DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the communication filed 2/26/2024.
Claims 1-20 are presented for examination.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirely as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) or (f).
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/26/2024 and 1/20/2026. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Regarding to Claim 20, Claim 20 recites “a computer program stored on a computer-readable recording medium”. However, there is no evidence from the specification excludes the broadest reasonable interpretation of such claimed computer-readable recording medium from signal per se. Such as, [00101] from the specification states that “The computer readable medium may be, for example, a removable recording medium (CD, DVD, Blu-ray disc, USB storage device, removable hard disk) or a fixed recording medium (ROM, RAM, computer equipped hard disk)”. Such statement may only provide certain particular examples for the claimed computer-readable recording medium without clearly excluding the claimed computer-readable recording medium from signal per se. Signals are directed to a non-statutory subject matter. Thus, Claim 20 is rejected under 35 U.S.C. 101 for directing to a non-statutory subject matter. Examiner suggests amend the claim element as “non-transitory computer-readable recording medium” in order to draw the claim to non-transitory subject matter.
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.
Claims 1, 3-9, 12 and 14-20 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Alt et al. (US 20220058060 A1, hereafter Alt).
Regarding to claim 1, Alt discloses: A method for performing a job, performed by a computing device (see [0004], [0007]-[0008], [0040], [0043]; “Data intensive computing tasks such as machine learning (ML), artificial intelligence (AI), data mining, and scientific simulation (often called applications, jobs or workloads)”, “a flowchart of one example of a method for creating application instances in a computing system is shown” and “the user may be prompted to select the option that will be used to run the job (step 370)”), the method comprising:
determining a first job that is performed under conditions specified by a user (see Fig. 3, [0004], [0040]-[0041], [0043]; “Data intensive computing tasks such as machine learning (ML), artificial intelligence (AI), data mining, and scientific simulation (often called applications, jobs or workloads)”, “A user is prompted to specify an application (step 300)”, “Based on the information input by the user … other information provided by the user (e.g., the size of the data file to be processed, the location of the data file to be processed, whether the application will be run in interactive mode or batch mode, etc.). Customer-specific business rules (e.g., geography-based restrictions or compute provider-based restrictions) may also be specified by the user and/or system administrator and applied” and “the user may be prompted to select the option that will be used to run the job”. The information or rules specified or provided by the user can be considered as the claimed conditions specified by a user);
determining an edge terminal of performing the first job (see Fig. 3, [0043]; “the user may be prompted to select the option that will be used to run the job (step 370). These system resources options may for example include a list of bare metal systems and cloud providers with instance options capable of executing the user's application with the logical topology specified. The determination of which resources to offer”. Also see [0033]; “Bare metal computing devices 130A and 130B may for example include workstations or servers optimized for machine learning computations”); and
activating an artificial intelligence-based model required for the edge terminal to perform the first job (see Fig. 3, [0045]; “Once the user makes their selection, the application may be instantiated and deployed (step 372)”. Also see [0004], [0040]; “Data intensive computing tasks such as machine learning (ML), artificial intelligence (AI), data mining, and scientific simulation (often called applications, jobs or workloads)” and “a machine learning application that a data scientist has written might require training data sets or production data to be processed”. The instantiation and deployment of machine learning application executed at selected bare metal system to execute the workload require activating the AI or ML based model to be executed at the selected bare metal system),
wherein the conditions include at least one of time zone information, regional information and event information (see [0041]; “other information provided by the user (e.g., the size of the data file to be processed, the location of the data file to be processed, whether the application will be run in interactive mode or batch mode, etc.). Customer-specific business rules (e.g., geography-based restrictions or compute provider-based restrictions) may also be specified by the user and/or system administrator and applied”. At least “geography-based restrictions” from [0041] can be considered as claimed regional information, at least “the size of the data file to be processed, the location of the data file to be processed, whether the application will be run in interactive mode or batch mode” from [0041] can be considered as claimed event information).
