DETAILED ACTION
1. This communication is in response to the Application No. 18/269,598 filed on June 26, 2023 and preliminary amendments also filed June 26, 2023 in which Claims 1-10 are presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statements submitted on 06/26/2023 and 08/11/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
4. Claim 1 is objected to because of the following informalities:
Claim 1 recites “[…] configured for redundant backup of the master mode;” but should instead recite “[…] configured for redundant backup of the master node;”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
5. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 1, 4, 6, 10, and their respective dependents are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
7. Claim 1 recites “[…] taking into account the number of idle cores of the computing nodes, the number of failures of the computing nodes […]” without any previous recitation of “cores” or “failures” in claim 1. There is insufficient antecedent basis for these limitations in the claim. This rejection applies to Claim 1 and its respective dependent claims 2-10.
8. Claim 4 recites “[…] the more the number of idle cores of the computing nodes, the easier the computing nodes to be selected in preference; the less the number of failures of the computing nodes, the easier the computing nodes to be selected in preference; the greater a time difference between a recent failure time of the computing nodes and a previous failure time of the computing nodes, the easier the computing nodes to be selected in preference […]”. However, the terms “more”, “easier”, “less”, and “greater” in Claim 4 are relative terms which render the claim indefinite. The terms “more”, “easier”, “less”, and “greater” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner notes that it is not clear what number of idle cores is considered to be “more” such that it will be “easier” to select the computing nodes, what number of failures is considered to be “less” such that it will be “easier” to select the computing nodes, and what time difference is considered to be “greater” such that it will be “easier” to select the computing nodes. Further, it is unclear what defines it being “easier” to select computing nodes of preference. Applicant’s specification also does not provide such a standard, as these terms are merely recited in the specification (See Par. [0014], Par. [0037], Par. [0039-0040]) without further explanation. The claim does not define any threshold/degree/requisite, thus, rendering the claim indefinite. This rejection applies to Claim 4 and its respective dependent Claim 7.
9. Claim 6 recites “[…] Kc represents an influence parameter of the number of remaining brain-inspired chip resources […]” and “[…] Ks represents an influence parameter of the number of failures of the computing node […]”. The term “influence” in Claim 6 is a relative term which renders the claim indefinite. The term “influence” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Examiner notes that it is not clear what the term “influence” refers to in context of the claim limitations – further, the claim does not define any threshold/degree/requisite as to what is considered to “influence” the number of remaining brain-inspired chip resources and the number of failures of the computing node, as related to such a quantifiable parameter. Applicant’s specification also does not provide such a standard, as these terms are merely recited in the specification (See Par. [0019] and Par. [0042]) without further explanation. Thus, the claim is rendered indefinite.
10. Claim 10 recites “[…] the computing nodes actually stored by the present computing node” without any previous recitation of a “present computing node” in claim 10 nor claim 3 which it is dependent upon. Both claims 3 and 10 recite a plurality of computing nodes, hence it is unclear which is considered to be “the present computing node”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 101
11. 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.
12. Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-10 recite “a neural model storage system”. The means to implement the system may be interpreted as software per se, as the system is not tangibly embodied on any sort of physical medium. This applies to Independent claim 1 and its respective dependent claims 2-10 by virtue of their dependency.
Claim Rejections - 35 USC § 103
13. 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.
14. Claims 1-3 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Bequet et al. (hereinafter Bequet) (US PG-PUB 20190012403), in view of Pakatci et al. (hereinafter Pakatci) (US PG-PUB 20230020268).
