DETAILED ACTION
This office action is in response to the remarks filed on April 6, 2026 in application 18/536,718.
Claims 1-3, 6-7, 9-11, 14-15, 17-19 are presented for examination. Claims 1, 9, 17 are amended. Claims 4-5, 8, 12-13, 16, 20 are cancelled.
IDS submitted on March 5, 2024 was acknowledged.
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 .
Response to Arguments
Applicant's arguments filed April 6, 2026 have been fully considered but they are not persuasive.
Applicant stated that the limitation “in response to performing the modifications to the operations of the computer system, verifying that a current fan noise level associated with the integrated fan of the computer system is less than the identified fan noise level” is not taught in Bard where Bard teach of a fan-speed adjustment procedure. Applicant stated that the fan speed and fan noise level are not the same. Applicant further stated that table 6 of Bard as mapped describes a pre-modification while the claimed teach of a post-modification.
Examiner disagreed. Fig. 2 of applicant’s drawing shown a determining that the identified fan noise level is undesirable, then perform modification to reduce the operating speed of the fan (fig. 2, 206, 208). Bard describe determining the acoustic noise produced based on the revolution per mins (RPM) of the fan (para. 52) and the acoustic transfer function 200 uses fan speed to estimate a calculated sound level 202 that is currently being produced by the device (para. 56, fig. 2).
Furthermore, for the lacking of a post-modification teaching as argued by applicant, Bard teach of the acoustic control system can be implemented using a number of control-loop feedback where the control loop attempts to keep the calculated sound level below and/or close to the target sound level (para. 59, fig. 3). With the acoustic control system in a feedback loop, the pre-modification would become the post-modification being feed into the next pre-modification cycle.
For these reasons, the rejections are maintained.
Claim Rejections - 35 USC § 103
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 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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 6-7, 9-10, 14-15, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bard (US 2009/0092261) in further view of Hudman et al. (US 2024/0096306).
In regard to claim 1, Bard teaches a method for managing the performance of a computer system based on context comprising:
identifying a fan noise level associated with an integrated fan of the computer system based on sound input detected by an audio input device associated with the computer system (receive set of acoustic characteristics for the device, these acoustic characteristics include acoustic state matrices for each noise-producing component in the device as well as an aggregation method table, fig. 8, para. 123);
identifying a current context associated with the computer system (aggregate the estimated acoustic noise for the components to produce an aggregated estimate of the acoustic noise produced by the device, fig. 8, 830, para. 123);
determining that the identified fan noise level is undesirable for the current context (determine whether the aggregated estimate exceeds a given acoustic annoyance level, fig. 8, 840, para. 123);
in response to determining that the identified fan noise level is undesirable, performing modifications to operations of the computer system, wherein the modifications are adapted to reduce an operating speed of the integrated fan (adjust a setting for a component within the device based on the aggregate estimate, fig. 8, 860, para. 123, flexible framework that measures, adjusts, and responds to acoustic annoyance data, para. 127, Table 6); and
in response to performing the modifications to the operations of the computer system, verifying that a current fan noise level associated with the integrated fan of the computer system is less than the identified fan noise level (compare looked up speed with current fan speed, if fan is not already at this speed, determine if fan speed change is upward or downward, para. 127, table 6, the acoustic control system can be implemented using a number of control-loop feedback where the control loop attempts to keep the calculated sound level below and/or close to the target sound level, para. 59, fig. 3).
Bard does not explicitly teach but Hudman et al. teach wherein identifying the current context (ANR process receive data indicative of a noise, para. 14-16, use of microphone, para. 39) includes identifying a status of a particular software application being executed by the computer system (application (e.g., a video game), para. 12), wherein the particular software application is a voice communication application (HMD may be to capture the sound of the user’s voice, para. 39), and wherein the identified status indicates that a voice communication session is currently active (determining whether and when to open the microphone of the HMD for performing ANR with improved accuracy, para. 16).
It would have been obvious to modify the method of Bard by adding Hudman et al. active reduction of fan noise. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in executing an application using ANR techniques (para. 11-17).
In regard to claim 2, Bard teaches the method of claim 1, wherein identifying the current context includes identifying a presence of a user in front of the computer system (annoyance model can incorporate one or more of the following: the hearing ability of a user, the noise sensitivity of a user, a general acoustic expectation of a user, ect., para. 23, receive a set of input from a user and/or environment the device is presently in, para. 24).
