DETAILED ACTION
This action is in response to the application field on 05/17/2023. Claims 1-8 are pending and have been examined.
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 05/17/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“an acquisition module” as claimed in claim 5 stating “an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway”.
“a structurized module” as claimed in claim 5 stating “a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model”.
“a quantification module” as claimed in claim 5 stating “a quantification module configured to calculate a risk of thermal runaway in LIB”.
“a prediction module” as claimed in claim 5 stating “ a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway”.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6-8 are dependent on claim 5, which is a system claim. However, claims 6-8 refers to “the method of claim 5”. It is unclear whether claims 6-8 are referring to system claim 5 or another method claim. Therefore, claims 6-8 fail to distinctly claim the subject matter and is indefinite. For the purposes of examination, claims 6-8 will refer to system claim 5. The examiner recommends the preamble of claims 6-8 to be amended to recite “The apparatus of claim 5…” to overcome this rejection under 35 U.S.C. 112(b).
Regarding claim 5, “an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway; a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model; a quantification module configured to calculate a risk of thermal runaway in LIB; and a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. In this instance, the corresponding structure refers to computer implemented means-plus function. The written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification does not provide sufficient details of any structure that is used to implement the acquisition module, the structurized module, the quantification module, and the prediction module Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Claims 6-8 inherit the rejection of claim 5 above.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 5-8 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 5 “an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway; a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model; a quantification module configured to calculate a risk of thermal runaway in LIB; and a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway” as described above, does not provide adequate structure to perform the claimed function. (See 112(b) rejection above). Therefore, the specification does not appear to provide sufficient detail such that one of ordinary skill can reasonably conclude that the inventor had possession of the claimed invention. Claims 6-8 inherits the rejection of claim 5 above.
Claim Objections
Claim 1-8 objected to because of the following informalities:
All claims reference lithium-ion batteries as LIB. Claim 1 should first refer LIB as lithium-ion batteries, followed with (LIB) so that every LIB references in the claims are referring to lithium-ion batteries.
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.
Claims 1-8 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more,
Regarding claim 1:
Subject Matter of Eligibility Analysis Step 1:
Claim 1 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 1 recites
perform risk quantification by using a Bayesian algorithm (this limitation is a mental process as it encompasses a human mentally calculating the risk quantification if a Bayesian algorithm was given).
mapping a fault tree structure to a dynamic Bayesian network model for thermal runaway in LIB to obtain quantitative results of a risk of thermal runaway in LIB (this limitation is a mental process as it encompasses a human mentally mapping a fault tree to a dynamic Bayesian network model).
Therefore, claim 1 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 1 further recites additional elements of
acquiring knowledge of a mechanism for thermal runaway in LIB (this element does not integrate the abstract idea into a practical application because it is merely data gathering, which is an insignificant extra-solution activity (see MPEP 2106.05(g))).
describing an evolution process of thermal runaway in LIB by adopting a fault tree (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
taking the quantitative results of a dynamic Bayesian network as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway in LIB (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
the fault tree being configured to systemically induce the knowledge of the mechanism for thermal runaway and graphically represent evolution of thermal runaway in LIB by virtue of events and a logic relationship therebetween (this element does not integrate an abstract idea because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
the mapping being configured to convert the fault tree structure and parameters into the corresponding dynamic Bayesian network to represent a more complex node relationship, and perform risk quantification by using a Bayesian algorithm (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
the machine learning model referring to a support vector regression model configured to predict a trend of the risk of thermal runaway (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
Therefore, claim 1 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 1 do not provide significantly more than the abstract idea itself, taken alone and in combination because
acquiring knowledge of a mechanism for thermal runaway in LIB is well understood, routine, and conventional. The court has ruled that “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is recognized as a computer function that is well‐understood, routine, and conventional (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
describing an evolution process of thermal runaway in LIB by adopting a fault tree is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
taking the quantitative results of a dynamic Bayesian network as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway in LIB is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the fault tree being configured to systemically induce the knowledge of the mechanism for thermal runaway and graphically represent evolution of thermal runaway in LIB by virtue of events and a logic relationship therebetween recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
the mapping being configured to convert the fault tree structure and parameters into the corresponding dynamic Bayesian network to represent a more complex node relationship, and perform risk quantification by using a Bayesian algorithm is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
the machine learning model referring to a support vector regression model configured to predict a trend of the risk of thermal runaway uses a computer tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
Therefore, claim 1 is subject-matter ineligible.
