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 .
Response to Amendment
The amendment filed 07/06/2026 has been entered. Claims 1-4, 7-11, 14-18 and 20 remain pending in the application. Applicant’s amendments to the claims have overcome the 112 rejections previously set forth in the Final Office Action mailed 04/06/2026.
Response to Arguments
Applicant’s arguments, filed 07/06/2026, with respect to the rejections of the claims under 103 have been fully considered and are persuasive because of the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Miller et al. (US Patent 7,818,338) in view of Adinarayan et al. (US Patent 11,227,018) in view of Iscen (US Pub. 2009/0327195) and further in view of Byron et al. (US Pub. 2018/0137419).
Applicant’s arguments, filed 07/06/2026, with respect to the rejections of the claims under 101 have been fully considered and are not persuasive.
Applicant argues (pages 4-5)
Claim 1 as amended forecloses that construction. Claim 1 now recites that "generating the computer-resident knowledge graph database includes injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships and labels between entities of the plurality of entities", and further recites that "composing the optimization pipeline includes resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline."
These recited features are not observations, evaluations, judgments, or opinions. As the USPTO's August 4, 2025 memorandum states, "a claim does not recite a mental process when it contains limitation(s) that cannot practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitation(s)," and examiners are reminded "not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." (Reminders memo, p. 2). The recited features of "resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates" and "defining variable visibility across the optimization pipeline" are beyond the capability of the human mind and are not a pencil-and-paper exercise. Support for these recited features appears at paragraph [0028] of the specification ("It includes resolution of potential conflicts including mapping of variables that are used across multiple constraints and variable definition and visibility across the optimization pipeline") and at paragraph [0095] ("One or more causal links may be injected/added between one or more sensors").
The Office's position that "a claim that requires a computer may still recite a mental process (MPEP 2106.04(a)(2)(III)(C))" (Office Action, p. 6) does not reach these recited features. Claim 1 does not invoke a computer merely as a tool to carry out a mental step; rather, it recites the particular operations of "injecting one or more causal links between the one or more sensors", "resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates", and "defining variable visibility across the optimization pipeline", which the human mind is not equipped to perform.
In response
The examiner respectfully disagrees.
Claim 1 as amended recites the new limitations of “generating the computer-resident knowledge graph database includes injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships and labels between entities of the plurality of entities”. These limitations can be performed in a human mind using observations, evaluations, judgments, or opinions. A human can generate a graph comprising multiple nodes/entities and the connections/edges between them with the aid of pencil and paper. The human can generate causal links/reasons, rules, constraints between the entities/sensors of the graph. For example, turning on a heater causes the temperature sensor to rise, (or the temperature sensor is rising because the heater is on). For another example, an outdoor temperature sensor detects a sharp drop in temperature, an indoor thermostat sensor detects the indoor temperature falling a short time later. The user can also create rules, conditions, root causes representing the relationships between the entities and generating labels for the entities when building a graph. For example, if X occurs to entity Y, then entity Z may fail.
Claim 1 as amended also recites the new limitations of “resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates", and "defining variable visibility across the optimization pipeline”. Similarly, these limitations can be performed in a human mind.
As mentioned above, the user can generate a graph comprising multiple entities and the relationships between the entities within the graph. The user can further generate an optimization problem includes problems/symptoms, action, solution for solving problem, rules such as if…then, and proving a clear path of moving from one entity to the other based on rules, conditions, constraints, etc., For example, the user can generate a reasoned relationship between the entities such as: symptom A causes by B and C, take action D to solve the issue.
Therefore, the amended limitation recites an abstract idea.
Applicant argues (pages 5-7)
Claim 1 as a whole integrates any recited exception into a practical application
the recited features reflect the technological improvement identified in the specification. The specification explains that the disclosure addresses the "huge scalability issues of configuring optimization solutions which either results in highly specialized solution which do not address all practical needs or generalized frameworks that require an optimization expert to work with." (Specification, [0021]) (emphasis added). Claim 1 reflects that improvement through its recited features, including "at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data" (see specification, [00114], "Data in external data sources may be identified using a key"), in combination with the recited feature "wherein composing the optimization pipeline includes resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline" (see specification, [0028]). The precedential Appeals Review Panel decision Ex Parte Desjardins credits this same consideration. There, the panel "evaluated the claims as a whole" and held that a specification-disclosed improvement reflected in the claim "integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two" (Desjardins memo, pp. 1-2). Example 47, claim 3 from the July 2024 Subject Matter Eligibility Examples, is eligible on the same basis: it "recites a judicial exception (abstract idea), but the claim as a whole integrates the judicial exception into a practical application." (July 2024 Subject Matter Eligibility Examples, Example 47, claim 3).
In response
Example 47, Claim 3 is eligible because even it recites an abstract idea, but the claim as a whole integrates the judicial exception into a practical application by reciting an improvement to a technology field of network intrusion detection, an improvement on network security, an improvement on dropping potentially malicious packets and blocking future traffic from the source address that reflect the improvements described in the specification.
In contrast, optimization configuration using semantic knowledge graphs is not a "technological field" or a "technological problem" or a "technological solution." As explained above, the process of configuring and solving optimization problem can be performed in a human mind, thus the claim recites an abstract idea, and an improvement in an abstract idea is not consider an improvement on the functioning of a computer or to any other technology or technical field.
Applicant further argues that “the "generic computer" characterization impermissibly oversimplifies the claim. The August 4, 2025 memorandum reminds examiners "not to oversimplify claim limitations and expand the application of the 'apply it' consideration", and identifies the pertinent question as "the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome" (Reminders memo, p. 4). The recited features "resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline" set forth a particular way of "composing an optimization pipeline for the optimization problem" (claim 1), not a generic instruction to "apply it" (see also MPEP 2106.0S(a), discussing En.fish andMcRO).”