Regarding Claim 1, Bequet teaches a neural model storage system (Bequet, Par. [0082], “More specifically, the storage of objects (e.g., data objects, task routines, job flow definitions, instance logs of performances of analyses, and/or DAGs) may be effected using a grid of storage devices that are coupled to and/or incorporated into one or more federated devices.”, thus, a storage system is disclosed) for an operating system of a brain-inspired computer (Bequet, Par. [0086], “Among the objects that may be stored in a federated area may be numerous data objects that may include data sets. […] By way of still another example, a data set may include data descriptive of a neural network, such as weights and biases of the nodes of a neural network that may have been derived through a training process in which the neural network is trained to perform a function.”, thus, a storage system for an operating system of a brain-inspired computer (neural network model) is disclosed. Further recitation of how the neural network equates to a “brain-inspired computer” is disclosed in Par. [0403]), comprising a master node (Bequet, Figure 4, label 402 depicting a “control node” analogous to the master node), a backup master node (Bequet, Figure 4, label 404 and 406 depicting a “control node” which may be used as a backup for the control node label 402 – see Bequet supporting Par. [0171]), and computing nodes (Bequet, Figure 4, labels 410-420 depicting a plurality of “worker nodes” analogous to the computing nodes),
wherein the master node is configured for maintaining resources of an entire brain-inspired computer, which comprise a relationship between a neural model and the computing nodes, and information of the computing nodes (Bequet, Par. [0170], “A control node, such as control node 402, may be designated as the primary control node. A server, computer or other external device may connect to the primary control node. Once the control node receives a project, the primary control node may distribute portions of the project to its worker nodes for execution. For example, when a project is initiated on communications grid 400, primary control node 402 controls the work to be performed for the project in order to complete the project as requested or instructed. The primary control node may distribute work to the worker nodes based on various factors, such as which subsets or portions of projects may be completed most efficiently and in the correct amount of time.”, therefore, the master/control node is configured for maintaining resources of a neural network (See Par. [0086] & [0121] which mentions how the projects/datasets used may comprise a neural network), which comprises a relationship between the neural model and the various computing nodes (how the model may be distributed across nodes) and information of the computing nodes (which projects may be completed most efficiently and in a correct amount of time by a corresponding node)), selecting the computing nodes by constructing weights (Bequet discloses selecting computing nodes taking into account the following limitations, as illustrated by the claim mapping below. However, Bequet does not explicitly disclose selecting the computing nodes by constructing weights – See introduction of Pakatci reference below for teaching of selecting the computing nodes by constructing weights), taking into account the number of idle cores of the computing nodes (Bequet, Par. [0168], “Control nodes may maintain knowledge of the status of the nodes in the grid (i.e., grid status information), accept work requests from clients, subdivide the work across worker nodes, coordinate the worker nodes, among other responsibilities.”, thus, computing nodes are selected by “taking into account” (using broadest reasonable interpretation – nodes are selected by “considering” or “based at least in part on”) the number of idle cores of the computing nodes (see Par. [0220] which explicitly recites where a status of a device may comprise it being active, on standby, etc.)), the number of failures of the computing nodes, and failure time (Bequet, Par. [0183], “The process may also include receiving a failure communication corresponding to a node in the communications grid in operation 506. For example, a node may receive a failure communication including an indication that the primary control node has failed, prompting a backup control node to take over for the primary control node. […] If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.”, therefore, as shown by Figure 5, which illustrates processes for adjusting a communications grid (i.e., selecting computing nodes of the grid for project execution), nodes are selected by “taking into account” (using broadest reasonable interpretation – nodes are selected by “considering” or “based at least in part on”) the number of failures and failure time of the computing nodes), and being subject to a constraint of available storage space of the computing nodes (Bequet, Par. [0025], “The processor is also caused to, in response to current availability of sufficient remaining processing resources to enable a first neuromorphic performance of the analytical function with at least a subset of the sets of input values of the first data set through use of a neural network defined by at least a set of hyperparameters, and at least partly in parallel with the first non-neuromorphic performance: assign, as part of the first assignment, at least a portion of the remaining processing resources to the first neuromorphic performance;”, thus, nodes are selected based on constraint of available storage space of the computing nodes);
