DETAILED ACTION
A summary of this action:
Claims 16-29 have been presented for examination.
This action is Final.
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 .
Response to Arguments
Following Applicants arguments and amendments, and in light of the 2019 Patent Eligibility guidance, the 101 rejection of the Claims is Maintained.
Applicant’s Argument: Applicant’s arguments directed to 101 rejection are based on newly amended subject matter." Here Applicant argues that Patent Office erred in concluding that the additional elements fails to consider material statements of technological improvements that the specification links to these additional limitations and do not recite a mental process or any other judicial exception fail to integrate the alleged mental process into a practical application.
Examiner’s Response: Examiner respectfully disagrees because MPEP 2106.05(a) states, “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology.” Here, Applicant submits the same claims as originally filed and cites specification paragraphs to allegedly be coupleable to the claims. However, none of these cited paragraphs are explicitly written in the original or resubmitted claim set. Furthermore, resubmitting the same claims does not rise to the level of overcoming the 101 rejection of being a mental process or because the claim limitations fail to integrate the mental process into a practical applications. Accordingly, all arguments are addressed in the 101 rejection of the claims below.
Therefore, the 101 rejection of the claims is Maintained.
Following Applicants arguments and amendments, the 103 rejection of the claims is Maintained.
Applicant’s Argument: Applicant’s arguments directed the 103 rejection are based on newly amended subject matter. Here, Applicant argues that the claim limitations overcome the prior art because Burmester does not disclose that the PLC includes input interfaces as recited in the claim, which Applicant alleges Applicant’s claims deliver respective runtime data to the respective input interface, is connectable. Applicant further argues that Burmester explains that "[a] triple graph grammar specification is a declarative definition of a mapping between two meta-models," which is irrelevant to the recited ability of the claimed aspect model to characterize runtime data received by the claimed data integration device via its input interfaces from connectable respective devices and that Neil does not overcome these deficiences.
Examiner’s Response: All arguments are addressed in the 103 rejection of the claims below.
Therefore, the 103 rejection is Maintained.
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 16-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process or mathematical concept without significantly more.
Step 1: Claims 16-27 are directed to a method, which is a process and is a statutory category invention. Claim 28 is directed to a non-transitory machine-readable memory medium system, which is a manufacturer and a statutory invention. Claim 29 is directed to a computer, which is a machine and is a statutory category invention. Therefore, claims 16-29 are directed to patent eligible categories of invention.
Claim 16
Step 2A, Prong 1: Independent claims 16, 28 and 29 similarly recite an abstract idea because the claims are derived from Mental Processes based on concepts performed in the human mind or with the aid of pencil and paper or in the alternative Mathematical Concepts using mathematical relationships, mathematical formulas or equations, or mathematical calculations.
Claim 16 and similarly recited in claims 28 and 29 generating a data integration device, including respective input interfaces to each of which a respective device, cover mental processes of assessing devices within input interfaces as described in [0137] of the specification.
Claim 16 and similarly recited in claims 28 and 29 have the limitation which delivers respective runtime data to the respective input interface, is connectable, cover mental processes of analyzing runtime data based on the respective connected input interface as described in the [Abstract] of the specification.
Claim 16 and similarly recited in claims 28 and 29 have the limitation the respective runtime data of each of the respective devices being characterized using at least one respective aspect model that is associated with the respective input interface and that characterizes an aspect of the respective runtime data, cover mental processes of analyzing the runtime data using the respective aspect model related to an input interface as described in [0113] of the specification.
Claim 16 and similarly recited in claims 28 and 29 have the limitation providing, from a metamodel, to each respective input interface associated with the respective aspect model, respective rules that are defined by the metamodel, cover mental processes of analyzing the respective rules defined by a metamodel as described in [0113] of the specification.
Claim 16 and similarly recited in claims 28 and 29 have the limitation setting up each respective aspect model according to the respective rules, cover mental processes of assessing the respective rules of each respective aspect model as described in [0113] of the specification.
Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper.
