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 action is responsive to the following communications: amendment & remarks filed on June 17, 2026.
This application has been examined. Claims 1, 3-9, 11-17, 19-20 are pending.
Claim Rejections — 35 U.S.C. 101
3. 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.
4. Claims 1, 3-9, 11-17, and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1 and 3-8 recite a method. Claims 9 and 11-16 recite a system. Claims 17 and 19-20 recite a non-transitory computer-readable medium. Therefore, claims 1 and 3-8 are directed to a process, claims 9 and 11-16 are directed to a machine, and claims 17 and 19-20 are directed to a manufacture. The claims are within the statutory categories.
With respect to claims 1, 9 and 17:
2A Prong 1: the claim recites a judicial exception.
generating an output representative of a predicted environment of the user device, wherein the model is trained to predict, based on the one or more audio features, a classification of a user environment the user device is located within; (mental process - an observation, evaluation, or judgment of the surrounding environment (e.g., public vs. private) that can be performed in the human mind or with pen and paper; and a mathematical concept - per the specification’s weighted feature scores [Wingdings font/0xE0] composite score [Wingdings font/0xE0] threshold comparison, FIG. 3).
2A Prong 2: This judicial exception is not integrated into a practical application. The additional elements are:
a processing device / a user device / an environment recognition model (a generic computer / generic machine-learning model recited at a high level of generality and invoked merely as a tool to perform the exception - mere instructions to apply an exception; see MPEP 2106.05(f), 2106.05(a)).
extracting one or more audio features from audio data captured by a processing device of a user device; (insignificant extra-solution activity - mere data gathering / pre-processing; see MPEP 2106.05(g)).
updating one or more settings of the user device based at least in part on the predicted environment of the user device. (generically-recited application of the classification result to change an unspecified setting - insignificant post-solution activity; the independent claims recite no particular technical setting or improvement; see MPEP 2106.05(g))..
2B: The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
a processing device / a user device / an environment recognition model (mere instructions to apply an exception - see MPEP 2106.05(f); the generic computer and generically-recited trained model are well-understood, routine, and conventional - see MPEP 2106.05(d)).
extracting one or more audio features from audio data captured by a processing device of a user device; (insignificant extra-solution activity - mere data gathering; and WURC: receiving or capturing data via a device - see MPEP 2106.05(d)(II)(i)).
updating one or more settings of the user device based at least in part on the predicted environment of the user device. (insignificant post-solution activity; and WURC: storing and retrieving / updating information in memory - see MPEP 2106.05(d)(II)(iii)).
Considered both individually and as an ordered combination, the additional elements are well-understood, routine, and conventional and do not amount to significantly more than the abstract idea.
With respect to claims 3, 11 and 19:
2A Prong 1: the claim recites a judicial exception.
wherein the one or more audio features comprise one or more of speech content, voice identities, music genres, ambient sounds, or separate sound sources (mental process - merely specifies the audio content observed and evaluated in reaching the environment judgment; a person perceives speech, music, and ambient sounds and judges the surroundings accordingly; the further specification is also part of the insignificant extra-solution data gathering of Prong Two and neither integrates the exception nor adds significantly more).
With respect to claims 4, 12 and 20:
2A Prong 1: the claim recites a judicial exception.
wherein the output indicates whether the predicted environment is a public environment or a private environment (mental process - evaluation or judgment; classifying surroundings as public vs. private is a characterization a person makes in the mind).
With respect to claims 5, 6, 13 and 14:
2A Prong 2 / 2B: these claims further specify the additional element of updating a setting; they do not integrate the exception into a practical application and do not add significantly more.
increasing a security profile of the user device when the predicted environment is a public environment; decreasing a security profile of the user device when the predicted environment is a private environment (generically-recited application of the abstract judgment to adjust a device security profile - insignificant post-solution activity, see MPEP 2106.05(g); considered in 2B, adjusting a device security setting in response to a determined environment is a generically-recited, conventional device function and does not amount to significantly more - see MPEP 2106.05(d); see also the Examiner note below).
With respect to claims 7 and 15:
2A Prong 2 / 2B: additional element - does not integrate the exception and does not add significantly more.
wherein updating the one or more settings comprises adjusting one or more of an authentication requirement, a display brightness, a display effect, a volume, an access permission, a lockout duration, or a device configuration (generically-recited adjustment of conventional device settings responsive to the classification result - insignificant post-solution activity, see MPEP 2106.05(g); WURC device configuration in 2B, see MPEP 2106.05(d)) (see also the § 112(b) rejection as to “field of view of view”).
With respect to claims 8 and 16:
2A Prong 2 / 2B: additional element - does not integrate the exception and does not add significantly more.
further comprising providing video or visual data as additional input to the environment recognition model (insignificant extra-solution activity - mere data gathering of additional input data, see MPEP 2106.05(g); the generically-recited model remains a tool applying the exception, see MPEP 2106.05(f); WURC in 2B, see MPEP 2106.05(d)).
