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 .
Claim Rejections - 35 USC § 102
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) 1,4-9, 12, 20, 32, 35-40, 51, 54 and 63 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Aghababaiie Beni et al. (U.S. Pub. 2024/0095249 A1)
With respect to claims 1, 32 and 63, Aghababaie Beni et al. discloses a computer-implemented method comprising:
transforming a query plan tree into a query strategy tree (i.e., “Interpretable execution plan 130 contains operational nodes as tree nodes arranged as a tree data structure, such as created when a query parser parses database statement 110.”(0028)), the query plan tree constructed from an input data query associated with a computation workload (i.e., “a cost-base plan optimizer uses SQL database catalog information and database statement 110 to construct a computation tree that describes the best-executing (i.e. least computer resources, e.g. fastest) decomposition of database statement 110 into SQL operators that are relational operators, which function as row sources of retrieved or computed data tuples that flow up the tree from leaves to root. In other words, the query tree (or DAG) operates as a dataflow graph”(0038));
compiling the query strategy tree into at least one dataflow graph (i.e., “a cost-base plan optimizer uses SQL database catalog information and database statement 110 to construct a computation tree that describes the best-executing (i.e. least computer resources, e.g. fastest) decomposition of database statement 110 into SQL operators that are relational operators, which function as row sources of retrieved or computed data tuples that flow up the tree from leaves to root. In other words, the query tree (or DAG) operates as a dataflow graph”(0038));
transmitting the at least one dataflow graph for execution via a virtual platform (i.e., “Each SQL operator is an object with a small set of virtual methods that are used to initialize (e.g. interconnect with other operators to form a tree or DAG) itself and to return row source results”(0039));
monitoring the execution of the at least one dataflow graph (fig. 9 shows the virtual machine monitor (VMM) 930); and
outputting, based on a result of the execution monitored, a response to the input data query (i.e., “Each SQL operator is an object with a small set of virtual methods that are used to initialize (e.g. interconnect with other operators to form a tree or DAG) itself and to return row source results”(0039));,
the result received from the virtual platform (i.e., “Each SQL operator is an object with a small set of virtual methods that are used to initialize (e.g. interconnect with other operators to form a tree or DAG) itself and to return row source results.”(0039)) and representing at least one computational result of processing the computation workload by the virtual platform (i.e., “ Each SQL operator is an object with a small set of virtual methods that are used to initialize (e.g. interconnect with other operators to form a tree or DAG) itself and to return row source results.”(0039)).
With respect to claims 4 and 35, Aghababaie Beni et al. discloses wherein transforming the query plan tree into the query strategy tree includes: generating the query strategy tree from the query plan tree, the query strategy tree including at least one action node, an action node of the at least one action node corresponding to a respective portion of the computation workload; and determining at least one resource for executing the action node of the query strategy tree generated (i.e., “Interpretable execution plan 130 contains operational nodes as tree nodes arranged as a tree data structure, such as created when a query parser parses database statement 110. In an embodiment, interpretable execution plan 130 is also an abstract syntax tree (AST) for a database language such as SQL.”(0028) and “a cost-base plan optimizer uses SQL database catalog information and database statement 110 to construct a computation tree that describes the best-executing (i.e. least computer resources, e.g. fastest) decomposition of database statement 110 into SQL operators that are relational operators, which function as row sources of retrieved or computed data tuples that flow up the tree from leaves to root. In other words, the query tree (or DAG) operates as a dataflow graph’(0038)).
With respect to claims 5 and 36, Aghababaie Beni et al. discloses the computer-implemented method of Claim 4, wherein the action node includes at least one stage, wherein a stage of the at least one stage corresponds to a unique portion of the respective portion of the computation workload, and wherein determining the at least one resource includes determining at least one respective resource for executing each stage of the at least one stage (i.e., “For FIG. 4, runtime phase 312 of FIG. 3 may occur as follows when a live (e.g. ad hoc) query is received. Runtime phase 312 occurs in two stages for FIG. 4 that respectively generate instantiation 440 and expression 430. In a first stage that entails template instantiation, the following sequence occurs.”(0140)).
With respect to claims 6 and 37, Aghababaie Beni et al. discloses wherein the query plan tree is annotated with at least one statistic relating to the computation workload and wherein transforming the query plan tree into the query strategy tree is based on a statistic of the at least one statistic (i.e., “ DBMS 100 may directly execute the object code that was generated by step 604, which may entail statis and/or dynamic linking of the object code into the address space of DBMS 100. The result of executing the object code is semantically the same as the result that would have been provided if interpretable execution plan 130 were interpreted instead of compiled.”(0159)).
With respect to claims 7 and 38, Aghababaie Beni et al. discloses wherein the transforming includes: distributing at least a portion of the computation workload equally across at least two action nodes of at least one level of action nodes of the query strategy tree (i.e., “Output data from one or a few nodes may be accepted as input by a node in a next level in the tree of interpretable execution plan 130”(0030)).
