Dynamic mitigation of fan noise during e-conference based on real-time and retroactive data

According to one embodiment, a method, computer system, and computer program product for mitigating computer fan noise. The embodiment may include retrieving a previously generated baseline profile for a user participating in a real-time e-conference call via a computing device. The embodiment may include identifying real-time hardware and software metrics of the computing device. The embodiment may include calculating a current fan indicative noise (FIN) score for the call based on the baseline profile and the identified metrics. The embodiment may include determining whether the current FIN score is above a threshold value. In response to determining that the FIN score is above a threshold value, the embodiment may include determining whether the computing device is at risk of damage. In response to determining that the computing device is not at risk of damage, the embodiment may include limiting operation of one or more fans of the computing device.

BACKGROUND

The present invention relates generally to the field of computing, and more particularly to electronic-conference (e-conference) calls and computing device fan operation.

A computer fan is any fan inside, or attached to, a computer case used for active cooling. Fans are used to draw cooler air into the case from the outside, expel warm air from inside the case, and move air across a heat sink to cool a particular component, for example a central processing unit (CPU) or a graphics processing unit (GPU). Both axial and sometimes centrifugal (blower/squirrel-cage) fans are used in computers. Computer fans are commonly implemented in standard sizes and are powered and controlled using 3-pin or 4-pin fan connectors. Noise from a computer fan has been found to be roughly proportional to the fifth power of fan speed. Some axial fans may rotate at speeds of up to around 38,000 revolutions per minute (RPM) for smaller sizes. Computer fans may be controlled by sensors and circuits that reduce their speed when internal computer temperature is not high.

SUMMARY

According to one embodiment, a method, computer system, and computer program product for mitigating computer fan noise. The embodiment may include retrieving a previously generated baseline profile for a user. The user is participating in a real-time e-conference call via a computing device. The embodiment may include identifying real-time hardware and software metrics of the computing device. The embodiment may include calculating a current fan indicative noise (FIN) score for the real-time e-conference call based on evaluation of the previously generated baseline profile and the identified real-time hardware and software metrics. The embodiment may include determining whether the current FIN score is above a threshold value. In response to determining that the current FIN score is above a threshold value, the embodiment may include determining whether the computing device is at risk of damage. In response to determining that the computing device is not at risk of damage, the embodiment may include limiting operation of one or more fans of the computing device.

DETAILED DESCRIPTION

The present invention relates generally to the field of computing, and more particularly to electronic-conference (e-conference) calls and computing device fan operation. The following described exemplary embodiments provide a system, method, and program product to, among other things, determine a fan indicative noise (FIN) level of a user's computer during an e-conference call and, accordingly, partially or completely, deactivate the fan of the user's computer when the user speaks on the e-conference call. Therefore, the present embodiment has the capacity to improve the technical fields of e-conference calls and computing device fan operation by dynamically reducing or eliminating the noise produced by a fan of the user's computer, thus mitigating disturbing fan noise and promoting intelligibility of a user's audio during an e-conference.

As previously described, a computer fan is any fan inside, or attached to, a computer case used for active cooling. Fans are used to draw cooler air into the case from the outside, expel warm air from inside the case, and move air across a heat sink to cool a particular component, for example a CPU or a GPU. Both axial and sometimes centrifugal fans are used in computers. Computer fans are commonly implemented in standard sizes and are powered and controlled using 3-pin or 4-pin fan connectors. Noise from a computer fan has been found to be roughly proportional to the fifth power of fan speed. Some axial fans may rotate at speeds of up to around 38,000 RPM for smaller sizes. Computer fans may be controlled by sensors and circuits that reduce their speed when internal computer temperature is not high.

