DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to applicant’s amendment filed on 04/30/2026.
Claims 1-12 are pending and examined.
Response to Arguments
Applicant's arguments filed 04/30/2026 with regards to the claim objections have been fully considered and are persuasive, as the suggested language has been amended accordingly. The claim objection for claim 5 have been withdrawn.
Applicant's arguments filed 04/30/2026 with regards to 35 U.S.C. 112 have been fully considered and are persuasive, as the language for the respective claims resulting in a lack of antecedent basis has been removed. The rejections under 35 U.S.C. 112(b) have been withdrawn.
Applicant's arguments filed 04/30/2026 with regards to 35 U.S.C. 103 have been fully considered but they are not persuasive. Applicant argued that “The cited references do not teach or suggest an input data stream being converted into batch processing data and real-time processing data, especially through use of a distributed computational graph,” and that “The cited references do not teach or suggest linear transformations as part of a workflow for batch processing and branch or iterative transformations as part of a workflow for real-time processing.” Examiner respectfully disagrees, see 35 U.S.C. 103 rejections below for a detailed analysis. With regards to the first point, Bartlett is interpreted to disclose the limitation as amended. For example, the MAG engine receiving a real-time trade which comprises real-time trade information correlates to an input data stream. The real-time trade information being used with the computed static information in each of the static computation nodes of the computation graph to result in dynamic information contained in each of the dynamic computation nodes correlates to converting an input data stream into real-time processing data through a distributed computational graph. The batch processing module of the MAG engine generating a computation graph with static and dynamic nodes, where the dynamic information in the dynamic computation nodes use real-time information to be computed, and the dynamic information in each of the dynamic computation nodes are further used to calculate credit risk scores, correlates to converting an input data stream into batch processing data through a distributed computational graph. Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with Bartlett because reducing the amount of computation by implementing computational kernels in the directed graph, improving core efficiency, and using thread parallelism improve the latency and throughput of a software system.
With regards to the second point, while Pueyo may not explicitly teach that the pre-determined first data processing workflow includes linear data transformation and that the pre-determined second data processing workflow includes branch or iterative transformations, linear data transformations are a popular type of data transformation as evidenced by Bartlett’s computation path in the computation graph including a conversion from Canadian dollars to American dollars. Additionally, branch or iterative transformations are a popular type of data transformation as evidenced by Garg’s iterative calculations continuing for a node over all neighbors and all data sources. Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with Garg because neighbor graphs can be used to include one user at a given node to other users connected to the one user to express the abstraction. Creating neighbor graphs for subsequent nodes as part of a signature graph analyzes data sources inducing a probability distribution for each user and their neighbors, which when used with algorithms such as an LTR algorithm, may include learning rules to improve its fit to the data.
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) 1-4 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Pueyo et al. (U.S. Patent No. US 20110154341 A1), hereinafter “Pueyo” in view of Bartlett et al. (U.S. Patent No. US 20160078532 A1), hereinafter “Bartlett” and Garg et al. (U.S. Patent No. US 20140143332 A1), hereinafter “Garg.”
With regards to Claim 1, Pueyo teaches:
A system for multitemporal data analysis, comprising:
a computing device comprising a memory, a processor, and a non-volatile data storage device (Paragraphs 17-18 and 23, “With reference to FIG. 1, an exemplary system for implementing the invention may include a general purpose computer system 100. Components of the computer system 100 may include, but are not limited to, a CPU or central processing unit 102, a system memory 104, and a system bus 120 that couples various system components including the system memory 104 to the processing unit 102… The computer system 100 may include a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer system 100 and includes both volatile and nonvolatile media. For example, computer-readable media may include volatile and nonvolatile computer storage media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data... The task manager library may also include a configurator that extracts data and parameters of the map-reduce application from a configuration file to configure the map-reduce application for execution, a scheduler that determines an execution plan based on input and output data dependencies of mappers and reducers, a launcher that iteratively launches the mappers and reducers according to the execution plan, and a task executor that requests the map-reduce library to invoke execution of mappers and reducers.” Extracting data and parameters from a map-reduce application and determining an execution plan based on dependencies correlates to a system for multitemporal data analysis. The system including a system memory, CPUs and nonvolatile computer storage media correlates to a computing device comprising memory, a processor, and a nonvolatile data storage device);
