efficient and effective manner (Brimmer ¶0056)], wherein Brimmer discloses delivering an audio sound track to the subject; receiving sensor data from one or more wearable sensors configured to be worn by the subject, wherein the sensor data comprise motion data, sound data, physiological data, or combinations thereof of the subject that is indicative of an activity of the subject during delivery of the audio sound track [The system 10 may include an agitation sensor 30A (motion, voice-activated, and/or the like) and/or electronic controller 35 operationally connected to the audio playback device 15 that initiates the device 15 to skip playback of a song that is associated with increasing agitation and/or override playback to initiate performance of a song weighted for its calming effect (Brimmer ¶0020)]; determining that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject; and associating the audio sound track with the subject [Machine learning algorithms, as described above, and/or manual feedback may be employed to better correlate combinations of sensor signals 710 to a specific patient state 700 that, once identified, may be paired with specifically assembled playlists 25, as well as to automatically edit and reefing the respective playlists 25 to delete music that is correlated with negative efficacy (i.e., music that increases undesired behaviors or that fails to increase desired behaviors) and add music that is identified as likely to have the desired effect on behavior. Likewise, the controller 35 may correlate those songs most effective in treating the patient and assign so-identified songs high priority and/or frequent repeat appearances in a respective playlist 25 (Brimmer ¶0064)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Ganesh to employ wherein the control system is further configured to deliver an audio sound track to the subject, receive sensor data from the one or more wearable sensors during delivery of the audio sound track, determine that the audio sound track is effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, and associate the audio sound track with the subject; and wherein the audible sound therapy comprises, the associated audio sound track that was determined to be effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject, so as to allow for identification of particular audible sound therapy known to be effective to prevent onset of agitation for the subject or decrease severity of agitation of the subject, and to allow for personalization of audible sound therapy to each subject.
Regarding claim 15, Ganesh in view of Brimmer teaches
The system of claim 14, wherein the at least one processor compares the motion data, the sound data, the physiological data, or combinations thereof of the subject with target motion data, target sound data, target physiological data, or combinations thereof, respectively [Ganesh Col 5:20-36, 6:11-15, 27-30].
Regarding claim 17, Ganesh in view of Brimmer teaches
The system of claim 14 further comprising an audio speaker, earbuds, and/or headphones, wherein the audible sound therapy is delivered via the audio speaker, earbuds, and/or headphones without requiring assistance from a caregiver [Therapy devices (not shown) may include… miniature audible speakers, earphone connectors, wireless BLUETOOTH™ transmitters for earphones,… and the like (Ganesh Col 5:1-6); Ganesh Col 8:26-41, Fig. 10, wherein the therapy is noted as being delivered independent of any caregiver interaction].
Regarding claim 18, Ganesh in view of Brimmer teaches
The system of claim 14, wherein the subject has autism spectrum disorder (ASD) [Ganesh Abstract]; wherein the sensor data being equal to or exceeding target data correlates with the onset of a pre-meltdown stage for the subject [When the sensor values exceed the pre-configured threshold values, the system determines that the patient has reached the pre-meltdown phase, also called antecedent to the meltdown phase (Ganesh Col 3:9-12)]; and wherein the audible sound therapy prevents the onset of a meltdown stage for the subject or decreases the severity of a meltdown stage for the subject [When the system and method detect that the patient has entered the pre-meltdown phase, the system triggers a set of activities. These activities include providing multiple options to deliver a therapeutic calming response to the patient to prevent further escalation of the patient's stress levels (Ganesh Col 3:13-18)].
Regarding claim 21, Ganesh in view of Brimmer teaches
The system of claim 14, the motion data comprises frequency and/or motion intensity; wherein the target motion data comprise target motion frequency and/or target motion intensity, respectively [Ganesh Col 5:20-36, 6:11-15, 27-30]; and wherein, when the motion data of the subject is equal to or exceed the target motion data, the audible sound therapy is delivered to the subject at least until the target motion frequency and/or target motion intensity exceed the motion frequency and/or motion intensity, respectively, or the subject [Ganesh Col 8:26-41, Fig. 10, see Examiner’s analysis above].
Claim(s) 11-13 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ganesh in view of Brimmer, as applied to claims 10 and 14 above, in further view of Inz (US-20220165393-A1, previously presented).