Dependent claims 17-27 further narrow the abstract ideas, identified in the independent claims. See analysis below.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. Claim 16 recites the additional element of “data integration device” as in independent claim 16, 28, and dependent claim 21, “respective input interfaces” as in independent claims 16, 28, and 29 and dependent claim 22, and 23, “respective device” as in independent claims 16, 28, and 29 and dependent claims 21, “respective output interface” as in dependent claims 23-25, “respective aspect processing device” as in dependent claims 22-24 and 27, “non-transitory machine-readable medium” as in independent claim 28, “computer” as in independent claim 28 and 29, this limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h). Alternatively, this additional element merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)).
Dependent claims 17-27 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above. The additional elements have been considered both individually and as an ordered combination in to determine whether they integrate the exception into a practical application. Therefore, the dependent claims do not integrate the claimed invention into a practical application.
Step 2B: The claims do not amount to significantly more. The judicial exception does not amount to significantly more. Claim 16 recites the additional element of “data integration device” as in independent claim 16, 28, and dependent claim 21, “respective input interfaces” as in independent claims 16, 28, and 29 and dependent claim 22, and 23, “respective device” as in independent claims 16, 28, and 29 and dependent claims 21, “respective output interface” as in dependent claims 23-25, “respective aspect processing device” as in dependent claims 22-24 and 27, “non-transitory machine-readable medium” as in independent claim 28, “computer” as in independent claim 28 and 29, this limitation does not amount to significantly more. See MPEP 2106.05(h). Alternatively, this additional element merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)).
Dependent claims 17-27 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above. The additional elements have been considered both individually and as an ordered combination in to determine whether they amount to significantly more. Therefore, the dependent claims do not amount to significantly more.
Therefore, the claims as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception.
As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claim 17 recites “wherein a structure of each respective aspect
model is a respective directed graph,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 18 similarly recite “wherein nodes identify individual data points and/or groups of data points in each respective directed graph,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 19 similarly recite “wherein the structure of each respective aspect model contains at least one respective subgraph that describes properties of the data points identified by the nodes and/or properties of the data points of the group identified by the nodes,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 20 similarly recite “wherein at least one respective subgraph is also unambiguously reachable during traversal of the graph up to a predefinable node,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 21 similarly recite “wherein the data integration device is configured to interpret data received from each respective device corresponding to the properties described in the subgraph,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 22 similarly recite “wherein each respective input interface is connected to a respective aspect processing device with which the respective aspect model is associated,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 23 similarly recite “wherein each respective processing is configured to provide a portion of those runtime data at a respective output interface which are provided to the respective input interface with which the respective aspect processing device is associated,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 24 similarly recite “wherein the portion of runtime data provided at the respective output interface is contained in an aspect of the runtime data which characterizes that respective aspect model which is associated with the respective aspect processing device to which the respective output interface belongs,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 25 similarly recite “wherein the individual data points and/or the data points of the groups of data points are those data points that are provided at the respective output interface,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 26 similarly recite “wherein the properties established by the respective subgraph, predefined in a hierarchical structure of selectable classes, are predefined by the metamodel,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Dependent claims 27 similarly recite “wherein each respective aspect processing device is created from the respective aspect models,” which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes.”
Accordingly, claims 16-29 are ineligible and rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without anything significantly more.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 16-29 and are rejected under are rejected under 35 U.S.C. 103 as being unpatentable over BURMESTER (Tool integration at the meta-model level the Fujaba approach), herein BURMESTER, in view of NEILL (US 20210374143 Al, herein NEILL.
Claim 16
BURMESTER teaches a method for generating a data integration device BURMESTER ([5.2 Simple graph grammar rules] “As an example, we employ a constraint from our previously mentioned case study. In our case study, we are generating code (method for generating data) for programmable logic controllers (data integration device) (PLC’s) [22]. To keep the transformation of the class diagram into a non-object-oriented target language quite simple, we have to restrict the used class diagrams to not include any multiple inheritance between classes.”)
