DETAILED ACTION
Claims 1-3, 5, 8-11, 13, and 16-17 are presented for examination.
This office action is in response to submission of application on 05-JANUARY-2026.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 25-MARCH-2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The amendment filed 05-JANUARY-2026 in response to the previous office action mailed 05-SEPTEMBER-2025 has been entered. Claims 1-3, 5, 8-11, 13, and 16-17 remain pending in the application.
With regards to the non-final office action' s rejections under 103, the amendments to the claims necessitated a new consideration of the art. After this consideration, the examiner respectfully disagrees with the applicant' s arguments that the art referenced in the previous office action does not teach the amendment claim limitations. A new 103 rejection over the prior art has been provided.
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 1-3, 5-11, 13-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Orsi et al. (Pub. No. WO 2014093198 A1, filed December 9th 2013, hereinafter Orsi) in view of Lorenz et al. (Pub. No. US 20050246302 A1, filed August 10th 2004, hereinafter Lorenz) further in view of Washington et al. (Pub. No. US 20010035879 A1, filed June 20th 2001, hereinafter Washington) further in view of DeAnna et al. (Pub. No. US 20120311526 A1, filed June 2nd 2011, hereinafter DeAnna).
Regarding claim 1:
Claim 1 recites:
A method comprising: receiving, by a processing device executing a rule engine, a set of rules, wherein each rule comprises a predicate associated with a constraint of the rule; and generating, based on the set of rules, a plurality of nodes of a network implementing a rule-based system, wherein each node comprises an identification of a corresponding predicate and a meta-program associated with the corresponding predicate, and wherein the meta-program is used to generate, based on the corresponding predicate, a source code associated with a respective node; and generating, based on the plurality of nodes, a network class implementing the network, receiving a working memory element comprising an asserted fact that references a constraint of a particular node of the plurality of nodes; determining a particular node of the plurality of nodes of the network to evaluate based on the network class and the asserted fact that references the constraint of the particular node; and evaluating, based on the constraint referenced by the working memory element, the particular node of the plurality of nodes of the network.
Orsi discloses receiving, by a processing device executing a rule engine, a set of rules, wherein each rule comprises a predicate associated with a constraint of the rule:
Orsi teaches receiving data from users and constructing rule sets from the user inputs (Paragraph 169). This would be analogous to receiving a set of rules as the user input is here analogous to the rules. Furthermore, rule-predicate relationships are recorded in a table (Paragraph 8) indicating that each rule comprises a predicate associated with a constraint of the rule.
Orsi discloses generating, based on the set of rules, a plurality of nodes of a network implementing a rule-based system, wherein each node comprises an identification of a corresponding predicate and a meta-program associated with the corresponding predicate:
Orsi teaches building a tree from the rules established by users (Paragraph 35). The tree would be analogous to nodes of a network implementing a rule-based system. Furthermore, each node corresponds to a pattern occurring in the conditions of a rule (Paragraph 35) which would comprise an identification of a corresponding predicate. The rule engine also has algorithmic features including pure logic programming (Paragraph 36) which would be analogous to a meta-program associated with corresponding predicate as the logic of a rule would be analogous to instructions on how to convert to rule to Java source code as outlined in the present application’s specification.
Orsi discloses wherein the meta-program is used to generate, based on the corresponding predicate, a node source code:
Orsi teaches that the rule engine can be incorporated into developing applications (Paragraph 36), which indicates that source code is generated for respective nodes of the rule engine as the application must automate the process of the rule engine in order to incorporate it.
Orsi discloses receiving a working memory element comprising an asserted fact that references a constraint of a particular node of the plurality of nodes; determining a particular node of the plurality of nodes of the network to evaluate based on the network class and the asserted fact that references the constraint of the particular node:
Orsi teaches that a node has a memory of facts which result in the pattern that occurs in the left-hand-side of a rule (Paragraph 35). This would be analogous to a working memory element as it is an asserted fact of the rules engine. Likewise, this is analogous to determining, based on a constraint reference by the working memory element and the network class, a particular node of the plurality of nodes of the network as the pattern determines which nodes contain matching patterns. This would be a form of evaluating the node as the pattern of the node is compared against the fact being propagated through the network, therefore evaluating the node for that fact.
Orsi does not disclose the network class, which is disclosed by Lorenz below.
Orsi discloses evaluating, based on the constraint referenced by the working memory element, the particular node of the plurality of nodes of the network:
Orsi teaches that nodes are annotated when a fact that reached them matches their pattern (Paragraph 35). This would be analogous to limitation as the fact would be the working memory element and the annotation would be the evaluation of the particular node as the particular node would be evaluated against the fact passing through the network in this process.
Lorenz discloses generating, based on the plurality of nodes, a network class implementing the network:
Lorenz in the same field of endeavor of rule engines teaches a class that represents Boolean network (Paragraph 104). This is analogous to a network class implementing the network.
Orsi and Lorenz are analogous art to the present application because they are in the same field of endeavor.