In response
As stated above and in the 101 rejections below, the process of generating an optimization problem includes resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline is a process that can be performed in a human mind without the help of a computer. The claim recites using computer component for configuring the optimization problem but as mentioned above, a claim that requires a computer may still recite a mental process (MPEP 2106.04(a)(2)(11l)(C)). In this case, the computer is only used as a tool to implement a process that could be implemented in the mind or with the aid of pencil and paper. The claim does not recite how the computer component is used in such a specific way for configuring such that the human mind cannot be mentally performed. An improvement in an abstract idea is not consider an improvement on the functioning of a computer or to any other technology or technical field.
Applicant argues (pages 8-9)
The recited features amount to significantly more
Applicant's position is that eligibility is established at Step 2A and the analysis need not reach Step 2B. In the alternative, the claim is also eligible at Step 2B. The recited electronic-key retrieval, the instantiation of atomic optimization templates, and the conflict-resolved pipeline composition together are an unconventional combination, and the Office has not shown – as MPEP 2106.05(d) requires -that this combination is well-understood, routine, and conventional.
In response
The argument includes some limitations that were not addressed as additional limitations, but as abstract ideas. It is not necessary to show the well-understood, routine, conventional nature of 1) the abstract idea of optimization problem configuration using semantic graphs, or 2) using a generic computer as a tool to implement the abstract idea. In the 101 rejections section, the rejections identified additional limitations as insignificant extra-solution data gathering or data outputting and provided evidence as required by MPEP § 2106.05(d) by citing limitations that the courts have identified to be well-understood, routine, or conventional as described in MPEP § 2106.05(d)(II). The rejection clearly addresses all additional elements and show how these elements individually or in combination do not amount to significantly more than the abstract idea.
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 1-4, 7-11, 14-18 and 20 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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “generating, by a graph construction component executing on one or more processors and based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database encoding a plurality of entities comprising one or more sensors associated with a knowledge domain”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph construction component executing on one or more processors” nothing in the claim element precludes the step from practically being performed in the mind. For example, “generating” in the context of this claim encompasses the user generating a graph with multiple nodes/entities and the connections between the nodes based on their relationship.
The limitation of “injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships and labels between entities of the plurality of entities”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “injecting and adding” in the context of this claim encompasses the user generating rules, conditions and reasoned relationship between the nodes/entities of the generated graph. For example, turning on a heater causes the temperature sensor to rise, or the temperature sensor is rising because the heater is on. For another example, an outdoor temperature sensor detects a sharp drop in temperature, an indoor thermostat sensor detects the indoor temperature falling a short time later. The user can also create rules, conditions, root causes representing the relationships between the entities and generating labels for the entities when building a graph. For example, if X occurs to entity Y, then entity Z may fail.
Similarly, the limitation of “mapping, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns stored in the computer-resident knowledge graph database to identify at least one graph pattern relevant to solving the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph pattern selection component” nothing in the claim element precludes the step from practically being performed in the mind. For example, “mapping” in the context of this claim encompasses the user determining a similarity between the patterns.
Similarly, the limitation of “identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “identifying” in the context of this claim encompasses the user determines if any node in the generated graph containing element similar to the graph pattern.
Similarly, the limitation of “querying, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying … a first set of instances corresponding to the first set of entities that match the at least one graph pattern and are relevant to solving the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph query engine” nothing in the claim element precludes the step from practically being performed in the mind. For example, “querying … identifying” in the context of this claim encompasses the user performs the process of querying the generated knowledge graph to identify a first set of instances corresponding to the first set of entities from specified storage relevant to solving the optimization problem that match the at least one graph pattern and are relevant to solving the optimization problem configuring the optimization problem, such as by comparing the instances and the entities with the aid of pencil and paper.
Similarly, the limitation of “configuring the optimization problem … and composing an optimization pipeline for the optimization problem … resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph query engine” nothing in the claim element precludes the step from practically being performed in the mind. For example, “configuring … and composing” in the context of this claim encompasses the user generating problem solving technique based at least on the data of the generated graph, wherein, the problem solving technique includes action, solution for solving problem, rules such as if…then, and proving a clear path of moving from one entity to the other based on rules, conditions, constraints, etc.,. For example, the user can generate a reasoned relationship between the entities such as: symptom A causes by B and C, take action D to solve the issue.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “a graph construction component”, “one or more processors”, “a graph pattern selection component”, “a graph query engine” and “an optimization solver”. The additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions of generating, mapping, querying, identifying, configuring and executing) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim recites the additional elements of “generating, by a graph construction component, … a computer-resident knowledge graph database”, “mapping, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns”, “querying, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying …” and “executing the optimization pipeline by invoking an optimization solver on the instantiated atomic optimization templates to generate an optimized solution”. These additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional elements of “generating, … based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database, a plurality of entities comprising one or more sensors, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data”, “wherein each graph pattern includes a machine-executable sub-graph template with associated constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern based on at least one defined instance of a defined concept in the sub-graph template”, “querying … to return variable bindings identifying, based on the sub-graph template including the at least one defined instance of the defined concept, a first set of instances” and “configuring the optimization problem by instantiating atomic optimization templates with the first set of instances corresponding to the first set of entities and with the entity data retrieved from the respective external data source using the at least one graph-resident electronic key” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate into a practical application (see MPEP 2106.05(h)).
