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.
Claim(s) 1, 4, 5, 7, 8, 11, 12, 14, 15, 18 and 19 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because:
[STEP 1] The claims recite at least a process, machine, manufacture or composition of matter. The claim(s) is/are to a process, machine, manufacture or composition of matter, which is one of the statutory categories of invention (Step 1: YES).
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
[STEP2A PRONG I] The claim(s) 1, 8 and 19 recite(s) “receiving task execution data of user interaction with a computing system for performing a task; generating a task graph based on the task execution data; identifying patterns of sequences of actions for performing the task based on the task graph; and outputting the identified patterns.
The non-highlighted aforementioned, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “memory and processor” nothing in the claims element precludes the step from practically being performed in the mind and/or with pen/paper. For example, a person can receive task information, draw a task graph, identify patterns and output the identified patterns.
Accordingly, the claims recite a judicial exception, and the analysis must therefore proceed to Step 2A Prong Two.
[STEP2A PRONG II] This judicial exception is not integrated into a practical application. In particular, the claims 1, 8 and 19 only recites the additional element(s) – “generating a task graph”.
The aforementioned steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Furthermore, presenting one or more solution falls under as being Well-Understood, Routine and Conventional. Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. (Step 2A: YES).
[STEP2B] The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the aforementioned steps amounts to no more than mere instructions to apply the exception using a generic computer component, which cannot provide an inventive concept.
As noted previously, the claims as a whole merely describes how to generally “apply” the aforementioned concept in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
The claims are not patent eligible. (Step 2B: NO).
Claim(s) 4, 5, 7 (similarly claims 11, 12, 18 and 19) is/are dependent on supra claim(s) and includes all the limitations of the claim(s). Therefore, the dependent claim(s) recite(s) the same abstract idea. The claims recites the additional limitations of: “wherein identifying patterns of sequences of actions for performing the task based on the task graph comprises: receiving user input defining a start action, an end action, and an additional action; and identifying the sequences of the actions in the task graph that are between the start action and the end action and include the additional action”, “wherein identifying the sequences of the actions in the task graph between the start action and the end action comprises: filtering the task graph to identify the sequences of the actions that start with the start action and end with the end action”, “wherein generating a task graph based on the task execution data comprises: receiving user input modifying the task graph.”, which are no more than mere instructions to apply the exception using a generic computer component, generally linking the use of the judicial exception to a particular technological environment or field of use, insignificant extra-solution activity, or that are well understood, routine and conventional activities previously known to the industry. Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. The additional element(s) of perform the aforementioned steps amounts to no more than mere instructions to apply the exception using a generic computer component, which cannot provide an inventive concept.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity or well-known, routine, and conventional activity in Step 2A should be reevaluated in Step 2B. Here, the aforementioned step(s) was/were considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the specification does not provide any indication that the additional element(s) is/are anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here), and the Electric Power Group, LLC v. Alstom S.A., and Ameranth, court decisions cited in MPEP 2106.05(g) indicate that displaying data is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (Pub 20220197306) (hereafter Cella) in view of Scheepens et al. (Pub 20210200758) (hereafter Scheepens).
As per claim 1, Cella teaches:
A computer-implemented method comprising:
receiving task execution data of user interaction with a computing system for performing a task; ([Paragraph 1057], Each time the user interacts with the client application 8052, the client application 8052 may monitor the user's actions and may report the actions back to the expert agent system 8008. Over time, the expert agent system 8008 may learn how the particular user responds to certain situations…)
generating a task graph based on the task execution data; ([Paragraph 292], In example embodiments, the knowledge graph may be a prevalent example of when a graph database and graph database architecture may be used. In some examples, the knowledge graph may be used to graph a workflow. For a linear workflow, a directed acyclic graph may be used. For a contingent workflow, a cyclic graph may be used. The graph database (e.g., graph database architectures 1124) may include the knowledge graph or the knowledge graph may be an example of the graph database. In example embodiments, the knowledge graph may include ontology and connections (e.g., relationships) between the ontology of the knowledge graph. In an example, the knowledge graph may be used to capture an articulation of knowledge domains of a human expert such that there may be an identification of opportunities to design and build robotic process automation or other intelligence that may replicate this knowledge set.)
