Patent Application 18223265 - METHOD EXECUTED BY ELECTRONIC DEVICE ELECTRONIC - Rejection

Patent Application 18223265 - METHOD EXECUTED BY ELECTRONIC DEVICE ELECTRONIC

Title: METHOD EXECUTED BY ELECTRONIC DEVICE, ELECTRONIC DEVICE AND STORAGE MEDIUM

Application Information

• Invention Title: METHOD EXECUTED BY ELECTRONIC DEVICE, ELECTRONIC DEVICE AND STORAGE MEDIUM
• Application Number: 18223265
• Submission Date: 2025-05-13T00:00:00.000Z
• Effective Filing Date: 2023-07-18T00:00:00.000Z
• Filing Date: 2023-07-18T00:00:00.000Z
• National Class: 704
• National Sub-Class: 251000
• Examiner Employee Number: 81576
• Art Unit: 2658
• Tech Center: 2600

Rejection Summary

• 102 Rejections: 1
• 103 Rejections: 1

Cited Patents

The following patents were cited in the rejection:

• US 0323290🔗
• US 0178069🔗

Office Action Text

    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 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.


Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract without significantly more.
When considering subject matter eligibility under 35 USC 101, it must be
determined whether the claim is directed to one of the four statutory categories of
invention, i.e., process, machine, manufacture, or composition of matter.
Specifically, claims 1 - 20 are directed to a process/machine. They hereby fall under one of the four statutory classes of invention.
If the claim does not fall within one of the statutory categories, it must then be
determined whether the claim is directed to a judicial exception (i.e., law of natural, natural phenomenon, and abstract idea).
Claims 1 - 20 recite steps of observation, evaluation, and judgement that can be
practically performed mentally by a human or using a pen and a piece of paper.
	The limitations of “extracting, according to the guidance features, target audio features corresponding to the audio signal; obtaining guidance features corresponding to an audio signal to be processed, the guidance features indicating distinguishable features of at least one signal type of at least one signal category; determining, according to the target audio features, a target signal type of the audio signal from among the at least one signal type of the at least one signal category” in claim 1 - 20, is a process/machine that, under its broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting “an electronic device; a memory storing computer-executable instructions for audio signal processing; and a processor communicatively coupled to the memory” nothing in the claim element precludes the steps from practically being performed in a human mind.
	The mere nominal recitation of an electronic device; a memory storing computer-executable instructions for audio signal processing; and a processor communicatively coupled to the memory, wherein the instructions do not take the claim limitations out of the mental processes grouping.
If a claim limitation, under its broadest reasonable interpretation, covers mental
processes but for the recitation of generic computer components, then it falls within the
"Mental Processes" grouping of abstract ideas (concepts performed in the human mind
including an observation, evaluation, judgement, and opinion). Accordingly, the claims
1 - 20 recite an abstract idea.
	This judicial exception is not integrated into a practical application. In particular,
the claims recite the additional elements “performing corresponding processing according to the target signal type of the audio signal.; controlling an external electronic device based on the signal detection result.”.
The limitation “obtaining the audio signal to be processed that has been collected by an audio collection device; extracting, according to the guidance features, target audio features corresponding to the audio signal;”, amount to data- gathering steps which is considered to be insignificant extra-solution activity, (See
MPEP 2106.05(g)).
Accordingly, these additional elements do not integrate the abstract idea into a
practical application because they do not impose any meaningful limits on practicing the
abstract idea, thus fail to integrate the abstract idea into a practical application. See
MPEP 2106.05(g). 
	The insignificant extra-solution activities identified above, which include the data gathering receiving outputting steps, are recognized by the courts as well- understood,
routine, and conventional activities when they are claimed in a merely generic manner
(e.g., at a high level of generality) or as insignificant extra-solution activity (See MPEP
2106.05(d)(Il) (i) Receiving or transmitting data over a network, e.g., using the Internet
to gather data, buySAPE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPO2d 1093,
1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v)
Presenting (displaying) offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63,
115 USPO2d at 1092- 93). The claims are not patent eligible. 
	Claims 1 - 20 do not include additional elements that are sufficient to amount to
significantly more than the judicial exception. As discussed above with respect to
integration of the abstract idea into a practical application, the additional element of
performing corresponding processing according to the target signal type of the audio signal.; controlling an external electronic device based on the signal detection result steps amount to no more than mere instructions to apply an exception using a generic computer. Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept.
Claims 1 - 20 as a whole, do not amount to significantly more than the abstract
idea itself. This is because the claims do not affect an improvement to the functioning of
a computer itself; and the claims do not move beyond a general link of the use of an
abstract idea to a particular technological environment. Accordingly, the claims 1 - 20
recite an abstract idea.  

