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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or
composition of matter, or any new and useful improvement thereof, may obtain a patent therefor,
subject to the conditions and requirements of this title.
Claims 1-20 are directed towards a system, method, and product which are
one of the four statutory categories.
However, they are rejected under 35 U.S.C. 101 because the claimed invention is directed
to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea)
without significantly more. The claims of 1, 9, and 17 are directed to a mental process with
the aid of a computer.
Limitations Directed Towards a Mental Process with the Aid of the Computer:
Generating a digital workflow twin, wherein the digital workflow twin is a digital twin of an original network
Identifying one or more potential impact regions with the digital workflow twin
Generating one or more recommendations and determining whether to propose the one or more recommendations to a user
Converting at least one of the one or more recommendations into at least one new rule
Generating a new network workflow, wherein the new network workflow is the original network with the new rule integrated
Part II. 2A-prong two (additional elements that integrate the judicial exception into a practical
application)
Under step 2A-Prong two (part 1 of Mayo test), this judicial exception is not integrated with a
practical application under the second prong of Step 2A. In particular, the claims recite the
additional elements beyond the recited abstract idea. Such as, “computer readable medium storage system…processor… memory…”
The courts have recognized the following computer functions as well-understood, routine, and
conventional functions when they are claimed in a merely generic manner (e.g., at a high level of
generality) as well-understood, routine, conventional. (MPEP 2106.05(d)
Accordingly, these additional elements do not integrate the abstract idea into a practical
application because they do not impose any meaningful limits on practicing the abstract idea.
The claims are directed to an abstract idea with no significantly more elements.
As a result, Examiner asserts that claims 1,9, and 17 are similarly directed to the abstract idea.
Since these claims are directed to an abstract idea, the Office must determine whether the
remaining limitations "do significantly more" than describe the abstract idea.
Part III. Determine whether any Element, or Combination, Amounts to "Significantly More" than Abstract Idea itself
The Alice framework, we turn to step 2B (Part 2 of Mayo) to determine if the claim is sufficient
to ensure that the claim amounts to "significantly more" than the abstract idea itself. These
additional elements recite conventional computer components and conventional functions of:
“computer readable medium storage system…processor… memory…”
Claims 1,9, and 17 do not include any limitations amounting to significantly more than the
abstract idea, alone.
In addition, Fig. 1 of the Applicant's specifications detail any combination of a generic computer system program to perform the system. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The dependent claims 2, 4, 8, 10-12,16, and 18 further limit the abstract idea without adding significantly more. Accordingly, the Examiner concludes that there are no meaningful limitations in the claims that transform the judicial exception into a patent eligible application such that the claim amounts to significantly more than the judicial exception itself.
Further, Examiner notes that the additional limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually. Claims 2, 4, 8, 10, 11-12, and 16,18 are rejected as ineligible subject matter under 35 U.S.C. 101 based on a rationale similar to independent claims 1,9, and 17.
Moreover, the dependent claims of 3, 5, 6, 7, 13, 14, 15, and 19 have the additional 35 U.S.C. 101 rejection of organizing human activity with the aid of a computer. Here, you can easily see how the claims are directing by instructions users of a computer in the application of the claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-6, 8-14, and 16-20 are rejected under 35 U.S.C. 102(a) (1) as being anticipated by Cella (US Pub. No. 2022/0163960) (hereinafter, Cella).
