DETAILED ACTION
Authorization for Internet Communications
The examiner encourages Applicant to submit an authorization to communicate with the examiner via the Internet by making the following statement (from MPEP 502.03):
“Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.”
Please note that the above statement can only be submitted via Central Fax, Regular postal mail, or EFS Web (PTO/SB/439).
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 .
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.
Examiner Notes
Examiner cites particular columns 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 entirety 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.
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.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Regarding claim 1 this part of the eligibility analysis evaluates whether the claim falls within any statutory category. MPEP §2106.03. The claim recites a method; thus, the claim is directed to a method which is one of the statutory categories of invention.
Step 2A Prong 1: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04(II) and the October 2019 Update, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The limitations “determining a number of worker nodes,” “breaking the data into plurality of chunks,” “distributing the data chucks,” “processing the data chucks,” “gathering the processed data,” as drafted, recite functions that, under its broadest reasonable interpretation, covers functions that could reasonably be performed in the mind, including with the aid of pen and paper, but for the recitation of generic computer components. That is, the limitations as drafted, are functions that, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the functions through observation, evaluation, judgment and/or opinion, or even with the aid of pen and paper. Thus, these limitations recite and fall within the “Mental Processes” grouping of abstract ideas. See MPEP §2106.04(a)(2). Accordingly, claim 1 recites a judicial exception (i.e. an abstract idea).
Step 2A, Prong 2, This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. 2019 PEG Section III(A)(2), 84 Fed. Reg. at 54-55.
In this case, this judicial exception is not integrated into a practical application. The claim recites the following additional elements “a computer program,” “virtual machine,” “a plurality of nodes,” “worker nodes,” “receiving node,” and “gathering node” and is recited at a high level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(f).
The claims include additional elements “receiving a subscription request,” “receiving information about data,” and “distributing the processed data.” The additional elements are not a practical application. It only amounts to insignificant extra-solution activity of data input/output. See MPEP 2106.05(g).
Step 2B, This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. MPEP 2106.05.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a computer program,” “virtual machine,” “a plurality of nodes,” “worker nodes,” “receiving node,” and “gathering node” are merely a generic computer or generic computer components to apply the judicial exception which cannot provide an inventive concept.
The claims include additional elements “receiving a subscription request,” “receiving information about data,” and “distributing the processed data.” It only amounts to insignificant extra-solution activity of data input/output. See MPEP 2106.05(g). This additional element does not recite significantly more than a judicial exception, because it is recognized as well-understood, routine, and conventional activity of storing and retrieving information in memory. See MPEP 2106.05(d)(II)(iv).
Claim 2, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein the subscription request comprises an identification of a type of the processed data, an identification of a data format for receiving the processed data, and/or an identification of a data channel to receive the processed data” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 3, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein the type of data comprises transaction-related data or account-related data” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 4, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein the data format comprises a flat file or a message” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 5, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein the data channel comprises a representational state transfer ("REST")/hyper-text transfer protocol ("HTTP") channel” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 6, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein the receiving node and the gathering node are worker nodes” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 7, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein the information about the data comprises a size of the data and/or a type of the data” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 8, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein at least one of the worker nodes processes more than one data chunk” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 9, is a dependent claim rejected for the same reasons as claim 1. Furthermore, the claims do not add additional elements and does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The additional element of “wherein the receiving node adds an additional worker node after distributing the data chunks, and distributes at least one of the data chunks to the additional worker node” does not render the judicial exception as a practical limitation or make a combination that is significantly more than the judicial exception because these additional elements are merely instructions to implement an abstract idea on a computer. MPEP 2106.04(d).
Claim 10, is rejected for the same reasons as claim 1. In particular, the claim recites two additional elements –at least one data source, and a subscriber--. The data source and subscriber are
recited at a high-level of generality (i.e., as a generic component) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claims 11-18, are dependent claims rejected for the same reasons as claim 2-9 above.
Claims 10-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-
statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent
eligible subject matter because they are not processes, machines, manufactures and compositions of
matter, but rather software per se.