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)(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.

Claims 1 – 5, 9- 16, 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sloan (US PAP 2022/0323290).
As per claims 1, 12, Sloan teaches a method of audio signal processing executed by an electronic device, comprising:
obtaining guidance features corresponding to an audio signal to be processed, the guidance features indicating distinguishable features of at least one signal type of at least one signal category (“detect speech and non-speech vocalizations in the audio”; paragraphs 12, 13);
extracting, according to the guidance features, target audio features corresponding to the audio signal (“receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands… associating speech and non-speech vocalizations with controls of the sexual stimulation device.”; Abstract; paragraphs 12, 13);
determining, according to the target audio features, a target signal type of the audio signal from among the at least one signal type of the at least one signal category (“the audio comprising either speech, a non-speech vocalization, or both; detect speech and non-speech vocalizations in the audio…Non-speech vocalizations (e.g., sighs, grunts, etc.) within the audio do not contain recognizable speech, and are sent directly to the voice characteristic analyzer”; Abstract; paragraphs 12, 13, 141); and
performing corresponding processing according to the target signal type of the audio signal (“analyzing the voice data to detect spoken commands, and generating control signals based on the commands”; Abstract; paragraphs 12, 13, 141).

As per claims 2, 13, Sloan further discloses determining, according to the guidance features, a category detection result of the audio signal, the category detection result indicating at least one probability of the audio signal matching a corresponding signal type of the at least one signal type of a corresponding signal category of the at least one signal category, wherein a first signal category of the at least one signal category has at least one first signal type, and wherein a second signal category of the at least one signal category has a plurality of second signal types; and based on the category detection result indicating that the audio signal matches the first signal category, determining the at least one first signal type of the first signal category as the target signal type of the audio signal, wherein the extracting of the target audio features corresponding to the audio signal comprises, based on the category detection result indicating that the audio signal does not match the first signal category, extracting the target audio features corresponding to the audio signal according to the guidance features (“the audio comprising either speech, a non-speech vocalization, or both; detect speech and non-speech vocalizations in the audio…Non-speech vocalizations (e.g., sighs, grunts, etc.) within the audio do not contain recognizable speech, and are sent directly to the voice characteristic analyzer”; Abstract; paragraphs 12, 13, 141).

As per claims 3, 14, Sloan further discloses the extracting of the target audio features corresponding to the audio signal comprises: extracting the target audio features corresponding to the audio signal according to the guidance features and the category detection result (“analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns… the audio comprising either speech, a non-speech vocalization, or both; detect speech and non-speech vocalizations in the audio; where speech is detected, transcribe the detected speech to text using an automated speech recognition engine; send the text to a speech analyzer; and where a non-speech vocalization is detected, send the non-speech vocalization to a voice characteristic analyzer; paragraphs 11 – 13).

As per claims 4, 15, Sloan further discloses determining, according to the category detection result, a target classifier from a plurality of classifiers corresponding to the plurality of second signal types of the second signal category, wherein the determining of the target signal type of the audio signal comprises determining, based on the target classifier, the target signal type of the audio signal according to the target audio features (“Voice data manager 2900 is responsible for detecting and analyzing speech and for analyzing voice characteristics of vocalizations (whether speech or non-speech).”; paragraphs 140 – 143).

As per claims 5, 16, Sloan further discloses the obtaining of the guidance features corresponding to the audio signal comprises: extracting, by a first encoder, the guidance features corresponding to the audio signal, wherein the extracting of the target audio features corresponding to the audio signal comprises extracting, by a second encoder, the target audio features corresponding to the audio signal according to the guidance features (“analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns… the audio comprising either speech, a non-speech vocalization, or both; detect speech and non-speech vocalizations in the audio; where speech is detected, transcribe the detected speech to text using an automated speech recognition engine; send the text to a speech analyzer; and where a non-speech vocalization is detected, send the non-speech vocalization to a voice characteristic analyzer; paragraphs 11 – 13).