Regarding claim 11, Ganesh in view of Brimmer teaches
The method of claim 10, wherein acquiring sensor data from one or more wearable sensors further comprises receiving, by at least one computing device, the sensor data from the one or more wearable sensors [The environmental sensor system 102, the physiological sensor system 103, and therapy device 104 can be distributed and worn at different parts of the body (Ganesh Col 4:48-50)]; wherein the at least one computing device comprises the at least one processor [Ganesh Col 5:9-15, Fig. 7]; wherein comparing the sensor data of the subject with target data further comprises providing, by the at least one computing device, an input to a model; wherein the model is configured to evaluate the sensor data of the subject with respect to the target data to determine the presence or the absence of a pre-meltdown stage for the subject; wherein the at least one processor, based upon a determination of the presence of a pre-meltdown stage for the subject, signals the at least one controller; and wherein the at least one controller delivers the audible sound therapy to the subject [Ganesh Col 6:30-34, wherein the input of data into an assessment to determine whether the data exceeds a threshold is considered to read on a model].
However, Ganesh in view of Brimmer fails to explicitly disclose wherein the model is trained with sensor data from the subject and/or with sensor data from at least one additional subject.
Inz discloses systems and methods for detecting the onset of a behavioral disorder using a combination of motion, sound, and physiological sensor data of a subject for the purposes of mitigating or eliminating the severity of the onset behavioral disorder [The following physiological indicators and physiological measures can be used in various combinations to detect the onset of a panic attack or other behavioral disorders: heart rate; heart rate variability (HRV); electrocardiogram (ECG); core body temperature; heat flow off the body; respiratory rate; galvanic skin response (GSR); electromyography (EMG); electroencephalography—Fast Fourier transform analysis (EEG-FFT); electrooculogram (EOG); blood pressure; hydration level; muscle pressure; activity level; skin temperature; body position and posture; acceleration; and voice tone (Inz ¶0009); By continuously receiving feedback on the user's physiological status in the form of detection signals, the user is able to detect the onset of the disorder, to observe a connection between one's mental state and the feedback signals, and, with practice and guidance, learn how to control, mitigate, or eliminate the disorder (Inz ¶0010)], wherein Inz discloses the use of at least one machine learning model that is trained with sensor data from the subject and/or with data from at least one additional subject, wherein the at least one machine learning model is configured to evaluate the sensor data of the subject with respect to target data and provide an evaluation result comprising an indication that the sensor data of the subject is equal to or exceed the target data; wherein the indication that the sensor data of the subject is equal to or exceed the target data is an evaluation score being equal to or greater than a threshold score value [In the supervised learning mode, explicit labels are provided by the user 110, by a therapist 180, or by another party that indicate the mental, emotional, or behavioral status of the user 110. These labels are paired with the features and a mapping from features to labels is formed via associative or supervised learning (Inz ¶0080); The detection and classification signals may vary according to the disorder but a preferred embodiment for most disorders comprises a multi-level detection signal that indicates that a disorder event has been detected if the level is above a threshold. The level of the detection signal corresponds to the severity of the disorder event. The number of levels of the detection signal corresponds to the number of severity levels (e.g., in the previous example above there are four levels: normal, mild, moderate, and intense). In an alternative embodiment, the detection signal is a binary signal that indicates the presence or absence of a disorder event (Inz ¶0092); The signal processing subsystem 210, or more specifically the machine learning subsystem 360 of FIGS. 6 and 9, generates a disorder event detection signal. The disorder event detection signal is based on the classification signals generated by the supervised learning subsystem 630 of the machine learning subsystem 360 of FIG. 9. In an example embodiment, if any of the non-normal disorder classes are active then the disorder event detection signal indicates that a disorder event has been detected (Inz ¶0093)].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Ganesh in view of Brimmer to employ wherein the model is trained with sensor data from the subject and/or with sensor data from at least one additional subject, as a trained machine learning model may allow for personalized detection for the subject to enhance accuracy [Thus, the system also includes a subsystem to adjust and improve the algorithm given feedback from a specific user through machine learning. The algorithm becomes personalized to a user through user feedback where false positives and negatives for symptoms or psychological events are indicated. Through machine learning, the system adjusts to errors indicated by the user and becomes more customized to an individual (Inz ¶0017)], and is further considered to amount to mere application of a known technique [machine learning for classification/event detection] to a known device (method, or product) ready for improvement to yield predictable results [MPEP § 2143(I)(D)].