BURMESTER also teaches the respective runtime data of each of the respective devices being characterized using at least one respective aspect model that is associated with the respective input interface and that characterizes an aspect of the respective runtime data BURMESTER ([Conclusion] “Based on the data integration provided by the patterns, a consistency mechanism is presented. This consistency mechanism (being characterized) uses triple graph grammars (using at least one respective aspect model) for a graphical (respective input interface), though formal (associated with the respective input interface), specification of structural consistency (characterizes an aspect) between instances of the integrated meta-model elements (of the respective runtime data). This formal specification (characterizes an aspect) is used (using) for managing consistency (at least one respective aspect model) during runtime (runtime data). Automatic repair actions (respective devices) may be included in the consistency specification (characterizes an aspect of the respective runtime data).”)
BURMESTER does not explicitly teach including respective input interfaces to each of which a respective device, which delivers respective runtime data to the respective input interface, is connectable, providing, from a metamodel, to each respective input interface associated with the respective aspect model, respective rules that are defined by the metamodel, and setting up each respective aspect model according to the respective rules.
However, NEILL teaches including respective input interfaces to each of which a respective device, which delivers respective runtime data to the respective input interface, is connectable, providing, from a metamodel, to each respective input interface associated with the respective aspect model, respective rules that are defined by the metamodel, and setting up each respective aspect model according to the respective rules.
NEILL teaches including respective input interfaces to each of which a respective device NEILL ([0195] “FIG. 12A shows an example of the parallel streaming compute engine 114 (respective input interfaces), configured for processing and computation on CPS-G datasets. The compute engine 114 is based on the dataflow, streaming computational model. Generally, datasets (e.g., CPS-G structures) are injected into the compute engine through one of two input interfaces (respective input interfaces). The first interface 1202 is for real-time streaming directly from one in-memory database (a respective device) to that of the compute engine in-memory database (a respective device). The second interface 1204 is for data from the CPS-G persistent database (a respective device) to that of the compute engine in-memory database (a respective device).”) See also NEILL ([Figure 12 A] and [Figure 13])
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NEILL also teaches delivers respective runtime data to the respective input interface NEIL ([0194] “Client applications or domain controllers submit through the CPS-G (n-model sub-graph interface 1102 (also called an n-model slicer) model definitions based on entities and programmatic constructs which define and generate the effective CPS-G model sub-graph. The CPS-G model subgraph 1104 then assigns one or more CPS-G Model generator engines 1106a-n to construct the generative CPS-G model sub-graph and store the generated model and datasets in respective in-memory CPS-G model cache 1108a-n for distribution to either real-time or persistent storage. A configurable workflow engine 1116 manages the runtime and lifecycle activities of each of the module components. The generated CPS-G (compact pattern stream graph) data models (input interface) can be further processed (deliver) for a range of low-latency, high-performance domain specific application use-cases, particularly those requiring real-time visualization of data (real time data), or low-latency, responsive command and control, orchestration and coordination use-cases.”)
NEIL also teaches is connectable NEIL ([0196] “The compute engine interconnection design is implemented as a fully meshed IPC network (connectable) 1211 in hardware (multi-core, server processors, or within integrated circuit, SoC realizations), software, or standard high-speed network based wired and wireless methods.”)
NEILL teaches providing, from a metamodel, to each respective input interface associated with the respective aspect model, respective rules that are defined by the metamodel NEILL ([0033] “In some implementations, the process includes enabling, by one or more transports (each respective input interface) and integration interfaces (each respective input interface), client access in accordance to the transport type (associated with the respective aspect model), protocol type, and interface specifications (metamodel respective rules) of each client application or device or network thereof.”) See also NEILL ([0034] “In some implementations, the process includes providing (providing), using the structured data model (metamodel), real-time viewership measurement, content or streaming media interactivity, real-time analysis, or targeted digital content delivery based applications. In some implementations, the real-time analysis comprises targeted generation of content items for at least one of television, mobile devices, or web-based devices.”) See also NEILL ([0012] “In addition to efficient storage and access to streaming datasets, the CPS-G enables the implementation of dynamic virtual data model structures (subsequently referred to as CPS-G slices). CPS-G slices can include a CPG-G generated from specified sub-graphs of nodes organized for efficient access or graph data processing that represent a specified subset of data of interest over dynamically configurable time-period windows, spatial domains, or other entities or feature sets of interest for implementing any of the use-case application scenarios previously described. In some implementations, CPS-G slices are generated through execution of one or more CPS-G graph query operations within the CPS-G model generator module comprising one or more CPS-G model (metamodel) generator engines (one or more parallel software processes). A function of the CPS-G model generator engines is to produce CPS-G sub-graphs from the CPS-G full graph based on programmatically defined input requirement specifications or rules (respective rules that are defined by the metamodel). The rules (respective rules) are derived in accordance with one or more query constructs (defined by the metamodel).”)