The additional element of “receiving an optimization problem associated with the knowledge domain” amounts to insignificant extra-solution activities of data gathering, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does 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 elements of “a graph construction component”, “one or more processors”, “a graph pattern selection component”, “a graph query engine” and “an optimization solver” to perform the “generating, mapping, querying, identifying, configuring and executing” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional elements of “generating, by a graph construction component, … a computer-resident knowledge graph database”, “mapping, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns”, “querying, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying …” and “executing the optimization pipeline by invoking an optimization solver on the instantiated atomic optimization templates to generate an optimized solution” amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional elements of “generating, … based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database, a plurality of entities comprising one or more sensors, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data”, “wherein each graph pattern includes a machine-executable sub-graph template with associated constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern based on at least one defined instance of a defined concept in the sub-graph template”, “querying … to return variable bindings identifying, based on the sub-graph template including the at least one defined instance of the defined concept, a first set of instances” and “configuring the optimization problem by instantiating atomic optimization templates with the first set of instances corresponding to the first set of entities and with the entity data retrieved from the respective external data source using the at least one graph-resident electronic key” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
The additional element of “receiving an optimization problem associated with the knowledge domain” is recited at a high level of generality and amount to extra-solution activity of receiving and transmitting data (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “identifying the first set of entities in the computer-resident knowledge graph database using reasoning data associated with the at least one graph pattern, wherein the at least one graph pattern is predefined”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does 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 “identifying the first set of entities in the computer-resident knowledge graph database using reasoning data associated with the at least one graph pattern, wherein the at least one graph pattern is predefined” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “the machine-executable sub-graph template comprises one of the atomic optimization templates configured to encode a portion of the optimization problem, wherein the portion of the optimization problem is selected from the group consisting of an optimization objective, an optimization constraint, and an optimization action”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does 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 “wherein the sub-graph template comprises an atomic optimization template configured to encode a portion of the optimization problem, wherein the portion of the optimization problem is selected from the group consisting of an optimization objective, an optimization constraint, and an optimization action” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “composing the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “composing” in the context of this claim encompasses the user utilizes the entities and the relationships expressed in the knowledge graph to generate an ordering of the entities, thus composing the optimization problem using the entities.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites an additional element of “composing the optimization problem using one or more optimization pipelines in the computer-resident knowledge graph database, wherein data linked to the computer-resident knowledge graph database is used to formulate the optimization problem”. This limitation amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see MPEP 2106.05(h)).
Step 2B:
The claim does 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 “composing the optimization problem using one or more optimization pipelines in the computer-resident knowledge graph database, wherein data linked to the computer-resident knowledge graph database is used to formulate the optimization problem” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “generating a graphical explanation of the optimized solution from executing the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generating” in the context of this claim encompasses the user generates a graphical representation of the provided result, such as with the aid of pencil and paper.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Indeed, the claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Step 2B:
The claim does 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 claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a system which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “generate, by a graph construction component executing on one or more processors and based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database encoding a plurality of entities comprising one or more sensors associated with a knowledge domain”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph construction component executing on one or more processors” nothing in the claim element precludes the step from practically being performed in the mind. For example, “generate” in the context of this claim encompasses the user generating a graph with multiple nodes/entities and the connections between the nodes based on their relationship.
The limitation of “injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships and labels between entities of the plurality of entities”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “injecting and adding” in the context of this claim encompasses the user generating rules, conditions and reasoned relationship between the nodes/entities of the generated graph. For example, turning on a heater causes the temperature sensor to rise, or the temperature sensor is rising because the heater is on. For another example, an outdoor temperature sensor detects a sharp drop in temperature, an indoor thermostat sensor detects the indoor temperature falling a short time later. The user can also create rules, conditions, root causes representing the relationships between the entities and generating labels for the entities when building a graph. For example, if X occurs to entity Y, then entity Z may fail.
Similarly, the limitation of “map, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns stored in the computer-resident knowledge graph database to identify at least one graph pattern relevant to solving the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph pattern selection component” nothing in the claim element precludes the step from practically being performed in the mind. For example, “map” in the context of this claim encompasses the user determining a similarity between the patterns.
Similarly, the limitation of “identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “identifying” in the context of this claim encompasses the user determines if any node in the generated graph containing element similar to the graph pattern.
Similarly, the limitation of “query, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying … a first set of instances corresponding to the first set of entities that match the at least one graph pattern and are relevant to solving the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph query engine” nothing in the claim element precludes the step from practically being performed in the mind. For example, “query … identifying” in the context of this claim encompasses the user performs the process of querying the generated knowledge graph to identify a first set of instances corresponding to the first set of entities from specified storage relevant to solving the optimization problem that match the at least one graph pattern and are relevant to solving the optimization problem configuring the optimization problem, such as by comparing the instances and the entities with the aid of pencil and paper.
Similarly, the limitation of “configure the optimization problem … and composing an optimization pipeline for the optimization problem … resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph query engine” nothing in the claim element precludes the step from practically being performed in the mind. For example, “configure … and composing” in the context of this claim encompasses the user generating problem solving technique based at least on the data of the generated graph, wherein, the problem solving technique includes action, solution for solving problem, rules such as if…then, and proving a clear path of moving from one entity to the other based on rules, conditions, constraints, etc.,. For example, the user can generate a reasoned relationship between the entities such as: symptom A causes by B and C, take action D to solve the issue.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “A system”, “one or more computers”, “a graph construction component”, “one or more processors”, “a graph pattern selection component”, “a graph query engine” and “an optimization solver”. The additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions of generating, mapping, querying, identifying, configuring and executing) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim recites the additional elements of “generate, by a graph construction component, … a computer-resident knowledge graph database”, “map, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns”, “query, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying …” and “execute the optimization pipeline by invoking an optimization solver on the instantiated atomic optimization templates to generate an optimized solution”. These additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional elements of “generate, … based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database, a plurality of entities comprising one or more sensors, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data”, “wherein each graph pattern includes a machine-executable sub-graph template with associated constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern based on at least one defined instance of a defined concept in the sub-graph template”, “querying … to return variable bindings identifying, based on the sub-graph template including the at least one defined instance of the defined concept, a first set of instances” and “configure the optimization problem by instantiating atomic optimization templates with the first set of instances corresponding to the first set of entities and with the entity data retrieved from the respective external data source using the at least one graph-resident electronic key” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate into a practical application (see MPEP 2106.05(h)).