perform batch processing of the batch processing data according to a pre-determined first data processing workflow (Paragraphs 29-31, “In general, a map-reduce application may have a map stage, where part of the input data distributed across mapper servers may be loaded and processed by executable code of a mapper to produce partial results, and a reduce stage, where one or more reducer servers receive and integrate the partial results of data distributed and processed by executable code of mappers to produce final results of data processing by the map-reduce application… The mapper server 202 may include a mapper 228 that has functionality for processing a part of the input data distributed across mapper servers 202 and sending partial results from processing to a reducer server 230 for integration to produce final results for output… Multiple tasks can be specified in a configuration file, and the task management library will execute them all, one after the other, allowing for the usage of the results of one task as input for the next one. Additionally, a task can be specified to be executed concurrently with other tasks in the configuration file, where the data the task uses does not depend on any task which has not yet finished execution. In order for the task manager library to manage chaining and parallelizing execution of tasks of a map-reduce application in a map-reduce framework, tasks and parameters of the map-reduce application need to be specified in the configuration file.” The mappers processing a part of the input data to produce partial results based on the order specified in a configuration file correlates to performing batch processing of the batch processing data according to a pre-determined first data processing workflow);
and
perform real-time processing of the real-time processing data according to a pre-determined second data processing workflow (Paragraphs 29 and 31, “In general, a map-reduce application may have a map stage, where part of the input data distributed across mapper servers may be loaded and processed by executable code of a mapper to produce partial results, and a reduce stage, where one or more reducer servers receive and integrate the partial results of data distributed and processed by executable code of mappers to produce final results of data processing by the map-reduce application... Multiple tasks can be specified in a configuration file, and the task management library will execute them all, one after the other, allowing for the usage of the results of one task as input for the next one. Additionally, a task can be specified to be executed concurrently with other tasks in the configuration file, where the data the task uses does not depend on any task which has not yet finished execution. In order for the task manager library to manage chaining and parallelizing execution of tasks of a map-reduce application in a map-reduce framework, tasks and parameters of the map-reduce application need to be specified in the configuration file. For instance, mapper, reducer and wrapper executable code referenced by their qualified name may be specified in the configuration file. A set of pathnames of input files or folder can be specified for input data of a single task.” The reducer servers integrating the partial results of data processed by the mappers to produce final results of data processing correlates to performing real-time processing of the real-time data. Tasks specified to be executed concurrently with other tasks and chained in succession based on a configuration file referencing mappers and reducers separately correlates to the real-time processing being done according to a second predetermined workflow).
Pueyo does not explicitly teach that the pre-determined first data processing workflow includes linear data transformation and that the pre-determined second data processing workflow includes branch or iterative transformations. However, linear data transformations are a popular type of data transformation as evidenced by Bartlett (Paragraph 32, “For instance, a computation path in a computation graph may include a conversion from the Canadian dollar to the American dollar, followed by a conversion from the American dollar to the Australian dollar. Techniques of this disclosure may, in some examples, calculate a conversion from the Canadian dollar to the Australian dollar during a batch process where pipeline kernels are generated, allowing one extra conversion to be skipped when real-time processing is occurring.” The computation path in the computation graph including a conversion from Canadian dollars to American dollars correlates to linear data transformations). Additionally, branch or iterative transformations are a popular type of data transformation as evidenced by Garg (Paragraph 92, “Generating signature graph 112 may include analyzing a plurality of data sources 97 for communications 68 between one user 82 (node 102) and connected other users 82 (neighbors 104). Analysis may include inducing a probability distribution 114 from data source 97 for the given node 102 with respect to each other neighbor 104… Iterative calculation may continue for node 102 over all neighbors 104 and all data sources 97. Iterative calculation may ultimately evaluate some or all users 82 as given node 102 (node i) to calculate a distribution 114 for each user 82, that user's neighbors 104, and some or all data sources 97.” The iterative calculations continuing for a node over all neighbors and all data sources correlates to iterative transformations).
