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 .
Examiner’s Comments
And/or as recited in the claims is read as ‘and’ or ‘or’.
An/or as used in claim 12 is read as ‘and’ or ‘or’.
‘Configured to’, as used in the claims is drawn to the structure of a digital processor based audio device with a sensor.
Examiner notes applicant’s para. 6 which describes the processes related to a ‘tinnitus management action’ as recited in claim 11.
Prior art to Pontoppidan (US 20120308060 A1) clearly teaches modifying tinnitus treatment based specifically on positive detection of speech.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 11-17,27-40 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Tran (US 20200268260 A1).
As per claim 11, Tran discloses an apparatus, comprising:
a body carried portable device (in ear device) including an input subsystem (the inputs to the device per para 154) and an output subsystem (the speaker of the in ear device, para 145), wherein the device includes a product of and/or resulting from machine learning (para. 164) that is used by the device to determine when and/or if to initiate a tinnitus management action (automatically selects an optimal set of hearing aid parameters per para 164 noting this is part of a tinnitus management action in view of the adaptive masking algorithm in para 154 ).
As per claim 12, the apparatus of claim 11, wherein: the product of an/or resulting from machine learning is also used by the device to determine what type of tinnitus management action should be executed based on input into the input subsystem (determine an audio response chart for a user based on a plurality of environments (restaurant, office, home, theater, party, concert, among others) to determine a current environment per para 164)
wherein the management action at least one of remediates the effects of tinnitus or prevents a noticeable tinnitus scenario from occurring (partially masked para 154).
As per claim 13, the apparatus of claim11, wherein:
the input subsystem is configured to automatically obtain data indicative of at least physiological features past and/or present of a person who is using the device for tinnitus management purposes (para 168 the speech heard or spoken by the user and processed per 168,169 ); and
the input into the subsystem is the obtained data (all cited data and signals must be input to and stored, recovered, processed and then output via the digital processor in order to perform the cited functions; additionally the speech and background noise as received by the device per para 168).
As per claim 14, the apparatus of claim11, wherein: the input subsystem is configured to automatically obtain data indicative of at least ambient environmental conditions past and/or present of a person who is using the device for tinnitus management purposes (a plurality of environments (restaurant, office, home, theater, party, concert, among others) per para 164); and
the input into the subsystem is the obtained data (the data is used with the tinnitus management).
As per claim 15, the input subsystem is configured to automatically obtain data indicative of speech in an ambient environment (through the microphone per the above cited speech);
the device is configured to analyze the input and determined that the speech is likely speech that a user of the device seeks to understand (para 154, the audio signal is analyzed for peaks and troughs, where the audio signal can be speech, where detecting peaks and troughs in an audio signal/speech comprises detecting the speech); and
the device automatically adjusts a tinnitus therapy based on the analysis (para 154).
As per claim 16, the apparatus of claim11, wherein: the device is configured to automatically initiate tinnitus masking using the product based on the input into the input subsystem (para. 154 masking algorithm).
As per claim 17, the apparatus of claim 11, wherein: the device is configured to log data 23
indicative of at least one of ambient environmental conditions past and/or present of a person who is using the device for tinnitus management purposes or ambient environmental conditions past and/or present of a person who is using the device for tinnitus management purposes (a plurality of environments (restaurant, office, home, theater, party, concert, among others) per para 164);
and the device is configured to correlate the logged data to tinnitus related events (the input in the context of the machine learning and tinnitus functions per para 164 to adapt the hearing aid parameters).