Claims 10-18 disclose “a system comprising a distributing data processing system comprising a plurality of node; at least one data source; and a subscriber.” However, the claimed subject matter appears to be software elements. ¶ [0028] and ¶ [0030] describes worker nodes as java instances, and does not further describe data source and subscriber, but according to the specification describes these can be implemented as software see ¶[0052]. Therefore, the claims are not eligible patent subject matter due to the broadest reasonable interpretation being software per se.
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.
Claim(s) 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Johannsen et al. (U.S. PG PUB 2019/0384659) in view of Pal et al. (U.S. PG PUB 2020/0364223).
Regarding claim 1, Johannsen teaches a method comprising:
receiving, at a computer program in a distributed data processing system, a subscription request from a subscriber to receive processed data from the distributed data processing system comprising a plurality of nodes (see ¶[0047] “According to some example embodiments, the first master node 170A may become a subscriber to the trace stream published by the first worker node 180A, the second worker node 180B, and the third worker node 180C in response to the client 140 requesting to receive trace messages from the first worker node 180A, the second worker node 180B, and the third worker node 180C”),
receiving, by a receiving node of the plurality of nodes, information about data to be processed from one or more data source (see ¶[0051] “For instance, the client 140 may query the first master node 170A in order to retrieve the trace messages stored at the first master node 170A, for example, in the trace log. Alternatively and/or additionally, the first master node 170A may send, to the client 140, the trace messages output by the first worker node 180A, the second worker node 180B, and/or the third worker node 180C. These trace messages may be displayed at the client 140, for example, by the user interface”), wherein the receiving node is the first of the plurality of nodes to respond to the subscription request (see ¶[0004] “The first master node may subscribe to the trace stream published by the first worker node but not a second trace stream published by the second worker node. The subscription may be based at least on the client requesting to receive trace messages output by first worker node but not trace messages output by the second worker node.”);
determining, by the receiving node, a number of worker nodes needed to process the data based on the information about the data (see ¶[0044] “For example, the trace messages output by one or more of the m quantity of worker nodes during the execution of the data processing pipeline corresponding to the graph 350 may be held in a trace log at one of the n quantity of master nodes.”);
distributing, by the receiving node, the data chunks to the worker nodes (see ¶ [0004] “The first worker node may execute a first portion of the data processing pipeline and a second worker node may execute a second portion of the data processing pipeline. The first master node may coordinate the execution of the data processing pipeline by the first worker node and the second worker node.”);
processing, by the worker nodes, the data chunks (see ¶ [0022] “The progress and/or performance of executing the data processing pipeline may be monitored via the trace messages, which may provide information regarding events that occur during the execution of various data processing operations in the data processing pipeline”);
gathering, by a gathering node of the plurality of nodes, the processed data from the worker nodes (see ¶ [0056] “At 402, the first master node 170A may receive, from the client 140, a request to receive one or more trace messages output by a worker node executing at least a portion of a data processing pipeline that includes a sequence data processing operations performed on data stored in a database.”); and
distributing, by the gathering node, the data stream(see ¶ [0022] “In some example embodiments, during the execution of a data processing pipeline by a distributed computing system, one or more worker nodes may each publish a trace stream that includes one or more trace messages. The progress and/or performance of executing the data processing pipeline may be monitored via the trace messages, which may provide information regarding events that occur during the execution of various data processing operations in the data processing pipeline”, see ¶ [0047] “According to some example embodiments, the first master node 170A may become a subscriber to the trace stream published by the first worker node 180A, the second worker node 180B, and the third worker node 180C in response to the client 140 requesting to receive trace messages from the first worker node 180A, the second worker node 180B, and the third worker node 180C.”).