As per claim 1,
Cella teaches
a method for dynamic workflow adjustment, the method comprising:
(Abstract)
generating a digital workflow twin, wherein the digital workflow twin is a digital twin of an original network;
(paragraph 388, discussing how the digital twin corresponds to an industrial environment; noting “… The digital twin datastore stores an industrial-environment digital twin including real-world-element digital twins embedded therein. The industrial-environment digital twin corresponds to an industrial environment. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the industrial environment…”)
identifying one or more potential impact regions within the digital workflow twin;
(paragraphs 387 and 388, discussing how the monitored connectivity is affected using the digital twin; noting “The one or more processors are configured to monitor connectivity of the real-world elements with the connected device, determine whether the monitored connectivity matches identifying criteria for a network-connectivity state, and represent an effect of the network-connectivity state on each real-world-element digital twin. In embodiments, the one or more processors are further configured to simulate, via a digital twin simulation system, effects of the network-connectivity state on each of the real-world elements and store, via the digital twin datastore, the effect of the network-connectivity state…”)
generating one or more recommendations and determining whether to propose the one or more recommendations to a user;
(paragraph 389, noting “…automatically implement, in response to determining occurrence of the network-connectivity state, a mitigating action…”)
converting at least one of the one or more recommendations into at least one new rule;
and
generating a new network workflow, wherein the new network workflow is the original network with the at least one new rule integrated
(paragraph 987-988 wherein the paragraphs are teaching by examples how the recommendations alters events such as scheduling and maintenance; noting “… The monitoring application 8150 may provide recommendations regarding scheduling repairs and/or maintenance…”)
As per claim 2,
Cella teaches,
the method of claim 1, wherein the original network is a graphical representation of an original workflow, wherein the graphical representation is comprised of nodes and edges corresponding to logical rules and physical entities utilized in performing a workflow within an organization
(paragraph 21, noting “…In embodiments, each edge represents a relationship between two respective digital twins. In embodiments, embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective industrial entity represented by the respective discrete digital twin and the industrial environment. In embodiments, each edge represents a spatial relationship between two respective digital twins, and an operational relationship between two respective digital twins…In embodiments, each entity node of the one or more entity nodes includes one or more properties of a respective properties of the respective industrial entity represented by the entity node…”)
As per claim 3,
Cella teaches,
the method of claim 2, wherein the one or more scenarios are defined by the user in a workflow interface, wherein the user selects at least one or more specific nodes, edges, or scenarios to be simulated
(paragraph 1963, noting “… Testbed rights 13064 may include rights to implement of specific use cases and scenarios, as well as rights to produce testable outcomes to confirm that an implementation conforms to expected results, for example…”)
As per claim 4,
Cella teaches,
the method of claim 2, wherein the one or more potential impact regions within the digital workflow twin are events or activities within the workflow which exceed a predefined threshold under one or more scenarios
(paragraph 384, 871, 873 all teaching by examples)
As per claim 5,
Cella teaches,
the method of claim 4, wherein the predefined threshold is manually set by the user within a workflow interface
(paragraph 21, noting “…, the present disclosure includes receiving user input relating to one or more steps performed in an industrial process relating to the industrial environment…”)
As per claim 6,
Cella teaches,
the method of claim 1, wherein the one or more potential impact regions within the digital workflow twin are identified using one or more machine learning models and one or more performance metrics
(paragraph 589, noting “…FIG. 4 also shows on-device sensor fusion 80, such as for storing on a device data from multiple analog sensors 82, which may be analyzed locally or in the cloud, such as by machine learning 84, including by training a machine based on initial models created by humans that are augmented by providing feedback (such as based on measures of success) when operating the methods and systems disclosed herein…”)
As per claim 8,
Cella teaches,
the method of claim 6, wherein a digital twin of the new network workflow is supported by the one or more machine learning models, wherein the one or more machine learning models are continuously retrained based on additional data received
(paragraphs 2057 & 2058).
As per claims 9-14 and 16:
Claims 9-14 and 16 disclose similar limitations as above claim limitations 1-6 and 8, however, they are disclosed in a computer system. Cella discloses such a system (see, claim 43). Therefore, claims 9-14 and 16 are rejected based on the rationale of claims 1-6 and 8 above.
As per claims 17-20:
Claims 17-20 disclose similar limitations as above claims 1-4, however they are disclosed in a computer program product. Cella discloses such a product (see, Abstract). Therefore, claims 17-20 are rejected based on the rationale of claims 1-4 above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cella in view of Riedmiller (US Pub. No. 20210049467) (Hereinafter, Riedmiller).