NEILL also teaches setting up each respective aspect model according to the respective rules NEIL ([0174] “FIG. 8B illustrates a generated CPS-G (compact pattern stream graph) data structure 810 including a forest of simple trees. For the data structure 810, each root node 818a-c of the forest (each respective aspect model) represents a device or positional identifier, and three relevant pattern streams and a pattern timestamps are encoded (setting up) and stored in the graph structure by pattern entities representing some feature of interest (according to the respective rules) (feature 1), the individual data elements (A, B, C, timestamp), stream patterns (A, AB, C), and frequency or occurrences (2, 2, 3).”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER by providing, from a metamodel, to each respective input interface associated with the respective aspect model, respective rules that are defined by the metamodel. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 16 is rejected based on the combination of these references.
Claim 28
Claim 28 is rejected because it is the non-transitory machine-readable memory medium on which is embodiment of claim 16 with similar limitations to claim 16, and is such rejected using the same reasoning found in claim 16. See NEILL ([0042] “In some implementations, one or more non-transitory computer-readable media store instructions for real time processing of a data stream using a graph-based data model, the instructions causing, when executed by at least one processor, the at least one processor to perform operations to perform one or more of the processes previously described.”) Accordingly, claim 28 is rejected based on the combination of these references.
Claim 29
Claim 29 is rejected because it is the non-transitory machine-readable memory medium on which is embodiment of claim 16 with similar limitations to claim 16, and is such rejected using the same reasoning found in claim 16. See NEILL ([0218] “Some implementations of subject matter and operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them.”) Accordingly, claim 29 is rejected based on the combination of these references.
Claim 17
Claim 17 is rejected because the combination of BURMESTER and NEILL teach claim 16. BURMESTER does not explicitly teach wherein a structure of each respective aspect model is a respective directed graph.
However, NEILL teaches wherein a structure of each respective aspect model is a respective directed graph ([Abstract] “A method for processing of a data stream using a graph based data model (each respective aspect model) includes receiving a data stream including data messages; disassembling the data messages data elements and metadata; generating a structured data model (each respective aspect model) comprising the set of data elements (structure) based on the type of the data elements (structure) and the pattern of the data messages (structure); instantiating a workflow to process the structured data model (each respective aspect model); configuring a CPS-G model sub-graph (a respective directed graph) to add to the CPS-G model based on the type of the data elements and the pattern of the data messages; adding the CPS-G model sub-graph (a respective directed graph) to the CPS-G model to form the CPS-G dataset; and storing the CPS-G dataset including the CPS-G model sub-graph (a respective directed graph) in a data store for further processing by streaming computation and machine learning algorithms.
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein a structure of each respective aspect model is a respective directed graph. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 17 is rejected based on the combination of these references.
Claim 18
Claim 18 is rejected because the combination of BURMESTER and NEILL teach claim 17. BURMESTER does not explicitly teach wherein nodes identify individual data points and/or groups of data points in each respective directed graph.
However, NEILL teaches wherein nodes identify individual data points and/or groups of data points in each respective directed graph NEILL ([0009] “pointers to subtrees of the current CPS prefix structure, pointers to other CPS prefix structures, pointers to other graph nodes or sub-graph structures (in each respective directed graph) including virtualized graph or sub-graph structures (in each respective directed graph) (a graph or set of nodes and edges created from a given sub-graph of the full graph topology), or pointer nodes (nodes identify) enabling access through interfaces (individual data points and/or groups of data points) providing runtime access from applications or other data processing system modules.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein nodes identify individual data points and/or groups of data points in each respective directed graph. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 18 is rejected based on the combination of these references.