18-26. (Cancelled)
As per claim 27, a system, comprising: a sound capture apparatus configured to capture ambient sound (para 148 microphone); and
an electronics package (the digital processor required to perform the cited functions) configured to receive data based on at least an outputted signal from the sound capture apparatus and analyze the data to determine based thereon that there exists a statistical likelihood of a future tinnitus event in the near term of a person using the system (para 154, the audio signal is analyzed for peaks and troughs, where the audio signal can be speech, where detecting peaks and troughs in an audio signal/speech comprises detecting the speech, as well as the likelihood of a tinnitus event during the peak and or trough) (additionally the predicted weighted combination of listening criteria as determined by the values of the coefficients and the acoustic environment and/or psychoacoustic parameters used by the learning system. The system further eventually allows the user to ‘train’ the device to adjust its operation automatically per para 166),
wherein the system is configured to automatically initiate an output that preemptively reduces the likelihood of the future tinnitus event upon the determination (either of the two functions cited directly above function to produce an automated output to adapt the device parameters, in view of the above cited tinnitus masking functions which preemptively reduces the likelihood of the future tinnitus event through the masking or adapted parameters).
As per claim 28, wherein: the system is configured to automatically initiate the output without affirmative input from the person (the parameters and comparison results are initiated automatically as part of an algorithm performed by a digital processor, which requires no user input from the point where the machine learning receives inputs).
As per claim 29, the system of, wherein: the data received by the electronics package further includes data based on physiological data relating to the person (the detected speech when spoken by the person); and
the electronics package is configured to evaluate the data based on physiological data in combination with the data based on the outputted signal an determine based thereon that there exists a statistical likelihood of a future tinnitus event in the near term of a person using the system (para 154: to spectrally modify the audio signal in accordance with a predetermined masking algorithm which modifies the intensity of the audio signal at selected frequencies. The described predetermined masking algorithm provides intermittent masking of the tinnitus where the tinnitus is completely masked during peaks in the audio signal/outputted signal)(in view of para. 154: the masking algorithm may be modified to account for any hearing loss of the patient, where the parameters indicating hearing loss are physiological data).
As per claim 30, the system of claim27, wherein: the system includes a hearing prosthesis, the hearing prosthesis including the sound capture device (the microphone for the device per para 4).
As per claim 31, the system of claim27, wherein: the electronics package includes logic that applies a dynamic and individualized probability metric to determine that there exists the statistical likelihood of a future tinnitus event in the near term of a person using the system (the machine learning cited above provides a dynamic and individualized implementation of the probability metric of the likelihood of tinnitus via the detected peak or trough of the speech trough tinnitus masking function per para 154).
As per claim 32, the system of claims27, 28, 29, 30 or31, of claim27, wherein: the system is configured to automatically log data indicative of at least one of ambient environmental conditions past and/or present of the person or physiological conditions past and/or present of the person (required as part of the determination of determine an audio response chart for a user based on a plurality of environments (restaurant, office, home, theater, party, concert, among others to determine a current environment per para 164) ;
the system is configured to automatically correlate the logged data to tinnitus related events of the person and automatically develop a tinnitus management regime (the function above is correlated with the tinnitus processing per the claim 1 rejection as they are performed on the same device with the same set of data, where machine learning cited above automatically develops the regime/processing parameters over time); and
the electronics package is configured to execute the tinnitus management regime to analyze the data to determined based on the data that there exists the statistical likelihood of the future tinnitus event in the near term of the person using the system (the tinnitus masking function per 154, the detection of the peak or trough of speech is an indication of a high likelihood of a particular tinnitus event relative to the other parameters being adapted via machine learning adapting the regime).
As per claim 33, the system of claims27, 28, 29, 30, 31 or32of claim27, wherein: the ambient environmental conditions include the presence of speech (per the masking function in para 154, noting speech via a microphone includes ambient condition information).
As per claim 34, a system, comprising: a tinnitus onset predictive subsystem (the means of determining the peaks and troughs with the masking functions per para 154); and a tinnitus management output subsystem (the means of performing the responses to the detection of peaks and troughs of the speech and/or outputting the audio to the user via the hearing device of the masking function).
As per claim 35, the system of claim 34, wherein: the system further comprises a tinnitus onset predictive metric development subsystem (any of the parameters updated or output via the machine learning cited above as used by the masking function per the claim 34 rejection).
As per claim 36, the system of claim 35, wherein: the system includes a trained neural network, wherein the trained neural network is part of the tinnitus onset predictive subsystem (para 167 in view of the masking function cited above); and
the tinnitus onset predictive metric development subsystem contributes to the training of the trained neural network (the interface between the hearing device and the cited neural network to perform the functions cited above).