The combination of Orsi and Lorenz would provide the advantage of easy deployment and portability, as discussed in Lorenz (Lorenz, Paragraph 104).
Washington discloses wherein generating the network class comprises, for each node of the plurality of nodes: generating a node source code for the node based on the corresponding meta- program associated with the predicate identified by the node; and inlining the node source code in the network class:
Washington in the same field of endeavor of self-instantiating nodes teaches a programmatically generated source code that is associated with a node, which may replace a node in a graphical program (Paragraph 23) wherein the replaced node is a node with default functionality (Paragraph 26). The programmatically generated source code associated with a node would be equivalent to generating a node source code for the node. Furthermore, the replacement of the node with the node source code would be inlining the node source code wherein the network class is the node with default functionality.
Furthermore, Washington teaches that functionality is specified for the node from which the source code may be generated (Paragraph 23). A specified functionality would be a corresponding meta-program associated with the corresponding predicate identified by the node.
While the nodes of Washington are not the same type of nodes as in Lorenz and Orsi, the method of generating source code node to replace an existing default in combination with the previously taught limitations would be obvious, as described below.
Washington is analogous art to the present application because they are both in the same field of endeavor of generation of nodes that are connected to a desired program functionality (Washington, Paragraph 3). The present application is also considered to be in this field as the present application regards a rules engine process that uses nodes to implement a desired functionality, i.e. the rules and logic of the rules engine.
The combination of Orsi in view of Lorenz and Washington would provide the advantage of programmatically generating nodes based on program functionality (Washington, Paragraph 17), which would be useful for Orsi and Lorenz as it would make the processing of big data more manageable through the abstraction provided by Washington (see Orsi, Paragraph 3) as well as allowing for greater cohesion between nodes they may be made with the same functionality in mind (see Lorenz, Paragraph 8).
DeAnna discloses wherein the meta-program associated with the corresponding predicate identified by the node comprises a set of instructions for generating, based on the corresponding predicate, the node source code from a code representation of the corresponding predicate identified by the corresponding node:
The examiner understands this claim limitation to be describing a process wherein a set of instruction (i.e., the meta-program) generates source code that performs the functionality of a given node (e.g., node source code) based on a particular statement about the functionality or the logic of how the node should work (the corresponding predicate) and the code that would enable this logic (the code representation of the predicate). It has been examined under this light.
DeAnna teaches generating source code for each node type of an application, wherein the node type would describe the particular functionality of the node (Paragraph 116), wherein the particular functionality of a node would be analogous to the corresponding predicate. Therefore DeAnna teaches generating source code based on the corresponding predicate identified by the corresponding node.
Furthermore, DeAnna teaches capturing a meta-data definition describing at least the plurality of node types (Paragraph 7) wherein the meta-data is processed as per an application object model to generate code for the application (Paragraph 30). This would include the source code as described above. Therefore, the meta-data as processed by the application object model would be a meta-program comprising instructions for generating source code.
Previously the applicant has argued that DeAnna does not teach a previous limitation regarding the generation of node source code and the inlining of said code. The examiner has previously agreed with this, and only argues now that the teachings of DeAnna would disclose the above limitation in combination with the teachings of Orsi, Lorenz, and Washington, and has provided what would be equivalent to the node source code for the purposes of said combination.
In this case, the meta-program of Washington as discussed above may be used in combination with the teachings of DeAnna in order to provide a corresponding meta-program associated with the corresponding predicate identified by the node that could be used with the functionality of DeAnna’s meta-program such that DeAnna’s meta-program may apply individually to nodes.
Furthermore, the node source code of Washington may be used as an explicit connection of a particular node to source code, rather than the broad types of DeAnna.
Essentially, Washington provides DeAnna with greater granularity for applying its systems to individual nodes, as can be seen by the functionality of Washington as described in above limitations.
DeAnna is analogous art to the present application because they are both in the same field of endeavor of node-based networks.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Orsi and the teachings of Lorenz. This would have provided the advantage of easy deployment and portability, as discussed in Lorenz (Lorenz, Paragraph 104).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Orsi in view of Lorenz and the teachings of Washington. This would have provided the advantage of of programmatically generating nodes based on program functionality (Washington, Paragraph 17), which would be useful for Orsi and Lorenz as it would make the processing of big data more manageable through the abstraction provided by Washington (see Orsi, Paragraph 3) as well as allowing for greater cohesion between nodes they may be made with the same functionality in mind (see Lorenz, Paragraph 8).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Orsi in view of Lorenz and the teachings of Washington and the teachings of DeAnna. This would have provided the advantage of broader application of generation of code generation methods for heterogeneous networks (DeAnna, paragraph 3) which would enable the methods of Orsi, Lorenz, and Washington to have greater reach.
Regarding claim 2, which depends upon claim 1:
Claim 2 recites:
The method of claim 1, wherein the node source code is a source code used to instantiate the node.
Orsi in view of Lorenz further in view of Washington further in view of DeAnna teaches the method of claim 1 upon which claim 2 depends. Furthermore, regarding the limitation of claim 2:
Orsi teaches the instantiation of a rule (Paragraph 57) which would be analogous to the instantiation of the node as the node is representative of the rule.