Pueyo does not explicitly teach:
a plurality of programming instructions stored in the memory and operable on the processor, of the computing device, wherein the plurality of programming instructions, when operating on the processor, causes the computing device to:
convert an input data stream into batch processing data and real-time processing data through a distributed computational graph;
perform computations on the input data stream based on the structure and content of the distributed computational graph;
wherein converting the input data stream includes utilizing one or more data pipelines to move data from the distributed computational graph among a plurality of instantiated workers, wherein the nodes, edges, and instantiated workers of the distributed computational graph represent state information for processing of the input data stream;
However, Bartlett teaches:
a plurality of programming instructions stored in the memory and operable on the processor of the computing device (Paragraphs 17 and 19, “MAG engine 2 may include a MAG memory 6 and MAG baseline 20. MAG engine 2 may also include a processor to execute one or more modules, including execute object module 8, prepare response module 10, trade parser module 12, incoming trade module 14, and look-up engine 16. MAG engine 2 may also include optimizing compiler 24, objects 26, and kernel library 28… MAG memory 6 may, in some cases, further be configured for long-term storage of information as non-volatile memory space and retain information after power on/off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.” The MAG engine including a MAG memory and processor to execute one or more modules correlates to a plurality of programming instructions stored in the memory and operable on the processor of the computing device), wherein the plurality of programming instructions, when operating on the processor, causes the computing device to:
convert an input data stream into batch processing data and real-time processing data through a distributed computational graph (Paragraphs 42 and 52, “For instance, MAG engine 2 may generate a computation graph comprising one or more static computation nodes, one or more dynamic computation nodes, and one or more computation edges… After the respective static information contained in each of the one or more static nodes of the pipeline kernel is computed, MAG engine 2 may receive the real-time trade. In these cases, the real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score. MAG engine 2 may compute, based at least in part on the real-time information in the real-time trade and the respective computed static information contained in each of the one or more static computation nodes of the pipeline kernel, the respective dynamic information contained in each of the one or more dynamic computation nodes of the pipeline kernel. MAG engine 2 may compute, based at least in part on the respective computed dynamic information contained in each of the one or more dynamic computation nodes, the real-time credit risk score... For instance, MAG engine 2 may include a batch processing module 42 and a real-time processing module 44. Batch processing module 42 may be operable by the one or more processors 30 to execute techniques of this disclosure corresponding to a batch process. For instance, batch processing module 42 may generate a computation graph comprising one or more static computation nodes and one or more dynamic computation nodes… In some examples, the one or more dynamic computation nodes of the computation graph each comprise dynamic information that uses the real-time information to be computed. In these cases, the real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score. Batch processing module 42 may also compute, before receiving the real-time trade, the respective static information contained in each of the one or more static nodes of the computation graph.” The MAG engine receiving a real-time trade which comprises real-time trade information correlates to an input data stream. The real-time trade information being used with the computed static information in each of the static computation nodes of the computation graph to result in dynamic information contained in each of the dynamic computation nodes correlates to converting an input data stream into real-time processing data through a distributed computational graph. The batch processing module of the MAG engine generating a computation graph with static and dynamic nodes, where the dynamic information in the dynamic computation nodes use real-time information to be computed, and the dynamic information in each of the dynamic computation nodes are further used to calculate credit risk scores, correlates to converting an input data stream into batch processing data through a distributed computational graph);
perform computations on the input data stream based on the structure and content of the distributed computational graph (Paragraph 42, “For instance, MAG engine 2 may generate a computation graph comprising one or more static computation nodes, one or more dynamic computation nodes, and one or more computation edges… After the respective static information contained in each of the one or more static nodes of the pipeline kernel is computed, MAG engine 2 may receive the real-time trade. In these cases, the real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score. MAG engine 2 may compute, based at least in part on the real-time information in the real-time trade and the respective computed static information contained in each of the one or more static computation nodes of the pipeline kernel, the respective dynamic information contained in each of the one or more dynamic computation nodes of the pipeline kernel. MAG engine 2 may compute, based at least in part on the respective computed dynamic information contained in each of the one or more dynamic computation nodes, the real-time credit risk score.” The MAG engine receiving a real-time trade which comprises real-time trade information correlates to an input data stream. The real-time trade information being used with the computed static information in each of the static computation nodes of the computation graph to result in dynamic information, which is further used to compute the real-time credit risk score, correlates to performing computations on the input data stream based on the structure and content of the distributed computational graph);