As per claim 37, the system of claim34, wherein:
the tinnitus onset predictive subsystem is an expert sub-system of the system (the trained set of parameters used by the device per para 92, trained) that includes a code of and/or from a machine learning algorithm to analyze data relating to a user of the system in real time (per the neural network implementation, and also per para 227) and
wherein the machine learning algorithm is a trained system trained based on a statistically significant population of tinnitus afflicted persons (the user, using the device as it trains and adapts via the machine learning neural networks).
As per claim 38, the system of, claim34, wherein: the tinnitus onset predictive subsystem is configured to automatically analyze a linguistic environment metric (the peak of speech) in combination with a non-linguistic environment metric (the trough of speech of the masking function cited above) correlated to the linguistic environment metric, all inputted into the system, and based on the analysis, automatically determine whether or not a tinnitus event is imminent (per the masking function and the various responses to the peak versus trough of detected speech).
As per claim 39, the system of claim 38, wherein: the system is configured to identify speech of a user of the system (per the masking function cited above); and
the linguistic environment metric is the speech of the user (per the speech peak and troughs detected in the masking function).
As per claim 40, the system of claim34, wherein: the tinnitus management output subsystem diverts a user of the system's attention, thus mitigating the effects of tinnitus (the masking for tinnitus as cited above).
41-43. (Cancelled).
As per claim 44, the apparatus of claim 11, wherein: the body carried portable device has a user interface that is part of the input subsystem and is a body worn device (the input microphones are a user interface because the user can hear with them, the hearing device is worn on the ear which is body worn).
As per claim 45, the system of claim 27, wherein: the output is different from masking sounds (the part of the optimal set of hearing aid parameters which do not act on the masking function, but alongside the masking function, including the nonmasked sounds/frequencies being output to the user as part of a hearing aid and/or any of the sounds input via the monitoring function (noting the abstract).
As per claim 46, The system of claim 27, wherein: the system is devoid of a light sensor sub-system (the device 144 only optionally comprises a light sensor, as 52 can be EEG, ECG, temperature, pressure, among others).
As per claim 47, the system of claim 34, further comprising: a tinnitus management type and/or stimulus determination subsystem configured to analyze input into the system and determine what type and/or what stimulus should be outputted by the tinnitus management output subsystem (the determination of the parameters in combination with the masking system, which determines a particular sound/frequency per para 154) (also para 154: the intensity of the audio signal may be modified across the spectrum of the signal and may also be modified to account for any hearing loss that the patient may have incurred).
As per claim 48, the system of claim 40, wherein: the tinnitus management output subsystem is also configured to manage tinnitus by way of masking (per the claim 1 and 47 rejections).
Response to Arguments
The submitted arguments have been considered but are moot in view of the new grounds of rejection.
Per applicant’s arguments, the examiner will attempt to respond to applicant’s arguments within the amount of time given (they have taken away much, much time). Many of applicant’s arguments seem tangential or not actually responsive to the actual art cited by the examiner. Applicant is encouraged to present the most relevant arguments first, based on a complete reading of the prior art in view of the cited portions.
For example, the first argument (bottom of page 8) is that the cited prior art is not a body worn device. Did applicant even review the cited prior art ? The examiner cited elements from para 145, which is shown in fig. 3d which is body worn. Applicant is encouraged to read the entire cited prior art for all references to previously cited elements 52,64,56,58 so that they may understand the prior art, and then stop mischaracterizing the prior art.
Per applicant’s arguments pages 9-25 the examiner notes the same as above where applicant has grossly mischaracterized the examiner’s cited art and is encouraged to read the entire reference, and put forth their most convincing arguments first. Mischaracterization and misrepresentation is an indication of ‘AI-Slop’. Counselor, I know you do not use AI in that way, certainly you can provide more relevant arguments than given on the bottom of page 8 of your remarks.
In the short time that I have remaining, let me discuss a notable difference between the present application and the cited art to Tran (US 20200268260 A1).
