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 .
This Office Action is in response to correspondence filed 31 October 2024 in reference to application 18/933,611. Claims 1-20 are pending and have been examined.
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.
Claim(s) 1-3, 12, 13, 15, 16, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reed et al. (US PAP 2018/0275956) in view of Bendersky et al. (US PAP 2024/0403564).
Consider claim 1, Reed teaches a method (abstract) comprising:
receiving, by a computing system, audio data generated by one or more hearing instruments worn at or near one or more ears of a user (0084, receiving user voice input at microphone of prosthesis, 0089, 0162 speech input example);
providing, by the computing system, a virtual personal assistant to the user, wherein the virtual personal assistant is configured to generate, based on the audio data, output to assist the user (0084-89, assistant 1002, described in great detail in figure 7 and para 0120-61, 0513, speech output); and
providing, by the computing system, the output to the one or more hearing instruments, wherein the one or more hearing instruments are configured to generate auditory stimuli based on the output (0513, speech output, 0067, prosthesis can be used to provide the speech output from the assistant).
Reed does not specifically teach wherein providing the virtual personal assistant comprises applying, by the computing system, a Large Language Model (LLM) to generate a response, and the output is based on the response.
In the same field of providing a personal assistant in a headset environment, Bendersky teaches wherein providing the virtual personal assistant comprises applying, by the computing system, a Large Language Model (LLM) to generate a response, and the output is based on the response (0024-23, assistant with an LLM, 0026, 0028, LLM receives prompt and generates response).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use an LLM as taught by Bendersky in the system of Reed in order to allow for the system to learn context of the user and therefore improving response results (Bendersky Summary).
Consider claim 2, Bendersky teaches the method of claim 1, wherein:
providing the virtual personal assistant comprises generating, by the computing system, a prompt based on the audio data (0026, processing audio to generate a text prompt), and
applying the LLM comprises applying, by the computing system, the LLM to the prompt to generate the response (0028, LLM receives prompt, generates output).
Consider claim 3, Bendersky teaches The method of claim 1, wherein the audio data is first audio data and the one or more hearing instruments are configured to generate the first audio data by applying signal processing to second audio data generated by microphones of the one or more hearing instruments (0026, converting sound input into MFCC data for further processing).
Consider claim 12, Reed teaches the method of claim 1, wherein the output generated by the virtual personal assistant includes a recommended or automatic adjustment to one or more aspects of the one or more hearing instruments (0089, assistant can adjust settings of the prosthesis).
Consider claim 13, Reed teaches The method of claim 12, wherein the audio data includes a request from the user to improve sound quality of the one or more hearing instruments (0089, having trouble hearing, ).
Consider claim 15, Bendersky teaches The method of claim 1, wherein providing the virtual personal assistant comprises extracting, by the computing system, semantic content of speech represented by the audio data (0169, inferring semantic intent of utterance).
Consider claim 16, Reed teaches A computing system (abstract) comprising:
one or more memories (0078, memories); and
one or more processors (0077, processors) configured to:
receive audio data generated by one or more hearing instruments worn at or near one or more ears of a user (0084, receiving user voice input at microphone of prosthesis, 0089, 0162 speech input example);
provide a virtual personal assistant to the user, wherein the virtual personal assistant is configured to generate, based on the audio data, output to assist the user (0084-89, assistant 1002, described in great detail in figure 7 and para 0120-61, 0513, speech output); and
provide the output to the one or more hearing instruments, wherein the one or more hearing instruments are configured to generate auditory stimuli based on the output (0513, speech output, 0067, prosthesis can be used to provide the speech output from the assistant).
Reed does not specifically teach wherein providing the virtual personal assistant comprises applying, by the computing system, a Large Language Model (LLM) to generate a response, and the output is based on the response.
In the same field of providing a personal assistant in a headset environment, Bendersky teaches wherein providing the virtual personal assistant comprises applying, by the computing system, a Large Language Model (LLM) to generate a response, and the output is based on the response (0024-23, assistant with an LLM, 0026, 0028, LLM receives prompt and generates response).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use an LLM as taught by Bendersky in the system of Reed in order to allow for the system to learn context of the user and therefore improving response results (Bendersky Summary).
Claim 18 contains similar limitations as claim 12 and is therefore rejected for the same reasons.
Consider claim 20, Reed teaches One or more non-transitory computer-readable media having instructions stored thereon that, when executed by one or more processors of a computing system (0078, memories), cause the one or more processors to:
receive audio data generated by one or more hearing instruments worn at or near one or more ears of a user (0084, receiving user voice input at microphone of prosthesis, 0089, 0162 speech input example);
provide a virtual personal assistant to the user, wherein the virtual personal assistant is configured to generate, based on the audio data, output to assist the user (0084-89, assistant 1002, described in great detail in figure 7 and para 0120-61, 0513, speech output); and
provide the output to the one or more hearing instruments, wherein the one or more hearing instruments are configured to generate auditory stimuli based on the output (0513, speech output, 0067, prosthesis can be used to provide the speech output from the assistant).