the backup master node is configured for redundant backup of the master mode (Bequet, Par. [0171], “Any remaining control nodes, such as control nodes 404 and 406, may be assigned as backup control nodes for the project. In an embodiment, backup control nodes may not control any portion of the project. Instead, backup control nodes may serve as a backup for the primary control node and take over as primary control node if the primary control node were to fail.”, therefore, the backup master node (backup control node) is configured for redundant backup of the master/control node);
the computing nodes comprise a set of brain-inspired chips configured for deploying the neural model, and the brain-inspired chips comprise a set of cores as a basic unit of computing resource management (Bequet, Par. [0026], “ The assignment of at least a portion of remaining processing resources to the first neuromorphic performance may include an assignment of at least a subset of one or more remaining processor cores, or an assignment of at least a portion of each of one or more neuromorphic devices; each of the remaining processor cores may be programmable to instantiate at least a portion of the neural network; each of the neuromorphic devices may include at least one of a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC); and each of the neuromorphic devices may include sets of circuits that each implement an artificial neuron able to be included in the neural network.”, thus, the computing nodes may comprise a set of brain-inspired chips (i.e., integrated circuits, FPGA, etc.) for deploying the neural model and the brain-inspired chips may comprise a set of cores (processor cores) as a basic unit of computing resource management for each neuron), which is configured for keeping the neural model stored in a form of a model file and performing computing tasks (Bequet, Par. [0090], “In some embodiments, job flow definitions may be stored within federated area(s) as a file or other type of data structure in which a job flow definition is represented as a DAG. Alternatively or additionally, a file or other type of data structure may be used that organizes aspects of a job flow definition in a manner that enables a DAG to be directly derived therefrom. Such a file or data structure may directly indicate an order of performance of tasks, or may specify dependencies between inputs and outputs of each task to enable an order of performance to be derived”, therefore, the computing nodes (contained within federated area) may be configured for keeping the neural model (part of the job flow definition) in form of a model file and performing corresponding computing tasks); and
the master node is further configured for maintaining the number of remaining cores of the computing nodes (Bequet, Par. [0168], “Control nodes may maintain knowledge of the status of the nodes in the grid (i.e., grid status information), accept work requests from clients, subdivide the work across worker nodes, coordinate the worker nodes, among other responsibilities. Worker nodes may accept work requests from a control node and provide the control node with results of the work performed by the worker node. A grid may be started from a single node (e.g., a machine, computer, server, etc.). This first node may be assigned or may start as the primary control node that will control any additional nodes that enter the grid.”, thus, the master/control node is further configured for maintaining the number of remaining cores of computing nodes, as the control node is responsible for subdividing work across worker nodes – this is similarly supported by Par. [0025] & Par. [0029] that recites that a state of “remaining processing resources” is analyzed in order to evaluate processing resource availability).
Bequet discloses selecting the computing nodes, taking into account the number of idle cores of the computing nodes, the number of failures of the computing nodes, the failure time, and being subject to a constraint of available storage space of the computing nodes, as outlined by the rejection above. However, Bequet does not explicitly disclose selecting the computing nodes by constructing weights, taking into account the number of idle cores of the computing nodes, the number of failures of the computing nodes, and failure time, and being subject to a constraint of available storage space of the computing nodes.