Johannsen does not expressly disclose, however, Pal teaches the processed data comprising the gathered processed data chunks (see ¶ [0118] “Other examples of data generated in a big data ecosystem include application program data, system logs, network packet data, error logs, stack traces, and performance data.” and see ¶ [0815] “At the processing phase 3606, the worker nodes 3306 may parse the portions of buckets located during the intake phase 3604 in order to identify information relative to a search. For example, the worker node 3306 may parse the portions of buckets (e.g., individual files or records) to identify specific lines or segments that contain values specified within the search, such as one or more error types desired to be located during the search. Where the search is conducted according to map-reduce techniques, the processing phase 3606 can correspond to implementing a map function. Where the search requires that results be time-ordered, the processing phase 3606 may further include sorting results at each partition into a time-ordering.” See ¶ [0919] “As further described herein, the partial results from a particular data intake and query system can be distributed to various worker nodes 3306 in a variety of ways. In certain embodiments, multiple worker nodes 3306 can receive partial results from a particular external data system 12 and/or one worker node 3306 can concurrently receive partial results from multiple external data systems 12. As mentioned, data chunks corresponding to the partial results from each external data system 12 can include a local search identifier that uniquely identifies the search to which the data chunk belongs within the external data system 12. In certain embodiments, the external data system 12 and/or the worker nodes 3306 may translate or transform query results from a format or language supported by the external data system 12 to a format or language supported by the data intake and query system 16A. The external data system 12 and/or the worker nodes 3306 may determine the supported format to convert the query results based on an entry in an external query configuration file of the external data system 12 and/or of the data intake and query system 16A.”),
an instance of a virtual machine at each of the worker nodes (see ¶[0142] “Host devices 106 may broadly include any number of computers, virtual machine instances, and/or data centers that are configured to host or execute one or more instances of host applications 114”);
and the information comprises a size of the data to be processed (see ¶[0190] “In some embodiments, a forwarder receives the raw data and may segment the data stream into “blocks”, possibly of a uniform data size, to facilitate subsequent processing steps.”);
breaking, by the receiving node, the data into a plurality of data chunks based on the number of worker nodes (see ¶ [0468] “In step 2318, the worker nodes sort the newly timestamped partial search results and create chunks (e.g., micro-batches) upon completion of collecting all of the partial search results from the data sources. In some embodiments, the chunks may be created to contain a default minimum or maximum number of partial search results (e.g., a default chunk size). As such, the worker nodes can create time-ordered partial search results obtained from data sources that did not provide time-ordered partial search results.” ¶ [1143] “In certain cases, the number of record groups of the plurality of record groups can be based on a number of compute resources allocated by the worker node 3306 to process incoming chunks of data. For example, if three processors are allocated to process incoming chunks of data, the record can be assigned to one of three record groups. However, it will be understood that fewer or more record groups can be used. For example, the number of record groups may be greater than or less than the number of compute resources allocated to process incoming chunks, etc.”),
the processed data chunks from the worker node into a data stream (see ¶[0445] “For example, the retrieved events can be sharded in chunks based on the field names passed as part of a search query process of the data intake and query system. The event chunks can then be exported from the peer indexers 206 in parallel over the network to the worker nodes 214.” See ¶[0466] “The worker node can use the time values (e.g., timestamps) associated with the events or event chunks to arrange the events and/or the event chunks in a time-order. Lastly, in step 2314, the worker nodes may stream the time-ordered partial search results in parallel as time-ordered chunks via the search service (e.g., to the DFS master or search service provider of the DFS system)”)
distributing the data stream over to a subscriber over a data channel (see ¶ [0470] “The time-ordered partial search results can be streamed in parallel from multiple worker nodes to the service provider, which can stream each search stream to the search head of the data intake and query system. As such, time-ordered search results can be produced from diverse data types of diverse data systems when the scope of a search query requires doing so.” See ¶[0165] “In some embodiments, a cloud-based data intake and query system 306 may comprise a plurality of system instances 308. In general, each system instance 308 may include one or more computing resources managed by a provider of the cloud-based system 306 made available to a particular subscriber. The computing resources comprising a system instance 308 may, for example, include one or more servers or other devices configured to implement one or more forwarders, indexers, search heads, and other components of a data intake and query system, similar to system 108. As indicated above, a subscriber may use a web browser or other application of a client device 302 to access a web portal or other interface that enables the subscriber to configure an instance 308.”).