The additional element of “receiving an optimization problem associated with the knowledge domain” amounts to insignificant extra-solution activities of data gathering, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does 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 elements of “A system”, “one or more computers”, “a graph construction component”, “one or more processors”, “a graph pattern selection component”, “a graph query engine” and “an optimization solver” to perform the “generate, map, query, identifying, configure and execute” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional elements of “generate, by a graph construction component, … a computer-resident knowledge graph database”, “map, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns”, “query, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying …” and “execute the optimization pipeline by invoking an optimization solver on the instantiated atomic optimization templates to generate an optimized solution” amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional elements of “generate, … based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database, a plurality of entities comprising one or more sensors, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data”, “wherein each graph pattern includes a machine-executable sub-graph template with associated constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern based on at least one defined instance of a defined concept in the sub-graph template”, “querying … to return variable bindings identifying, based on the sub-graph template including the at least one defined instance of the defined concept, a first set of instances” and “configure the optimization problem by instantiating atomic optimization templates with the first set of instances corresponding to the first set of entities and with the entity data retrieved from the respective external data source using the at least one graph-resident electronic key” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
The additional element of “receiving an optimization problem associated with the knowledge domain” is recited at a high level of generality and amount to extra-solution activity of receiving and transmitting data (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
Claim 9: is substantially similar to claim 2 and thus rejected for similar reasons as claim 2.
Claim 10: is substantially similar to claim 3 and thus rejected for similar reasons as claim 3.
Claim 11: is substantially similar to claim 4 and thus rejected for similar reasons as claim 4.
Claim 14: is substantially similar to claim 7 and thus rejected for similar reasons as claim 7.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a computer program product which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “program instructions to generate, by a graph construction component executing on one or more processors and based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database encoding a plurality of entities comprising one or more sensors associated with a knowledge domain”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph construction component executing on one or more processors” nothing in the claim element precludes the step from practically being performed in the mind. For example, “generate” in the context of this claim encompasses the user generating a graph with multiple nodes/entities and the connections between the nodes based on their relationship.
The limitation of “injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships and labels between entities of the plurality of entities”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “injecting and adding” in the context of this claim encompasses the user generating rules, conditions and reasoned relationship between the nodes/entities of the generated graph. For example, turning on a heater causes the temperature sensor to rise, or the temperature sensor is rising because the heater is on. For another example, an outdoor temperature sensor detects a sharp drop in temperature, an indoor thermostat sensor detects the indoor temperature falling a short time later. The user can also create rules, conditions, root causes representing the relationships between the entities and generating labels for the entities when building a graph. For example, if X occurs to entity Y, then entity Z may fail.
Similarly, the limitation of “map, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns stored in the computer-resident knowledge graph database to identify at least one graph pattern relevant to solving the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph pattern selection component” nothing in the claim element precludes the step from practically being performed in the mind. For example, “map” in the context of this claim encompasses the user determining a similarity between the patterns.
Similarly, the limitation of “identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “identifying” in the context of this claim encompasses the user determines if any node in the generated graph containing element similar to the graph pattern.
Similarly, the limitation of “program instructions to query, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying … a first set of instances corresponding to the first set of entities that match the at least one graph pattern and are relevant to solving the optimization problem” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph query engine” nothing in the claim element precludes the step from practically being performed in the mind. For example, “query … identifying” in the context of this claim encompasses the user performs the process of querying the generated knowledge graph to identify a first set of instances corresponding to the first set of entities from specified storage relevant to solving the optimization problem that match the at least one graph pattern and are relevant to solving the optimization problem configuring the optimization problem, such as by comparing the instances and the entities with the aid of pencil and paper.
Similarly, the limitation of “program instructions to configure the optimization problem … and composing an optimization pipeline for the optimization problem … resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline” as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “a graph query engine” nothing in the claim element precludes the step from practically being performed in the mind. For example, “configure … and composing” in the context of this claim encompasses the user generating problem solving technique based at least on the data of the generated graph, wherein, the problem solving technique includes action, solution for solving problem, rules such as if…then, and proving a clear path of moving from one entity to the other based on rules, conditions, constraints, etc.,. For example, the user can generate a reasoned relationship between the entities such as: symptom A causes by B and C, take action D to solve the issue.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “A computer program product”, “one or more computer readable storage media”, “a graph construction component”, “one or more processors”, “a graph pattern selection component”, “a graph query engine” and “an optimization solver”. The additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions of generating, mapping, querying, identifying, configuring and executing) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim recites the additional elements of “generate, by a graph construction component, … a computer-resident knowledge graph database”, “map, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns”, “query, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying …” and “execute the optimization pipeline by invoking an optimization solver on the instantiated atomic optimization templates to generate an optimized solution”. These additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The additional elements of “generate, … based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database, a plurality of entities comprising one or more sensors, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data”, “wherein each graph pattern includes a machine-executable sub-graph template with associated constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern based on at least one defined instance of a defined concept in the sub-graph template”, “querying … to return variable bindings identifying, based on the sub-graph template including the at least one defined instance of the defined concept, a first set of instances” and “configure the optimization problem by instantiating atomic optimization templates with the first set of instances corresponding to the first set of entities and with the entity data retrieved from the respective external data source using the at least one graph-resident electronic key” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not integrate into a practical application (see MPEP 2106.05(h)).
The additional element of “receiving an optimization problem associated with the knowledge domain” amounts to insignificant extra-solution activities of data gathering, which does not amount to significantly more than the abstract idea (MPEP 2106.05(g)). Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does 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 elements of “A computer program product”, “one or more computer readable storage media”, “a graph construction component”, “one or more processors”, “a graph pattern selection component”, “a graph query engine” and “an optimization solver” to perform the “generate, map, query, identifying, configure and execute” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional elements of “generate, by a graph construction component, … a computer-resident knowledge graph database”, “map, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns”, “query, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying …” and “execute the optimization pipeline by invoking an optimization solver on the instantiated atomic optimization templates to generate an optimized solution” amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional elements of “generate, … based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database, a plurality of entities comprising one or more sensors, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data”, “wherein each graph pattern includes a machine-executable sub-graph template with associated constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern based on at least one defined instance of a defined concept in the sub-graph template”, “querying … to return variable bindings identifying, based on the sub-graph template including the at least one defined instance of the defined concept, a first set of instances” and “configure the optimization problem by instantiating atomic optimization templates with the first set of instances corresponding to the first set of entities and with the entity data retrieved from the respective external data source using the at least one graph-resident electronic key” amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
The additional element of “receiving an optimization problem associated with the knowledge domain” is recited at a high level of generality and amount to extra-solution activity of receiving and transmitting data (MPEP 2106.05(g)). The courts have found limitations directed to receiving and transmitting information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”).