However, Pakatci teaches selecting the computing nodes by constructing weights, taking into account the number of idle cores of the computing nodes, the number of failures of the computing nodes, and failure time, and being subject to a constraint of available storage space of the computing nodes (Pakatci, Par. [0259], “In the example method depicted in FIG. 4 , a load model (412) that predicts performance load on the storage system (408) based on characteristics of workloads (420, 422, 424) executing on the storage system (408) may be generated (410). The term ‘performance load’ used herein may refer to a measure of load on a storage system that is generated in dependence upon multiple system metrics. For example, the performance load on the storage system (408) may be generated in dependence upon the amount of read bandwidth being serviced by the storage system, the amount of write bandwidth being serviced by the storage system, the amount of IOPS being serviced by the storage system, the amount of computing load being placed on the storage system, the amount of data transfer load being placed on the storage system, and many other factors. In such an example, the performance load on the storage system (408) may be calculated according to some formula that takes as inputs the weighted or unweighted combination of such factors described in the preceding sentence.”, therefore, the performance load of the storage system, and correspondingly selecting certain computing nodes to perform a task (See Par. [0315] for support on selecting a preferred configuration based on the weighted performance factors), is based on the construction of weights with respect to a combination of factors such as the number of idle cores (see Par. [0344] for explicit recitation of idle components), failure information including failures over a period of time (see Par. [0107] for explicit recitation of failures),and being subject to a constraint of available storage space, as recited above by the citation of Par. [0259]).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the neural model storage system, as disclosed by Bequet to include selecting the computing nodes by constructing weights, taking into account the number of idle cores of the computing nodes, the number of failures of the computing nodes, and failure time, and being subject to a constraint of available storage space of the computing nodes, as disclosed by Pakatci. One of ordinary skill in the art would have been motivated to make this modification to construct weights, comprising various performance metrics, that improves accuracy by combining multiple metrics into a single, more reliable, load estimate which may enable improved and more efficient resource allocation across the computing nodes (Pakatci, Par. [0315], “Selecting (1304) the preferred configuration in dependence upon a plurality of factors may be carried out, for example, by applying a formula that takes utilizes each of the factors as input to identify the configuration change that has the best score when applying the formula. In some examples, each factor may be applied equally but in other embodiments each factor may be given weighted consideration, such that some factors impact the selection (1106) in a more substantial way than other factors. As such, the example method depicted in FIG. 13 also includes receiving (1102), from a user, weightings associated with each of the factors.”)
Regarding Claim 2, Bequet in view of Pakatci teaches the neural model storage system for the operating system of the brain-inspired computer of claim 1,
wherein the brain-inspired chips comprise a two-dimensional grid structure, and each grid represents one of the cores (Bequet, Par. [0176], “The grid may add new machines at any time (e.g., initiated from any control node). Upon adding a new node to the grid, the control node may first add the new node to its table of grid nodes. The control node may also then notify every other control node about the new node. The nodes receiving the notification may acknowledge that they have updated their configuration information.”, thus, the brain-inspired chips (of the worker nodes) may comprise a two-dimensional (tabular) grid structure, where each grid represents one of the cores/nodes. This is also depicted by Figure 4);
based on a two-dimensional distribution of brain-inspired chip resources, the operating system of the brain-inspired computer is configured to take the cores as the basic unit of the resource management (Bequet, Par. [0186], “Similar to in FIG. 4, communications grid computing system (or just “communications grid”) 600 includes data processing nodes (control node 602 and worker node 610). Nodes 602 and 610 include multi-core data processors. Each node 602 and 610 includes a grid-enabled software component (GESC) 620 that executes on the data processor associated with that node and interfaces with buffer memory 622 also associated with that node.”, thus, the operating system of the neuromorphic computer is configured to take the cores as the basic unit of resource management, to manage corresponding worker nodes based on the grid of resources) and abstract a unified address space from brain-inspired computing hardware resources (Pakatci, Par. [0231], “ A containerized application can be represented as a collection of such containers that together represent all the elements of the application combined with the various run-time environments needed for all those elements to run. As a result, the containerized application may be abstracted away from host operating systems as a combined collection of lightweight and portable packages and configurations, where the containerized application may be uniformly deployed and consistently executed in different computing environments that use different container-compatible operating systems or different infrastructures”, therefore, a unified address space (See Par. [0101] which outlines how a segment is a logical container of data which is an address space) may be abstracted from the computing hardware resources)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the neural model storage system of claim 1, as disclosed by Bequet in view of Pakatci to include abstract[ing] a unified address space from brain-inspired computing hardware resources, as disclosed by Pakatci. One of ordinary skill in the art would have been motivated to make this modification to enable uniform deployment of hardware resources which may be consistently executed across different computing environments with different infrastructures, allowing applications to access the same data regardless of where it resides on the system (Pakatci, Par. [0231], “A containerized application can be represented as a collection of such containers that together represent all the elements of the application combined with the various run-time environments needed for all those elements to run. As a result, the containerized application may be abstracted away from host operating systems as a combined collection of lightweight and portable packages and configurations, where the containerized application may be uniformly deployed and consistently executed in different computing environments that use different container-compatible operating systems or different infrastructures”).