Regarding claim 12, Ganesh in view of Brimmer and Inz teaches
The method of claim 11, wherein evaluating the sensor data of the subject with respect to the target data further comprises determining, by the at least one computing device, that the sensor data of the subject is equal to or exceed the target data [Ganesh Col 6:30-34]; wherein determining, by the at least one computing device, that the sensor data of the subject is equal to or exceed the target data further comprises comparing at least one evaluation score with at least one threshold score value and determining that the evaluation score is equal to or exceeds the threshold score value [see § 103 modification above; Inz ¶¶0092-0093].
Regarding claim 13, Ganesh in view of Brimmer and Inz teaches
The method of claim 12, wherein step (f) of delivering audible sound therapy to the subject further comprises delivering audible sound therapy to the subject by the at least one controller at least until the threshold score value exceeds the evaluation score [wherein based on the § 103 modification of claims 11-12 above, the determination to of the target data being exceed is based on an evaluation score exceeding a threshold score value].
Regarding claim 16, Ganesh in view of Brimmer teaches
The system of claim 14, wherein the control system further comprises (i) at least one model, wherein the at least one model is configured to evaluate the sensor data of the subject with respect to target data [Ganesh Col 6:30-34, wherein the input of data into an assessment to determine whether the data exceeds a threshold is considered to read on a model]; and (ii) a non-transitory computer readable medium that stores instructions [Ganesh Col 8:59-63] that when executed by the processor, causes the processor to: receive, using the control system, an input comprising sensor data of the subject; provide, by the control system, the input to the at least one model; determine, by the control system, an evaluation result comprising an indication that the sensor data of the subject is equal to or exceeds the target data by using the at least one model; and deliver, by the control system, audible sound therapy to the subject, wherein the audible sound therapy comprises the associated audio sound track, and wherein the associated audio sound track is configured to be repeated at least until the target data exceed the sensor data of the subject [Ganesh Col 6:11-15, 27-34, 42-48].
However, Ganesh in view of Brimmer fails to explicitly disclose wherein the model is at least one machine learning model that is trained with sensor data from the subject and/or with data from at least one additional subject; wherein the indication that the sensor data of the subject is equal to or exceeds the target data is an evaluation score being equal to or greater than a threshold score value.
Inz discloses systems and methods for detecting the onset of a behavioral disorder using a combination of motion, sound, and physiological sensor data of a subject for the purposes of mitigating or eliminating the severity of the onset behavioral disorder [Inz ¶¶0009-0010)], wherein Inz discloses the use of at least one machine learning model that is trained with sensor data from the subject and/or with data from at least one additional subject, wherein the at least one machine learning model is configured to evaluate the sensor data of the subject with respect to target data and provide an evaluation result comprising an indication that the sensor data of the subject is equal to or exceed the target data; wherein the indication that the sensor data of the subject is equal to or exceed the target data is an evaluation score being equal to or greater than a threshold score value [Inz ¶¶0080, 0092-0093].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Ganesh in view of Brimmer to employ wherein the model is at least one machine learning model that is trained with sensor data from the subject and/or with data from at least one additional subject and wherein the indication that the sensor data of the subject is equal to or exceeds the target data is an evaluation score being equal to or greater than a threshold score value, as a trained machine learning model may allow for personalized detection for the subject to enhance accuracy [Thus, the system also includes a subsystem to adjust and improve the algorithm given feedback from a specific user through machine learning. The algorithm becomes personalized to a user through user feedback where false positives and negatives for symptoms or psychological events are indicated. Through machine learning, the system adjusts to errors indicated by the user and becomes more customized to an individual (Inz ¶0017)], and is further considered to amount to mere application of a known technique [machine learning for classification/event detection] to a known device (method, or product) ready for improvement to yield predictable results [MPEP § 2143(I)(D)].
Response to Arguments
Applicant’s arguments, see Applicant’s Remarks p. 13-14, filed 20 April 2026, with respect to documents cited but not attached in the IDS filed 12 September 2025 have been fully considered and are persuasive. The Examiner acknowledges the corrected IDS filed 20 April 2026.
Applicant’s arguments, see Applicant’s Remarks p. 14, with respect to the previously presented drawing objections have been fully considered and are persuasive. The drawing objections have been withdrawn.
Applicant's arguments, see Applicant’s Remarks p. 15, with respect to the previously presented claim objections have been fully considered but they are not entirely persuasive.
The Examiner notes that not all of the previously presented claim objections were addressed or argued against. See above for maintained objections.
Applicant’s arguments, see Applicant’s Remarks p. 15, with respect to the previously applied rejections under § 112(b) have been fully considered and are persuasive. The rejection of claim 16 under § 112(b) has been withdrawn.