Claim 19
Claim 19 is rejected because the combination of BURMESTER and NEILL teach claim 18. BURMESTER does not explicitly teach wherein the structure of each respective aspect model contains at least one respective subgraph that describes properties of the data points identified by the nodes and/or properties of the data points of the group identified by the nodes.
However, NEILL teaches wherein the structure of each respective aspect model contains at least one respective subgraph that describes properties of the data points identified by the nodes and/or properties of the data points of the group identified by the nodes NEILL ([0025] “some implementations, the CPS-G model (respective aspect model) comprises a complete graph (data points) comprising an assembly of nodes (identified by the nodes) and edges (and/or properties of the data points of the group), with one or more compact pattern (structure) stream subgraph structures (contains at least one respective subgraph) embedded within the graph. In some implementations, the CPS-G model comprises an assembly of sub-graphs organized as a forest data-model structure wherein each graph node (identified by the nodes) in the forest data-model structure includes one of a dataset entities, a feature, or a platform (describes properties of the data points).”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein the structure of each respective aspect model contains at least one respective subgraph that describes properties of the data points identified by the nodes and/or properties of the data points of the group identified by the nodes. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 19 is rejected based on the combination of these references.
Claim 20
Claim 20 is rejected because the combination of BURMESTER and NEILL teach claim 19. BURMESTER does not explicitly teach wherein at least one respective subgraph is also unambiguously reachable during traversal of the graph up to a predefinable node.
However, NEILL teaches wherein at least one respective subgraph is also unambiguously reachable during traversal of the graph up to a predefinable node NEILL ([0167] “The generic CPS-G graph 804 includes each of the graph nodes NID 1-NIDN as identifiers for device identifiers and in this example represented as a Forrest graph structure. Each sub-graph 808a-h is made up of one or more CPS prefix structures, some of which have node pointers 806a-d (up to a predefinable node) to other sub-graph structures (at least one respective subgraph) to facilitate efficient graph traversal (unambiguously reachable during traversal of the graph). This embodiment enables the support of high performance search and computation across selected subgraph node entities without having to process, traverse, or search through unrelated node entities.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein at least one respective subgraph is also unambiguously reachable during traversal of the graph up to a predefinable node. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 20 is rejected based on the combination of these references.
Claim 21
Claim 21 is rejected because the combination of BURMESTER and NEILL teach claim 21. BURMESTER does not explicitly teach wherein the data integration device is configured to interpret data received from each respective device corresponding to the properties described in the subgraph.
However, NEILL teaches wherein the data integration device is configured to interpret data received from each respective device corresponding to the properties described in the subgraph NEILL ([0168] “Each CPS-G generator engine (data integration device) 704a, 704b, 704c ... 704n receives its assigned input window streaming datasets (configured to interpret data received) associated to a workflow (from each respective device), constructing the corresponding CPS-G tree sub-graph structures (corresponding to the properties described in the subgraph) that are linked in the CPS-G forest data structure 804. The subgraphs (e.g., prefix trees) can be generated in parallel. Each sub-graph (including associated multi-dimensional data structures or tensor data models) is configured for storing both metadata and pointers to other graph nodes.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein the data integration device is configured to interpret data received from each respective device corresponding to the properties described in the subgraph. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 21 is rejected based on the combination of these references.
Claim 22
Claim 22 is rejected because the combination of BURMESTER and NEILL teach claim 17. BURMESTER does not explicitly teach wherein each respective input interface is connected to a respective aspect processing device with which the respective aspect model is associated.
However, NEILL teaches wherein each respective input interface is connected to a respective aspect processing device with which the respective aspect model is associated NEILL ([0082] “The data processing system (respective input interface) performs the data summarization techniques to also perform labeling of data streams, data-model matching (the respective aspect model) (as previously described), and pattern analysis to extract relevant data elements for application-specific processing (e.g., configured to satisfy stated requirements of the application).”)