Claim 16: is substantially similar to claim 2 and thus rejected for similar reasons as claim 2.
Claim 17: is substantially similar to claim 3 and thus rejected for similar reasons as claim 3.
Claim 18: is substantially similar to claim 4 and thus rejected for similar reasons as claim 4.
Claim 20: is substantially similar to claim 7 and thus rejected for similar reasons as claim 7.
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 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.
Claims 1-4, 8-11 and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. (US Patent 7,818,338) in view of Adinarayan et al. (US Patent 11,227,018) in view of Iscen (US Pub. 2009/0327195) and further in view of Byron et al. (US Pub. 2018/0137419).
As per claim 1, Miller teaches a computer-implemented method, comprising:
generating, a computer-resident knowledge graph database [Fig. 1, Col. 5, lines 51-55, “The service system 120 interacts with the service agent 118 to diagnose problems and provide domain knowledge and problem resolution to the enterprise computer system 102 based upon knowledge maintained in its corresponding knowledge base 122”];
responsive to receiving an optimization problem associated with the knowledge domain domain [Col. 6, lines 53-55, “Referring to FIG. 3, an exemplary approach 150 is illustrated for performing problem determination. A request is received at 152 for assistance and problem determination”], mapping, by a graph pattern selection component, the optimization problem to a pre-defined library of graph patterns stored in the computer-resident knowledge graph database to identify at least one graph pattern relevant to solving the optimization problem [Fig. 3, Col. 6, lines 54-66, “A request is received at 152 for assistance and problem determination. For example, the service agent 118 or other service requester may contact the analyzer server 108 for assistance to solve a problem. The analyzer server 108 responds by initiating a query back to the service requester to request pattern matching information at 154. The process of querying the service requester at 154 may be an iterative process utilized to perform problem diagnosis. For example, the service system 120 may identify a plurality of patterns 130 as candidate patterns. The candidate patterns may comprise all of the patterns 130 or a subset of the patterns 130 stored in the knowledge base 122”; Col. 1, lines 54-63, “receiving a request for resolution knowledge from a service requester, and identifying a plurality of patterns as candidate patterns, each candidate pattern having at least one element that characterize a corresponding problem. The method may further comprise repeating until at least one stopping criterion is met: providing information to the service requester that identifies a select element of each remaining candidate pattern, receiving information from the service requestor that enables identification of ones of the candidate patterns having its select element matched to corresponding information considered by the service requester”], wherein each graph pattern includes a machine-executable sub-graph template with associated constraints for identifying a first set that match the at least one graph pattern based on at least one defined instance of a defined concept in the machine-executable sub-graph template [Col. 5, line 59 to Col. 6, line 2, “The analyzer server 108 maintains a plurality of patterns 130 that are stored, for example, in the knowledge base 122. As shown, there are M patterns 130, where M is any integer. Each pattern 130 is defined by one or more elements that characterize a corresponding problem. Elements may include for example, one or more of a description of an event or events, a description of other information such as settings, parameters, configurations, etc., rules, conditions, sequences, steps and/or other criteria that relate to, characterize or are otherwise associated with the corresponding problem pattern”; Col. 8, lines 41-58, “A diagnostic is requested at 202 from a service requester such as the service agent 118. The service system 120 responds to the service request by identifying candidate patterns and by initiating a first query back to the service agent 118 as described more fully herein. The first query comprises a request to the service agent 118 to identify whether or not matches can be associated to the first element of each candidate pattern. The service agent 118 responds to the first query at 206 by providing sufficient information to allow the service system 120 to identify candidate patterns having their first element matched to problem data in the corresponding log files and other information 116 monitored by the service agent 118. For example, the service agent 118 may return specific event listings or otherwise uniquely identify each element for which a match was found or where the associated element was otherwise satisfied”; Examiner interprets the element associated with each pattern as the template which comprising the constraints, rules, conditions, symptoms, etc., for identifying a match; Col. 9, lines 58-60 and Col. 10, lines 38-43, “Referring to FIG. 5, an exemplary way to organize problem information is to describe the patterns in the know ledge base 122 in terms of symptom definitions … the symptoms that correspond to the patterns 130 may not only describe problems but further may be used to encode rules and/or provide an action to achieve solutions that stem from or relate to the current "symptom" or pattern that corresponds with a root cause problem”];
querying, by executing the at least one graph pattern on the computer-resident knowledge graph database using a graph query engine, to return variable bindings identifying, based on the machine-executable sub-graph template including the at least one defined instance of the defined concept, a first set of instances that match the at least one graph pattern and are relevant to solving the optimization problem [Col. 6, lines 54-63, “A request is 55 received at 152 for assistance and problem determination. For example, the service agent 118 or other service requester may contact the analyzer server 108 for assistance to solve a problem. The analyzer server 108 responds by initiating a query back to the service requester to request pattern matching information at 154. The process of querying the service requester at 154 may be an iterative process utilized to perform problem diagnosis”; Col. 7, lines 34-44, “A query module, control module or other component of the service system sends a query to the service requester at 154, e.g., by identifying an element from each candidate pattern or otherwise identifying information that must be identified, e.g., in the information of the service requester to satisfy a corresponding candidate pattern element. A pattern matching module or other component of the service system 120 identifies ones of the candidate patterns at 156, having its select element matched to a corresponding event or other information, e.g., based upon query results returned by the service requestor”; Col. 12, lines 30-35, “A solution to a root cause of a problem may be based upon a recognition of those separated events and/or other factors. For example, a timestamp or other metadata associated with the events may be utilized to determine whether or not an event matches to a pattern element queried by the service system”];