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 2, Johannsen does not expressly disclose, however, Pal teaches wherein the subscription request comprises an identification of a type of the processed data, an identification of a data format for receiving the processed data, and/or an identification of a data channel to receive the processed data (see ¶[0128] “For example, a data center, servers, or network appliances may generate many different types and formats of machine data (e.g., system logs, network packet data (e.g., wire data, etc.), sensor data, application program data, error logs, stack traces, system performance data, operating system data, virtualization data, etc.) from thousands of different components, which can collectively be very time-consuming to analyze.”).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 3, Johannsen does not expressly disclose, however, Pal teaches wherein the type of data comprises transaction-related data or account-related data (see ¶[0895] “In certain cases, the worker node 3306 can request the external data system 12 to return any portion or all search configuration data or any portion or all search configuration data that is accessible based on the account or user credentials used to access the external data system 12.”).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 4, Johannsen does not expressly disclose, however, Pal teaches wherein the data format comprises a flat file or a message (see ¶[0132] messages).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 5, Johannsen does not expressly disclose, however, Pal teaches wherein the data channel comprises a representational state transfer (REST)/hyper-text transfer protocol (HTTP) channel (see ¶ [0143] “The communication between a client device 102 and a host application 114 may, for example, be based on the Hypertext Transfer Protocol (HTTP) or any other network protocol.”).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 6, Johannsen teaches wherein the receiving node and the gathering node are worker nodes (see ¶ [0021] “The data processing operations corresponding to each subgraph may be executed at one or more computing nodes serving as worker nodes while a computing node serving as the master node may coordinate the execution of one or more data processing pipelines by the worker nodes. For instance, one or more master nodes may form a pipeline engine configured to coordinate the execution of one or more data processing pipelines.”).
Regarding claim 7, Johannsen does not expressly disclose, however, Pal teaches wherein the information about the data comprises a size of the data and/or a type of the data (see ¶ [0836] “For example, during or after the conclusion of the intake phase 3604, each partition worker node 3306 implementing that phase 3604 may communicate to the query coordinator 3304 information regarding the collections of messages received during a given time-window (e.g., the number, size, or formatting of messages, etc.).”).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 8, Johannsen does not expressly disclose, however, Pal teaches wherein at least one of the worker nodes processes more than one data chunk (see ¶ [1116] “In addition, to address possible issues caused by redistributing and/or reducing data at ingest and/or waiting to redistribute/reduce until all of the data has been received and assigned to partitions, the worker node 3306 can combine similar data as the data is assigned to particular partitions at ingest. While the logical assignment of records based on content can increase the likelihood that records with similar data are assigned to the same group (and thus the same partition), it will be understood that combining similar records during ingest can independently improve the functioning of the system 16.”).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 9, Johannsen does not expressly disclose, however, Pal teaches wherein the receiving node adds an additional worker node after distributing the data chunks, and distributes at least one of the data chunks to the additional worker node (see ¶[0669] “As such, the query coordinator 3304 can estimate that a larger number of partitions will be used in the processing layer and allocate additional worker nodes 3306 or processors 3406 to the processing layer 3606 or use multiple processing layers 3606 to process the data. In some cases, more partitions, worker nodes 3306, and/or processors 3406 can be allocated to the search layers for queries of larger datasets.”).
Hence, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Johannsen by adapting Pal to analyze large sets of data in order to gather insights and to quickly perform the analysis (see ¶ [0006] of Pal).
Regarding claim 10, is an independent system claim corresponding to method claim 1 and is rejected for the same reasons. In addition, Johannsen teaches distributed data processing system comprising a plurality of nodes (see Fig. 1A nodes), wherein the plurality of nodes comprises a receiving node, a plurality of worker nodes, and a gathering node, and each node comprises an instance of a virtual machine; at least one data source (see Fig. 1A Database 120); and a subscriber (see Fig. 1 Client 140).
Regarding claims 11-18, are system claims corresponding to claims 2-9 above, and are rejected for the same reasons.