See also NEILL ([0083] “The data processing system (respective input interface) is configured to ingest data (connected to) from a large number of devices and types of applications (a respective aspect processing device), including the Internet-of-things (IoT), mobile devices (e.g., smartphones or cellular-enabled devices), and autonomous devices (such as drones, robotic devices, and vehicles) (each respective input interfaces). The data processing system is configured for processing data at a high-scale within real-time constraints (e.g., processing data streams as they arrive). The data processing system is configured for processing data in multiple formats, types, and range specifications in accordance with networks of many different kinds of applications and devices and their respective streaming data capabilities (with which the respective aspect model is associated). For example, the data processing system is configured to process data streams that are highly redundant in nature ( e.g., a majority of the data are redundant). In this context, data patterns indicate where relevant data or data outliers exist in the data.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein the data integration device is configured to interpret data received from each respective device corresponding to the properties described in the subgraph. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 22 is rejected based on the combination of these references.
Claim 23
Claim 23 is rejected because the combination of BURMESTER and NEILL teach claim 22. BURMESTER does not explicitly teach wherein each respective processing is configured to provide a portion of those runtime data at a respective output interface which are provided to the respective input interface with which the respective aspect processing device is associated.
However, NEILL teaches wherein each respective processing is configured to provide a portion of those runtime data at a respective output interface which are provided to the respective input interface with which the respective aspect processing device is associated NEILL ([0173] “Interconnection among CPS-G generators 704a-n, CPS-G forest/graph generator 706, and external interfaces 702, 710, and 716 (each respective processing) is accomplished (is configured) through a meshed inter-process communications (IPC) network 708 in hardware (multi-core, server processors, or within integrated circuit, system-on-chip (SoC) realizations, software, or standard high-speed network based wired and wireless methods (to provide a portion of those runtime data). Access to CPS-G datasets are available through the two input/output interfaces 710, 716 (at a respective output interface) for access in either streamed real-time output (at a respective output interface), or from persistent storage or CPS-G database 712. Orchestration of the CPS-G components is managed by the module workflow engine 714. The workflow engine 714 manages the configuration and runtime execution of components (each respective processing is configured to provide a portion of those runtime data) including managing the storage processing of persisting the in-memory database 712 structures to persistent CPS-G database storage. In this way, datasets for long term time periods (with which the respective aspect processing device is associated) are available in addition to the in-memory data.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein each respective processing is configured to provide a portion of those runtime data at a respective output interface which are provided to the respective input interface with which the respective aspect processing device is associated. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 23 is rejected based on the combination of these references.
Claim 24
Claim 24 is rejected because the combination of BURMESTER and NEILL teach claim 23. BURMESTER does not explicitly teach wherein the portion of runtime data provided at the respective output interface is contained in an aspect of the runtime data which characterizes that respective aspect model which is associated with the respective aspect processing device to which the respective output interface belongs.
However, NEILL teaches wherein the portion of runtime data provided at the respective output interface is contained in an aspect of the runtime data which characterizes that respective aspect model which is associated with the respective aspect processing device to which the respective output interface belongs NEILL ([0173] “Interconnection among CPS-G generators 704a-n, CPS-G forest/graph generator 706, and external interfaces 702, 710, and 716 is accomplished through a meshed inter-process communications (IPC) network 708 in hardware (multi-core, server processors, or within integrated circuit, system-on-chip (SoC) realizations, software, or standard high-speed network based wired and wireless methods (which is associated with the respective aspect processing device). Access to CPS-G datasets are available through the two input/output interfaces 710, 716 (provided at the respective output interface) for access in either streamed real-time output, or from persistent storage (is contained in an aspect of the runtime data) or CPS-G database 712 (to which the respective output interface belongs). Orchestration of the CPS-G components is managed by the module workflow engine 714. The workflow engine 714 (which characterizes that respective aspect model) manages the configuration and runtime execution of components (wherein the portion of runtime data) including managing the storage processing of persisting the in-memory database 712 structures to persistent CPS-G database storage. In this way, datasets for long term time periods (with which the respective aspect processing device is associated) are available in addition to the in-memory data.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein the portion of runtime data provided at the respective output interface is contained in an aspect of the runtime data which characterizes that respective aspect model which is associated with the respective aspect processing device to which the respective output interface belongs. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 24 is rejected based on the combination of these references.