E-conferencing is a method of holding meetings with others in different locations via the Internet. While one of the most important benefits of e-conferencing is the reduction in travel time and operational costs incurred in conducting in-person meetings, businesses can employ e-conferencing to improve productivity and efficiency by speeding up decision-making, solving problems faster, and collaborating more easily. A user can set up or join an e-conference by using software on a desktop computer, laptop or tablet computer linked to the Internet. The user can speak and listen during an e-conference by using a microphone and speakers attached to the computer or by telephone. When communicating over e-conferences on a laptop, it is not uncommon for the user to deal with not just noisy background audio, but also a noisy computer itself. In fact, one of the loudest sources of noise the user may have to deal with is their laptop's fan. A computer using CPU and GPU cooling fans can together generate over 60 decibels (dBA), an amount similar in noise to a lawn mower at 10 meters away. As such, the noise from the user's laptop, or other computing device, may interfere with and negatively affect the intelligibility of the user's audio (e.g., the user's speech) when communicating in an e-conference. Consequently, the benefits and effectiveness of the e-conference may be reduced. It may therefore be imperative to have a system in place to analyze and identify fan indicative noise of a laptop which is negatively affecting intelligibility of a user's transmitted audio in an e-conference and mitigate the noise produced by the fan while the user is communicating during the e-conference. Thus, embodiments of the present invention may be advantageous to, among other things, analyzing laptop fan noise and other hardware metrics (e.g., internal temperature) produced during an e-conference, identifying when the fan noise is adversely affecting intelligibility of a user's audio, mitigating (partially or completely) the noise produced by the fan when the user speaks during the e-conference while ensuring that the laptop is not at risk of overheating. The present invention does not require that all advantages need to be incorporated into every embodiment of the invention.

According to at least one embodiment, when a user joins an e-conference call a baseline profile containing computing system hardware metrics, average fan noise levels, and past e-conference behavior of the user may be retrieved. A fan indicative noise (FIN) score may be calculated for the current e-conference call. If the FIN score for the current e-conference call is above a threshold, the proposed system may mitigate operation of the fan when the user speaks during the e-conference call provided that the computing system is not at risk of damage. According to at least one other embodiment, if the past e-conference behavior of the user indicates spot unmuting behavior, the proposed system may mitigate operation of the fan when the user unmutes their computing system to speak during the e-conference provided that the computing system is not at risk of damage. According to at least one further embodiment, a presenter group of computing systems may be created and during an e-conference call a computing system of the group may be identified as an optimal system for presentation use based on an evaluation of each computing system's fan decibel level.

The following described exemplary embodiments provide a system, method, and program product to identify laptop fan noise which is negatively impacting intelligibility of a user's audio feed when communicating during an e-conference call and mitigate the fan noise when the user is communicating or is unmuted during the e-conference call. The system, method, and program product additionally determines whether or not the laptop is at risk of overheating before implementing fan noise mitigation actions.

Referring toFIG.1, an exemplary networked computer environment100is depicted, according to at least one embodiment. The networked computer environment100may include a client computing device102and a server112, interconnected via a communication network114. According to at least one implementation, the networked computer environment100may include a plurality of client computing devices102and servers112, of which only one of each is shown for illustrative brevity. Additionally, in one or more embodiments, the client computing device102and the server112may each host a fan noise mitigation program110A,110B. In one or more other embodiments, the fan noise mitigation program110A,110B may be partially hosted on client computing device102and server112so that functionality may be separated among the devices.

Client computing device102may include a processor104and a data storage device106that is enabled to host and run a software program108and a fan noise mitigation program110A and communicate with the server112via the communication network114, in accordance with one embodiment of the invention. Client computing device102may be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing capable of running a program and accessing a network. Additionally, the client computing device102may include one or more internal temperature sensors and one or more fans (internal or external) to actively cool internal components (e.g., processor104, data storage device106) of the client computing device102. As will be discussed with reference toFIG.4, the client computing device102may include internal components402aand external components404a, respectively.

The server computer112may be a laptop computer, netbook computer, personal computer (PC), a desktop computer, or any programmable electronic device or any network of programmable electronic devices capable of hosting and running a fan noise mitigation program110B and a database116and communicating with the client computing device102via the communication network114, in accordance with embodiments of the invention. As will be discussed with reference toFIG.4, the server computer112may include internal components402band external components404b, respectively. The server112may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS). The server112may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud.