As per claim 7,
Cella does not teach, however, Riedmiller does teach,
the method of claim 6, wherein the one or more machine learning models includes at least a Graph Neural Network, wherein the Graph Neural Network is trained to predict nodes or edges that correspond to the one or more potential impact regions
(Abstract with Figure 6 and corresponding text)
Therefore, it would have been obvious to one of ordinary skill in the art at the time of filing to incorporate the teachings of Riedmiller within the invention of Cella with the motivation of r understanding and controlling a physical system. (See, Riedmiller Abstract)
As per claims 15,
Claim 15 discloses similar limitations to claim 7 above, however, in a computer system. Cella discloses such a system (see, Abstract), therefore, claim 15 is rejected under similar rationale as claim 7.
As per claim 21-25:
Allowable Subject Matter
Claims 21-25 have allowable subject matter.
The following is a statement of reasons for the indication of allowable subject matter: The prior art as a whole and within the subject matter of the applicant’s invention, fails to disclose:
“…generating a graph representation of an original network comprising logical rules and
physical entities forming a multimodal dynamic graph comprising a correspondence of existing
nodes and edges from an original workflow; predicting regions of the multimodal dynamic graph having an impact exceeding a predetermined threshold on a predetermined business case using artificial intelligence model based simulations including a graph neural network (GNN), to identify a scenario to be improved; performing a set of simulations on the scenario identified using a representation from the original network using a case including nodes, edges and situations defined by a user; generating additional predictions for rules given different predicted topologies originated from the set of simulations and real data using GNN to create a recommendation for the scenario identified; converting the recommendation into new rules inserted in a system knowledge repository enabling network/workflow reconfiguration; and publishing a new workflow and links representing the new rules converted in connections
and links in the original network”
As maintained by the Examiner, the combination of elements as presented in claims 21-25 are not taught or fairly suggested by the prior art of record as claimed in independent claim 21, thus, the application has allowable subject matter.
The closest prior art found by the Examiner are the following:
Riedmiller: 20210049467
Riedmiller teaches a graph neural network system implementing a learnable physics engine for understanding and controlling a physical system. The physical system is considered to be composed of bodies coupled by joints and is represented by static and dynamic graphs. A graph processing neural network processes an input graph e.g. the static and dynamic graphs, to provide an output graph, e.g. a predicted dynamic graph. The graph processing neural network is differentiable and may be used for control and/or reinforcement learning. The trained graph neural network system can be applied to physical systems with similar but new graph structures (zero-shot learning).
Riedmiller does not teach,
predicting regions of the multimodal dynamic graph having an impact exceeding a predetermined threshold on a predetermined business case using artificial intelligence model-based simulations including a graph neural network (GNN), to identify a scenario to be improved as required by the current claims.
Guan: 20240330679
Guan teaches, a method for making predictions pertaining to entities represented within a heterogeneous graph includes: identifying, for each node in the heterogeneous graph structure, a set of node-target paths that connect the node to a target node; assigning, to each of the node-target paths identified for each node, a path type identifier indicative of a number of edges and corresponding edge types in the associated node-target path; and extracting a semantic tree from the heterogeneous graph structure. The semantic tree includes the target node as a root node and defines a hierarchy of metapaths that each individually correspond to a subset of the node-target paths in the heterogeneous graph structure assigned to a same path type identifier. The semantic tree is encoded, using one or more neural networks by generating a metapath embedding corresponding to each metapath in the semantic tree. Each of the resulting metapath embeddings encodes aggregated feature-label data for nodes in the heterogeneous graph structure corresponding to the path type identifier corresponding to the metapath associated with the metapath embedding. A label is predicted for the target node in the heterogeneous graph structure based on the set of metapath embeddings.
Guan does not teach,
predicting regions of the multimodal dynamic graph having an impact exceeding a predetermined threshold on a predetermined business case using artificial intelligence model-based simulations including a graph neural network (GNN), to identify a scenario to be improved as required by the current claims.