Claim 25
Claim 25 is rejected because the combination of BURMESTER and NEILL teach claim 23. BURMESTER does not explicitly teach wherein the individual data points and/or the data points of the groups of data points are those data points that are provided at the respective output interface.
However, NEILL teaches wherein the individual data points and/or the data points of the groups of data points are those data points that are provided at the respective output interface NEILL ([0173] “Access to CPS-G datasets (individual data points and/or the data points of the groups of data points) are available through the two input/output interfaces 710, 716 (are provided at the respective output interface) for access in either streamed real-time output, or from persistent storage or CPS-G database 712.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein the individual data points and/or the data points of the groups of data points are those data points that are provided at the respective output interface. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 25 is rejected based on the combination of these references.
Claim 26
Claim 26 is rejected because the combination of BURMESTER and NEILL teach claim 19. BURMESTER teaches predefined in a hierarchical structure of selectable classes, are predefined by the metamodel ([Consistency management environment] “For the specific requirements of the outlined tool-integration framework, a flexible consistency management environment has been implemented (predefined in a hierarchical structure of selectable classes) (cf. [30]). The part of the consistency management architecture that is relevant for execution is summarized in Fig. 17. Consistency rules (predefined in a hierarchical structure) can be organized in the form of catalogs and different categories (of selectable classes) to permit each plug-in to add its own rules in an organized manner (predefined in a hierarchical structure of selectable classes). Such predefined catalogs (selectable classes) can then be loaded on demand or at startup of a plug-in to configure the Fujaba Tool Suite for the specific needs of an individual tool-integration scenario.”) See also BURMESTER ([Figure 17].)
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BURMESTER Figure 17 Reference
BURMESTER does not explicitly teach wherein the properties established by the respective subgraph.
However, NEILL teaches wherein the properties established by the respective subgraph NEILL ([0178] “A sub-graph 904 is formed by (by the respective subgraph) the module 112 based on a client template or rule set, or based on other configurable logic (wherein the properties established). In some implementations, no sub-graph is generated. In some implementations, each CPS prefix tree is a subgraph. In some implementations, a rule set (wherein the properties established) defines a sub-graph (by the respective subgraph) such that it includes only elements from features 1, 2 and 3; entities A, B, E, F, and G; and a summary of patterns A, E, F, AB, and FG. The sub-graph can be any configuration desired for later use in machine learning models, generations of visualizations, or other applications.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein the properties established by the respective subgraph. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 26 is rejected based on the combination of these references.
Claim 27
Claim 27 is rejected because the combination of BURMESTER and NEILL teach claim 22. BURMESTER does not explicitly teach wherein each respective aspect processing device is created from the respective aspect models.
However, NEILL teaches wherein each respective aspect processing device is created from the respective aspect models NEILL ([0096] “The adaptive windowing module 104 includes one or more processing devices (each respective aspect processing device) and associated memory configured (is created) to select and forward data in temporal order through reservoir sampling channels (from the respective aspect models). The sampling channels (from the respective aspect models) implement configurable sliding windows and sample policies to incoming data-streams. In some implementations, two reservoir sampling channels (from the respective aspect models) are used for scalability and to increase processing performance.”)
It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the teachings of NEILL with BURMESTER as the references deal with a method for generating a data integration device. NEILL would modify BURMESTER wherein each respective aspect processing device is created from the respective aspect models. The benefits of doing so provides a data integration device that is flexibly coupleable to a plurality of (runtime) data, it being ensured that the various specific properties of the particular (runtime) data may be taken into account in the interpretation in order to ensure a correct interpretation of the particular(runtime) data. (NEILL [0003]). Accordingly, claim 27 is rejected based on the combination of these references.
Conclusion
THIS ACTION IS MADE FINAL. 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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/M.K.V./Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186