According to the present embodiment, the fan noise mitigation program110A,110B may be a program capable of creating and maintaining a baseline profile including past e-conference call mute behavior of a user and correlations/trends between hardware metrics and fan noise levels of a computing system of the user, identifying fan indicative noise of the computing system of the user that is negatively impacting intelligibility of the user's transmitted audio during an e-conference call, mitigating, either partially or completely, the fan noise when the user is communicating or is unmuted during the e-conference call, determining whether the computing system of the user is at risk of overheating, creating a presenter group of computing systems for users to share applications remotely during an e-conference call, identifying an optimal computing system of the presenter group based on background fan noise, and redirecting the user to switch to an alternative audio input for participation during an e-conference call. The fan noise mitigation program110A,110B may be implemented as a stand-alone application or as a plug-in to known e-conference software applications. For example, the fan noise mitigation program110A,110B may be a plug-in to the software program108which may be an e-conference software application. The user baseline profile generation method is explained in further detail below with respect toFIG.2. The computer fan noise mitigation method is explained in further detail below with respect toFIG.3.

Referring now toFIG.2, an operational flowchart for creating a user baseline profile in a user baseline profile generation process200is depicted according to at least one embodiment. At202, the fan mitigation (FM) program110A,110B requires a user to opt-in to system usage upon commencement of a user baseline profile generation or commencement of an e-conference call. In an embodiment where the FNM program110A,110B is implemented as a plug-in to a known e-conference software application, the FNM program110A,110B may require the user to opt-in upon opening the e-conference software application.

At204, the FNM program110A,110B generates a user baseline profile for the user. According to one embodiment, the FNM program110A,110B may generate the baseline profile for the user during an e-conference configuration call initiated by the user on a computing device (e.g., a desktop or a laptop). In generating the user baseline profile, the FNM program110A,110B observes/identifies hardware and software metrics of the user's computing device during the configuration call. For example, in a scenario in which the user has initiated a configuration call on a laptop, the FNM program110A,110B observes spin speeds and dBA levels produced by one or more fans of the laptop to identify an average dBA level produced by the one or more fans during the configuration call. The FNM program110A,110B also observes/identifies laptop CPU and/or GPU utilization percentages, laptop memory utilization percentages, open/active software applications and their laptop computing resource utilization percentages, and internal laptop temperatures during the configuration call. From the aforementioned observed hardware and software metrics, the FNM program110A,110B may identify correlations between CPU/GPU utilization percentages and laptop fan spin speeds or internal laptop temperatures, correlations between memory utilization percentages and laptop fan spin speeds or internal laptop temperatures, correlations between internal laptop temperatures and laptop fan spin speeds, correlations between internal laptop temperatures and laptop overheating risk levels, correlations between open/active software applications and laptop fan spin speeds, and correlations between laptop fan spin speeds and resulting dBA levels. Observed metrics and identified correlations resulting from the configuration call may be included, by the FNM program110A,110B, within the generated user baseline profile for the user. Additionally, the FNM program110A,110B may also identify and include, within the generated user baseline profile, information of the user's laptop such as internal hardware specifications, installed software applications, average internal laptop temperature, normal (i.e., within hardware operating tolerance) internal temperature range, critical (i.e., exceeding hardware operating tolerance) internal temperature range, and one or more temperature thresholds for laptop fan activation. The FNM program110A,110B may store generated user baseline profiles within data storage device106or database116.