As per claim 9, Sloan further discloses performing signal processing on the audio signal to obtain a processed audio signal, wherein the performing of the signal processing comprises performing at least one of signal spreading, signal enhancement, and filtering a DC bias from the audio signal, wherein the obtaining of the guidance features corresponding to the audio signal to be processed comprises obtaining the guidance features based on at least one of the processed audio signal and processed audio features extracted from the processed audio signal based on the guidance features (“The pattern of voice activity (aka a voice pattern) may be a frequency pattern, an amplitude pattern, some combination of the two, or some derivative of either or the combination (e.g., a pattern discovered by passing the voice signal data through a filter, algorithm, or function such as a Kalman filter or a Fourier transform). The voice data manager 2801 associates voice pattern (or recognized speech) with an objective of task”; paragraphs 136, 147).

As per claim 10, Sloan further discloses obtaining the audio signal to be processed that has been collected by an audio collection device; determining a signal detection result of the audio signal to be processed based on the audio signal to be processed, wherein the audio signal to be processed comprises at least one audio frame, wherein the signal detection result comprises the target signal type of each of the at least one audio frame (“The pattern of voice activity (aka a voice pattern) may be a frequency pattern, an amplitude pattern, some combination of the two, or some derivative of either or the combination (e.g., a pattern discovered by passing the voice signal data through a filter, algorithm, or function such as a Kalman filter or a Fourier transform).”; paragraphs 136, 147).

As per claim 11, Sloan further discloses the performing corresponding processing according to the target signal type of the audio signal comprises at least one of: determining a user state based on the signal detection result; determining a current environment of a user according to the signal detection result; and controlling an external electronic device based on the signal detection result (“Voice data manager 2801 associates voice pattern (or recognized speech) with an objective of task (e.g., reducing the speed of stimulation), creating voice pattern (or recognized speech)/objective pairs that can be used either to generate controls for stimulation device via a control signal generator.”; paragraphs 136, 147).

As per claim 20, Sloan further discloses a non-transitory computer-readable storage medium storing a computer program for audio signal processing that, when executed by a processor of an electronic device, causes the processor to execute the method according to claim 1(paragraph 165).

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.

Claims 8, 19, are rejected under 35 U.S.C. 103 as being unpatentable over Sloan (US PAP 2022/0323290) in view of Vaughan et al. (US PAP 2023/0178069).
As per claims 8, 19, Sloan does not specifically teach extracting initial audio features corresponding to the audio signal; and obtaining, by an attention network, the target audio features based on the guidance features and the initial audio features, wherein the attention network comprises at least one of a channel attention network and a spatial attention network.
Vaughan et al. disclose that the attention network 26 is configured to summarize the encoded feature vector 25 output by the encoder 23 and output a context vector 27. The context vector 27 is used by the decoder 28 for each decoding step. The attention network 26 may take information (such as weights) from previous decoding steps (that is, from previous speech frames decoded by decoder) in order to output the context vector 27. The function of the attention network 26 may be understood to be to act as a mask that focusses on the important features of the encoded features 25 output by the encoder (paragraphs 104 - 106).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to use an attention network as taught by Vaughan et al. in Sloan, because that would help detecting voice stress patterns, and generating control signals based on the detected patterns (Sloan; paragraph 11).

Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.  Kang teaches speech signal processing method of determining verbal and nonverbal speech. Hanes tech conversion of non-verbal commands.  Hardee et al. teach INTERACTING WITH A PROCESSING STSYEM USING INTERACTIVE MENU AND NON-VERBAL SOUND INPUTS.  Goslin et al. teach TECHNIQUES FOR PROVIDING NON-VERBAL SPEECH RECOGNITION IN AN IMMERSIVE PLAYTIME ENVIRONMENT.
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/LEONARD SAINT-CYR/Primary Examiner, Art Unit 2658