identifying patterns of sequences of actions for performing the task based on the task graph; and ([Paragraph 108], In embodiments, the artificial intelligence system is configured to analyze usage patterns associated with one or more users and learn user preferences with respect to materials, orientations, and/or print strategies. [Paragraph 462], As noted elsewhere herein and in documents incorporated by reference, artificial intelligence (such as any of the techniques or systems described throughout this disclosure) in connection with value chain network entities 652 and related processes and applications may be used to facilitate, among other things: (a) the optimization, automation and/or control of various functions, workflows, applications, features, resource utilization and other factors, (b) recognition or diagnosis of various states, entities, patterns, events, contexts, behaviors, or other elements; and/or (c) the forecasting of various states, events, contexts or other factors… [Paragraph 1814], In embodiments, the artificial intelligence system 10212 may be configured to analyze usage patterns associated with one or more users and learning user preferences with respect to outputs, timing, materials, colors, shapes, orientations, and/or print strategies. For example, the system 10212 may develop a profile, such as by the additive manufacturing unit 10102, by location, by user, by organization, by role, or the like, that indicates what materials were used for manufacturing, what processes were used for manufacturing, what shapes were produced, what finishing steps were undertaken, what colors were used, what functions were enabled, and the like. The profile may be used to determine, infer, or suggest preferences of users, organizations, or the like. For example, an organization's preferred brand colors may be recognized, such that conforming materials and coatings are recommended and/or preconfigured in development of additive manufacturing steps.)
outputting the identified patterns. ([Paragraph 76], FIG. 76 through FIG. 103 are schematic diagrams of embodiments of neural net systems that may connect to, be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure.)
Although Cella discloses pattern recognition.
Cella does not explicitly disclose patterns of sequences of actions.
Scheepens teaches patterns of sequences of actions. ([Paragraph 15], Process mining involves the analysis of a process to identify trends, patterns, and other process analytical measures. In accordance with embodiments of the present invention, process mining may be performed based on an edge table representing execution of the process. Each row of the edge table identifies a transition from a source event to a destination event of the execution of the process.)
Scheepens also teaches task graph ([Paragraph 15], Process mining involves the analysis of a process to identify trends, patterns, and other process analytical measures. In accordance with embodiments of the present invention, process mining may be performed based on an edge table representing execution of the process. Each row of the edge table identifies a transition from a source event to a destination event of the execution of the process. Accordingly, metrics associated with the transition and/or the destination event may be computed from the edge table. An example of a process is shown in FIG. 1A as process 100 for document processing, which may be implemented as a robotic process automation (RPA) process. Another example of a process is shown in FIG. 1B as process 150 for invoice process, which may be implemented as a business workflow.)
It would have been obvious to a person with ordinary skill in the art, before the effective filing date of the invention, to combine the teachings of Cella wherein task execution data of user interactions is received, task graph is generated to graph the user workflow, user patterns are identified and outputted for machine learning, into teachings of Scheepens wherein patterns of sequence of actions are recognized based on task graph/workflow to be implemented as a robotic process automation, because this would enhance the teachings of Cella wherein recognizing sequence of actions/patterns, machine learning can be leveraged to produce/enhance robotic process automation.
As per claim 2, rejection of claim 1 is incorporated:
Scheepens teaches wherein identifying patterns of sequences of actions for performing the task based on the task graph comprises: identifying the patterns of the sequences of the actions for performing the task using a language model. ([Paragraph 15], Process mining involves the analysis of a process to identify trends, patterns, and other process analytical measures. In accordance with embodiments of the present invention, process mining may be performed based on an edge table representing execution of the process. Each row of the edge table identifies a transition from a source event to a destination event of the execution of the process.)