Regarding Claim 3, Bequet in view of Pakatci teaches a storage method based on the neural model storage system for the operating system of the brain-inspired computer of claim 1 (Bequet, Claim 21, “A computer-implemented method comprising: […]”, thus, a method is disclosed (see the remainder of Bequet claim 21), which is based on the neural model storage system for the operating system of the brain-inspired computer described in claim 1), wherein a storage of the neural model comprises:
at step 1, the master node selecting the computing nodes to store the neural model, by constructing weights (Pakatci, Par. [0259], “In the example method depicted in FIG. 4 , a load model (412) that predicts performance load on the storage system (408) based on characteristics of workloads (420, 422, 424) executing on the storage system (408) may be generated (410). The term ‘performance load’ used herein may refer to a measure of load on a storage system that is generated in dependence upon multiple system metrics. For example, the performance load on the storage system (408) may be generated in dependence upon the amount of read bandwidth being serviced by the storage system, the amount of write bandwidth being serviced by the storage system, the amount of IOPS being serviced by the storage system, the amount of computing load being placed on the storage system, the amount of data transfer load being placed on the storage system, and many other factors. In such an example, the performance load on the storage system (408) may be calculated according to some formula that takes as inputs the weighted or unweighted combination of such factors described in the preceding sentence.”, therefore, the performance load of the storage system, and correspondingly selecting certain computing nodes to perform a task (See Par. [0315] for support on selecting a preferred configuration based on the weighted performance factors), is based on the construction of weights with respect to a combination of factors such as the number of idle cores (see Par. [0344] for explicit recitation of idle components), failure information including failures over a period of time (see Par. [0107] for explicit recitation of failures),and being subject to a constraint of available storage space, as recited above by the citation of Par. [0259]), taking into account the number of idle cores of the computing nodes (Bequet, Par. [0168], “Control nodes may maintain knowledge of the status of the nodes in the grid (i.e., grid status information), accept work requests from clients, subdivide the work across worker nodes, coordinate the worker nodes, among other responsibilities.”, thus, computing nodes are selected by “taking into account” (using broadest reasonable interpretation – nodes are selected by “considering” or “based at least in part on”) the number of idle cores of the computing nodes (see Par. [0220] which explicitly recites where a status of a device may comprise it being active, on standby, etc.)), the number of failures of the computing nodes, and failure time (Bequet, Par. [0183], “The process may also include receiving a failure communication corresponding to a node in the communications grid in operation 506. For example, a node may receive a failure communication including an indication that the primary control node has failed, prompting a backup control node to take over for the primary control node. […] If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.”, therefore, as shown by Figure 5, which illustrates processes for adjusting a communications grid (i.e., selecting computing nodes of the grid for project execution), nodes are selected by “taking into account” (using broadest reasonable interpretation – nodes are selected by “considering” or “based at least in part on”) the number of failures and failure time of the computing nodes), and being subject to the constraint of available storage space of the computing nodes(Bequet, Par. [0025], “The processor is also caused to, in response to current availability of sufficient remaining processing resources to enable a first neuromorphic performance of the analytical function with at least a subset of the sets of input values of the first data set through use of a neural network defined by at least a set of hyperparameters, and at least partly in parallel with the first non-neuromorphic performance: assign, as part of the first assignment, at least a portion of the remaining processing resources to the first neuromorphic performance;”, thus, nodes are selected based on constraint of available storage space of the computing nodes);
at step 2, the master node sending the neural model to the selected computing nodes, maintaining the relationship between the neural model and the computing nodes (Bequet, Par. [0170], “Once the control node receives a project, the primary control node may distribute portions of the project to its worker nodes for execution. For example, when a project is initiated on communications grid 400, primary control node 402 controls the work to be performed for the project in order to complete the project as requested or instructed. The primary control node may distribute work to the worker nodes based on various factors, such as which subsets or portions of projects may be completed most efficiently and in the correct amount of time”, therefore, the master/control node sends the neural model (See Par. [0086] & [0121] which mentions how the projects/datasets used may comprise a neural network) to the selected computing nodes, maintaining the relationship between the neural model and computing nodes);