Applicants Disclosure is very generalized, the prior art is not.
Tran discloses the implementation level details of an ear/body worn device that treats tinnitus, monitors, and also is a hearing aid. The device uses AI to train (note TRAN fig. 7A is almost exactly matched to applicant’s Fig. 7, 620). The training is applied to the hearing, monitoring and to the tinnitus treatment/masking. Additionally, the generic training of the hearing aid alone could be read as tinnitus treatment as it involves equalizing the frequency response of the user’s hearing which would include any chronic tinnitus.
The biggest issue moving forward appears to be the lack of implementation level details in applicant’s specification. Applicant does not provide a single embodiment, but rather several drastically different ones (Fig. 1a, Fig. 1d, Fig. 2c, Fig. 7a).
Additionally, applicant’s specification is silent as to the implementation or processing at a signal level. What is the machine learning stage actually doing ? What signals is it acting upon? How does the audio get converted from an A/D converter into the multiple signals directly to applicant’s 620 in fig. 7 ? that does not make sense because an A/D only has one output. Further, the modern AI LLM machine learning chips which classify, are not currently able to fit into any head worn devices, or even within many consumer devices. Many of the modern AI ‘classifiers’ require a full size personal computer full of graphics cards. How is applicant’s system using AI? What is the AI chip in the phone or ear device actually doing ? What generation AI chipset actually fits in a hearing device ? Does applicant’s device rely on an always on connection to a server that uses the AI chip ? What functions are actually being performed in blocks 708 and 777 in applicant’s fig. 7 ? I look forward to discussing the actual implementation details of the invention with you at some point. Feel free to bring along an engineer who worked on the product. If we cannot get some implementation level details, then the best ways to move forward are to pick a single embodiment and we can work together to overcome the prior art, with significant amendments to all independent claims.
Below is an example of the differences between the prior art disclosure (detailed) and applicant’s disclosure (generalized).
For example:
TRAN: description of the machine learning filter:
A digital filter that not only amplifies sound but can also isolate speech from background noise and automatically adjust the volumes of each separately. The system extract features that could distinguish voices from noise based on common changes in amplitude, frequency, and the modulations of each. For example, the features can cover frequencies of the sounds and their intensities (loud or soft). The deep neural network uses the features during an unsupervised training to distinguish speech from noise and adjusts the amplifier control settings. During operation and based on user responses under supervised training, the neural network learns from its success and failures and improves on the amplifier settings. This iteratively learning of noise in different environments enables users to understand spoken words obscured by noise.
APPLICANT: description of stage 708:
there can be a preprocessing component 708, which can be optional, and can include a digital to analog converter or an analog-to-digital converter or any other device that can preprocess the results (seriously counselor, what am I supposed to do with this disclosure ?)
APPLICANT: description of AI stage 620
620. In at least some exemplary embodiments, the network will have already been loaded with pre-taught weights (more on this below). The neural network of device 620 (which can be a deep neural network that perform signal processing / audio processing / light processing, etc.) then determines whether or not a tinnitus episode is statistically likely to occur in the short run and/or whether or not a tinnitus episode is occurring and/or what type of stimulus should be provided to the person who suffers from tinnitus to prevent and/or mask the tinnitus episode. Results of this are provided to data receiving device 777, which can correspond to the tinnitus mitigation device and/or a processor or a sub processor of a hearing prosthesis or any other device that can controllably provide stimulation to a person suffering from tinnitus.
Counselor, your move.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER KRZYSTAN whose telephone number is 571-272-7498, and whose email address is alexander.krzystan@uspto.gov
The examiner can usually be reached on m-f 7:30-4:00 est.
If attempts to reach the examiner by telephone or email are unsuccessful, the examiner’s supervisor, Carolyn Edwards can be reached on (571) 270-7136.
The fax phone numbers for the organization where this application or proceeding is assigned are 571-273-8300 for regular communications and 571-273-8300 for After Final communications.
/ALEXANDER KRZYSTAN/Primary Examiner, Art Unit 2653
Examiner Alexander Krzystan
September 28, 2026