wherein converting the input data stream includes utilizing one or more data pipelines to move data from the distributed computational graph among a plurality of instantiated workers, wherein the nodes, edges, and instantiated workers of the distributed computational graph represent state information for processing of the input data stream (Paragraphs 22, 33, 54, and 88, “In a computation graph, nodes represent computations and edges represent data dependence between computations. A node may include a computation kernel and its internal data called states. States are typically vectors or dense matrices called sheets. A sheet may comprise a two-dimension data structure organized by scenarios and time points. In one example, sheets may be in memory sequentially along the scenario dimension. There are two types of nodes in a computation graph, consolidation nodes and transformation nodes. Both types of nodes may produce a new result, while only consolidation nodes may modify its own states… Techniques of this disclosure may also exploit both single-instruction multiple data (SIMD) parellelism and thread-level parallelism… In some examples, the one or more static computation nodes of the computation graph each contain static information. In some examples, the one or more dynamic computation nodes of the computation graph each comprise dynamic information. The instructions may cause processors 30 to, before receiving the real-time trade, determine a pipeline kernel in the computation graph. The pipeline kernel may comprise at least one of the one or more static computation nodes, at least one of the one or more dynamic computation nodes, and a path originating from one of the one or more static computation nodes or one of the one or more dynamic computation nodes along at least one of the one or more computation edges… For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may be executed in a different order, or the functions in different blocks may be processed in different but parallel processing threads, depending upon the functionality involved.” The computation nodes including states and edges representing data dependence between computations in a computation graph correlates to the nodes and edges of the distributed computational graph representing state information for processing of the input data stream. The pipeline kernel comprising static and dynamic computation nodes containing information and connected by at least one computation edge correlates to utilizing one or more data pipelines to move data from the distributed computational graph. The system utilizing parallel processing threads to process specific functions of the computation nodes correlates to utilizing one or more data pipelines to move data from the distributed computational graph among a plurality of instantiated workers, wherein the nodes, edges, and instantiated workers of the distributed computational graph represent state information for processing of the input data stream);
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with wherein the pre-determined second data processing workflow includes branch or iterative transformations as taught by Garg because neighbor graphs can be used to include one user at a given node to other users connected to the one user to express the abstraction. Creating neighbor graphs for subsequent nodes as part of a signature graph analyzes data sources inducing a probability distribution for each user and their neighbors, which when used with algorithms such as an LTR algorithm, may include learning rules to improve its fit to the data (Garg: paragraphs 90, 92 and 100).
Additionally, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with a plurality of programming instructions stored in the memory and operable on the processor of the computing device, wherein the plurality of programming instructions, when operating on the processor, causes the computing device to: convert an input data stream into batch processing data and real-time processing data through a distributed computational graph; perform computations on the input data stream based on the structure and content of the distributed computational graph; wherein converting the input data stream includes utilizing one or more data pipelines to move data from the distributed computational graph among a plurality of instantiated workers, wherein the nodes, edges, and instantiated workers of the distributed computational graph represent state information for processing of the input data stream, and the pre-determined first data processing workflow includes linear data transformation as taught by Bartlett because reducing the amount of computation by implementing computational kernels in the directed graph, improving core efficiency, and using thread parallelism improve the latency and throughput of a software system (Bartlett: paragraph 31).
With regards to Claim 7, the system of Claim 1 performs the same steps as the method of Claim 7, and Claim 7 is therefore rejected using the same rationale set forth above in the rejection of Claim 1.
With regards to Claim 2, Pueyo in view of Bartlett and Garg teaches the system of Claim 1 above. Pueyo further teaches:
wherein a function is executed based at least in part by the results of the batch and the real-time processing (Paragraphs 30-31, “The mapper server 202 may include a mapper 228 that has functionality for processing a part of the input data distributed across mapper servers 202 and sending partial results from processing to a reducer server 230 for integration to produce final results for output. The reducer server 230 may include a reducer 232 that has functionality for receiving partial results of processing parts of the input data from one or more mappers 228, and outputting final results of data processing by the map-reduce application… Multiple tasks can be specified in a configuration file, and the task management library will execute them all, one after the other, allowing for the usage of the results of one task as input for the next one.” The mapper servers processing a part of the input data which is sent to the reducer server as partial results for further processing and integration, where the results of one task can be used as input for the next one, correlates to a function executed at least in part by results of the batch and real-time processing).
With regards to Claim 8, the system of Claim 2 performs the same steps as the method of Claim 8, and Claim 8 is therefore rejected using the same rationale set forth above in the rejection of Claim 2.