Regarding claim 3, which depends upon claim 1:
Claim 3 recites:
The method of claim 1, wherein the network class is an executable code representation of the plurality of nodes.
Orsi in view of Lorenz further in view of Washington further in view of DeAnna teaches the method of claim 1 upon which claim 3 depends. However, Orsi does not teach the limitation of claim 3:
Lorenz teaches network node factory classes that create the Boolean network based on a configuration from a user (Paragraph 211). This would be analogous to the network class being an executable code representation of the plurality of nodes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Orsi in view of Washington further in view of DeAnna and the teachings of Lorenz. This would have provided the advantage of better performance with large amounts of data, as discussed in Lorenz (Paragraph 23).
Regarding claim 5, which depends upon claim 4:
Claim 5 recites:
The method of claim 1, wherein inlining the source code in the network class comprises: replacing the node in the network class with the node source code.
Orsi in view of Lorenz further in view of Washington further in view of DeAnna teaches the method of claim 1 upon which claim 5 depends. However, Orsi in view of Lorenz does not teach the limitation of claim 5:
Washington teaches a programmatically generated source code that is associated with a node, which may replace a node in a graphical program (Paragraph 23) wherein the replaced node is a node with default functionality (Paragraph 26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Orsi, Washington, and DeAnna and the teachings of Lorenz. This would have provided the advantage of easy deployment and portability, as discussed in Lorenz (Lorenz, Paragraph 104).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Orsi in view of Lorenz further in view of DeAnna and the teachings of Washington. This would have provided the advantage of programmatically generating nodes without direct user input (Washington, Paragraph 17).
Regarding claim 8, which depends upon claim 1:
Claim 8 recites:
The method of claim 1, wherein the set of rules is defined using an executable model language.
Orsi in view of Lorenz further in view of Washington further in view of DeAnna teaches the method of claim 1 upon which claim 8 depends. Furthermore, regarding the limitation of claim 8:
Orsi teaches runtime execution of the rules (Paragraph 100) which indicates that wherein the set of rules are defined using an executable model language.
Claims 9-11, 13, and 16 recite a system that parallels the method of claims 1-3, 5, and 8 respectively. Therefore, the analysis discussed above with respect to claims 1-3, 5, and 8 also applies to claims 9-11, 13, and 16 respectively. Accordingly, claims 9-11, 13, and 16 are rejected based on substantially the same rationale as set forth above with respect to claims 1-3, 5, and 8 respectively.
Claims 17 recites a non-transitory computer readable storage medium that parallels the method of claim 1. Therefore, the analysis discussed above with respect to claim 1 also applies to claim 17. Accordingly, claim 17 is rejected based on substantially the same rationale as set forth above with respect to claim 1.
Response to Arguments
Applicant’s arguments filed 05-JANUARY-2026 have been fully considered, but the examiner believes that not all are fully persuasive.
Regarding the applicant’s remarks on the non-final office action’s 103 rejection of the claims, the applicant argues that Orsi, Lorenz, Washington, and DeAnna do not teach the amended limitations of these claims. As such, the applicant argues that all claims dependent on the above would additionally not be obvious under 103. However, the examiner believes that Orsi, Lorenz, Washington, and DeAnna do teach the amended limitations and respectfully requests applicant’s consideration of the following:
Regarding the applicant’s arguments that the prior art does not teach the amended limitations:
Orsi teaches that a node has a memory of facts which result in the pattern that occurs in the left-hand-side of a rule (Paragraph 35). This would be analogous to a working memory element as it is an asserted fact of the rules engine. Likewise, this is analogous to determining, based on a constraint reference by the working memory element and the network class, a particular node of the plurality of nodes of the network as the pattern determines which nodes contain matching patterns.
Firstly, the applicant alleges that Orsi does not teach the evaluation of a node. However, the examiner believes that this would be a form of evaluating the node as the pattern of the node is compared against the fact being propagated through the network (Paragraph 35), therefore evaluating the node for that fact.
Secondly, the applicant argues that Orsi does not disclose the network class that is disclosed by Lorenz in the full rejection (Lorenz, Paragraph 104). However, it would be obvious to combine Orsi and Lorenz for reasons of the improvement listed below. In the combined invention, the network class of Lorenz would be able to implement the network of Orsi as referenced by the applicant in the arguments, and the network class of Lorenz is used to create a network (Lorenz, Paragraph 211).
Furthermore, the applicant argues one of ordinary skill in the art would not have motive to combine DeAnna and Lorenz. However, the office action never directly combines DeAnna and Lorenz. Rather, DeAnna is combined with the combination of Orsi, Lorenz, and Washington. As Orsi is the primary reference of the 103 rejection, the obviousness of DeAnna’s inclusion and the improvement that it provides is best understood through a combination of Orsi and DeAnna, wherein Orsi is also combined with Lorenz, and further combined with Washington. The combination of Orsi and DeAnna is not contested by the applicant.
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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/A.J.M./Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142