With respect to claims 8 and 39, Aghababaie Beni et al. discloses wherein the transforming includes: applying at least one optimization to the query strategy tree (i.e., “a cost-base plan optimizer uses SQL database catalog information and database statement 110 to construct a computation tree that describes the best-executing (i.e. least computer resources, e.g. fastest) decomposition of database statement 110 into SQL operators that are relational operators, which function as row sources of retrieved or computed data tuples that flow up the tree from leaves to root. In other words, the query tree (or DAG) operates as a dataflow graph”(0038)).
With respect to claims 9 and 40, Aghababaie Beni et al. discloses the computer-implemented method of Claim 8, wherein the at least one optimization includes a node-level optimization, an expression-level optimization, or a combination thereof (i.e., “Partial evaluation entails executing some computational expressions that occur in interpretable execution plan 130 during plan compilation and before actually accessing database content such as table rows.”(0073)).
With respect to claims 20 and 51, Aghababaie Beni et al. discloses further comprising: adapting the query strategy tree based on at least one statistic associated with the computation workload (i.e., “compile-time constant 180 may contain (and interpretable execution plan 130 may contain) database schema information and statistics that includes any combination of: [0079] a database dictionary, [0080] an identifier of a database table, [0081] a statistic of a database table”(0078)).
With respect to claims 23 and 54, Aghababaie Beni et al. discloses the computer-implemented method of Claim 20, wherein the adapting includes regenerating a dataflow graph of the at least one dataflow graph by performing at least one of: (i) reordering dataflow nodes of the dataflow graph, (ii) removing an existing dataflow node of the dataflow graph, and (iii) adding a new dataflow node to the dataflow graph (i.e., “The logic that step 204 generates may be so streamlined that the control flow paths that are present in the streamlined logic are only or almost only the few control flow paths that are actually needed by interpretable execution plan 130 for database statement 110. ”(0093)).
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, 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 21 and 52 are rejected under 35 U.S.C 103 as being unpatentable over Aghababaie et al. (U.S. Pub. 2024/0095249 A1) in view of Shwarts et al. (U.S. Pub. 2023/0092152 A1)
With respect to claims 21 and 52, Aghababaie et al. and Lynch disclose all limitations recited in claim 20 except for wherein a statistic of the least one statistic includes a runtime statistical distribution of data values in a data source associated with the computation workload. However, Shwarts et al. discloses wherein a statistic of the least one statistic includes a runtime statistical distribution of data values in a data source associated with the computation workload (i.e., “With reference to FIGS. 2A to 2B, an embodiment of a method 200 may include, at runtime, applying two or more respective controls to statistical data from two or more respective data sources in accordance with respective configuration information for each data source at box 221, and storing the statistical data in a memory in accordance with the applied two or more controls at box 222” (0040) and “Optimizations based on runtime statistical information may be beneficial because the system may be tuned to the actual needs of the workload. Information such as performance counters and telemetry spread around the device may be monitored and provided to various agents to make decisions accordingly.”(0032) and “a counter that collects information about all the workloads in the system may be limited to only include information on a specific application or virtual machine.”(0050)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Shwarts et al.’s feature in order to improve performance of the CPU for the stated purpose has been well known in the art as evidenced by teaching of Shwarts et al’ (0003).
Claims 24-25 and 55-56 are rejected under 35 U.S.C 103 as being unpatentable over Aghababaie et al. (U.S. Pub. 2024/0095249 A1) in view of Guha et al. (U.S. Pub. 2023/0162032A1)
With respect to claim 24, and 55. Aghababaie Beni et al. discloses all limitations recites in claim 1 except for further comprising: generating, based on a dataflow graph of the at least one dataflow graph, a plurality of dataflow subgraphs; and configuring dataflow subgraphs of the plurality of dataflow subgraphs to, when executed via the virtual platform, perform a data movement operation in parallel. But Guha et al. discloses further comprising: generating, based on a dataflow graph of the at least one dataflow graph, a plurality of dataflow subgraphs; and configuring dataflow subgraphs of the plurality of dataflow subgraphs to (i.e., “Algebraic graph compiler 622 may include a model analyzer and compiler (MAC) level that makes high-level mapping decisions for (sub-graphs of the) dataflow graph based on hardware constraints.”(0092)), when executed via the virtual platform, perform a data movement operation in parallel (i.e., “Algebraic graph compiler 622 may also transform the graphs via autodiff and GradNorm, perform stitching between sub-graphs, interface with template generators for performance and latency estimation, convert dataflow graph operations to AIR operation, perform tiling, sharding (database partitioning) and other operations, and model or estimate the parallelism that can be achieved on the dataflow graphs.”(0092)). )). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Guha et al.’s feature in order to have efficient accelerator for system when execute the subgraph for the stated purpose has been well known in the art as evidenced by teaching of Guha et al’ (0010).
With respect to claim 25 and 56, Guha et al. discloses the computer-implemented method of Claim 24, wherein the data movement operation includes at least one of: (i) streaming data from a data source associated with the computation workload and (ii) transferring data to or from at least one VM of the virtual platform (i.e., “The allocation module 825 may function in conjunction with a partitioner that partitions the compute graph into executable sub-graphs and inserts virtual memory units (i.e., buffers) into the compute graph that enables dataflow execution of the sub-graphs on reconfigurable dataflow processing units such as the RDUs 850.’(0113)).