According to at least one further embodiment, the FNM program110A,110B may generate, or update, a user baseline profile based on observed hardware/software metrics, identified correlations, observed user behaviors, and identified information of the user's laptop during a real-time e-conference call (i.e., an e-conference call that is not a configuration call). For example, during an e-conference call, other than a configuration call, the FNM program110A,110B may observe spot unmuting behavior of a user. Spot unmuting behavior is when a user typically remains on mute during an e-conference call and unmutes only to speak during the call. When spot unmuting behavior is observed, the FNM program110A,110B may identify an average period of time the user spends unmuted when speaking. Furthermore, the FNM program110A,110B may track observed hardware/software metrics, identified correlations, and observed user behaviors from multiple real-time e-conference calls and generate, or update, a user baseline profile based on the observed/tracked information from one or more real-time e-conference calls. For example, the FNM program110A,110B may update a user's existing user baseline profile to include observed spot unmuting behavior and average unmute time or to update hardware/software specifications or identified correlations.

At206, the FNM program110A,110B calculates a baseline fan indicative noise (FIN) score for the e-conference configuration call. The FIN score represents an association between operation of hardware and software components of the user's computing device (e.g., a laptop) and their effect on the intelligibility of the user's transmitted audio during the e-conference call. For example, a FIN score which exceeds a threshold value may be an indication that noise produced by one or more fans of a user's laptop is negatively impacting the ability of a microphone of the user's laptop to capture the user's speech and thus negatively impact the intelligibility, as received by another user or by the FNM program110A,110B itself, of the user's transmitted audio during an e-conference call. The FNM program110A,110B may base calculation of the baseline FIN score on evaluation of one or more factors including: information within a user's baseline profile as described above; speech communication and laptop fan noise testing between the user and another user on the e-conference configuration call; speech-to-text accuracy of the user's transmitted audio at varying laptop fan spin speeds; and word2vec natural language processing (NLP) of extracted text from the user's transmitted audio. The calculated FIN score for the e-conference configuration call may be included, by the FNM program110A,110B, within the generated user baseline profile.

As an illustrative example, when evaluating the speech communication and laptop fan noise testing between the user and another user on the e-conference configuration call, the FNM program110A,110B may perform NLP on extracted text from the speech of the two users to identify phrases which indicate a lack of intelligibility of the user's transmitted audio. Such phrases may include, but are not limited to, “I can't hear you”, “can you repeat that”, or “what did you say”. Moreover, repeated phrases between the users may also be an indication of a lack of intelligibility. The NLP on the extracted text from the speech of the two users may be performed as they communicate with each other while one or more fans of the user's laptop spin at varying speeds (e.g., from 0 RPM to a max RPM) as controlled by the FNM program110A,110B. Additionally, the FNM program110A,110B may identify correlations between increasing utterances of phrases indicating a lack of intelligibility and increasing RPMs of the one or more fans of the user's laptop and may also identify a threshold RPM limit which minimizes or eliminates utterances of phrases indicating a lack of intelligibility.

As another illustrative example, when evaluating the speech-to-text accuracy of the user's transmitted audio at varying laptop fan spin speeds during the e-conference configuration call, the FNM program110A,110B may display a test script for the user to read out loud and compare extracted text from the user's audio (i.e., the user's out loud reading) to the test script. The FNM program110A,110B may prompt the user to read the test script out loud while one or more fans of the user's laptop spin at varying speeds, as controlled by the FNM program110A,110B, and compare those readings to the test script. Additionally, the FNM program110A,110B may identify correlations between decreasing accuracy of the user's captured speech-to-text readings and increasing RPMs of the one or more fans of the user's laptop and may also identify a threshold fan RPM limit above which accuracy of the user's captured speech-to-text readings falls below a speech-to-text accuracy threshold due to the negative impact of fan noise on the ability of a microphone of the user's laptop to correctly capture the user's speech.

As yet another illustrative example, the FNM program110A,110B may analyze the extracted text of the user's transmitted audio during the e-conference configuration call at varying laptop fan spin speeds utilizing known word2vec NLP techniques. The word2vec algorithm uses a neural network model to learn word associations from a corpus of text. In embodiments of the invention, the corpus of text may include text from users having similar make/model laptops and laptop fan spin speeds. Once trained, the neural network model can detect synonymous words or suggest additional words for a partial sentence. As the name implies, word2vec represents each distinct word with a particular list of numbers called a vector which are chosen carefully such that a simple mathematical function (e.g., the cosine similarity between the vectors) indicates a level of semantic similarity between the words represented by those vectors. The FIN score and the level of semantic similarity may be inversely related.