Applicant's arguments, see Applicant’s Remarks p. 15-19, with respect to the previously applied rejections under § 101 have been fully considered but they are not persuasive.
The Applicant asserts that 1. amended independent claims 1 and 14 now include two new elements relating to a determination of personalized target data and a sensor-validated association of an audio track, wherein the Applicant notes that neither of these elements is/are performable in the human mind; wherein the Applicant further notes that step (a) [determining, for a subject, at least one of target motion data, target sound data, target physiological data, or combinations thereof…] requires physical, sensor-based measurement of the specific subject’s baseline behavior, such that no observer can mentally determine another individual’s precise pre-meltdown sensor thresholds; that step (b) [delivering an audio track to the subject; receiving sensor data… determining that the audio sound track is effective…] is a physical hardware-driven calibration procedure, wherein no person can mentally measure physiological, motion, and sound sensor responses during audio playback with the precision required to determine whether a specific track produced a measurable calming effect in a specific individual; and that step (c) [acquiring sensor data… wherein the one or more wearable sensors are integrated with a control system…] affirmatively recites computer elements and integrated sensors that cannot be performed in the mind of a human. However, the Examiner disagrees that step (a) [and corresponding function in claim 14] requires physical, sensor-based measurement of the specific subject’s baseline behavior, such that no observer can mentally determine another individual’s precise pre-meltdown sensor thresholds, as the subject’s habitual motions are defined by visually or audibly observing physical phenomenon [Applicant’s Specification ¶00020], such that determination of the target motion/sound/physiological data may still be performed in the mind or by hand. Furthermore, the Examiner disagrees that that step (b) [and corresponding function in claim 14] is a physical hardware-driven calibration procedure, wherein no person can mentally measure physiological, motion, and sound sensor responses during audio playback with the precision required to determine whether a specific track produced a measurable calming effect in a specific individual, as delivering an audio sound track and determining effectiveness is/are considered to be abstract ideas of organizing human activity and a mental process, respectively, as analyzed above, wherein for the sake of compact prosecution, even if delivering an audio sound track was not considered to be an abstract idea is still considered to fail to be an additional element that integrates the abstract idea into a practical application at Step 2A Prong 2 or allow the claim(s) to be significantly more than the abstract idea at Step 2B as further analyzed above due to the delivery of the audio sound track defining a necessary precursor for mental analysis steps and the use of generic audio delivery devices and lack of sufficient particularity regarding the audio sound track itself as a particular treatment; and wherein the reception of sensor data is considered to amount to mere extra-solution data gathering. Finally, the Examiner notes that while step (c) [and corresponding function in claim 14] recite structural limitations in the form of sensors and a control system, the sensors and control system are further analyzed at Step 2A Prong 2 and Step 2B as referring to well-understood, routine, and conventional structures.
The Applicant asserts that 2. the amended claims are integrated into a practical application, as amended independent claims 1 and 14 recite a hardware system of wearable sensors, and controller, which the Applicant notes are not generic computers performing generic steps and are instead particular machines performing a particular therapeutic function for a particular individual; and wherein the Applicant further asserts that amended independent claims 1 and 14 both relate to delivery of not any familiar audio track, but specifically the specific track sensor-validated in the calibration step for that individual, such that the generic medication analogy as recited in the Office Action [p. 18-20 of Non-Final Rejection dated 20 January 2026] is not applicable, as the claims recite a specific therapeutic stimulus and the sensor-confirmed criterion for its effectiveness in the specific subject, which makes the therapeutic stimulus highly personalized for each subject. However, the Examiner disagrees with the Applicant’s assertion that the amended claims recite particular machines performing a particular therapeutic function for a particular individual, as the only structural elements as recited [considered to refer to one or more wearable sensors, a control system that comprises at least one processor and at least one controller, and an audio speaker, earbuds, and/or headphones] have been analyzed at Step 2A Prong 2 and Step 2B as referring to well-understood, routine, and conventional structures, wherein the claim language lacks any particular recitation of the identified structures as being any more than well-understood, routine, and conventional. Furthermore, the Examiner disagrees that the argued specific track being sensor-validated in the calibration step for an individual defines a particular treatment, as under the broadest reasonable interpretation of “delivering an audible sound therapy” that “comprises the associated audio sound track that was determined to be effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject” is considered to refer to a method of organizing human activities at Step 2A Prong 1, as an “effective” audio sound track may be verbally delivered from one person to another; and wherein under a narrower interpretation wherein the audio sound track itself is not considered to be a particular treatment or prophylaxis, as the audio sound track is recited at a high level of generality [Conversely, consider a claim that recites the same abstract idea and "administering a suitable medication to a patient." This administration step is not particular, and is instead merely instructions to "apply" the exception in a generic way (MPEP § 2106.04(d)(2)(a), wherein the Examiner notes that the audio sound track being associated with the subject as being “effective to prevent onset of a meltdown stage for the subject or decrease severity of a meltdown stage for the subject” is considered to be similar to the cited portion of the MPEP, which recites a medication recited at a high level of generality that is suitable to treat the patient, as the audio sound track while being considered to be specifically associated with the subject is still in itself recited at a high level of generality (characterized by a track rhythm and a track beat, or further comprise a song, a music album, an audiobook chapter, an audio book, a recited poem, a collection of recited poems, or combinations thereof)].