Allowable Subject Matter
Claims 2-3 and 33-34 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the claimed further comprising: generating, based on the input data query associated with the computation workload, a query logic tree including at least one query element node; and constructing, based on the query logic tree generated, the query plan tree in an intermediate representation (IR), wherein the IR is compatible with at least one type of computation workload, wherein the at least one type of computation workload includes a type of the computation workload associated with the input data query, wherein the IR is architecture-independent, and wherein the IR represents at least one query operation of the input data query; wherein the at least one type of computation workload includes a Structured Query Language (SQL) query plan, a data ingestion pipeline, an artificial intelligence (AI) or machine learning (ML) workload, a high-performance computing (HPC) program, another type of computation workload, or a combination thereof.
Claims 10-18 and 41-49 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the claimed wherein the compiling includes: selecting, based on at least one resource associated with an action node of at least one action node of the query strategy tree, a virtual machine (VM) of at least one VM of the virtual platform; translating the action node of the at least one action node of the query strategy tree into a dataflow graph of the at least one dataflow graph; and assigning the dataflow graph for execution by the VM selected; wherein: selecting the VM is further based on at least one of: (i) a workload of the VM, (ii) at least one resource of the VM for processing the computation workload, and (iii) compatibility of the computation workload with the VM; wherein a scheduling mode for the query strategy tree is a store-forward mode, and wherein the method further comprises: identifying the action node of the at least one action node of the query strategy tree by traversing the query strategy tree in a breadth-first mode; wherein the action node of the at least one action node of the query strategy tree is a parent action node associated with at least one child action node of the query strategy tree, and wherein: the translating and the assigning are performed responsive to determining that execution of a respective dataflow graph of the at least one dataflow graph has completed, the respective dataflow graph corresponding to a child action node of the at least one child action node. wherein a scheduling mode for the query strategy tree is a cut-through mode, and wherein: the selecting includes causing the VM to reserve the at least one resource associated with the action node of the at least one action node of the query strategy tree; and the translating and the assigning are performed responsive to traversing the query strategy tree in a post-order depth-first mode; wherein the VM selected includes at least one programmable dataflow unit (PDU) based execution node, and wherein: the selecting is further based on at least one resource of a PDU based execution node of the at least one PDU based execution node; wherein a dataflow node of the dataflow graph corresponds to a query operation, and wherein: the selecting includes mapping the query operation to the PDU based execution node; wherein the VM selected includes at least one non-PDU based execution node, and wherein: the selecting is further based on at least one resource of a non-PDU based execution node of the at least one non-PDU based execution node; wherein the non-PDU based execution node is a central processing unit (CPU) based execution node, a graphics processing unit (GPU) based execution node, a tensor processing unit (TPU) based execution node, or another type of non-PDU based execution node.
Claims 19 and 50 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest wherein the monitoring includes: detecting an execution failure of a dataflow graph of the at least one dataflow graph on a first VM of the virtual platform; and assigning the dataflow graph for execution on a second VM of the virtual platform.
Claims 22 and 53 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest wherein the adapting is responsive to identifying a mismatch between the runtime statistical distribution of the data values and an estimated statistical distribution of the data values.
Claim 26-30 and 57-61 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the claimed wherein the compiling includes: selecting a virtual machine (VM) of at least one VM of the virtual platform, the selecting based on at least one resource associated with a stage of at least one stage of an action node of at least one action node of the query strategy tree; translating the stage into a dataflow graph of the at least one dataflow graph; and assigning the dataflow graph for execution by the VM selected; wherein a scheduling mode for the query strategy tree is a store-forward mode, and wherein the computer-implemented method further comprises: identifying the action node by traversing the query strategy tree in a breadth-first mode; wherein the action node is a parent action node of at least one child action node of the query strategy tree, wherein the stage of the action node is associated with a stage of at least one stage of the at least one child action node, and wherein: the translating and the assigning are performed responsive to determining that execution of a respective dataflow graph of the at least one dataflow graph has completed, the respective dataflow graph corresponding to the stage of the at least one stage of the at least one child action node; wherein the action node is a child action node of a parent action node of the query strategy tree and wherein the stage of the action node is associated with a stage of at least one stage of the parent action node; wherein a scheduling mode for the query strategy tree is a cut-through mode, and wherein: the selecting includes causing the VM to reserve the at least one resource associated with the stage of the at least one stage of the action node of the at least one action node of the query strategy tree; and the translating and the assigning are performed responsive to traversing the query strategy tree in a post-order depth-first mode.
Claims 31 and 62 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, since the prior art of record and considered pertinent to the applicant’s disclosure does not teach or suggest the claimed wherein the transforming includes: distributing at least a portion of the computation workload equally across at least two stages of an action node of at least one action node of the query strategy tree.
Conclusion
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/HUNG T VY/Primary Examiner, Art Unit 2163 June 3, 2026
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