configuring the optimization problem by instantiating atomic optimization templates with the first set of instances corresponding to the first set of entities and with the entity data, and composing an optimization pipeline for the optimization problem [Col. 9, lines 63-65, “Each symptom definition 302 may include explanations, samples and solutions that identify an action or actions to be performed in order to resolve the underlying problem(s).”; Col. 10, lines 38-43, “the symptoms that correspond to the patterns 130 may not only describe problems but further may be used to encode rules and/or provide an action to achieve solutions that stem from or relate to the current "symptom" or pattern that corresponds with a root cause problem”; Examiner interprets the process of identifying the problem, identifying action or actions to be performed to resolve the problem is to configuring the optimization problem and composing an optimization pipeline for the optimization problem], wherein composing the optimization pipeline includes resolving one or more conflicts among variables that are used across multiple constraints of the instantiated atomic optimization templates and defining variable visibility across the optimization pipeline [Col. 9, line 58 to Col. 10, line 43, “an exemplary way to organize problem information is to describe the patterns in the knowledge base 122 in terms of symptom definitions 302 … Each symptom definition 302 may include explanations, samples and solutions that identify an action or actions to be performed in order to resolve the underlying problem(s). A symptom is thus a form of knowledge that indicates a possible problem or other situation of interest and may be used as a predefined problem definition … The symptom schema may include a description of the symptom including the kinds of problems or situations associated with the symptom when a symptom occurrence is recognized. The symptom schema may also include examples of problems or situations where the symptom is likely to occur, solutions to the problem or situation … Each symptom may be recorded with at least one event correlation rule, each correlation rule having at least one predicate thereof that can be corresponded to events 114 recorded in the log files and other information 116. For example, within a system, if error X occurs to component Y, then product Z may crash, fail, terminate, etc. However, the problem associated with product Z may be detected by recognizing that symptoms A, B and C are simultaneously present and correspond to the error X in component Y. As such, a solution can be identified, and/or a problem may be prevented, mitigated, compensated for or otherwise corrected if the problem and knowledge of a solution can be brought to the attention of the service requestor. As such, the symptoms that correspond to the patterns 130 may not only describe problems but further may be used to encode rules and/or provide an action to achieve solutions that stem from or relate to the current "symptom" or pattern that corresponds with a root cause problem”; Based on the reciting above, it can be seen that the optimization problem in Miller includes the problems or situations, rules, conditions associated with the problems, and solutions/actions for solving problems, thus resolving the conflicts, as well as tracking and controlling how visible, accessible a variable is to the other of the graph as it moves through the optimization pipeline using rules, conditions, constraints, etc.,]; and
executing the optimization pipeline by invoking an optimization solver on the instantiated atomic optimization templates to generate an optimized solution [Col. 5, lines 51-55, “The service system 120 interacts with the service agent 118 to diagnose problems and provide domain knowledge and problem resolution to the enterprise computer system 102 based upon knowledge maintained in its corresponding knowledge base 122”; Col. 10, lines 38-43, “provide an action to achieve solutions that stem from or relate to the current "symptom" or pattern that corresponds with a root cause problem”].
Miller does not explicitly teach
generating, based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database encoding a plurality of entities comprising one or more sensors associated with a knowledge domain, wherein generating the computer-resident knowledge graph database includes injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships and labels between entities of the plurality of entities, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data;
constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern;
a first set of instances corresponding to the first set of entities that match the at least one graph pattern and are relevant to solving the optimization problem (emphasis added);
the entity data retrieved from the respective external data source using the at least one graph-resident electronic key;
Adinarayan teaches
generating, based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database encoding a plurality of entities comprising one or more sensors associated with a knowledge domain [Col. 2, lines 12-15, “generating knowledge graph reasoning queries based on user selected sources of data of entities of a domain”; Col. 5, lines 11-24, “Generally, a knowledge graph acquires and integrates information into an ontology. Knowledge graph 144 is a network of entities, entity semantic types, properties, and relationships between entities organized in a graph which covers various topical domains. In one embodiment, knowledge graph 144 is modeled to correspond to IoT device 130. For example, IoT device 130 may be a smart building, which includes sensors, meters, and assets that are IoT enabled. In this example, knowledge graph 144 includes semantic descriptions, type, and location of entities (e.g., sensors, meters, assets, etc.) of the smart building. Additionally, knowledge graph 144 includes relationships between the entities in a graph form. In another embodiment, knowledge graph 144 includes one or more domains of IoT device 130. For example, knowledge graph 144 may include a respective domain that corresponds to each floor of a smart building”], wherein generating the computer-resident knowledge graph database includes adding labels between entities of the plurality of entities [Col. 5, lines 46-50, “generation program 200 can map semantic labels (e.g., metadata, device type, device identification, message topic, varying data values of a device, etc.) of IoT device 130 to corresponding entities of knowledge graph 144”], wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data [Col. 4, line 64 to Col. 5, line 7, “Storage device 142 can be implemented with any type of storage device, for example, persistent storage 305, which is capable of storing data that may be accessed and utilized by server 140, client device 120, and IoT device 130, such as a database server, a hard disk drive, or a flash memory … storage device 142 stores a plurality of information, such as data of sensor 134 and knowledge graph 144”; Col. 9, lines 9-14, “generation program 200 retrieves data from storage device 142 that corresponds to a user request for data of sensor 134 utilizing application 124. For example, generation program 200 returns temperature data from a sensor of a thermostat (e.g., an entity) of a building (e.g., IoT device 130) in response to a query from a user”; Examiner interprets the storage device 142 as the external data source storing the entity data, and interprets any device or program such as application 124, server 140, client device 120 etc., which can access the storage device to retrieve entity data as the key];
the entity data retrieved from the respective external data source using the at least one graph-resident electronic key [Col. 9, lines 9-14, “generation program 200 retrieves data from storage device 142 that corresponds to a user request for data of sensor 134 utilizing application 124. For example, generation program 200 returns temperature data from a sensor of a thermostat (e.g., an entity) of a building (e.g., IoT device 130) in response to a query from a user”; Examiner interprets the storage device 142 as the external data source storing the entity data, and interprets any device or program such as application 124, server 140, client device 120 etc., which can access the storage device to retrieve entity data as the key];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for controlling the distribution of problem resolution knowledge of Miller to include generating, based on one or more internet of things (IoT) data sources, a computer-resident knowledge graph database encoding a plurality of entities associated with a knowledge domain, wherein the computer-resident knowledge graph database includes at least one graph-resident electronic key that identifies and retrieves entity data from a respective external data source storing the entity data of Adinarayan. Doing so would allow the devices/entities to communicate, interact, be monitored, and controlled over the internet (Adinarayan, Col. 1, lines 25-31).