Cellan teaches language model ([Paragraph 533], In embodiments, the unified set of adaptive intelligent systems includes a set of biometric systems. In embodiments, the unified set of adaptive intelligent systems includes a set of natural language processing systems. [Paragraph 1833], In embodiments an instruction set for additive manufacturing may be automatically generated from a text description, such as using a blend of natural language-based artificial intelligence and other artificial intelligence for handling and/or generating images and/or spatial representations, such as using the DALL-E language model from OpenAI…)
As per claim 3, rejection of claim 2 is incorporated:
Cellan teaches wherein the language model is a large language model. ([Paragraph 533], In embodiments, the unified set of adaptive intelligent systems includes a set of biometric systems. In embodiments, the unified set of adaptive intelligent systems includes a set of natural language processing systems. [Paragraph 1833], In embodiments an instruction set for additive manufacturing may be automatically generated from a text description, such as using a blend of natural language-based artificial intelligence and other artificial intelligence for handling and/or generating images and/or spatial representations, such as using the DALL-E language model from OpenAI…)
As per claim 4, rejection of claim 1 is incorporated:
Scheepens teaches wherein identifying patterns of sequences of actions for performing the task based on the task graph comprises: receiving user input defining a start action, an end action, and an additional action; and identifying the sequences of the actions in the task graph that are between the start action and the end action and include the additional action. ([Fig. 1A] [Paragraph 15], Process mining involves the analysis of a process to identify trends, patterns, and other process analytical measures. In accordance with embodiments of the present invention, process mining may be performed based on an edge table representing execution of the process. Each row of the edge table identifies a transition from a source event to a destination event of the execution of the process. [Paragraph 16], Process 100 is shown in FIG. 1A as an RPA workflow for automatic document processing performed using RPA robots. However, it should be understood that process 100 may be any suitable process that can be modelled as a workflow, such as, e.g., a business workflow. Process 100 comprises activities 102-126. As shown in FIG. 1A, process 100 is modeled as a directed graph where each activity 102-126 is represented as a node and each transition between activities is represented as edges linking the nodes. The transition between activities represents the execution of process 100 from a source activity to a destination activity.)
Cella also teaches ([Paragraph 658], Similarly, the process digital twin may be seen as comprised of digital twins of multiple sub-processes representing entities selected from among supply chain entities, demand management entities and value chain network entities. For example, the digital twin of a packaging process is comprised of digital twins of sub-processes for picking, moving, inspecting and packing the product. As another example, the digital twin of warehousing process may be seen as comprised of digital twins of multiple sub-processes including receiving, storing, picking and shipping of stored inventories. [Paragraph 1278], In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use an autoencoder, autoassociator or Diabolo neural network, which may be similar to a multilayer perceptron (MLP) neural network, such as where there may be an input layer, an output layer and one or more hidden layers connecting them.)
As per claim 5, rejection of claim 4 is incorporated:
Cella teaches wherein identifying the sequences of the actions in the task graph between the start action and the end action comprises: filtering the task graph to identify the sequences of the actions that start with the start action and end with the end action. ([Paragraph 18], A robot fleet management platform for configuring robot fleet resources includes a set of one or more processors that execute a set of computer-readable instructions. The set of one or more processors collectively execute a job parsing system that applies a set of filters to job content received in association with a job request to identify portions thereof suitable for robot automation. A task definition system establishes a set of robot tasks that each define at least a type of robot and a task objective, the set of robot tasks being based at least in part on the portions of the job request that are suitable for robot automation and meet a first fleet objective of a set of fleet objectives. [Paragraph 22], In other features, the job configuration system includes a job parsing system that applies content and structural filters to job content received in association with a job request to identify portions thereof suitable for robot automation. In other features, the job configuration system includes a task definition system that establishes a set of robot tasks that each define at least a type of robot and a task objective, the set of robot tasks are based at least in part on the portions of the job request that are suitable for robot automation and meet a first fleet objective of the set of fleet objectives. )
Scheepens also teaches ([Fig. 1A] [Paragraph 15], Process mining involves the analysis of a process to identify trends, patterns, and other process analytical measures. In accordance with embodiments of the present invention, process mining may be performed based on an edge table representing execution of the process. Each row of the edge table identifies a transition from a source event to a destination event of the execution of the process. [Paragraph 36], In one embodiment, transitions between events in an edge table can be represented directly in BI charts. The edge table may be filtered or enhanced, and the resulting edge table may be shown directly as a process graph and/or a BI chart. The edge table may act as a normal table in a BI system, resulting in all BI functionality, such as, e.g., filtering, selection, calculating metrics, joining to other tables, adding new (derived) attributes, etc., available on transitions in the edge table.)