Regarding to Claim 3, the rejection of Claim 1 is incorporated and further Alt discloses: wherein the determining of the first job includes recommending a job suitable to be performed under the conditions (see [0040]; “A user is prompted to specify an application (step 300). Using the example presented above the user would specify Jupyter as the application that they are using. There may be multiple options for a particular application. For example, an application (e.g., a Jupyter notebook) can be run in multiple modes, such as interactive or batch mode”. Specifying or recommending a particular application in a particular mode. Also see [0009]-[0010]; “the user may be asked to describe their application. For example, they may be prompted to select one or more application types … This description/classification information may be used to select a similar application”).
Regarding to Claim 4, the rejection of Claim 1 is incorporated and further Alt discloses: wherein the determining of the edge terminal includes designating the edge terminal as a first dynamic group (see [0041]-[0043]; “a recommended logical node or cluster topology is displayed (step 330). A logical topology is a high-level definition of how many processing nodes (e.g., master nodes and worker nodes) will be used”, “the user may be prompted to select the option that will be used to run the job (step 370). These system resources options may for example include a list of bare metal systems and cloud providers with instance options capable of executing the user's application”. Selecting one or more nodes, i.e., claimed first dynamic group, for executing the job).
Regarding to Claim 5, the rejection of Claim 4 is incorporated and further Alt discloses:
wherein the designating of the edge terminal as the first dynamic group includes:
transmitting information about the first job to a first edge terminal included in the first dynamic group (see [0061]; “Once the user has selected one of the options (step 1330), the user's application may be configured and deployed (step 1340) to the selected computing system resource (e.g., a container with the user's application may be created and copied over to the selected computing system resource)”. Also see [0012]; “predicted time to transfer data file to computing system”); and
transmitting, by the first edge terminal, the information about the first job to a second edge terminal included in the first dynamic group (see [0045], [0052]-[0055]; “some applications utilize master and worker nodes, while others have parameter servers and clients. Other roles that may also be assigned include producer and consumer”, “a TensorFlow application to identify objects using a gradient descent and Horovod to identify objects … the user might first deploy a first instance that acts as a consumer (e.g., the Jupyter notebook on a single node), and a second instance for training and prediction (e.g., a multi-node configuration with a Horovod master/controller node and four worker nodes that perform the gradient descent). In this example, the user would have six instances of the same application (i.e., the notebook, the master node that controls Horovod, and four worker nodes performing Horovod)”. For a particular ML application task to be performed, there are multiple devices to be selected for executing the same ML application task, such as at least one master node, i.e., claimed first edge terminal, would control other worker nodes, i.e., claimed second edge terminal, to perform such ML application task, and thus such master node would transmit certain job information to other worker nodes).
Regarding to Claim 6, the rejection of Claim 4 is incorporated and further Alt discloses:
wherein the designating of the edge terminal as the first dynamic group includes:
monitoring a performance result of the first job of a first edge terminal included in the first dynamic group (see [0008] and [0061]; “ranking a set of available computing resources by maintaining a performance database comprising historical performance data for a large number of prior applications … the database is checked to see if the application has already been run on any of the computing systems participating in the distributed computing marketplace” and “The historical data for example may include what percentage of time the application spent bound by different performance limits such as being CPU-bound, network-bound, I/O bound, memory latency bound, memory bandwidth bound … the user's application may be configured and deployed (step 1340) to the selected computing system resource (e.g., a container with the user's application may be created and copied over to the selected computing system resource)”); and
designating a second edge terminal included in the first dynamic group as a second dynamic group, when the first job is achieved as a result of performing the monitoring of the performance result, and the second dynamic group is distinguished from the first dynamic group and is designated as an edge terminal of performing a second task that is performed under the conditions (see [0008] and [0061]; “the database is checked to see if the application has already been run on any of the computing systems participating in the distributed computing marketplace. If it has already been executed, the existing performance data is used to determine which factors matter most to the application's performance … the database is checked to see if the application has already been run on any of the computing systems participating in the distributed computing marketplace” and “A historical application performance database may be searched to find performance data for prior executions of the application (if any exist), or for one or more similar applications”. Under one of the reasonable embodiments, a second device, i.e., claimed second edge terminal, from the group of devices selected to perform prior executions of the application, i.e., claimed first job, can be selected to perform a similar application, i.e., claimed second task. Note: since it is similar application (instead of identical application), then group of devices to be selected for executing such similar application, i.e., claimed second dynamic group, can be different from the group of devices selected for executing the application, i.e., claimed first dynamic group).
