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 .
Claim Status
Claims 1-16 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/25/2024 has been considered by the examiner.
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) 1-4, 6-8, 10 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over BASSON (US Pub. 2002/0161582) and Cruz-Hernandez (US Pub. 2015/0070148).
Regarding claim 1,
Basson teaches a speech training device for hearing impaired communication ([0004], [0005] and [0015] teaches a system for improved comprehension by hearing impaired persons) comprising:
one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to ([0020] teaches that the system’s components may be implemented in accordance with a processor and a memory; [0021] teaches including instructions or code for performing the methodologies of the invention).
Basson fails to expressly teach the further recited features of the claim.
Cruz-Hernandez teaches one or more memory devices configured to store instructions that, when executed on one or more processors ([0055] teaches steps in flow charts 400 and 500 which may be implemented in program code executed by a processor), cause the one or more processors to:
receive a sound input, the sound input comprising of a plurality of human speeches and a plurality of non-human speeches([0034] teaches that the device comprises I/O components 112 may be used to facilitate connection to microphones; [0030] teaches the system may isolate the source of the speech, e.g., isolate the speaker or speakers, thus teaching that sound input may comprise a plurality of human speeches and [0027] teaches analyzing an audio signal to identify or determine components within the audio signal. In some embodiments, a component may comprise an event within the audio signal, such as a discrete event, e.g., a gunshot, explosion, scream, or fight - any one of at least an explosion or scream corresponding to a plurality of non-human speeches);
decode a sound input signal from the sound input, the decoding comprising separating a single human speech from the plurality of human speeches and the plurality of non-human speeches ([0027] teaches that an audio signal may include audio components such as the sound of a voice (e.g., speech or singing), along with components associated with action (e.g., gunfire, automotive noises, and special effects), and background noise (e.g., music or mechanical sounds). In one illustrative embodiment, the system may analyze an audio file to determine the location of these components; [0065] teaches that the system determines or identifies, a component associated with background noise, the system may determine no haptic effect. [0071] teaches that the processor 102 may determine if a component such as speech or music is present wherein the processor may further access a database of audio signal data enabling the processor to isolate the source of an audio effect, e.g., to isolate a speaker or isolate a musical instrument or special effect (e.g., effects found in action movies such as gun shots, explosions, or engine sounds)); and
store the separated single human speech on the one or more memory devices by the one or more processors ([0071] teaches that the processor 102 may determine if a component such as speech or music is present; while [0073] teaches that the processor 102 may access the database to store a component - thus teaching that a speech component may be stored within said database).
Before the effective filing date of the invention it would have been obvious to combine the teachings of Basson and Cruz-Hernandez such that the speech training device of Basson decodes a received sound signal input as recited, because these modifications can significantly enhance the effectiveness of the speech training device, leading to better outcomes for users with speech related challenges.
Regarding claim 2,
Cruz-Hernandez teaches that the one or more processors separate the single human speech based on features of the single human speech ([0030] teaches the ability to isolate a single speaker, the system may analyze the frequency, pitch, or tone, and the system may isolate the components of the audio signal, e.g., components associated with speech).
Regarding claim 3,
Cruz-Hernandez teaches that the features of the single human speech comprise a frequency of the single human speech ([0028] teaches determining components of an audio signal based on frequency).
Regarding claim 4,
Basson teaches that the features of the single human speech comprise mouth position data (see the images 116, 118 and 1230 in fig. 1; [0022] teaches parsing/segmenting images into one or more corresponding words, by depicting lip and mouth movements or facial expressions of a user).
Regarding claim 6,
Cruz-Hernandez teaches that the one or more memory devices include one or more models of the sound input ([0073] teaches that a database may comprise a large database comprising a plurality of classifiers configured to enable high speed searches associated with audio effects).
Regarding claim 7,
Cruz-Hernandez teaches that one or more models include language models and accent models ([0078] teaches that the database may be large enough to include samples of speakers of different ages, genders, emotional states and accents/languages.)
Regarding claim 8,
Cruz-Hernandez teaches that the one or more models include proper mouth position ([0018] teaches processing utterance of a speaker 101 so as to preferably extract certain characteristics of these movements as a facial feature vector set. These characteristics may include, for example, lip/mouth position, tongue position, etc..).
Regarding claim 10,
Cruz-Hernandez teaches that the one or more processors compare the single human speech to the model of the sound input to determine whether the single human speech is a desired sound input ([0030] teaches that the system may isolate the source of the speech, e.g., isolate the speaker or speakers.)
Regarding claim 15,
Basson teaches that the speech training device is communicably connected to a user device, the user device comprising a display, the display displaying outputs of the speech training device (see display 108 in fig. 1).
Claim(s) 5 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over BASSON (US Pub. 2002/0161582) and Cruz-Hernandez (US Pub. 2015/0070148) in view of TRAN (US Pub. 2020/0268260).
Regarding claims 5 and 9,
The combined disclosures of Basson and Cruz-Hernandez teaches the speech training device of claims 2 and 6, but fails to expressly teach that the features of the single human speech comprises data on all controllable elements involved in sound production, and exhalation and time duration of speech.