The Applicant asserts that 3. the amended claims include significantly more than any abstract idea, as the cited references do not disclose delivery of a specific audio track to a subject while wearable sensor data is collected, and that data being used to determine and confirm the track’s therapeutic effectiveness as a meltdown prophylactic for that individual, with the validated track thereafter being associated with the subject for autonomous closed-loop delivery; and wherein individualized target threshold calibration based on the subject’s actual baseline habitual behavior is not disclosed as conventional. However, the Examiner disagrees with the Applicant arguments as the references cited with respect to additional elements identified as being well-understood, routine, and conventional, are cited in addition to the Applicant’s own disclosure of such additional elements being well-understood, routine, and conventional; and wherein as noted above in the Step 2B analysis, as the audio sound track is recited at a high level of generality, the cited references with respect to an “audio speaker, earbuds, and/or headphones” are considered applicable to deliver the claimed audio sound track. Furthermore, the Examiner disagrees with the Applicant’s arguments with respect to the amended calibration step, as the argued steps have been analyzed at Step 2A Prongs 1-2 and Step 2B as failing to integrate the abstract idea into a practical application or allow the claim as a whole to amount to significantly more in the § 101 analysis [refers to limitations either identified as being abstract ideas, extra-solution activity, or well-understood, routine, and conventional structures] and Examiner’s response to Applicant’s arguments above.
Applicant’s arguments, see Applicant’s Remarks p. 19-22, with respect to the rejection(s) of claim(s) 1, 14, and those dependent therefrom under § 102 and § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Ganesh (US-10231664-B2, previously presented) in view of Brimmer (US-20190126002-A1).
The Applicant asserts that neither Ganesh, nor the combination of Ganesh and Inz, discloses the amended subject matter of claims 1 and 14, wherein the Applicant specifically notes that the target data of Ganesh [Ganesh Col 6:11-15, 27-30, 51-55] is/are pre-configured parameters, but are not determined personalized target data based on the subject’s own habitual motions, sounds, and physiology. The Applicant further asserts that the limitations directed towards step (b) of claim 1 [and corresponding functionality of claim 14] fails to be taught by Ganesh, as the Applicant notes that while Ganesh discloses “playing favorite music” [Ganesh Col 6:42-48], Ganesh contains no disclosure of delivering a specific audio therapeutic track to test its therapeutic effectiveness as claimed, and wherein the “favorite music” of Ganesh is not necessarily therapeutically effective, but may just be “favorite” music without being therapeutically effective. However, the Examiner notes that Applicant’s arguments with respect to claim(s) 1 and 14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. Ganesh is presently modified in view of Brimmer (US-20190126002-A1) to teach the argued limitation of step (b), as Brimmer is considered to disclose similar methodology and functionality [Brimmer ¶¶0020-0021, 0056, 0064]. Furthermore, the Examiner disagrees with the Applicant’s argument that the “favorite music” of Ganesh is not necessarily therapeutically effective, but may just be “favorite” music without being therapeutically effective, as Ganesh explicitly discloses that the delivered audible sound therapy of “favorite” music is part of an implemented calming response [When the readings from the sensors cross the thresholds, the response initiated by microprocessor 410 comprises of the following: Calming response to the patient is implemented through the calming response module 104 (Ganesh Col 6:30-34); The actions that are dynamically controlled by the calming response module 104 include (example embodiments are shown in FIG. 2, FIG. 3, and FIG. 4)… playing favorite music, playing a discrete gentle audible alert (Ganesh Col 6:42-48)].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SEVERO ANTONIO P LOPEZ whose telephone number is (571)272-7378. The examiner can normally be reached M-F 9-6 EST.
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/SEVERO ANTONIO P LOPEZ/Examiner, Art Unit 3791
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