With regards to Claim 3, Pueyo in view of Bartlett and Garg teaches the system of Claim 1 above. Pueyo further teaches:
wherein at least a portion of the input data comes from a social media source (Paragraph 2, “Cloud computing involves many powerful technologies, including map-reduce applications, that allow large online companies to process vast amounts of data in a short period of time. Tasks such as analyzing traffic, extracting knowledge from social media properties or computing new features for a search index are complex by nature and recur on a regular basis. Map-reduce applications are often used to perform these tasks to process large quantities of data. A map-reduce application may be executed in a map-reduce framework of a distributed computer system where input data is divided and loaded for processing by several mappers, each executing on mapper servers, and partial results from processing by mappers are sent for integration to one or more reducers, each executing on reducer servers.” The map-reduce application extracting knowledge from social media properties to process large quantities of input data correlates to at least a portion of the input data coming from a social media source).
Pueyo does not explicitly teach that the input data is an input data stream. However, input data streams are a popular form of input data as evidenced by Bartlett above (Paragraph 42, “After the respective static information contained in each of the one or more static nodes of the pipeline kernel is computed, MAG engine 2 may receive the real-time trade. In these cases, the real-time trade is associated with a current exchange of assets for which a real-time credit risk score may be determined and comprises real-time information for use in computing the real-time credit risk score.” The MAG engine receiving a real-time trade which comprises real-time trade information correlates to an input data stream);
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with an input data stream as taught by Bartlett because real-time trades can include real-time information such as trade value sheets and trade parameters. When evaluating a trade on a computation graph, it typically refers to the process of absorbing the trade value sheet into some consolidation nodes of the graph and/or computing statistical measures on computation graph nodes. Real-time trades also allow for the use of determining a real-time credit risk score associated with a current exchange of assets (Bartlett: paragraphs 23 and 54).
With regards to Claim 9, the system of Claim 3 performs the same steps as the method of Claim 9, and Claim 9 is therefore rejected using the same rationale set forth above in the rejection of Claim 3.
With regards to Claim 4, Pueyo in view of Bartlett and Garg teaches the system of Claim 1 above. Pueyo further teaches:
wherein at least a portion of the input data comes from actions of a user while using an application (Paragraphs 21 and 31, “A user may enter commands and information into the computer system 100 through an input device 140 such as a keyboard and pointing device, commonly referred to as mouse, trackball or touch pad tablet, electronic digitizer, or a microphone. Other input devices may include a joystick, game pad, satellite dish, scanner, and so forth. These and other input devices are often connected to CPU 102 through an input interface 130 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB)… Users rarely run a single task in a map-reduce application for a data processing project and need to chain data processes, transforming the data, retrieving results and reusing obtained results. Multiple tasks can be specified in a configuration file, and the task management library will execute them all, one after the other, allowing for the usage of the results of one task as input for the next one.” The users entering commands and information such as a configuration file into the computer system through an input device correlates to at least a portion of the data input into the system coming from actions of a user while using an application).
Pueyo does not explicitly teach that the input data is an input data stream. However, input data streams are a popular form of input data as evidenced by Bartlett above (Paragraph 42);
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with an input data stream as taught by Bartlett because real-time trades can include real-time information such as trade value sheets and trade parameters. When evaluating a trade on a computation graph, it typically refers to the process of absorbing the trade value sheet into some consolidation nodes of the graph and/or computing statistical measures on computation graph nodes. Real-time trades also allow for the use of determining a real-time credit risk score associated with a current exchange of assets (Bartlett: paragraphs 23 and 54).
With regards to Claim 10, the system of Claim 4 performs the same steps as the method of Claim 10, and Claim 10 is therefore rejected using the same rationale set forth above in the rejection of Claim 4.
Claim(s) 5 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Pueyo in view of Bartlett, Garg and Bishop et al. (U.S. Patent No. US 20170083380 A1), hereinafter “Bishop.”
With regards to Claim 5, Pueyo in view of Bartlett and Garg teaches the system of Claim 1 above. Pueyo in view of Bartlett and Garg does not explicitly teach:
wherein at least a portion of the input data stream comes from a news outlet.