Reed does not specifically teach wherein providing the virtual personal assistant comprises applying, by the computing system, a Large Language Model (LLM) to generate a response, and the output is based on the response.
In the same field of providing a personal assistant in a headset environment, Bendersky teaches wherein providing the virtual personal assistant comprises applying, by the computing system, a Large Language Model (LLM) to generate a response, and the output is based on the response (0024-23, assistant with an LLM, 0026, 0028, LLM receives prompt and generates response).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use an LLM as taught by Bendersky in the system of Reed in order to allow for the system to learn context of the user and therefore improving response results (Bendersky Summary).
Claim(s) 4, 5, 8, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reed and Bendersky as applied to claims 1 above, and further in view of Zweig et al. (US PAP 2022/0343231).
Consider claim 4, Reed and Bendersky teach the method of claim 1, but does not specifically teach wherein the virtual personal assistant is configured to learn a routine of the user based at least in part on the audio data and generate the output based on the routine of the user.
In the same field of digital assistants, Zweig teaches wherein the virtual personal assistant is configured to learn a routine of the user based at least in part on the audio data and generate the output based on the routine of the user (0074-78, schedules are inferred based on sensor data, which at 0030-31 included microphones, 0079, reminders, ).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to learn schedules and generate reminders as taught by Zweig in the system of Reed and Bendersky in order to increase effectiveness of the digital assistant (Zweig 0006-07).
Consider claim 5, Zweig teaches the method of claim 4, wherein the output based on the routine of the user includes a reminder to perform an activity (i.e. 0079, reminder to call a friend).
Consider claim 8, Reed and Bendersky teach the method of claim 1, but does not specifically teach wherein the virtual personal assistant is configured to access a calendar and the output is based on events in the calendar.
In the same field of digital assistants, Zweig teaches wherein the virtual personal assistant is configured to access a calendar and the output is based on events in the calendar (0074-78, schedules are inferred based on sensor data, which at 0030-31 included the users calendar, 0079, reminders, ).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to learn schedules and generate reminders as taught by Zweig in the system of Reed and Bendersky in order to increase effectiveness of the digital assistant (Zweig 0006-07).
Consider claim 14, Reed and Bendersky teach the method of claim 1, but does not specifically teach wherein the virtual personal assistant is configured to receive health data for the user.
In the same field of digital assistants, Zweig teaches wherein the virtual personal assistant is configured to receive health data for the user (0032 using health related information for one or more users).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to use health related information as taught by Zweig in the system of Reed and Bendersky in order to increase effectiveness of the digital assistant (Zweig 0006-07).
Claim(s) 6, 7, 17 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reed and Bendersky as applied to claims 1 above, and further in view of Watts et al. (US PAP 2022/0270461).
Consider claim 6, Reed and Bendersky teach the method of claim 1, but does not specifically teach wherein the virtual personal assistant is configured to determine, based on the audio data, whether an event has occurred and to generate the output indicating whether the event has occurred.
In the same field of personal assistants, Watts teaches wherein the virtual personal assistant is configured to determine, based on the audio data, whether an event has occurred and to generate the output indicating whether the event has occurred (0027, detecting sounds to determine if user is taking medication).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to determine if events have occurred as taught by Watts in the system of Reed and Bendersky in insure compliance with a healthcare plan (Watts 0027).
Consider claim 7, Watts teaches The method of claim 6, wherein the event is the user taking medication (0027, reminding user to take medication).
Claim(s) 9-11, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reed and Bendersky as applied to claims 1 and 16 above, and further in view of Newell et al. (US PAP 2020/0210464).
Consider claim 9, Reed and Bendersky teach the method of claim 1, but does not specifically teach wherein the audio data represent a voice of a person with whom the user is interacting, and the output generated by the virtual personal assistant includes information about the person.
In the same field of digital assistants, Newell teaches wherein the audio data represent a voice of a person with whom the user is interacting (0019, 0059, determining participants in conversation with user), and the output generated by the virtual personal assistant includes information about the person (0020, 0043, providing information about the person speaking).
It would have been obvious to one of ordinary skill in the art at the time of effective filing to provide the user with information about conversation participants as taught by Newell in the system of Reed and Bendersky in order to better enhance an individual’s participation in social settings (Newell 0007-08).
Consider claim 10, Newell teaches the method of claim 9, wherein the information about the person includes information about interactions between the person and the user (0096, previous conversations may be used to generate queues, also see 0072-74).
Consider claim 11, Newell teaches the method of claim 9, wherein the virtual personal assistant is configured to learn the information about the person based on the audio data received from the one or more hearing instruments (0072-73, ongoing dialogue representation, used to generate advisory information).
Claim 17 contains similar limitations as claim 9 and is therefore rejected for the same reasons.
Consider claim 19, Reed teaches The computer system of of claim 17, wherein the audio data includes a request from the user to improve sound quality of the one or more hearing instruments (0089, having trouble hearing).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhang et al. (US PAP 2019/0149927) teaches a similar method of providing virtual assistants to a hearing device.
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DOUGLAS GODBOLD
Examiner
Art Unit 2655
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
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