As per claim 6, rejection of claim 4 is incorporated:
Cella teaches further comprising: determining a similarity measure between a first sequence of the sequences of the actions and a second sequence of the sequences of the actions. ([paragraph 32], In other features, the robot fleet platform includes a qualified data resolution system configured to evaluate at least one qualified data element in the job content for similarity to clarified data from a plurality of job requests, and based on an outcome of the evaluation to adjust the at least one qualified data element based on a similar clarified data element. [Paragraph 463], Search results or recommendations may, in embodiments, be based at least in part on collaborative filtering, such as by asking developers to indicate or select elements of favorable models, as well as by clustering, such as by using similarity matrices, k-means clustering, or other clustering techniques that associate similar developers, similar domain-specific problems, and/or similar artificial intelligence solutions.)
Scheepens also teaches ([Fig. 1A] [Paragraph 15], Process mining involves the analysis of a process to identify trends, patterns, and other process analytical measures. In accordance with embodiments of the present invention, process mining may be performed based on an edge table representing execution of the process. Each row of the edge table identifies a transition from a source event to a destination event of the execution of the process.)
As per claim 7, rejection of claim 1 is incorporated:
Scheepens teaches wherein generating a task graph based on the task execution data comprises: receiving user input modifying the task graph ([Paragraph 26], In one embodiment, for example where the process execution data is a BPMN (business process model and notation) process model, the edge table may be generated by storing each edge in the process model as a single transition between its source activity and its destination activity. The edge table may optionally include columns identifying the model node type of the node associated with the source activity and the node associated with the destination activity. The model node type represents the semantics of the node and may be one of the following: Activity, And gateway, Xor gateway, Start, or End. Other node types are also contemplated. The nodes types are determined from mining algorithms or direct input). The model node type stored in the edge table allows the node type of be uniformly reused in process graphs and BI charts.)
Cella also teaches ([Paragraph 292], In embodiments, the storage layer 624 may store data in one or more knowledge graphs (such as a directed acyclic graph, a data map, a data hierarchy, a data cluster including links and nodes, a self-organizing map, or the like) in the graph database architectures 1124. In example embodiments, the knowledge graph may be a prevalent example of when a graph database and graph database architecture may be used. In some examples, the knowledge graph may be used to graph a workflow. For a linear workflow, a directed acyclic graph may be used. For a contingent workflow, a cyclic graph may be used. The graph database (e.g., graph database architectures 1124) may include the knowledge graph or the knowledge graph may be an example of the graph database. In example embodiments, the knowledge graph may include ontology and connections (e.g., relationships) between the ontology of the knowledge graph. In an example, the knowledge graph may be used to capture an articulation of knowledge domains of a human expert such that there may be an identification of opportunities to design and build robotic process automation or other intelligence that may replicate this knowledge set. )
As per claims 8-14, these are system claims corresponding to the method claims 1-7. Therefore, rejected based on similar rationale.
As per claim 15-20, these are non-transitory computer-readable medium claims corresponding to the method claims 1-6. Therefore, rejected based on similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fullmer et al. (Pub 20210173704) discloses recognizing usage patterns that correspond to recurring actions or tasks initiated by a user using a device.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONG U KIM whose telephone number is (571)270-1313. The examiner can normally be reached 9:00am - 5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bradley Teets can be reached at 5712723338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DONG U KIM/Primary Examiner, Art Unit 2197
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