However, Bishop teaches:
wherein at least a portion of the input data stream comes from a news outlet (Paragraph 136, “Data sources 102 are entities such as a smart phone, a WiFi access point, a sensor or sensor network, a mobile application, a web client, a log from a server, a social media site, etc. In one implementation, data from data sources 102 are accessed via an API Application Programming Interface) that allows sensors, devices, gateways, proxies and other kinds of clients to register data sources 102 in the IoT platform 100 so that data can be ingested from them. Data from the data sources 102 can include events in the form of structured data (e.g. user profiles and the interest graph), unstructured text (e.g. tweets) and semi-structured interaction logs. Examples of events include device logs, clicks on links, impressions of recommendations, numbers of logins on a particular client, server logs, user's identities (sometimes referred to as user handles or user IDs and other times the users' actual names), content posted by a user to a respective feed on a social network service, social graph data, metadata including whether comments are posted in reply to a prior posting, events, news articles, and so forth.” The clients registering data sources which include data from news articles correlates to at least a portion of the input data coming from a news outlet).
Bishop does not explicitly teach that the input data is an input data stream. However, input data streams are a popular form of input data as evidenced by Bartlett above (Paragraph 42);
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with an input data stream as taught by Bartlett because real-time trades can include real-time information such as trade value sheets and trade parameters. When evaluating a trade on a computation graph, it typically refers to the process of absorbing the trade value sheet into some consolidation nodes of the graph and/or computing statistical measures on computation graph nodes. Real-time trades also allow for the use of determining a real-time credit risk score associated with a current exchange of assets (Bartlett: paragraphs 23 and 54).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with wherein at least a portion of the input data from a news outlet as taught by Bishop because APIs allow clients to register a variety of data sources so that data can be ingested from them. These data sources can be structured, unstructured, or semi-structured and include events such as device logs, clicks on links, recommendations, number of logins, server logs, identities, and metadata (Bishop: paragraph 136).
With regards to Claim 11, the system of Claim 5 performs the same steps as the method of Claim 11, and Claim 11 is therefore rejected using the same rationale set forth above in the rejection of Claim 5.
Claim(s) 6 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Pueyo in view of Bartlett, Garg and Sirota et al. (U.S. Patent No. US 8719415 B1), hereinafter “Sirota.”
With regards to Claim 6, Pueyo in view of Bartlett and Garg teaches the system of Claim 1 above. Pueyo in view of Bartlett and Garg does not explicitly teach:
wherein at least a portion of the input data stream comes from a distributed database.
However, Sirota teaches:
wherein at least a portion of the input data stream comes from a distributed database (Col. 3, lines 34-42, “As previously noted, a cluster for use in the distributed execution of a program may in at least some embodiments include multiple core computing nodes that participate in a distributed storage system for use by the cluster, such as to store input data used in the distributed program execution and/or output data generated by the distributed program execution. The distributed storage system may have various forms in various embodiments, such as a distributed file system, a distributed database, etc.” The input data being stored in a distributed storage system such as a distributed database correlates to a portion of the input data coming from a distributed database).
Sirota does not explicitly teach that the input data is an input data stream. However, input data streams are a popular form of input data as evidenced by Bartlett above (Paragraph 42);
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with an input data stream as taught by Bartlett because real-time trades can include real-time information such as trade value sheets and trade parameters. When evaluating a trade on a computation graph, it typically refers to the process of absorbing the trade value sheet into some consolidation nodes of the graph and/or computing statistical measures on computation graph nodes. Real-time trades also allow for the use of determining a real-time credit risk score associated with a current exchange of assets (Bartlett: paragraphs 23 and 54).
Therefore, it would have been obvious to one of ordinary skill in the art to which said subject matter pertains before the effective filing date of the claimed invention to combine Pueyo with wherein at least a portion of the input data comes from a distributed database as taught by Sirota because distributed storage systems provide various mechanisms to enhance data availability, such as by storing multiple copies of some groups of data to enhance the likelihood that at least one copy remains available of a core computing node storing another copy of that data group fails or otherwise becomes unavailable (Sirota: Col. 3, lines 42-48).
With regards to Claim 12, the system of Claim 6 performs the same steps as the method of Claim 12, and Claim 12 is therefore rejected using the same rationale set forth above in the rejection of Claim 6.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Amorim et al. (U.S. Patent No. US 20120290576 A1); teaching a method of data analysis from multiple devices using a database service module with a data storage subsystem to collect data from different devices. The data is stored in a meta-structure using primitives to classify the data. An analysis engine is used to analyze the data in a specific order and frequency to determine whether the data meets certain criteria in accordance with a stored set of rules.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chat Do can be reached at (571) 272-3721. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
The publicPAIR and privatePAIR systems are no longer available. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SELINA ELISA HU/Examiner, Art Unit 2193
/Chat C Do/Supervisory Patent Examiner, Art Unit 2193
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