Examiner note: Applicant may argue under Prong Two that updating device security settings based on the detected environment is a technological improvement (device security). The Examiner notes that the independent claims recite only a generic “updating one or more settings,” while the security-profile specifics appear in dependent claims 5, 6, 13, and 14. Amending the independent claims to recite a specific technical implementation/improvement (rather than a generic setting change resulting from the classification) would be a route to eligibility.
Claim Rejections - 35 USC § 112
The following is a quotation of the second paragraph of 35 U.S.C. 112:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7 and 15 are rejected under 35 U.S.C. 112(b) as failing to particularly point out and distinctly claim the subject matter regarded as the invention
Claims 7 and 15 are rejected under 35 U.S.C. 112(b) as indefinite. Each recites “adjusting a field of view of view of the display device,” in which “of view” is duplicated, rendering the limitation unclear. Correction to “a field of view of the display device” is required.
Claim Rejections - 35 USC § 103
5. 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 t which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
6. Claims 1, 3-9, 11-17, and 19-20 are rejected under 35 U.S.C. 103 as unpatentable over Guo et al. (“Guo”) (US 11,545,170 B2) in view of Wu (US 2020/0034575 A1).
In order to expedite and avoid piecemeal prosecution, the following rejection is made to the extent that the claims are understood, by considering those elements which are understood and interpreting their function in a manner which is consistent with the recited goals of the claims, and then applying the best available art.
The examiner relies on the entire teachings of Guo and Wu references; the applicant should carefully consider the entire teachings of the above-mentioned references to better understand the examiner’s position.
In regard to claim 1, Guo teaches a method comprising: extracting one or more audio features from audio data captured by a processing device of a user device (as shown in Fig. 4, which is reproduced below for ease of reference and convenience, Guo discloses a method for acoustic-based recognition of environments performed on a user device (method 300; FIG. 3); a microphone of client device 110 records the surrounding environment to generate audio data (input engine 205; audio input layer 405); the convolutional engine 215 (convolutional layers 410/415) processes the audio data to “generate audio feature data items that describe audio features … of the environment” (method 300, operation 320; FIG. 3);
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providing at least the one or more audio features as input to an environment recognition model to generate an output representative of a predicted environment, wherein the model is trained to predict, based on the one or more audio features, a classification of a user environment the user device is located within (in Guo, the audio feature data is provided to the acoustic classification neural network (recursive layer 220/425, attention layer 225/430, and classification engine 235/445), which generates a scene classification of the environment (operations 330-340; FIG. 3). The training engine 245 trains the network on audio training data recorded from different environments e.g., a restaurant, a park, an office using backpropagation (FIG. 9, method 900), and the classification layer outputs, for each of a plurality of scene categories (bus, cafe, car, city center, home, library, office, residential area, etc.), a likelihood that the environment is that category, selecting the highest. This is a model trained to predict a classification of the user environment the user device is located within). Guo uses the classification to select content overlay for a social-media message and does not disclose updating a setting of the user device based on the predicted environment. In the same field of endeavor, Wu teaches updating one or more settings of the user device based at least in part on the predicted environment of the user device (as shown in Fig. 1, which is reproduced below for ease of reference and convenience, Wu determines the user’s environment from motion and sound data (environmental security index, ¶ [0080]-[0081]) and, based on the determined environment, updates the device’s security/privacy settings adjusting screen privacy protection between an active-information-sharing and a passive-privacy-exposure state (¶[0074])).
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Guo teaches the extracting audio features and providing them to a trained neural network that classifies the user’s environment but applies the resulting classification to select social-media content overlay rather than to update a setting of the user device. Wu teaches determining a user’s environment and, based on it, updating the device’s security/privacy settings. It would have been obvious to one of ordinary skill in the art before the effective filing date to use the Guo’s trained acoustic environment classifier as the environment-determination mechanism in Wu’s environment-adaptive security system, and to update the device settings based on that predicted environment. Guo expressly identifies “context-aware services” and “security surveillance” as applications of its environment classification and touts that it “more accurately classif[ies] an environment using acoustic data,” providing an express motivation to substitute its trained classifier for Wu’s threshold-based environmental security index. The combination applies a known, more-accurate environment classifier (Guo) to a known environment-based settings-adjustment system (Wu) to yield the predictable result of adjusting device settings based on a more accurately classified environment (KSR; MPEP 2143(A)/(G)), with a reasonable expectation of success.
In regard to claim 3, Guo et al. disclose wherein the one or more audio features comprise at least one of speech content, voice identities, music genres, ambient sounds, or separate sound sources (in Guo, the network processes “signature sounds” of environments and audio features including speech/voice and environmental sounds (e.g., dishes clacking indicating a restaurant); the convolutional layers extract such audio features (FIG. 4)).
