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 .
Claims 1-20 are presented for Examination.
DETAILED ACTION
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 8-12,15-20 is/are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Trim et.al. (U.S Patent Application Publication 2022/0066499; hereinafter “Trim”].
Regarding Claims 1, 8, 15, Trim discloses, an apparatus to perform cloud-based artificial intelligence (AI) overclocking, the apparatus comprising:
interface circuitry [“ Computer system/server 12 may also communicate with one or more external devices 14 such as a keyboard, a pointing device, a display 24, etc.; one or more devices that enable a user to interact with computer system/server 12; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server 12 to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces 22. Still yet, computer system/server 12 can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter 20”, 0044; “ CPU clock management program 200 generates a communication channel. In one embodiment, CPU clock management program 200 creates a communication channel for user device 120 via network 110. For example, upon a client-side application (e.g., application 124) of CPU clock management program 200 initiating on a mobile device (e.g., user device 120), the client-side application transmits a stream-based channel request to CPU clock management program 200 through a node (e.g., eNodeB) of a 5G telecom network (e.g., network 110) via a radio access protocol. Additionally, the client-side application actuates data collectors, daemons, and operating system interconnects of the mobile device and collects raw data and statistics of the mobile device;
machine readable instructions[ “ Computer system/server 12 may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. “, 0038; “Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12, and it includes both volatile and non-volatile media, removable and non-removable media.”, 0041]; and
programmable circuitry to at least one of instantiate or execute the machine readable instructions to[“Program/utility 40, having a set (at least one) of program modules 42, may be stored in memory 28 by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments of the disclosure as described herein.”, 0043; “ server 140 is representative of any electronic device or combination of electronic devices capable of executing computer readable program instructions. Server 140 may include components as depicted in FIG. 1, in accordance with embodiments of the present invention.”, 0059] :
collect workload information, the workload information representing a workload to be executed by a first compute platform[ “CPU clock management program 200 collects information of user device 120. For example, CPU clock management program 200 retrieves local hardware information (e.g., CPU, number of cores, supported clock speeds, etc.) from a mobile device (e.g., user device 120) via a secured dedicated 5G logical channel (e.g., network 110). In this example, CPU clock management program 200 retrieves local dynamic information ..”, 0063;” CPU clock manager 96 is shown in FIG. 3 to reside in workloads layer 90”, 0050; “clock management program 200 determines the dynamic insights by filtering a data stream of the mobile device that includes collected information and query a metadata mapper to identify corresponding operational requirements (i.e., detecting a set of conditions that indicate a current or future workload of one or more cores of the mobile device in order to identify computational requirements of the mobile device).”, 0075] ;
cause generation of an output from an AI model based on the workload information, the output representing an overclocking frequency value to be used for operation of the first compute platform[ “..Embodiments of the present invention initiate a secured dedicated 5G logical channel for artificial intelligence (AI) based dynamic clock manager based on user equipment device subscribed services to a virtual network function (VNF) and end-to-end (E2E) domain ..”, 0011; “CPU clock management program 200 maps collected information of user device 120 with a machine learning algorithm. For example, CPU clock management program 200 parses (e.g., statistical, regular expression classifier (Regex), etc.) a data stream of a mobile device (e.g., user device 120) via a secure dedicated 5G logical channel that includes collected data (e.g., local hardware information, dynamic information, etc.) and extracts fields (e.g., static and dynamic data fields the mobile device) of the collected data. In this example, CPU clock management program 200 maps the extracted fields to a training model of a machine learning algorithm (e.g., artificial neural network (ANN)) for inferring situational insight from the collected data (i.e., training a ANN to identify external circumstances, events, activities, and circumstances that impact computational requirements of the mobile device based on the collected data)”, 0074; CPU clock management program 200 utilizes situational insight from collected data of user device 120 to determine a recommended CPU clock speed for a core of user device 120. For example, CPU clock management program 200 uses dynamic insight about hardware and software services utilization of a mobile device (e.g., user device 120) of a stream-based classifier and machine learning algorithm model (e.g., ANN) to recommend a CPU clock speed of one or more cores of the mobile device. In this example, CPU clock management program 200 recommends to underclock or overclock one or more cores of the mobile using the articulated information about the mobile device and an estimation of the machine learning algorithm model with respect to anticipated events and workloads based on an application access pattern of a user and corresponding application requirements.”, 0076;]; and
perform overclocking based on the overclocking frequency value[0013; 0015; “CPU clock management program 200 provides a mechanism, which detects emergency situations and modifies a core clock frequency of one or more cores of user equipment to accommodate situational computational needs of the user equipment”, 0062; “..underclock or overclock one or more cores of the mobile using the articulated information about the mobile device ..”, 0076; “..CPU clock management program 200 recommends a clock speed of 1.8 GHz for a core of the mobile device, based on frequent crashes of the gaming application under similarly forecasted workloads in an application history of the gaming application.”, 0077-0078;(i.e overclocking during an emergency or frequent crashes of the device) ; “then CPU clock management program 200 transmits the emergency message to the machine learning algorithm services to determine a CPU clock speed of a mobile device (e.g., user device 120) based on operational requirements of a source (e.g., service) of the emergency situation”, 0079; “For example, CPU clock management program 200 uses a CPU clock manager (e.g., application 124) to modify a CPU clock speed of a core of a mobile device (e.g., client device 120) and transmit a signal to an operating system of the mobile device to control scheduling of a running application based on a new clock speed. In this example, CPU clock management program 200 parses an acknowledgement message received from a client-side application (e.g., application 124...”, 0081].