Referring now toFIG.3, an operational flowchart for mitigating computer fan noise during an e-conference call in a computer fan noise mitigation process300is depicted according to at least one embodiment. At302, a user joins a real-time e-conference call with other users. Upon joining the e-conference call, the FNM program110A,110B requires the user to opt-in to system usage. Alternatively, user opt-in may be required upon opening e-conference software in which the FNM program110A,110B is implemented as a plug-in or upon installation of the FNM program110A,110B.

Next, at304, the FNM program110A,110B retrieves a previously generated user baseline profile for the user. The user baseline profile may be retrieved from data storage device106or database116. According to at least one other embodiment in which the user does not have a retrievable previously generated user baseline profile, the FNM program110A,110B generates a real-time user baseline profile for the user based on hardware/software metrics (e.g., laptop fan RPM and resulting dBA level, internal laptop temperature, CPU/GPU/memory utilization, open applications and their computing resource utilizations) and their correlations, user behaviors (e.g., muting behavior), and information of the user's laptop (e.g., hardware specifications, installed software applications, average internal laptop temperature, normal & critical internal temperature ranges, and one or more temperature thresholds for laptop fan activation) identified/observed during the real-time e-conference call. The FNM program110A,110B may store the generated real-time user baseline profile within data storage device106or database116for later retrieval as a previously generated user baseline profile during a future real-time e-conference call of the user. According to at least one further embodiment, the FNM program110A,110B may update the retrieved user baseline profile based on information identified/observed during the real-time e-conference call. For example, the FNM program110A,110B may update a retrieved user baseline profile to include information relating to spot unmuting behavior of the user observed during the real-time e-conference call or to update the average internal laptop temperature.

At306, the FNM program110A,110B calculates a current FIN score for the real-time e-conference call. The FNM program110A,110B may base calculation of the current FIN score on evaluation of one or more factors including: information within the user's retrieved or real-time user baseline profile; hardware/software metrics and information of the user's laptop identified during the real-time call; speech communication between the user and another user on the real-time call; speech-to-text accuracy of the user's transmitted audio at varying laptop fan spin speeds; and word2vec NLP of extracted text from the user's transmitted audio. For example, when evaluating the speech communication between the user and another user, the FNM program110A,110B may monitor voice chat between the users, or extracted text from the voice chat, for phrases which indicate a lack of intelligibility of the user's transmitted audio, as the occurrence of such phrases may result in an increased current FIN score. As another example, when evaluating the hardware/software metrics and information of the user's laptop identified during the real-time call, the FNM program110A,110B may calculate an increased current FIN score when laptop fan RPM or resulting dBA level is above a threshold value. As yet another example, the FNM program110A,110B may calculate an increased current FIN score when word2vec NLP results in a level of semantic similarity which falls below a threshold value. Moreover, any identified correlations between hardware and software metrics which are present within the user's retrieved or real-time user baseline profile may influence the calculation of the current FIN score by the FNM program110A,110B. Each factor may be evaluated alone or in combination with each other when calculating the current FIN score.

Next, at308, the FNM program110A,110B determines whether the current FIN score for the real-time e-conference call is above a threshold value. As noted above, the FIN score represents an association between operation of hardware and software components of the user's laptop and their effect on the intelligibility of the user's transmitted audio during the e-conference call. A FIN score which exceeds a threshold value may indicate that noise produced by one or more active fans of the user's laptop (triggered by operation of hardware/software components and increasing internal temperature) is negatively impacting the ability of a microphone of the laptop to capture the user's speech and thus is negatively impacting the intelligibility of the user's transmitted audio as received by another user or by the FNM program110A,110B itself. As a non-limiting examples, the threshold value may be a specified dBA level or a specified FIN value. In response to determining that the current FIN score is above the threshold value (step308, “Y” branch), the computer fan noise mitigation process300may proceed to step310. In response to determining that the current FIN score is not above the threshold value (step308, “N” branch), the computer fan noise mitigation process300may proceed to step318.