at step 3, the master node making a backup of the relationship between the neural model and the computing nodes to the backup master node (Bequet, Par. [0177], “For example, primary control node 402 may transmit snapshots (e.g., status information) of the communications grid so that backup control node 404 always has a recent snapshot of the communications grid. The snapshot or grid status may include, for example, the structure of the grid (including, for example, the worker nodes in the grid, unique identifiers of the nodes, or their relationships with the primary control node) and the status of a project (including, for example, the status of each worker node's portion of the project). The snapshot may also include analysis or results received from worker nodes in the communications grid. The backup control nodes may receive and store the backup data received from the primary control node.”, thus, the master/control node makes a backup of the relationship between the neural model (project) and the computing/worker nodes to the backup master/control node); and
at step 4, the master node and the selected computing nodes completing deploying the neural model (Bequet, Par. [0170], “The primary control node also coordinates and processes the results of the work performed by each worker node after each worker node executes and completes its job. For example, the primary control node may receive a result from one or more worker nodes, and the control node may organize (e.g., collect and assemble) the results received and compile them to produce a complete result for the project received from the end user.”, thus, the master/control node and the selecting computing nodes complete the deployment of the neural model project).
Regarding Claim 8, Bequet in view of Pakatci teaches the storage method based on the neural model storage system for the operating system of claim 3, further comprising recovering from failures of non-master nodes, wherein the neural model storage system comprises a set of hot backup computing nodes, and the recovering from failures of non-master nodes (Bequet, Par. [0181], “A worker node within the communications grid may also fail. If a worker node fails, work being performed by the failed worker node may be redistributed amongst the operational worker nodes.”, thus, the storage system may also recover from failures of worker/computing nodes by utilizing backup computing nodes to replace the original computing node) further comprises:
when a computing node fails, activating a new computing node, replacing a coordinate of abstracted resource where an original failed computing node is located, and taking over an operation of the failed computing node (Bequet, Par. [0176], “The grid may add new machines at any time (e.g., initiated from any control node). Upon adding a new node to the grid, the control node may first add the new node to its table of grid nodes. The control node may also then notify every other control node about the new node. The nodes receiving the notification may acknowledge that they have updated their configuration information.” & Par. [0183], “The process may also include reassigning a node or a portion of the project being executed by the failed node, as described in operation 508. For example, a control node may designate the backup control node as a new primary control node based on the failure communication upon receiving the failure communication. If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.”, thus, when a computing node fails, a new computing/worker node may be activated and added to the table of grid nodes, in which the new node may replace the old/failed node and take over its operations. For explicit recitation/teaching of abstracted resources, see the rejection of claim 2 above), wherein the new computing node is in communication with the master node via a communication protocol (Bequet, Par. [0189], “The GESC 620 at the nodes 602 and 620 may be connected via a network, such as network 108 shown in FIG. 1. Therefore, nodes 602 and 620 can communicate with each other via the network using a predetermined communication protocol such as, for example, the Message Passing Interface (MPI).”, therefore, the new computing node may be in communication with the master node via a communication protocol – this is supported by Figure 6); searching a relationship between the neural model and the computing nodes, finding the model file required by the failed computing node, obtaining the model file from the computing nodes that store the model file, storing contents of the neural model in a memory of the new computing node, and sending the neural model to the brain-inspired chips of the new computing node for configuring (Bequet, Par. [0183], “In an alternative embodiment, a node may receive a failure that a worker node has failed, prompting a control node to reassign the work being performed by the worker node. The process may also include reassigning a node or a portion of the project being executed by the failed node, as described in operation 508. For example, a control node may designate the backup control node as a new primary control node based on the failure communication upon receiving the failure communication. If the failed node is a worker node, a control node may identify a project status of the failed worker node using the snapshot of the communications grid, where the project status of the failed worker node includes a status of a portion of the project being executed by the failed worker node at the failure time.”, therefore, the relationship between the neural model and computing node (project & grid status) may be searched and found within the snapshot. Further, the model file (included in the snapshot/project status) may be obtained and stored in the new computing node (reassigned computing node) for further configuration and execution. Grid status updates according to reassignment is also disclosed in subsequent Par. [0184] & depicted by Figure 5).