Regarding to Claim 7, the rejection of Claim 4 is incorporated and further Alt discloses:
wherein the designating of the edge terminal as the first dynamic group includes:
monitoring a resource usage rate of a first edge terminal included in the first dynamic group; and designating an edge terminal that has secured resources to perform the first job as the first dynamic group, as a result of performing the monitoring of the resource usage rate (see [0008] and [0061]; “ranking a set of available computing resources by maintaining a performance database comprising historical performance data for a large number of prior applications that have been executed on many different computer systems with performance tracking enabled” and “The historical data for example may include what percentage of time the application spent bound by different performance limits such as being CPU-bound, network-bound, I/O bound, memory latency bound, memory bandwidth bound … the user's application may be configured and deployed (step 1340) to the selected computing system resource (e.g., a container with the user's application may be created and copied over to the selected computing system resource)”).
Regarding to Claim 8, the rejection of Claim 1 is incorporated and further Alt discloses:
wherein the determining of the edge terminal includes:
selecting a first edge terminal equipped with hardware resources necessary to perform the first job; and scheduling a job so that the first edge terminal performs the first job under the conditions (see [0035], [0037]; “designate a destination for the results of the application, and set one or more application requirements (e.g., parameters such as how many processors to use, how much memory to use, cost limits, application priority, etc.)”, “determine which of the distributed computing system 100 computing resources are available to complete those jobs, make recommendations on which available resources best meet the user's requirements, allocate resources to each job, and then bind and dispatch the job to those allocated resources”. Also see [0041], [0043] and [0045]; “Based on the information input by the user, a recommended logical node or cluster topology is displayed”, “the user may be prompted to select the option that will be used to run the job” and “the application may be instantiated and deployed (step 372)”).
Regarding to Claim 9, the rejection of Claim 1 is incorporated and further Alt discloses:
wherein the determining of the edge terminal includes:
selecting a first edge terminal on which the artificial intelligence-based model is distributed; and scheduling a job so that the first edge terminal performs the first job under the conditions (see [0043]; “the user may be prompted to select the option that will be used to run the job (step 370). These system resources options may for example include a list of bare metal systems and cloud providers with instance options capable of executing the user's application with the logical topology specified”. Also see [0033]; “Bare metal computing devices 130A and 130B may for example include workstations or servers optimized for machine learning computations”. If bare metal computing device is optimized for machine learning computations, then such type of device is required to include distributed AI or ML based module).
Regarding to Claim 12, Claim 12 is a system claim corresponds to method Claim 1 and is rejected for the same reason set forth in the rejection of Claim 1 above (note: Alao see Fig. 1, [0036]-[0037] for claimed limitations related to “A system for performing a job, the system comprising … wherein the computer program includes instructions for performing”).
Regarding to Claim 14, Claim 14 is a system claim corresponds to method Claim 4 and is rejected for the same reason set forth in the rejection of Claim 4 above.
Regarding to Claim 15, Claim 15 is a system claim corresponds to method Claim 5 and is rejected for the same reason set forth in the rejection of Claim 5 above.
Regarding to Claim 16, Claim 16 is a system claim corresponds to method Claim 6 and is rejected for the same reason set forth in the rejection of Claim 6 above.