In regard to claim 4, Guo et al. disclose wherein the output indicates whether the user device is located in a public environment (i.e. shared) or a private environment (i.e. privacy) (in Guo, determines whether the environment implicates a public/exposed vs. private condition and adjusts privacy accordingly (¶[0074],[0080]-[0081]); Guo classifies among environments such as office, home (private) and cafe, library, city center (public). Obvious to output a public/private classification).
In regard to claim 5, Wu et al. disclose further: responsive to determining that the user device is located in a public environment using the output of the environment recognition model, updating the one or more settings of the user device to increase a security profile of the user device (in Wu, increases/decreases device privacy protection based on the determined environment (¶[0074],[0081])). Same rational/motivation to combine as claim 1.
In regard to claim 6, Wu et al. disclose comprising: responsive to determining that the user device is located in a private environment using the output of the environment recognition model, updating the one or more settings of the user device to decrease a security profile of the user device (in Wu, increases/decreases device privacy protection based on the determined environment (¶[0074],[0081])). Same rational/motivation to combine as claim 1.
In regard to claims 7, Wu et al. disclose wherein updating the one or more settings of the user device comprises at least one of:
adjusting an authentication requirement of the user device; adjusting a brightness level of a display device associated with the user device;
adjusting one or more display effects of the display device;
adjusting a volume level of the user device;
adjusting a field of view of view of the display device;
adjusting access permissions to one or more data items with certain classifications stored on the user device;
adjusting a duration of a security lockout mechanism associated with the user device; and adjusting one or more device configuration settings associated with the user device (in Wu, adjusts device privacy/security settings based on environment; adjusting authentication requirements, display, and volume as recited are conventional device settings and obvious over Wu (Official Notice as needed). (See §112(b) as to “field of view of view.”)). Same rational/motivation to combine as claim 1.
In regard to claim 8, Guo et al. disclose further:
providing video data captured by the processing device as additional input to the environment recognition model, wherein the environment recognition model is configured to generate the output representative of the predicted environment of the user device using the video data and the audio data (in Guo, processes audio accompanying frames of a live video feed and combines acoustic classification with image/video scene detection (“video scene identification combined with image scene detection”); providing video data as additional input is taught/obvious over Guo).
Claim 17 (non-transitory machine-readable medium) and Claim 9 (system comprising processor and memory) recite the same operative limitations as method claim 1. The change in claim format from method to CRM or system does not confer patentability where the underlying operations are identical to those taught by the applied references. See MPEP § 2114; In re Bernhart, 417 F.2d 1395 (CCPA 1969). The element-by-element mapping set forth for claim 1 applies with equal force to claims 17 and 9.
Claims 11-16 (system) recite the same operative limitations as method claims 3-8. The change in claim format from method to system does not confer patentability where the underlying operations are identical to those taught by the applied references. See MPEP § 2114; In re Bernhart, 417 F.2d 1395 (CCPA 1969). The element-by-element mapping set forth for claims 3-8 applies with equal force to claims 11-16.
Claim 19-20 (non-transitory machine-readable medium) recite the same operative limitations as method claims 3-4. The change in claim format from method to CRM does not confer patentability where the underlying operations are identical to those taught by the applied references. See MPEP § 2114; In re Bernhart, 417 F.2d 1395 (CCPA 1969). The element-by-element mapping set forth for claims 3-4 applies with equal force to claims 19-20.
If Applicant traverses any statement of Official Notice, documentary evidence will be provided (MPEP 2144.03(C)).
Examiner's note:
Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the Applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passages as taught by the prior art or disclosed by the Examiner.
Response to Applicant’s Amendment & Arguments
Applicant’s arguments (pp. 8-10) that Wu does not disclose “an environment recognition model trained to predict a classification of a user environment” are persuasive as to Wu alone, and the rejection under 35 U.S.C. 102 has been withdrawn accordingly. However, the amended claims remain unpatentable. The precise feature Applicant added a model trained to classify the user environment from extracted audio features is expressly taught by Guo patent, which discloses a neural network trained on audio from different environments to classify the environment the user device is located within. Combined with Wu’s environment-based updating of device settings, the amended claims are obvious under 35 U.S.C. 103 as set forth above. The amendment therefore overcame the specific Wu anticipation ground but not the prior art.
Conclusion
7. Claims 1, 2-9, 11-17, 19-20 are rejected. Claims 2, 10, 18 are cancelled.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Raymond Phan, whose telephone number is (571) 272-3630. The examiner can normally be reached on Monday-Friday from 6:30AM- 3:00PM. The Group Fax No. (571) 273-8300.
Communications via Internet e-mail regarding this application, other than those under 35 U.S.C. 132 or which otherwise require a signature, may be used by the applicant and should be addressed to [raymond.phan@uspto.gov].
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Jung can be reached at (571) 270-3779. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Any inquiry of a general nature or relating to the status of this application should be directed to the TC 2100 central telephone number is (571) 272-2100.
/RAYMOND N PHAN/
Primary Examiner, Art Unit 2175
Prosecution Insights