At310, the FNM program110A,110B determines whether the user's retrieved or real-time user baseline profile indicates observed spot unmuting behavior and a corresponding average unmute time for spot voiced statements of the user. In response to determining that the retrieved or real-time user baseline profile indicates observed spot unmuting behavior of the user (step310, “Y” branch), the computer fan noise mitigation process300may proceed to step312. In response to determining that the retrieved or real-time user baseline profile does not indicate observed spot unmuting behavior of the user, for instance the user typically remains unmuted during an e-conference call, (step310, “N” branch), the computer fan noise mitigation process300may proceed to step314.

At312, the FNM program110A,110B determines whether the user is currently muted (i.e., a microphone of the user's computing device is muted) during the real-time e-conference call. In response to determining that the user is currently muted (step312, “Y” branch), the computer fan noise mitigation process300may proceed to step318. In response to determining that the user is not currently muted, for instance the user is making a spot statement, (step312, “N” branch), the computer fan noise mitigation process300may proceed to step314.

Next at314, the FNM program110A,110B determines whether hardware of the user's laptop is at risk of damage due to overheating before potentially mitigating the noise produced by one or more fans of the laptop. In making this determination, the FNM program110A,110B identifies and analyzes real-time information of the laptop including laptop fan RPM, internal laptop temperature, CPU/GPU/memory utilization, open applications and their computing resource utilizations, hardware specifications, average internal laptop temperature, normal & critical internal temperature ranges, and one or more temperature thresholds for laptop fan activation. The FNM program110A,110B may additionally analyze information within the user's retrieved or real-time user baseline profile (e.g., identified correlations/trends between hardware and software metrics, average unmute time for spot voiced statements of the user). For example, the FNM program110A,110B may reference correlations between CPU/GPU/memory utilization percentages and laptop fan RPM or internal laptop temperature, correlations between internal laptop temperatures and laptop fan RPMs, correlations between internal laptop temperatures and laptop overheating risk levels, and correlations between open/active software applications and laptop fan RPMs when determining an overheating risk of the laptop. The FNM program110A,110B may additionally determine if the average unmute time for spot voiced statements of the user exceeds a threshold amount of fan noise mitigation time (i.e., an amount of time fan noise mitigation is performed by the FNM program110A,110B) above which the user's laptop may overheat. In response to determining that the hardware of the user's laptop is at risk of damage due to overheating (step314, “Y” branch), the computer fan noise mitigation process300may proceed to step318. In response to determining that the hardware of the user's laptop is not at risk of damage due to overheating (step314, “N” branch), the computer fan noise mitigation process300may proceed to step316.

At316, the FNM program110A,110B mitigates the noise produced by one or more fans of the user's laptop by partially, or completely, limiting their operation (i.e., reducing the RPMs of the one or more fans) while the user is speaking during the real-time e-conference call. When partially limiting operation of the one or more fans, the FNM program110A,110B may run the fan(s) at an RPM which produces a dBA level, or corresponding FIN score, that is below a threshold value. According to at least one other embodiment, the FNM program110A,110B may also identify one or more software applications currently open on the user's laptop having a computing resource (e.g., CPU/GPU/memory) utilization percentage which exceeds a threshold value and decrease the computing resource utilization of one or more of the identified software applications, as these software applications may be strongly contributing to laptop fan usage and resulting RPM levels. The FNM program110A,110B may prompt the user for confirmation before decreasing computing resource utilization of an identified software application. Moreover, the FNM program110A,110B may prompt the user to shut down one or more of the identified applications if the user has not interacted with them for more than a threshold amount of time. According to at least one further embodiment, the FNM program110A,110B may cease or limit mitigation of the noise produced by one or more fans of the user's laptop (i.e., allow for normal or partial fan operation) in response to the user re-muting a microphone of the laptop or in response to internal laptop temperature entering a critical internal temperature range. For example, the FNM program110A,110B may limit the RPM of one or more laptop fans to a minimum speed which allows for cooling within the laptop.

