DETAILED ACTION
Claims 1-15 are presented for examination.
This office action is in response to submission of application on 12/19/2023.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/19/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 5, 10, and 15 are objected to because of the following informalities: “ wherein a physics-based policy for the reinforcement learning framework to map the sensor concept to the application concept” should be “wherein a physics-based policy for the reinforcement learning framework maps the sensor concept to the application concept”. Appropriate correction is required.
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.
The claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Claim 1 includes the steps of:
A processor-implemented method comprising:
receiving, via an input/output interface, a problem description from a user to recommend one or more sensors and associated pipelines for an application, wherein the problem description illustrates a set of domains of the application;
generating, via one or more hardware processors, a knowledge graph for the received problem description using a model driven development framework;
analyzing, via one or more hardware processors, the received problem description by traversing through the generated knowledge graph to recommend at least one relevant domain from the set of domains;
generating, via the one or more hardware processors, an application concept from the received problem description to map with a concept of one or more sensors by running a reinforcement learning (RL) agent; and
recommending, via the one or more hardware processors, at least one of the one or more sensors of the at least one relevant domain by performing a finite element analysis (FEA) on mapping outcome.
The broadest reasonable interpretation of the bolded limitations above are directed to a mental process able to be performed in the human mind through the use of a physical aid, like a pen and paper. A human can:
generate, i.e., draw with pen and paper, a knowledge graph for a problem description,
analyze the problem description by traversing through the generated knowledge graph to recommend at least one relevant domain from a set of domains,
generate an application concept from the received problem description to map with a concept of one or more sensors,
recommend relevant domain sensors by performing a finite element analysis (FEA) on mapping outcome.
The broadest reasonable interpretation of the following limitations also falls within the mathematical concepts groupings of abstract ideas because they cover mathematical relationships, mathematical formulas or equations, and mathematical calculations. See MPEP 2106.04(a)(2), subsection I.
performing a finite element analysis (FEA) on mapping outcome.
As drafted and under its broadest reasonable interpretation, this limitation recites an abstract idea of mathematical concepts and calculations because performing finite element analysis comprises application of mathematical formulas, i.e., applying FEA formulas.
Step 2A Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f).
A processor-implemented method comprising:
via an input/output interface,
via the one or more hardware processors,
using a model driven development framework,
by running a reinforcement learning (RL) agent.
As drafted and under their broadest reasonable interpretation, the following limitations recite additional elements which amount to mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
receiving, via an input/output interface, a problem description from a user to recommend one or more sensors and associated pipelines for an application, wherein the problem description illustrates a set of domains of the application;
The additional elements have been considered both individually and as an ordered combination in order to determine whether they integrates the exception into a practical application. Therefore, no meaningful claim limits are imposed practicing the abstract idea. Accordingly, at Step 2A, prong two, the additional elements do not integrate the judicial exception into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception.
The claim limitation(s) reciting generic computer elements amounts to no more than mere instructions to apply the exception using a generic computer.
The claim reciting the additional element(s) of “obtaining”, “receiving” and/or “transmitting” amount to necessary data gathering and output.
The additional elements have been considered both individually and as an ordered combination in order to determine whether they warrant significantly more consideration. Thus, the claim does not provide an inventive concept.
The claim is ineligible.
Claims 2-5 further recite limitations that encompass mental evaluations that are practically performed in the human mind, but for the recitation of generic computer components. The claims do not integrate the judicial exception into practical application. The claims do not recite additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 2-5 are ineligible.
Claims 6-10 and 11-15 are substantially similar to claims 1-5, and are rejected on the same basis as claims 1-5. These claims recite additional elements that amount to generic computer components recited at a high level of generality, with merely the words “apply it” or an equivalent with the judicial exception, merely including instructions to implement an abstract idea on the additional elements, or merely using the additional elements as a tool to perform an abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Surya et al. ("USWSBS: User-Centric Sensor and Web Service Search for IoT Application Using Bagging and Sunflower Optimization "), hereafter Surya, in view of Chevuru et al. (Pub. No.: 2024/0104480 A1), hereafter Chevuru, in further view of Heindl et al. (Pub. No.: US 2023/0273601 A1), hereafter Heindl.