In regard to claim 6, Bard teaches the method of claim 1, wherein the modifications to operations of the computer system include suspending background tasks being executed by the computer system (reduce the load associated with managing acoustic noise, for instance by reducing the associated amount of computation and data-access speed, para. 119).
In regard to claim 7, Bard teaches the method of claim 1, wherein the modifications to operations of the computer system include modifying an Energy Performance Preference (EPP) parameter of a processor of the computer system to reduce a maximum speed of the processor (adjusts a power level for a device component to reduce the acoustic noise produced, para. 61, throttle the power used to ensure that the device does not exceed the target sound level and the target temperature, para. 62).
In regard to claim 9, Bard teaches a system for managing computer system performance based on context, the system comprising:
a computer system including at least one processor, a memory, and an integrated fan, and configured to perform operations including:
identifying a fan noise level associated with the integrated fan based on sound input detected by an audio input device associated with the computer system (receive set of acoustic characteristics for the device, these acoustic characteristics include acoustic state matrices for each noise-producing component in the device as well as an aggregation method table, fig. 8, para. 123);
identifying a current context associated with the computer system (aggregate the estimated acoustic noise for the components to produce an aggregated estimate of the acoustic noise produced by the device, fig. 8, 830, para. 123);
determining that the identified fan noise level is undesirable for the current context (determine whether the aggregated estimate exceeds a given acoustic annoyance level, fig. 8, 840, para. 123);
in response to determining that the identified fan noise level is undesirable, performing modifications to operations of the computer system, wherein the modifications are adapted to reduce an operating speed of the integrated fan (adjust a setting for a component within the device based on the aggregate estimate, fig. 8, 860, para. 123, flexible framework that measures, adjusts, and responds to acoustic annoyance data, para. 127, Table 6); and
in response to performing the modifications to the operations of the computer system, verifying that a current fan noise level associated with the integrated fan of the computer system is less than the identified fan noise level (compare looked up speed with current fan speed, if fan is not already at this speed, determine if fan speed change is upward or downward, para. 127, table 6, the acoustic control system can be implemented using a number of control-loop feedback where the control loop attempts to keep the calculated sound level below and/or close to the target sound level, para. 59, fig. 3).
Bard does not explicitly teach but Hudman et al. teach wherein identifying the current context (ANR process receive data indicative of a noise, para. 14-16, use of microphone, para. 39) includes identifying a status of a particular software application being executed by the computer system (application (e.g., a video game), para. 12), wherein the particular software application is a voice communication application (HMD may be to capture the sound of the user’s voice, para. 39), and wherein the identified status indicates that a voice communication session is currently active (determining whether and when to open the microphone of the HMD for performing ANR with improved accuracy, para. 16).
Refer to claim 1 for motivational statement.
In regard to claim 10, Bard teaches the system of claim 9, wherein identifying the current context includes identifying a presence of a user in front of the computer system (annoyance model can incorporate one or more of the following: the hearing ability of a user, the noise sensitivity of a user, a general acoustic expectation of a user, ect., para. 23, receive a set of input from a user and/or environment the device is presently in, para. 24).
In regard to claim 14, Bard teaches the system of claim 9, wherein the modifications to operations of the computer system include suspending background tasks being executed by the computer system (reduce the load associated with managing acoustic noise, for instance by reducing the associated amount of computation and data-access speed, para. 119).
In regard to claim 15, Bard teaches the system of claim 9, wherein the modifications to operations of the computer system include modifying an Energy Performance Preference (EPP) parameter of a processor of the computer system to reduce a maximum speed of the processor (adjusts a power level for a device component to reduce the acoustic noise produced, para. 61, throttle the power used to ensure that the device does not exceed the target sound level and the target temperature, para. 62).