At318, the FNM program110A,110B allows for unmitigated (i.e., normal) operation of one or more fans of the user's laptop in response to determining that the calculated current FIN score for the real-time e-conference call fell below the threshold value, or in response to determining that hardware of the user's laptop is at risk of damage due to overheating, or in response to determining that the user is currently muted.

It may be appreciated thatFIG.2andFIG.3provide only an illustration of one implementation and do not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

According to a further embodiment, the FNM program110A,110B may be added to an e-conference software solution and may allow for the creation of a presenter group of computing devices (e.g., laptops) for users to share and control applications remotely within an e-conference. At a scheduled interval prior to an e-conference call, or in real-time, the FNM program110A,110B may evaluate each system within the presenter group to determine, for each system, an active fan decibel level and a fan trigger temperature (i.e., a temperature which activates the fan). Based on the evaluation of each systems' active fan decibel level and fan trigger temperature, the FNM program110A,110B may identify and select a single computing device of the presenter group as an optimal computing device to present from during the e-conference call. The optimal computing device of the presenter group may be the computing device having the lowest fan decibel level and thus allow a user to speak without interfering background fan noise. Furthermore, in instances where background fan noise of the optimal computing device is determined to exceed a threshold value, the FNM program110A,110B may identify another optimal computing device of the presenter group or may redirect the user of the optimal computing device from the computing device mic audio input to a phone/headset audio input for the e-conference call; thereby minimizing the background fan noise level.

The client computing device102and the server112may include respective sets of internal components402a,band external components404a,billustrated inFIG.4. Each of the sets of internal components402include one or more processors420, one or more computer-readable RAMs422, and one or more computer-readable ROMs424on one or more buses426, and one or more operating systems428and one or more computer-readable tangible storage devices430. The one or more operating systems428, the software program108and the FNM program110A in the client computing device102, and the FNM program110B in the server112are stored on one or more of the respective computer-readable tangible storage devices430for execution by one or more of the respective processors420via one or more of the respective RAMs422(which typically include cache memory). In the embodiment illustrated inFIG.4, each of the computer-readable tangible storage devices430is a magnetic disk storage device of an internal hard drive. Alternatively, each of the computer-readable tangible storage devices430is a semiconductor storage device such as ROM424, EPROM, flash memory or any other computer-readable tangible storage device that can store a computer program and digital information.

Each set of internal components402a,balso includes a R/W drive or interface432to read from and write to one or more portable computer-readable tangible storage devices438such as a CD-ROM, DVD, memory stick, magnetic tape, magnetic disk, optical disk or semiconductor storage device. A software program, such as the FNM program110A,110B, can be stored on one or more of the respective portable computer-readable tangible storage devices438, read via the respective R/W drive or interface432, and loaded into the respective hard drive430.

Each set of internal components402a,balso includes network adapters or interfaces436such as a TCP/IP adapter cards, wireless Wi-Fi interface cards, or 3G or 4G wireless interface cards or other wired or wireless communication links. The software program108and the FNM program110A in the client computing device102and the FNM program110B in the server112can be downloaded to the client computing device102and the server112from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces436. From the network adapters or interfaces436, the software program108and the FNM program110A in the client computing device102and the FNM program110B in the server112are loaded into the respective hard drive430. The network may comprise copper wires, optical fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers.

Each of the sets of external components404a,bcan include a computer display monitor444, a keyboard442, and a computer mouse434. External components404a,bcan also include touch screens, virtual keyboards, touch pads, pointing devices, and other human interface devices. Each of the sets of internal components402a,balso includes device drivers440to interface to computer display monitor444, keyboard442, and computer mouse434. The device drivers440, R/W drive or interface432, and network adapter or interface436comprise hardware and software (stored in storage device430and/or ROM424).

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and fan mitigation96. Fan mitigation96may relate to mitigating computing device fan-based noise.