TRAN teaches a hearing and monitoring system wherein features of the single human speech comprises data on all controllable elements involved in sound production, and exhalation and time duration of speech (Tran teaches systems and methods for assisting a user include a housing custom fitted to a user anatomy; a microphone to capture sound coupled to a processor to deliver enhanced sound to the user anatomy (see 0004); and teaches in [0118] that the device can include EEG sensors which measure a variety of EEG responses— at least in-part comprising alpha rhythm (wherein an alpha cycle is understood as representing a discrete temporal window or time duration, at least in-part) as well as multiple mechanical signals associated with speech and breathing.)
Before the effective filing date of the invention it would have been obvious to further modify Basson per the teachings of Tran for the purpose of allowing the processor to better understand how the produced speech is produced with regard to breath support, as well as to identify abnormal timing of speech utterances.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over BASSON (US Pub. 2002/0161582) and Cruz-Hernandez (US Pub. 2015/0070148) in view of LEAK (US Pub. 2015/0199965).
Regarding claim 11,
The combined teachings of Basson and Cruz-Hernandez teaches the speech training device of claim 10, but fails to expressly teach that the one or more memory devices store the comparison of the single human speech and the model of the sound input; and the one or more processors determine a correction plan for adjusting the single human speech if the single human speech is not similar to the desired sound input
LEAK teaches a system and method for recognition and automatic correction of voice commands wherein one or more memory devices store the comparison of the single human speech and the model of the sound input; and the one or more processors determine a correction plan for adjusting the single human speech if the single human speech is not similar to the desired sound input ([0033] teaches that information obtained from audio sources can be used to adjust speech recognition parameters and further that the native language of the speaker can be used to tune the parameters of the speech recognition model to produce better results.)
Before the effective filing date of the invention it would have been obvious to further modify Basson per the teachings of Leak for the purpose of producing a speech recognition model that is more likely to match the speaker's utterances with a voice command in database.
Claim(s) 14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over BASSON (US Pub. 2002/0161582) and Cruz-Hernandez (US Pub. 2015/0070148) in view of Thomson (US Patent 10,388,272).
Regarding claim 14,
The combined disclosures of Basson and Cruz-Hernandez teaches the speech training device of claim 1, but fails to expressly teach that the one or more memory devices cause the processors to encode the sound input signal and transmit the encoded sound input signal to an actuator, the actuator configured to provide a haptic sensation based on the encoded sound input signal.
THOMSON teaches one or more memory devices which cause the processors to encode the sound input signal and transmit the encoded sound input signal to an actuator, the actuator configured to provide a haptic sensation based on the encoded sound input signal (col. 19:6-8 teaches providing tactile feedback by haptic controls).
Before the effective filing date of the invention it would have been obvious to further modify Basson per the teachings of Thomson for the purpose of allowing users with various disabilities and impairments to receive alerts and information without needing audible prompts.
Regarding claim 16,
The combined disclosures of Basson, Cruz-Hernandez and Thomson teach a communication method for hearing impaired communication comprising:
providing the speech training device of claim 1 to a hearing impaired user (Basson teaches in [0017] teaches converting speech into a spectral feature which is usually indicated to a hearing impaired user; also see [0022]), the speech training device configured to teach the hearing impaired user how to determine non-speech sounds (Thomson teaches in col. 40:48-51 teaches decoder 510 which determines words laughter and background noise);
providing a haptic output device to a hearing impaired user, the haptic output device configured to be releasably coupled to the hearing impaired user (Thomson teaches in col. 6:37-47 teaches device 104 or 106 as a wearable device that provides haptic output);
receiving by the haptic output device, a sound input signal comprising a non-speech sound (Thomson teaches in col. 19:1-8 the capabilities for helping a hearing impaired user; col. 7:32-36 teaches that device 106 provides audio from microphone to device 104 or vice versa);
providing by the haptic output device, a haptic output signal to an actuator in electrical communication with the haptic output device (Thomson teaches col. 19:3-9 teaches either/both 104, 106 is configured to provide tactile feedback i.e., encoding for providing a haptic signal; col. 19:3-8 teaches 104/106 provides tactile feedback); and
actuating the actuator in response to the haptic output signal, wherein actuating the actuator provides a haptic sensation to the hearing impaired user (Thomson teaches in col. 19:6-8 teaches vibration of buttons or operation of force feedback, therefore an actuator is required for generating said vibrations of force feedback output).
Allowable Subject Matter
Claims 12 and 13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 12, The prior art fails to teach the device of claim 11, wherein the correction plan is depicted graphically, and a graph is used to show the comparison of the single human speech and the model of the sound input.
Regarding claim 13, The prior art fails to teach the device of claim 11, wherein the speech training device is communicably connected to a user device, the user device comprising a display that displays the correction plan.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONNE PENDLETON whose telephone number is (571)272-7497. The examiner can normally be reached M-F 9a-5pm.
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/DIONNE PENDLETON/Primary Examiner, Art Unit 2689