Regarding claim 1, Surya discloses:
A processor-implemented method comprising (page 353, second paragraph, lines 1-2 “The proposed framework’s implementation is performed in Google’s Colaboratory environment on a computer with an i7 processor and 16 GB RAM.”):
receiving, via an input/…, a problem description from a user to recommend one or more sensors and associated pipelines for an application (Fig. 1, Algorithm 1 lines “Input” and “Output” teaches receiving queries from users as problem descriptions, to recommend, as output, one or more sensors and associated pipelines for an application),
wherein the problem description illustrates a set of domains of the application (page 352, line 1 “ Queries that represent the users’ interest are collected and preprocessed” teaches the user input problem description to illustrate domains of the application),
… via one or more hardware processors, a knowledge graph for the received problem description using a model driven development framework (Fig. 1 and algorithm 1 teach ontologies as a knowledge graph for the received problem description using a model driven development framework),
analyzing, via one or more hardware processors, the received problem description by traversing through the … knowledge graph to recommend at least one relevant domain from the set of domains (Fig. 1, Algorithm 1, and page 352, first paragraph, lines 2-4 “the relevant query terms are aggregated based on the IoT and sensor Thesaurus. IoT Ontologies and Sensor Ontologies are also appended to the aggregated terms, and the semantic similarity among the concepts is calculated” teaches analyzing the received problem by traversing through the ontologies to recommend at least one relevant domain),
generating, via the one or more hardware processors, an application concept from the received problem description to map with a concept of one or more sensors by running … learning … (Fig. 1 and Algorithm 1 steps 5, 6, and 7 teaches generating an IOT application concept to map with a concept of sensors, from sensor ontologies, by running a classification model),
recommending, via the one or more hardware processors, at least one of the one or more sensors of the at least one relevant domain by performing … analysis … on mapping outcome (Fig. 1 and Algorithm 1 teaches recommending sensors of relevant domain by performing analyses on mapping outcomes).
While Surya discloses receiving, via an input…, a problem description from a user to recommend one or more sensors and associated pipelines for an application, they not explicitly disclose receiving this via an input/output interface.
Chevuru discloses:
receiving, via an input/output interface, … from a user (¶[0075] discloses receiving inputs from a user via an interface, i.e. input device).
Surya and Chevuru are analogous art because they are from the same field of endeavor, knowledge graphs and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Surya to include receiving, via an input/output interface, … from a user, based on the teachings of Chevuru. One of ordinary skill in the art would have been motivated to make this modification in order to allow a user to interact with the components of the system, as suggested by Chevuru (¶[0075]).
While Surya discloses a knowledge graph for the received problem description using a model driven development framework, they do not teach generating this knowledge graph.
Chevuru discloses:
generating, via one or more hardware processors, a knowledge graph…(Fig. 8 and ¶[0090] discloses generating a knowledge graph).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Surya to include generating, via one or more hardware processors, a knowledge graph, based on the teachings of Chevuru. One of ordinary skill in the art would have been motivated to make this modification in order to deliver intelligent actionable recommendations for sustained peak performance, as suggested by Chevuru (¶[0044]).
While Surya discloses generating, via the one or more hardware processors, an application concept from the received problem description to map with a concept of one or more sensors by running … learning …, they don’t disclose doing so by running a reinforcement learning (RL) agent.
Chevuru discloses:
generating, via the one or more hardware processors, an application concept … by running a reinforcement learning (RL) agent (Fig. 2, Fig 4, and ¶[0067] teaches generating an application concept, i.e. classes and properties, by running a reinforcement learning model),
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Surya to include generating, via the one or more hardware processors, an application concept … by running a reinforcement learning (RL) agent, based on the teachings of Chevuru. One of ordinary skill in the art would have been motivated to make this modification in order to deliver intelligent actionable recommendations for sustained peak performance, as suggested by Chevuru (¶[0044]).
While Surya discloses recommending, via the one or more hardware processors, at least one of the one or more sensors of the at least one relevant domain by performing … analysis … on mapping outcome, they do not disclose this analysis to be a finite element analysis (FEA).
Heindl discloses:
performing a finite element analysis (FEA) on mapping outcome (Fig. 1, Fig. 3, ¶[0041], and ¶[0062] discloses performing FEA on mapping outcomes).
Surya, Chevuru, Heindl are analogous art because they are from the same field of endeavor, knowledge graphs and machine learning models.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Surya, in view of Chevuru, to include performing a finite element analysis (FEA) on mapping outcome, based on the teachings of Heindl. One of ordinary skill in the art would have been motivated to make this modification in order to linking AI approaches in the context of factory automation, as suggested by Heindl (¶[0033]).