Regarding Claim 9, Bequet in view of Pakatci teaches the storage method based on the neural model storage system for the operating system of claim 3, further comprises recovering from failures of the master node (Bequet, Par. [0178], “As noted, the backup data may allow the backup control node to take over as primary control node if the primary control node fails without requiring the grid to start the project over from scratch.”, therefore, the storage system may recover from a failure of the master/control node, using backups):
storing the relationship between the neural model and the computing nodes to both the master node and the backup master node, which serve as a backup for each other (Bequet, Par. [0177], “For example, primary control node 402 may transmit snapshots (e.g., status information) of the communications grid so that backup control node 404 always has a recent snapshot of the communications grid. The snapshot or grid status may include, for example, the structure of the grid (including, for example, the worker nodes in the grid, unique identifiers of the nodes, or their relationships with the primary control node) and the status of a project (including, for example, the status of each worker node's portion of the project). The snapshot may also include analysis or results received from worker nodes in the communications grid.”, thus, the relationship between the neural model project and computing/worker nodes may be stored to a storage device in the form of snapshots by both the control node and backup control node, which serve as backup for each other. See Figure 4 which depicts the primary control node label 402 and its backups label 404 and 406); when the master node fails, the backup master node becoming a new master node, and taking over an operation of the failed master node (Bequet, Par. [0178], “As noted, the backup data may allow the backup control node to take over as primary control node if the primary control node fails without requiring the grid to start the project over from scratch.”, thus, when the master node fails, the backup master node may become the new master node and take over operations of the failed master node); the operating system of the brain-inspired computer electing a new backup master node, and the new master node sending the relationship between the neural model and the computing nodes to the new backup master node (Bequet, Par. [0175], “When a node is connected to the grid, the node may share its unique identifier with the other nodes in the grid. Since each node may share its unique identifier, each node may know the unique identifier of every other node on the grid. Unique identifiers may also designate a hierarchy of each of the nodes (e.g., backup control nodes) within the grid. For example, the unique identifiers of each of the backup control nodes may be stored in a list of backup control nodes to indicate an order in which the backup control nodes will take over for a failed primary control node to become a new primary control node.”, thus, when a new backup master/control node is designated as the primary master/control node, the new node may send snapshots (including the relationship between the neural model and computing nodes – see Par. [0177] for support) to the next backup master/control nodes in the hierarchy).
Regarding Claim 10, Bequet in view of Pakatci teaches the storage method based on the neural model storage system for the operating system of claim 3, further comprises recovering from a whole machine restart or failure (Bequet, Par. [0178], “As noted, the backup data may allow the backup control node to take over as primary control node if the primary control node fails without requiring the grid to start the project over from scratch.”, therefore, the storage system may recover from a whole machine restart or failure, using backups):
storing the relationship between the neural model and the computing nodes actually stored by the present computing node to a storage device (Bequet, Par. [0177], “For example, primary control node 402 may transmit snapshots (e.g., status information) of the communications grid so that backup control node 404 always has a recent snapshot of the communications grid. The snapshot or grid status may include, for example, the structure of the grid (including, for example, the worker nodes in the grid, unique identifiers of the nodes, or their relationships with the primary control node) and the status of a project (including, for example, the status of each worker node's portion of the project). The snapshot may also include analysis or results received from worker nodes in the communications grid.”, thus, the relationship between the neural model project and computing/worker nodes may be stored to a storage device in the form of snapshots), when the brain-inspired computer recovers from a whole machine restart or failure, the computing nodes sending the relationship between the neural model and the computing nodes thereof to the master node, and the master node aggregating relationships to formulate a global relationship between the neural model and the computing nodes (Bequet, Par. [0178], “As noted, the backup data may allow the backup control node to take over as primary control node if the primary control node fails without requiring the grid to start the project over from scratch. If the primary control node fails, the backup control node that will take over as primary control node may retrieve the most recent version of the snapshot received from the primary control node and use the snapshot to continue the project from the stage of the project indicated by the backup data. This may prevent failure of the project as a whole.”, therefore, when recovering from a restart/failure, the computing nodes may send the relationship between the neural model and computing nodes (backup data/status of project) to the backup master node (which is now designated as the master node after failure) to aggregate relationships and formulate a global relationship, in order to continue execution of the project. This is also depicted by Figure 5).