Response to Arguments
Regarding 101 rejections applicants argues that the claim recites “processing, by an instance of a virtual machine” and “plurality of nodes” and thus the claim is not a mental process. Applicant argues that the office action did not respond to the argument that the claims provide a technical solution to the problem and improve the operation of a computer.
Examiner disagrees. The claimed virtual machine and plurality of nodes are generic computing components. The applicants cite to the specification when describing the technical solution and those citations are not listed in the claims. In regards to the claims as the determining step or the breaking and distributing step fall under the abstract idea because they are either abstract ideas or additional elements that are not a practical application nor significantly more than the abstract idea.
Regarding 103 rejections, applicant’s argue that nothing in Pal discloses or suggests that trace messages are large or require sharding to be processed, and they are not processed only published. Pal does not disclose a large data set of trace messages, and appear to be independent of each other. There is no evidence that combining Johannsen and Pal would provide predictable results. Applicant argues that Johannsen does not disclose “gathering...the processed data chunks from the worker nodes into a data stream” and “distributing...the data stream to the subscriber over a data channel.”
Examiner disagrees. Examiner cited Johannsen as disclosing a significant amount of trace data. Johannsen describes using big data or complex data sets (see ¶[0021]). Thus, with significant amount of data, it would be useful to partition the data into smaller sets. While Johannsen was not cited for the partitioning of data, Pal was utilized to show that partitioning data into smaller sets is not a new or novel concept, or way of solving a problem. Pal’s data is processed because it is being sharded (see ¶[00445]), and that is a way of processing data, it is not only published as applicants claim. It is possible and predictable to combine both Johannsen and Pal because they are both in the field of processing data. Examiner does not find applicants arguments to be persuasive.
Johannsen and Pal teaches “gathering...the processed data chunks from the worker nodes into a data stream” and “distributing...the data stream to the subscriber over a data channel.” It is a combined teaching. Johannsen teaches gathering, by a gathering node of the plurality of nodes, the processed data from the worker nodes (see ¶ [0056] “At 402, the first master node 170A may receive, from the client 140, a request to receive one or more trace messages output by a worker node executing at least a portion of a data processing pipeline that includes a sequence data processing operations performed on data stored in a database.”); and distributing, by the gathering node, the data stream(see ¶ [0022] “In some example embodiments, during the execution of a data processing pipeline by a distributed computing system, one or more worker nodes may each publish a trace stream that includes one or more trace messages. The progress and/or performance of executing the data processing pipeline may be monitored via the trace messages, which may provide information regarding events that occur during the execution of various data processing operations in the data processing pipeline”, see ¶ [0047] “According to some example embodiments, the first master node 170A may become a subscriber to the trace stream published by the first worker node 180A, the second worker node 180B, and the third worker node 180C in response to the client 140 requesting to receive trace messages from the first worker node 180A, the second worker node 180B, and the third worker node 180C.”).
Pal teaches an instance of a virtual machine at each of the worker nodes (see ¶[0142] “Host devices 106 may broadly include any number of computers, virtual machine instances, and/or data centers that are configured to host or execute one or more instances of host applications 114”); and the information comprises a size of the data to be processed (see ¶[0190] “In some embodiments, a forwarder receives the raw data and may segment the data stream into “blocks”, possibly of a uniform data size, to facilitate subsequent processing steps.”).
Thus, it is combined teachings that disclose the claimed invention, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
He et al. (U.S. PG PUB 2019/0171367) teaches a data processing method, a worker node in a distributed data processing system receives first data from an upstream worker node. The first data has been stored in a buffer of the upstream worker node. The worker node sends a first portion of the first data to a persistent storage device of the distributed data processing system for persistent backup, and performs computational processing on the first data to generate second data. Prior to completing performing computational processing on the first data, the worker node sends acknowledgement information to the upstream worker node to instruct the upstream node to delete the first data from the buffer of the upstream worker node. The worker node then sends the second data to a downstream worker node in the distributed data processing system for further processing by the downstream worker node.
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Carina Yun
Patent Examiner
Art Unit 2194
/CARINA YUN/Examiner, Art Unit 2194