Regarding to Claim 17, Claim 17 is a system claim corresponds to method Claim 7 and is rejected for the same reason set forth in the rejection of Claim 7 above.
Regarding to Claim 18, Claim 18 is a system claim corresponds to method Claim 8 and is rejected for the same reason set forth in the rejection of Claim 8 above.
Regarding to Claim 19, Claim 19 is a system claim corresponds to method Claim 9 and is rejected for the same reason set forth in the rejection of Claim 9 above.
Regarding to Claim 20, Claim 12 is a product claim corresponds to method Claim 1 and is rejected for the same reason set forth in the rejection of Claim 1 above (note: Also see [0072] for claimed limitations related to “A computer program stored on a computer-readable recording medium”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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 2 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Alt et al. (US 20220058060 A1, hereafter Alt) and Pham et al. (US 20220188636 A1, hereafter Pham).
Regarding to Claim 2, the rejection of Claim 1 is incorporated, Alt does not disclose: wherein the edge terminal is a movable mobile edge terminal.
However, Pham discloses: an edge terminal of performing the first job, wherein the edge terminal is a movable mobile edge terminal (see [0046]; “the system 100 or another system can provide the trained parameter values of the student neural network to an edge device, e.g., a mobile phone, a smart personal assistant device, or other IoT device, over a wired or wireless network connection, so that the student neural network 110 can be used to perform the machine learning task on the edge device”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the metal computing devices selected for executing machine learning task from Alt by including moveable edge devices that are capable of perform machine learning task from Pham, and thus the combination of Alt and Pham would disclose the missing limitations from Alt, since it would provide more flexible computing resources to perform requested job via supplying moveable computing devices.
Regarding to Claim 13, Claim 13 is a system claim corresponds to method Claim 2 and is rejected for the same reason set forth in the rejection of Claim 2 above.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Alt et al. (US 20220058060 A1, hereafter Alt) and Kim et al. (US 20200125390 A1, hereafter Kim).
Regarding to Claim 10, the rejection of Claim 1 is incorporated, Alt does not disclose: wherein the activating of the artificial intelligence-based model includes distributing the artificial intelligence-based model on the edge terminal, when the artificial intelligence-based model is absent in the edge terminal.
However, Kim discloses: wherein the activating of the artificial intelligence-based model includes distributing the artificial intelligence-based model on the device, when the artificial intelligence-based model is absent in the device (see [0121]-[0122]; “Whether a machine learning model exists in the machine learning model DB is checked (S703), an algorithm is downloaded from the machine learning engine manager in response to non-existence of the machine learning model (S704), and the user behavior is analyzed in response to existence of the machine learning model”. The ML model from [0122] is used to analyze the user behavior data, such analysis is required to activate the ML model on the device. In this way, [0112] does teach determining whether the ML model is already existed on the device before activating or utilizing the ML model to perform its task or job).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the processes of utilizing machine learning application to perform job or task from Alt by including the process of determining whether a machine learning model exists or not before utilizing the machine learning model to perform job from Kim, and thus the combination of Alt and Kim would disclose the missing limitations from Alt, since it would provide a mechanism of ensuring existence of an object prior to utilizing the object (see [0122] of Kim).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Alt et al. (US 20220058060 A1, hereafter Alt) and Kim et al. (US 20200125390 A1, hereafter Kim) and Panergo et al. (US 20210051455 A1, hereafter Panergo).
Regarding to Claim 11, the rejection of Claim 1 is incorporated, Alt does not disclose: wherein the activating of the artificial intelligence-based model includes transmitting connection information about a first edge terminal on which the artificial intelligence-based model is distributed to the edge terminal, when the artificial intelligence-based model is absent in the edge terminal, and
the first edge terminal is located at a distance within a reference value from the edge terminal.
