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 .
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
In the “Cross Reference to Related Applications”, the parent application has now issued as a U.S. Patent. Please update the information. Correction is required
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-5, 9-13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-8 of U.S. Patent No. 12,217,767. Although the claims at issue are not identical, they are not patentably distinct from each other because the additional claim limitations in the ‘767 patent are not necessary to realize the functionality of the claims in the instant invention.
Claims 6-8, 14-16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,9 of U.S. Patent No. 12217,767 in view of Kida et al (20190156846).
As per claims 6-8, 14-16, the claims of the ‘767 does not explicitly teach the selection/direction of the processed audio signal to a selected device; Kida et al (20190156846) however, teaches the selection and the forwarding of the modified signals, to a device ( (as determining, that the output device is the voice device by the user – para 0022; and, can be music – para 0021; and outputting to an output device – fig. 7, subblock 1010) ). Therefore, it would have been obvious to one of ordinary skill in the art of modified signal operations and distribution, to specify the destination of the signal as well as the device, as taught by Kida et al (20190156846), because it would advantageously allow for a user to further use/hear/modify the signal and information, at their own device (see Kida et al (20190156846), para 0021, 0022.
Examiner notes the two mapping tables below. The first table, maps claim numbers to claim numbers. The second table contains the claim language of the claims of the instant application and the issued U.S.Patent. Follow the first mapping table to compare the claim language extracted from the second table.
19/038828
12,217,767
1
1
2
1
3
4
4
2
5
3
6
1+ Kida et al (20190156846)
7
1+ Kida et al (20190156846)
8
1+ Kida et al (20190156846)
9
5
10
5
11
8
12
6
13
7
14
9+ Kida et al (20190156846)
15
9+ Kida et al (20190156846)
16
9+ Kida et al (20190156846)
19/039828
12,217,767
1. An audio device comprising: a sound sensor portion; a sound separation portion; a sound determination portion; and a processing portion, wherein the sound sensor portion is configured to sense first sound, wherein the sound separation portion is configured to separate the first sound into second sound and third sound, wherein the sound determination portion is configured to store a feature quantity of a voice of a user, wherein the sound determination portion is configured to determine, with a machine learning model, whether the second sound has the stored feature quantity, wherein the processing portion is configured to analyze an instruction contained in the second sound and generate a signal when the feature quantity of the second sound is the stored feature quantity, and wherein the signal represents a content of the instruction and an output destination of the instruction.
2. The audio device according to claim 1, wherein the processing portion is configured to perform, on the third sound, processing for canceling the third sound to generate fourth sound.
3. The audio device according to claim 2, wherein the fourth sound is sound having a phase opposite to a phase of the third sound.
4. The audio device according to claim 1, wherein learning for the machine learning model is performed using supervised learning in which a voice is learning data and a label indicating whether the storing is to be performed is training data.
5. The audio device according to claim 1, wherein the machine learning model is a neural network model.
6. The audio device according to claim 1, wherein the processing portion is configured to determine the output destination in accordance with the kind of the instruction.
7. The audio device according to claim 1, wherein the processing portion is configured to determine an information terminal as the output destination when the second sound contains an instruction to change a kind of music or a volume of music, and wherein the information terminal is configured to play music.
8. The audio device according to claim 1, further comprising a transmission/reception portion, wherein the transmission/reception portion is configured to output the signal to the output destination.
9. An operation method of an audio device, comprising: sensing first sound; separating the first sound into second sound and third sound; determining, with a machine learning model, whether the second sound has a stored feature quantity; analyzing an instruction contained in the second sound when the feature quantity of the second sound is the stored feature quantity; determining an output destination of the instruction; generating a signal representing content of the instruction and the output destination of the instruction; and outputting the signal to the output destination.
10. An operation method of the audio device, according to claim 9, wherein processing for canceling the third sound is performed on the third sound to generate fourth sound.
11. The operation method of the audio device, according to claim 10, wherein the fourth sound is sound having a phase opposite to a phase of the third sound.
12. The operation method of the audio device, according to claim 9, wherein learning for the machine learning model is performed using supervised learning in which a voice is used as learning data and a label indicating whether storing is to be performed is used as training data.
13. The operation method of the audio device, according to claim 9, wherein the machine learning model is a neural network model.
14. The operation method of the audio device, according to claim 9, wherein the output destination is determined according to the kind of the instruction.
15. The operation method of the audio device, according to claim 9, wherein the second sound contains an instruction to change a kind of music or a volume of music.
16. The operation method of the audio device, according to claim 15, wherein an information terminal is determined as an output destination.
1. An audio device comprising: a sound sensor portion; a sound separation portion; a sound determination portion; a processing portion; and an output portion, wherein the audio device is an earphone or a headphone, wherein the sound sensor portion is configured to sense first sound, wherein the sound determination portion is configured to store a feature quantity of a voice of a user, wherein the sound determination portion is configured to determine, with a machine learning model, whether the first sound has the stored feature quantity, wherein the processing portion is configured to analyze an instruction contained in the first sound and, when the feature quantity of the first sound is the stored feature quantity, to generate a signal representing content of the instruction, and, when the feature quantity of the first sound is not the stored feature quantity, to not generate the signal representing content of the instruction, wherein second sound is sound to cancel noise in the first sound, wherein third sound is sound electrically input to the audio device, wherein fourth sound is synthesized sound of the second sound and the third sound, wherein the processing portion is configured to generate the second sound and the third sound, and wherein the output portion is configured to output the fourth sound.
2. The audio device according to claim 1, wherein learning for the machine learning model is performed using supervised learning in which a voice is learning data and a label indicating whether the storing is to be performed is training data.