In regard to claim 17, Bard teaches an article of manufacture comprising a non-transitory, computer-readable medium having computer-executable instructions thereon that are executable by a processor to perform operations for managing the performance of a computer system based on context, the operations comprising:
identifying a fan noise level associated with an integrated fan of the computer system based on sound input detected by an audio input device associated with the computer system (receive set of acoustic characteristics for the device, these acoustic characteristics include acoustic state matrices for each noise-producing component in the device as well as an aggregation method table, fig. 8, para. 123);
identifying a current context associated with the computer system (aggregate the estimated acoustic noise for the components to produce an aggregated estimate of the acoustic noise produced by the device, fig. 8, 830, para. 123);
determining that the identified fan noise level is undesirable for the current context (determine whether the aggregated estimate exceeds a given acoustic annoyance level, fig. 8, 840, para. 123);
in response to determining that the identified fan noise level is undesirable, performing modifications to operations of the computer system, wherein the modifications are adapted to reduce an operating speed of the integrated fan (adjust a setting for a component within the device based on the aggregate estimate, fig. 8, 860, para. 123, flexible framework that measures, adjusts, and responds to acoustic annoyance data, para. 127, Table 6); and
in response to performing the modifications to the operations of the computer system, verifying that a current fan noise level associated with the integrated fan of the computer system is less than the identified fan noise level (compare looked up speed with current fan speed, if fan is not already at this speed, determine if fan speed change is upward or downward, para. 127, table 6, the acoustic control system can be implemented using a number of control-loop feedback where the control loop attempts to keep the calculated sound level below and/or close to the target sound level, para. 59, fig. 3).
Bard does not explicitly teach but Hudman et al. teach wherein identifying the current context (ANR process receive data indicative of a noise, para. 14-16, use of microphone, para. 39) includes identifying a status of a particular software application being executed by the computer system (application (e.g., a video game), para. 12), wherein the particular software application is a voice communication application (HMD may be to capture the sound of the user’s voice, para. 39), and wherein the identified status indicates that a voice communication session is currently active (determining whether and when to open the microphone of the HMD for performing ANR with improved accuracy, para. 16).
Refer to claim 1 for motivational statement.
In regard to claim 18, Bard teaches the article of claim 17, wherein identifying the current context includes identifying a presence of a user in front of the computer system (annoyance model can incorporate one or more of the following: the hearing ability of a user, the noise sensitivity of a user, a general acoustic expectation of a user, ect., para. 23, receive a set of input from a user and/or environment the device is presently in, para. 24).
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Claims 3, 11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bard (US 2009/0092261) in further view of Hudman et al. (US 2024/0096306) in further view of Sinha et al. (US 2020/0133374).
In regard to claim 3, Bard and Hudman et al. does not explicitly teach but Sinha et al. teach the method of claim 2, wherein identifying the presence of the user is performed based on input captured by a camera associated with the computer system (user presence detector accesses the image data from the image sensor, para. 90).
It would have been obvious to modify the method of Bard and Hudman et al. by adding Sinha et al. manage power and performance. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in setting the performance parameters according to the balanced operation mode (para. 90).
In regard to claim 11, Bard and Hudman et al. does not explicitly teach but Sinha et al. teach the system of claim 10, wherein identifying the presence of the user is performed based on input captured by a camera associated with the computer system (user presence detector accesses the image data from the image sensor, para. 90).
Refer to claim 3 for motivational statement
In regard to claim 19, Bard and Hudman et al. does not explicitly teach but Sinha et al. teach the article of claim 18, wherein identifying the presence of the user is performed based on input captured by a camera associated with the computer system (user presence detector accesses the image data from the image sensor, para. 90).
Refer to claim 3 for motivational statement
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892.
Ziarnik (US 2003/0120394) reduce fan noise
Rijken et al. (US 2003/0218465) reduce fan noise
Lai (US 2004/0119430) reduce fan noise
Meir (US 2005/0174737) reduce/cancel fan noise
Hu et al. (US 6,965,175) reduce temperature and noise of fan
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Li et al. (US 2023/0402027) reduce temperature and noise
Khaleghimeybodi et al. (US 11,589,176) calibrating audio system
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Varma et al. (US 12,436,585) environmental condition (noise levels) of computing platforms
Nakami et al. (US 2011/0096176) noise reduction level for execution of image processing
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Daimo (US 12,205,612) detect noise feature amount
Culbert et al. (US 9,317,090) actively managed by balance cooling effort and noise
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOAN TRUONG whose telephone number is 408-918-7552. The examiner can normally be reached on 10AM-6PM PST M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached on 571-272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Loan L.T. Truong/Primary Examiner, Art Unit 2114 Loan.truong@uspto.gov
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