Orhan: 20220124543
Orhan teaches connection management techniques based on graph neural networks (GNN) and deep reinforcement learning (DRL) to optimize user association and load balancing. A graph structure of a communication network is considered for the GNN architecture and DRL is used to learn parameters of the GNN algorithm/model. Connection management is defined as a combinatorial graph optimization problem, and the DRL mechanism uses the underlying graph to learn weights of the GNN for an optimal user connections or associations. The connection management techniques can consider local network features to make better decisions to balance network traffic load while network throughput is also maximized. Implementations are provided based on edge computing frameworks include the Open RAN (O-RAN) architecture. Other embodiments may be described and/or claimed.
Orhan does not teach, predicting regions of the multimodal dynamic graph having an impact exceeding a predetermined threshold on a predetermined business case using artificial intelligence model-based simulations including a graph neural network (GNN), to identify a scenario to be improved as required by the current claims.
Keown: WO2024096775
Keown teaches, training and using a graph neural network, GNN. The method of training comprises: inputting first training information into a first graph convolution layer of the GNN, wherein the first training information comprises: initial performance metrics of the plurality of nodes, subsequent configurations for the plurality of nodes, and an indication of relationships between the plurality of nodes; applying weighting parameters to the training information in the first graph convolution layer to determine a set of first feature vectors for each of the plurality of nodes; deriving predicted performance metrics for each of the plurality of nodes from the set of first feature vectors; and updating the weighting parameters based a loss function, wherein the loss function is calculated based on: predicted performance metrics associated with a set of training nodes in the plurality of nodes, and actual performance metrics resulting from applying the corresponding subsequent configurations to the set of training nodes.
Keown does not teach, , predicting regions of the multimodal dynamic graph having an impact exceeding a predetermined threshold on a predetermined business case using artificial intelligence model-based simulations including a graph neural network (GNN), to identify a scenario to be improved as required by the current claims.
Zhu: 20220126445
Zhu teaches, solving task and motion planning problems. In at least one embodiment, a task and motion planning problem is modeled using a geometric scene graph that records positions and orientations of objects within a playfield, and a symbolic scene graph that represents states of objects within context of a task to be solved. In at least one embodiment, task planning is performed using symbolic scene graph, and motion planning is performed using a geometric scene graph.
Zhu does not teach, , predicting regions of the multimodal dynamic graph having an impact exceeding a predetermined threshold on a predetermined business case using artificial intelligence model-based simulations including a graph neural network (GNN), to identify a scenario to be improved as required by the current claims.
Non-Patent Literature: Liu, et al.
Liu teaches, review of GNN applications in interdependent infrastructure systems, including transportation networks, power distribution networks, water distribution networks, and communication networks. By leveraging spatial-temporal modeling, multi-modal data integration, and physics-informed learning, GNN-based approaches enhance predictive accuracy, system resilience, and decision-making efficiency.
Liu does not teach, predicting regions of the multimodal dynamic graph having an impact exceeding a predetermined threshold on a predetermined business case using artificial intelligence model-based simulations including a graph neural network (GNN), to identify a scenario to be improved as required by the current claims
While the teachings of the above prior art separately address different parts of the
claimed invention, these teachings would not be combinable by one of ordinary skill in the art at
the time of the invention with a reasonable expectation of success to provide a predictable
combination that would render the claimed invention obvious. Thus, the novelty of the claimed
invention is in the combination of limitations rather than any single limitation.
Any comments considered necessary by applicant must be submitted no later than the payment
of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.
Such submissions should be clearly labeled "Comments on Statement of Reasons for
Allowance."
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAHRA ELKASSABGI whose telephone number is (571)270-7943. The examiner can normally be reached Monday through Friday 11:30 to 8:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rutao Wu can be reached at 571.272.6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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ZAHRA . ELKASSABGI
Examiner
Art Unit 3623
/RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
Prosecution Insights