Miller and Adinarayan do not explicitly teach
generating the computer-resident knowledge graph database includes injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships between entities of the plurality of entities;
constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern;
a first set of instances corresponding to the first set of entities that match the at least one graph pattern and are relevant to solving the optimization problem (emphasis added);
Iscen teaches
generating the computer-resident knowledge graph database includes injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships between entities of the plurality of entities [paragraph 0005, “A causality graph includes nodes that represent observation, and root causes. Further meta-nodes are included to model how the state of a root cause affects its children. Links between nodes establish a causality relationship such that the state of the child is dependent on the state of the parent. Reasoning algorithms can then be applied over inference graphs to identify root causes given observations or symptoms”; Since Miller (as modified) teaches the knowledge graph 144 includes multiple entities (e.g., sensors) (Adinarayan, Col. 5, lines 11-24), while Iscen teaches the causal link is between the nodes/entities, therefore, the combination of Miller (as modified) and Iscen teaches the above claim limitation “generating the computer-resident knowledge graph database includes injecting one or more causal links between the one or more sensors”. Further, causal link between nodes/sensors can represent the reason relationship between nodes/sensors, for example, turning on a heater causing the temperature sensor to rise, or the temperature sensor is rising because the heater is on];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for controlling the distribution of problem resolution knowledge of Miller to include generating the computer-resident knowledge graph database includes injecting one or more causal links between the one or more sensors and adding one or more reasoned relationships between entities of the plurality of entities of Iscen. Doing so would help modelling fault propagation or causality throughout a system (Iscen, 0005).
Miller, Adinarayan and Iscen do not explicitly teach
constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern;
a first set of instances corresponding to the first set of entities that match the at least one graph pattern and are relevant to solving the optimization problem (emphasis added);
Byron teaches
constraints for identifying a first set of entities in the computer-resident knowledge graph database that match the at least one graph pattern [paragraph 0032, “a small set of actions (e.g., verbs) are manually tagged with features that indicate constraints on the attributes of entities with which that action may be utilized, e.g., the verb "pour" has a feature that it may be used with entities having a state of matter attribute of "liquid"”; paragraph 0060, “A knowledge base of instructions may be searched based on the entity and a desired state, as determined from the pre-condition feature of the action term. Thus, for example, if the entity is potatoes and the action term is "whip" and the constraint on the action term is "liquid" or "mixture" state, then the knowledge base of instructions may be searched for entries that result in a liquid or mixture state of potatoes, e.g., "chop the potatoes into small pieces and add the butter and the sour cream."”; It can be seen that the set of entities is identified based on the constraints, also, since Miller in Cols. 5-8 teaches each graph pattern includes a template with associated constraints for identifying a match, while Byron teaches constraints for identifying a first set of entities in the knowledge graph, therefore, the combination of Miller and Byron teaches the above claim limitation];
a first set of instances corresponding to the first set of entities that match the at least one graph pattern and are relevant to solving the optimization problem [paragraph 0024, “The illustrative embodiments are thus, directed to solutions for solving the problems”; paragraphs 0041-0045, “multiple instances of an entity may be generated with each instance having a different value of the attribute, e.g., a first instance of butter that is solid and a second instance of butter that is liquid. For those attribute values that meet or exceed the requirement specified by the threshold(s), those attribute values are maintained in association with the entity data structure in the knowledge base or ontology data structure … The result of this automated learning process is an expanded knowledge base that is expanded with regard to the attributes of the entities for the specific domain or in some cases additional entities and relationships in the knowledge base, e.g., a new entity of "liquid butter" may be generated and linked to a butter entity … For example, in the cooking domain, assume that a recipe includes the steps of melting the butter and then pouring the butter. As noted above, the first instance of an action term (melting) operating on the entity (butter) may be analyzed to associate a pre-condition feature (solid entity) of the action term (melting) to an attribute (state of matter) of the entity (butter).”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for controlling the distribution of problem resolution knowledge of Miller to include constraints for identifying a first set of entities that match the at least one graph pattern, and a first set of instances corresponding to the first set of entities that match the at least one graph pattern of Byron. Doing so would help building a larger set of domain knowledge including the constraints on entities in the domain (Byron, 0022).
As per claim 2, Miller, Adinarayan, Iscen and Byron teach the method of claim 1.
Miller teaches
the at least one graph pattern is predefined [Fig. 2, Col. 2, lines 3-7, “a module that receives a request for resolution knowledge from a service requestor and a module that identifies a plurality of patterns as candidate patterns, each candidate pattern having at least one element that characterize a corresponding problem”; Col. 5 line 62 to Col. 6, line 2, “Each pattern 130 is defined by one or more elements that characterize a corresponding problem. Elements may include for example, one or more of a description of an event or events, a description of other information such as settings, parameters, configurations, etc., rules, conditions, sequences, steps and/or other criteria that relate to, characterize or are otherwise associated with the corresponding problem pattern”].
Byron further teaches
identifying the first set of entities in the computer-resident knowledge graph database using reasoning data associated with the at least one graph pattern [Fig. 8B, “Reason for correct answer: melting the butter will put it into a liquid form”; paragraph 0102, “The QA pipeline then performs deep analysis on the language of the input question and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms … some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data”; paragraph 0192, “the reason 860 was incorrect is indicated to be that the ingredient (butter) is solid and pouring the butter requires the butter to be a liquid. The correct answer 870 would be to "melt the butter" and the reason 880 why this is a correct response is because melting the butter generates liquid butter which can then be poured”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for controlling the distribution of problem resolution knowledge of Miller to include identifying the first set of entities in the computer-resident knowledge graph database using reasoning data of Byron. Doing so would help generating a set of candidate answers to the received input question (Byron, 0102).