However, Kim discloses: wherein the activating of the artificial intelligence-based model includes transmitting connection information about a first edge terminal on which the artificial intelligence-based model is distributed to the device, when the artificial intelligence-based model is absent in the device (see [0121]-[0122]; “Whether a machine learning model exists in the machine learning model DB is checked (S703), an algorithm is downloaded from the machine learning engine manager in response to non-existence of the machine learning model (S704), and the user behavior is analyzed in response to existence of the machine learning model”. The ML model from [0122] is used to analyze the user behavior data, such analysis is required to activate the ML model on the device. In this way, [0112] does teach determining whether the ML model is already existed on the device before activating or utilizing the ML model to perform its task or job. In addition, in order to download or transmit the ML model from the source having ML model, it is understood that certain connection information like at least the source address information or at least the ML model itself is required to transmitted to the download destination. Note: claimed “connection information” is broad term, the artificial intelligence-based model itself to be distributed can also be considered as claimed “connection information” since such artificial intelligence-based model is the information to be transferred).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the processes of utilizing machine learning application to perform job or task from Alt by including the process of determining whether a machine learning model exists or not before utilizing the machine learning model to perform job from Kim, since it would provide a mechanism of ensuring existence of an object prior to utilizing the object (see [0122] of Kim).
In addition, Panergo discloses: the first edge terminal is located at a distance within a reference value from the device receiving object from the first edge terminal (see [0042]; “the transient storage location 122 is selected from a plurality of transient storage locations 122 by the ground computing device 102, the aircraft interface computing device 116, or a combination thereof. In some examples, the transient storage location 122 through which the data file 104 is to be transferred is selected based on the aircraft being geographically located within a threshold distance of the transient storage location at the time the data file will be transferred to the aircraft computing device, a flight plan of the aircraft”).
It would have been obvious to one with ordinary skill, in the art before the effective filing date of the claim invention, to modify the processes of transferring machine learning model code file between devices from the combination of Alt and Kim by including transferring files among two devices that such two devices are located within a threshold distance from Panergo, and thus the combination of Alt, Kim and Panergo would disclose the missing limitations from Alt, since it would provide a mechanism of ensuring data transmission is performed via “an expected strength/efficiency of the uplink/downlink connection” (see [0042] from Panergo).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Brar et al. (US 20230224223 A1) discloses: selecting specific host machines to execute machine learning task based on constraints specified by customer, wherein the constraints include location affinity (see [0144]-[0145] and [0166]).
Morgan (US 20120131594 A1) discloses: a set of host machines to deploy user’s workload, wherein the set of host machines can be located at different time zones (see abstract, [0006] and [0034]).
Albano et al. (US 20110145621 A1) discloses: dispatching a computing task to a computing resource based on the time zone information of computing resource candidates (see [0044] and [0047]-[0049]).
Meng et al. (US 20250094237 A1) discloses: a workload requested by a first client application executing in the western United States cannot be delegated to an endpoint in Asia that is fully equipped to execute the requested task (see [0010]-[0012]).
Koo et al. (US 20140181916 A1) discloses: when the electronic device is within a threshold distance of the personal cloud apparatus, receiving download information of the application using NFC, and downloading the application based on the download information of the application (see [0011]).
Belk et al. (US 20140364056 A1) discloses: distance between the mobile device and the digital media device satisfies a proximity threshold distance, the proximity threshold distance being less than a maximum distance at which the wireless signal from the digital media device is detectable by the mobile device (see claim 1)
Kobayashi (US 20170068532 A1) discloses: selecting, when a plurality of download sources hold the software of the version, one of the download sources on a basis of load information on a communication situation of each of the download sources or a distance between each of the download sources and the first data center, wherein the instructing includes instructing the first data center to download the software of the version from the download source selected at the selecting (see claim 2).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHI CHEN whose telephone number is (571)272-0805. The examiner can normally be reached on M-F from 9:30AM to 5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Y Blair can be reached on 571-270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Zhi Chen/
Patent Examiner, AU2196
/APRIL Y BLAIR/Supervisory Patent Examiner, Art Unit 2196
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