3. The audio device according to claim 1, wherein the machine learning model is a neural network model.
4. The audio device according to claim 1, wherein the second sound is sound having a phase opposite to a phase of the noise in the first sound.
5. An operation method of an audio device, comprising: sensing first sound; determining, with a machine learning model, whether a feature quantity of the first sound is a stored feature quantity of a voice of a user; analyzing an instruction contained in the first sound; generating a signal representing content of the instruction when the feature quantity of the first sound is the stored feature quantity and not generating the signal representing content of the instruction when the feature quantity of the first sound is not the stored feature quantity; generating second sound to cancel noise in the first sound; electrically inputting third sound to the audio device; generating fourth sound by synthesizing the second sound and the third sound; and outputting the fourth sound.
6. The operation method of the audio device, according to claim 5, wherein learning for the machine learning model is performed using supervised learning in which a voice is used as learning data and a label indicating whether storing is to be performed is used as training data.
7. The operation method of the audio device, according to claim 5, wherein the machine learning model is a neural network model.
8. The operation method of the audio device, according to claim 5, wherein the second sound is sound having a phase opposite to a phase of the noise in the first sound.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kida et al (20190156846).
As per claim 1, Kida et al (20190156846) teaches an audio device comprising: a sound sensor portion; a sound separation portion; a sound determination portion; and a processing portion, wherein the sound sensor portion is configured to sense first sound (as sound sensing – microphone – para 0020; sound separation – as acquiring signal + noise – para0041; sound determination – para 0026, determining from which device the sound came from; and processing – para 0026 – reducing the signal that does not belong, to a noise level),
wherein the sound separation portion is configured to separate the first sound into second sound and third sound (as, sensing a first sound and separating into a signal and a reverb signal – para 0026, as well as identifying a different source device – para 0026),
wherein the sound determination portion is configured to store a feature quantity of a voice of a user (as storing signal characteristics – as tracking and comparing, frequency characteristics of the signal to known patterns – para 0036, and para 0070 showing an observation signal database),
wherein the sound determination portion is configured to determine, with a machine learning model, whether the second sound has the stored feature quantity (as using machine learning models such as a DNN, LSTM, CNN, or RNN – para 0040),
wherein the processing portion is configured to analyze an instruction contained in the second sound and generate a signal when the feature quantity of the second sound is the stored feature quantity, and wherein the signal represents a content of the instruction and an output destination of the instruction (as, analyzing in a qualified signal based on position/distance, extracting keywords by using a generated keyword mask from the neural network – para 0041).
As per claim 2, Kida et al (20190156846) teaches the audio device according to claim 1, wherein the processing portion is configured to perform, on the third sound, processing for canceling the third sound to generate fourth sound (as, reducing/removing reverb from the sound, for further processing as a fourth (original signal including keyword signal and removing reverb signal) – para 0031).
As per claim 3, Kida et al (20190156846) teaches the audio device according to claim 2, wherein the fourth sound is sound having a phase opposite to a phase of the third sound (examiner notes, that applicants specification, defines opposite phase, with stating “inversion of the phase of the sound – e.g., para 0101; however, in para 0105, the inversion of the phase of the noise, is stated; it is not clear on what ‘inverting the phase of noise’, necessarily means; however, examiner notes that in both of these paragraphs, and others in the specification, ‘generating opposite phase signal’ is for the purpose of ‘canceling the noise’; therefore, the claim scope ‘sound have a phase opposite to a phase of the third sound’ is equivalent to, canceling the noise signal; Kida et al (20190156846) teaches filtering to cancel noise signals – para 0043, 0063, 0104 – Kida’s ‘reducing’).
As per claim 4, Kida et al (20190156846) teaches the audio device according to claim 1, wherein learning for the machine learning model is performed using supervised learning in which a voice is learning data and a label indicating whether the storing is to be performed is training data (as, the learning for the neural network model uses a learning signal – end of para 0041, and using labels for the differing features of the signal – para 0043 – information such as frequency characteristics, keyword signal, the actual range, etc.).
As per claim 5, Kida et al (20190156846) teaches the audio device according to claim 1, wherein the machine learning model is a neural network model (as neural network – para 0040).
As per claim 6, Kida et al (20190156846) teaches the audio device according to claim 1, wherein the processing portion is configured to determine the output destination in accordance with the kind of the instruction(as, determining the voice device to receive the modified signal – para 0021).
As per claim 7, Kida et al (20190156846) teaches the audio device according to claim 1, wherein the processing portion is configured to determine an information terminal as the output destination when the second sound contains an instruction to change a kind of music or a volume of music, and wherein the information terminal is configured to play music (as determining, that the output device is the voice device by the user – para 0022; and, can be music – para 0021).
As per claim 8, Kida et al (20190156846) teaches the audio device according to claim 1, further comprising a transmission/reception portion, wherein the transmission/reception portion is configured to output the signal to the output destination (as outputting to an output device – fig. 7, subblock 1010).
Claims 9-16 are method claims whose steps are performed by the audio device claims of claims 1-8 above, and as such, claims 9-16 are similar in scope and content to claims 1-8 above; therefore, claims 9-16 are rejected under similar rationale as presented against claims 1-8 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, to applicants current claim scope:
Finkelstein et al (20180260680) teaches machine learning on spoken keywords – abstract, para 0002, 0095
Baker et al (20180228006) teaches determining acoustic signals with voice commands, using machine learning, with room discrimination (para 0042, 0047, 0074, 0075).
Smith et al (20200349935) teaches audio signal re-routing and playback (para 0053, 0078).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 07/03/2026
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