As per claim 3, Miller, Adinarayan, Iscen and Byron teach the method of claim 1.
Miller teaches
the machine-executable sub-graph template comprises one of the atomic optimization templates configured to encode a portion of the optimization problem, wherein the portion of the optimization problem is selected from the group consisting of an optimization objective, an optimization constraint, and an optimization action [Col. 5, line 62 to Col. 6, line 2, “Each pattern 130 is defined by one or more elements that characterize a corresponding problem. Elements may include for example, one or more of a description of an event or events, a description of other information such as settings, parameters, configurations, etc., rules, conditions, sequences, steps and/or other criteria that relate to, characterize or are otherwise associated with the corresponding problem pattern”; Examiner interprets the element associated with each pattern as the template which comprising the constraints, rules, conditions, symptoms, etc., for identifying a match; Col. 9, lines 58-60 and Col. 10, lines 38-43, “Referring to FIG. 5, an exemplary way to organize problem information is to describe the patterns in the know ledge base 122 in terms of symptom definitions … the symptoms that correspond to the patterns 130 may not only describe problems but further may be used to encode rules and/or provide an action to achieve solutions that stem from or relate to the current "symptom" or pattern that corresponds with a root cause problem”].
As per claim 4, Miller, Adinarayan, Iscen and Byron teach the method of claim 1.
Miller teaches
composing the optimization problem using one or more optimization pipelines in the computer-resident knowledge graph database [Col. 9, lines 63-65, “Each symptom definition 302 may include explanations, samples and solutions that identify an action or actions to be performed in order to resolve the underlying problem(s)”; Col. 10, lines 38-43, “the symptoms that correspond to the patterns 130 may not only describe problems but further may be used to encode rules and/or provide an action to achieve solutions that stem from or relate to the current "symptom" or pattern that corresponds with a root cause problem”; Examiner interprets the process of identifying the problem, identifying action or actions to be performed to resolve the problem is to composing an optimization pipeline for the optimization problem, and interprets the process comprising explanations, samples and solutions that identify an action or actions to be performed in order to resolve the underlying problem(s) as to compose the optimization problem],
Adinarayan teaches
wherein data linked to the computer-resident knowledge graph database is used to formulate the optimization problem [Fig. 1, Col. 9, lines 9-14, “generation program 200 retrieves data from storage device 142 that corresponds to a user request for data of sensor 134 utilizing application 124. For example, generation program 200 returns temperature data from a sensor of a thermostat (e.g., an entity) of a building (e.g., IoT device 130) in response to a query from a user”; It can be seen that entity data that retrieve from storage device 142 is used to generate response to user request].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for controlling the distribution of problem resolution knowledge of Miller to include entity data linked to the computer-resident knowledge graph database is used to formulate the optimization problem of Adinarayan. Doing so would help generating response to a query from a user (Adinarayan, Col. 9, lines 9-14).
Claim 8: is substantially similar to claim 1 and thus rejected for similar reasons as claim 1.
Miller further teaches
A system … in a computing environment [Fig. 1];
one or more computers with executable instructions that when executed cause the system to: [Col. 14, lines 48-55, “These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart”];
Claim 9: is substantially similar to claim 2 and thus rejected for similar reasons as claim 2.
Claim 10: is substantially similar to claim 3 and thus rejected for similar reasons as claim 3.
Claim 11: is substantially similar to claim 4 and thus rejected for similar reasons as claim 4.
Claim 15: is substantially similar to claim 1 and thus rejected for similar reasons as claim 1.
Miller further teaches
A computer program product ... in a computing environment, the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instruction comprising: program instructions to ...; program instructions to ...; and program instructions to [Col. 14, lines 48-60, “These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart … These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner”];
Claim 16: is substantially similar to claim 2 and thus rejected for similar reasons as claim 2.
Claim 17: is substantially similar to claim 3 and thus rejected for similar reasons as claim 3.
Claim 18: is substantially similar to claim 4 and thus rejected for similar reasons as claim 4.
Claims 7, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Miller et al. in view of Adinarayan et al. in view of Iscen in view of Byron et al. and further in view of Huang et al. (US Pub. 2019/0057145).
As per claim 7, Miller, Adinarayan, Iscen and Byron teach the method of claim 1.
Miller teaches
generating explanation of the optimized solution from executing the optimization problem [Col. 9, lines 63-65, “Each symptom definition 302 may include explanations, samples and solutions that identify an action or actions to be performed in order to resolve the underlying problem(s)”].
Miller, Adinarayan, Iscen and Byron do not teach
generating a graphical explanation of the solution from executing the problem (emphasis added).
Huang teaches
generating a graphical explanation of the solution from executing the problem [paragraph 0031, “the information system can provide the result set to, for example, a user … Providing the result set to a user can include rendering the result set in a knowledge graph, as described for operation 120”; paragraph 0046, “The interface 400 can be a graphical user interface ... The interface 400 can include a custom knowledge graph 405 rendering a display of a result set”; (EN): The result set corresponds to the solution. Providing an answer is encompassed by the BRI of explaining an answer];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for controlling the distribution of problem resolution knowledge of Miller to include HUANG's digitally displayed customized knowledge graphs because "These custom knowledge graphs may be simpler and easier to analyze ... the ontology of these custom knowledge graphs, selected to fit the intent of a user and the relationships between a set of factors in the query and the elements of a result set generated from the query, enables information systems to algorithmically generate questions that can reduce or otherwise refine the scope of the query ... to provide more accurate results to users" (Huang, 0019).
Claim 14: is substantially similar to claim 7 and thus rejected for similar reasons as claim 7.
Claim 20: is substantially similar to claim 7 and thus rejected for similar reasons as claim 7.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Chen et al. (US Pub. 2023/0069074) describes a method for detecting a system failure, and conducting topological cause learning by extracting causal relations
Dasgupta et al. (US Pub. 2023/0079455) describes methods for generating causal insight summary.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TRI T NGUYEN/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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