Regarding Claims 2, 9, 16, Trim discloses wherein to cause generation of the output from the Al model, the programmable circuitry is to:
obtain a local copy of the AI model; and execute the AI model based on the workload information. [0019;0065; 0074; 0076]
Regarding Claims 3, 10, 17, Trim discloses wherein to cause the generation of the output from the Al model, the programmable circuitry is to transmit the workload information to a model generator and request that the model generator generate the output using the workload information[0043;0074;0076] .
Regarding Claims 4,11,18, Trim discloses wherein the workload information includes system telemetry data that includes at least one of voltage data, temperature data, individual core power data, and multiple core power data [0058; 0063-0064; 0073; (i.e. adjusting the clock speed based on the number of cores, computing power and operational requirements)].
Regarding Claims 5, 12, 19, Trim discloses wherein the Al model is trained by model generator circuitry separate from the apparatus, and the programmable circuitry is to tune the AI model [“CPU clock management program 200 parses the acknowledgement message and updates training data to the classification model that improve triggering and recommendations based on historical data.”, 0068; 0074; 0076; 0081].
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 of this title, 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 6, 13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Trim in view of Yan et.al. (U.S Patent Application Publication 2022/0113757; hereinafter “Yan”)
Regarding Claims 6,13, 20, Trim discloses the limitations outlined in claims 1, 8, 15.
However Trim does not expressly disclose determine importance of respective features of the Al model, the features associated with a first overclocking performance; and minimize the features of the AI model based on the importance.
In the same field of endeavor(e.g. intelligent tuning of overclocking frequency to execute an optimization model to select first values for overclocking parameters of a processor) , Yan teaches ,
determine importance of respective features of the Al model, the features associated with a first overclocking performance; and minimize the features of the AI model based on the importance[“ Upon completion of a trial (e.g., three benchmark runs with the processing unit 102 operating under selected overclocking parameters for the trial), the clock rate tuning circuitry 112 provides the trial score and selected overclocking parameters for the trial to the optimization model. Based on the trial scores generated for each trial in an experiment, the optimization model learns which overclocking parameter values and/or combinations of overclocking parameter values resulted in the highest benchmark scores, which overclocking parameter values and/or combinations of overclocking parameter values caused the processing unit to become unstable, etc. .. for a subsequent trial of an experiment, the optimization model can select value(s) of the overclocking parameter(s) and/or combination of the parameter value(s) that are different than the overclocking values that cause the processing unit 102 to crash during one of the previous trials of the experiment.”, 0030;(i.e. determining the benchmark scores / importance for respective models and updating parameter values/ features for subsequent model / trial if the previous model has caused the processor to crash or become unstable. Hence it is apparent to minimize or select the parameter values based on the importance of the stability of the processor) ; “ the trial control circuitry 206 executes the optimization model to select (e.g., determine, estimate, predict) trial values for the overclocking parameters to be used in the trial, where the trial values are selected as the expected optimal values based on the training (i.e., current training) of the optimization model 216. ..”, 0069; 0105; “.. implement an Artificial Intelligence/Machine Learning (AI/ML) model to generate the instructions), 0096; “ the model updating circuitry 210 updates or refines the optimization model 216 with the trial score for the trial and the associated overclocking parameter value(s) for that trial. ..”, 0076].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Trim with Yan. Yan’s teaching of tuning of an overclocking frequency and executing an optimization model will substantially improve Trim’s system to determine the overclocking frequency by analyzing the performance metrics/ benchmark scores of the trial models and selecting the optimal parameter values that promotes stability of the processing unit.
Claims 7, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Trim
Regarding claims 7, 14, Trim discloses, wherein the programmable circuitry is to, after performing overclocking on the first compute platform, adjust the overclocking frequency value.[ 0013; 0073-0076] i.e. updating the clock speed to underclock or overclock based on dynamically obtaining the workload information.
However Trim does not expressly disclose determine to collect subsequent workload information to adjust the overclocking frequency value.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Trim teaching to collect subsequent workload information to adjust the overclocking frequency value as Trim discloses updating the clock speed to underclock or overclock based on dynamically obtaining the workload information [ 0013; 0073-0076], to prevent bottlenecks and increase computing performance in emergency situations by transmitting an overclocking signal to the computing system, which addresses the emergency situations instead of a static throttling of core clocks by modifying the CPU clock speeds of cores independently and dynamically [0013].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Colbert et al., U.S Patent Application Publication 2024/0319760, teaches A processing device includes an automated overclocking system and a processor. The automated overclocking system is data-driven and includes an inference engine that executes a machine learning model configured to generate a first output based on a current configuration of the processing device. The first output includes a first set of overclocking parameters. The processor is configured to adjust one or more operating characteristics of at least one component of the processing device based on the first set of overclocking parameters.
Branover et al., U.S Patent Application Publication 2013/0246820, teaches an apparatus and method for dynamically adjusting power limits for processing nodes and other components, such as peripheral interfaces. The apparatus includes multiple processing nodes and other components, and further includes a power management unit configured to set a first frequency limit for at least one of the processing nodes responsive to receiving an indication of a first detected temperature greater than a first temperature threshold.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GAYATHRI SAMPATH whose telephone number is (571)272-5489. The examiner can normally be reached on 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, Jaweed Abbaszadeh can be reached on 5712701640. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GAYATHRI SAMPATH/ Examiner, Art Unit 2176
/JAWEED A ABBASZADEH/ Supervisory Patent Examiner, Art Unit 2176