Allowable Subject Matter
15. No prior art rejection is made for Claims 4-7. However, these claims are still rejected under 35 U.S.C. 112(b), 35 U.S.C. 101 – software per se, and are objected to as being dependent upon a rejected base claim.
16. Examiner has disclosed Bequet et al. (US PG-PUB 20190012403) and Pakatci et al. (US PG-PUB 20230020268), which are the closest prior art as compared to instant application claims 4-7. Bequet discloses a distributed/federated system, comprising control node(s) and worker nodes, which processes neuromorphic computations across the interconnected processing nodes in order to mimic biological neural/brain activity. Pakatci discloses evaluating recommended changes to a storage system, which may support artificial intelligence applications, including evaluating and recommending configuration changes based on predicted characteristics of workloads (i.e., load balancing). Although Bequet in view of Pakatci are utilized to reject Claims 1-3 and 8-10, Bequet and Pakatci do not explicitly disclose the specific limitations of Claim 4 including “[…] at step 1, the more the number of idle cores of the computing nodes, the easier the computing nodes to be selected in preference; the less the number of failures of the computing nodes, the easier the computing nodes to be selected in preference; the greater a time difference between a recent failure time of the computing nodes and a previous failure time of the computing nodes, the easier the computing nodes to be selected in preference; and the master node maintaining remaining storage space of the computing nodes, wherein computing nodes with remaining storage space less than required storage space of the model file are not selected for storing the model file; at step 2, the master node sending the model file to the computing nodes which are configured to store the model file via a communication protocol, the computing nodes receiving the model file and feeding back to the master node, the master node recording a correspondence between the model file and the computing nodes which are configured to store the model file via feedback, formulating and maintaining a neural model index table; at step 3, the master node synchronizing the neural model index table to the backup master node; and at step 4, the master node storing the model file to the computing nodes for model deployment, reading the model file from the computing nodes that store the model file, obtaining specific content of the neural model, and sending the specific content to the brain-inspired chips of the computing nodes to configure the cores of the brain-inspired chips.” and Claim 5 including “[…] at step 1.1, searching a first computing node; at step 1.2, obtaining remaining storage space of the first computing node, when the remaining storage space of the first computing node meets a storage requirement of the model file, obtaining the number of idle cores of the first computing node, the number of failures of the first computing node, and failure time of the first computing node, calculating a weight according to the number of idle cores of the first computing node, the number of failures of the first computing node, and failure time of the first computing node; when the remaining storage space of the first computing node does not meet the storage requirement of the model file, performing step 1.3; at step 1.3, determining whether a next computing node exists, if yes, searching the next computing node and performing the step 1.2 with the next computing node; if no, sending the model file to a computing node with the greatest weight.” in combination with the remaining limitations of the Independent claims. This applies equally to Claims 6-7, by virtue of their dependency on Claims 4-5.
Conclusion
17. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Devika S Maharaj whose telephone number is (571)272-0829. The examiner can normally be reached Monday - Thursday 8:30am - 5:30pm.
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/DEVIKA S MAHARAJ/Examiner, Art Unit 2123