Regarding claim 2, Surya, in view of Chevuru, in further view of Heindl, discloses the processor-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Surya further discloses:
… via the one or more hardware processors, a design of the application based on the recommended at least one sensor and an associated pre-processing pipeline (Algorithm 1 Step 7 teaches a design of the IoT application based on the recommended sensor and an associated pre-processing pipeline).
While Surya discloses a design of the application based on the recommended at least one sensor and an associated pre-processing pipeline, they don’t disclose generating this design of the application.
Heindl discloses:
generating, via the one or more hardware processors, a design of the application … (Fig. 2 and ¶[0052] teaches generating a design of the application)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Surya, in view of Chevuru, to include generating, via the one or more hardware processors, a design of the application, based on the teachings of Heindl. One of ordinary skill in the art would have been motivated to make this modification in order to linking AI approaches in the context of factory automation, as suggested by Heindl (¶[0033]).
Regarding claim 3, Surya, in view of Chevuru, in further view of Heindl, discloses the processor-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Surya further discloses:
wherein the knowledge graph includes one or more capabilities of the one or more sensors, and specifications of the set of domains (page 253, third paragraph, lines 1-7 ”The SSN ontology is used to portray sensors and their noticed properties… With various axiomatization levels, SSN and SOSA can uphold a broad scope of utilization, including social detecting, observing enormous industrial and scientific infrastructures, Web of Things, and Ontology Engineering.”).
Regarding claim 4, Surya, in view of Chevuru, in further view of Heindl, discloses the processor-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Surya further discloses:
wherein a knowledge-based … learning framework is designed to measure physical quantities (Fig. 1 and page 353, second paragraph, lines 2-4 “Environment based datasets such as MesoWest and Air Quality Sensor Dataset are exploited for the experimentation objectives and evaluation.” Teaches the learning framework of Fig. 1 to be designed to recommend sensors that measure physical quantities).
While Surya discloses wherein a knowledge-based … learning framework is designed to measure physical quantities, they don’t disclose this learning framework to be a reinforcement learning framework.
Chevuru discloses:
a knowledge-based reinforcement learning framework (Fig. 2 and ¶[0067] teaches a knowledge-based reinforcement learning framework).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Surya to include a knowledge-based reinforcement learning framework, based on the teachings of Chevuru. One of ordinary skill in the art would have been motivated to make this modification in order to deliver intelligent actionable recommendations for sustained peak performance, as suggested by Chevuru (¶[0044]).
Regarding claim 5, Surya, in view of Chevuru, in further view of Heindl, discloses the processor-implemented method of claim 1 (and thus the rejection of claim 1 is incorporated). Surya further discloses:
wherein a physics-based policy for the … learning framework to map the sensor concept to the application concept (Examiner’s Note: a physics-based policy is interpreted as any policy for a learning framework that uses physical sensor data, as per specification ¶[003]) (Fig. 1, Algorithm 1, and page 353, second paragraph, lines 2-4 “Environment based datasets such as MesoWest and Air Quality Sensor Dataset are exploited for the experimentation objectives and evaluation.” Teaches a physics based, i.e. physical data based, policy for the learning framework to map the sensor concept to the application concept).
While Surya discloses wherein a physics-based policy for the … learning framework to map the sensor concept to the application concept, thy don’t explicitly disclose this learning framework to be a reinforcement learning framework.
Chevuru discloses:
a reinforcement learning framework (Fig. 2 and ¶[0067] teaches a reinforcement learning framework).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Surya to include a reinforcement learning framework, based on the teachings of Chevuru. One of ordinary skill in the art would have been motivated to make this modification in order to deliver intelligent actionable recommendations for sustained peak performance, as suggested by Chevuru (¶[0044]).
Claims 6-10 and 11-15 are substantially similar to claims 1-5, and are rejected on the same basis as claims 1-5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's
disclosure.
U.S. Pub No. 20200301672 A1: Pries et al. teaches sensors, knowledge graphs, and machine learning.
Ishak et al. (“SPLAI: Computational Finite Element Model for Sensor Networks”) teaches sensors and finite element analysis.
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/H.Z.M./Examiner, Art Unit 2141
/MATTHEW ELL/Supervisory Patent Examiner, Art Unit 2141
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