Regarding claim 2:
Subject Matter of Eligibility Analysis Step 1:
Claim 2 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Because claim 2 is dependent on claim 1, the Subject Matter of Eligibility Analysis Step 2A Prong 1 from claim 1 is applied here. Therefore claim 2 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 2 further recites additional elements of
utilizing the fault tree to analyze a triggering process of an accident for thermal runaway in LIB from two aspects of human and material factors by taking thermal runaway in LIB as a top event to obtain a fault tree model, wherein the human factor refers to emergency response failure for early abnormal heating-up, and the material factor refers to abnormal heating-up caused by mechanical abuse, electrical abuse, thermal abuse, etc. (this element does not integrate an abstract idea into a practical application because it recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h))).
Therefore, claim 2 is not integrated into a practical application
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 2 do not provide significantly more than the abstract idea itself, taken alone and in combination because
utilizing the fault tree to analyze a triggering process of an accident for thermal runaway in LIB from two aspects of human and material factors by taking thermal runaway in LIB as a top event to obtain a fault tree model, wherein the human factor refers to emergency response failure for early abnormal heating-up, and the material factor refers to abnormal heating-up caused by mechanical abuse, electrical abuse, thermal abuse, etc. recites a field of use limitation to apply a judicial exception (see MPEP 2106.05(h)).
Therefore, claim 2 is subject-matter ineligible.
Regarding claim 3:
Subject Matter of Eligibility Analysis Step 1:
Claim 3 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 3 recites
mapping the fault tree structure to the dynamic Bayesian network, which comprises graphic and numerical conversion: during graphic conversion, a top event, an intermediate event and a basic event of the fault tree are respectively mapped as a leaf node, an intermediate node and a root node of the Bayesian network, and the nodes are connected in the same way as the corresponding events (this limitation is a mental process as it encompasses a human mentally mapping the nodes from the fault tree to the nodes of the Bayesian network).
during numerical conversion, an occurrence probability of the basic event is taken as a prior probability of the corresponding root node, and a conditional probability table is adopted to represent a relationship between the nodes (this limitation is a mental process as it encompasses a human mentally mapping the probabilities of nodes).
Therefore, claim 3 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 3 further recites additional elements of
acquiring the prior probability and dependency between nodes of the dynamic Bayesian network according to multi-source information such as statistical data, an open data set, and expert knowledge to obtain Bayesian-inference-based quantitative results of the risk of thermal runaway in LIB (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 3 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 3 do not provide significantly more than the abstract idea itself, taken alone and in combination because
acquiring the prior probability and dependency between nodes of the dynamic Bayesian network according to multi-source information such as statistical data, an open data set, and expert knowledge to obtain Bayesian-inference-based quantitative results of the risk of thermal runaway in LIB is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 3 is subject-matter ineligible.
Regarding claim 4:
Subject Matter of Eligibility Analysis Step 1:
Claim 4 recites a method, which is directed to a process, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 4 recites
dividing the quantitative results of the risk of thermal runaway of the dynamic Bayesian network into a training set and a test set (this limitation is a mental process as it encompasses a human mentally splitting data into subsets).
Therefore, claim 4 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 4 further recites additional elements of
taking the training set and the test set as inputs of the support vector regression model supporting parameter grid search to obtain the prediction results of the risk of thermal runaway (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 4 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 4 do not provide significantly more than the abstract idea itself, taken alone and in combination because
taking the training set and the test set as inputs of the support vector regression model supporting parameter grid search to obtain the prediction results of the risk of thermal runaway is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 4 is subject-matter ineligible.
Regarding claim 5:
Subject Matter of Eligibility Analysis Step 1:
Claim 5 recites an apparatus, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 5 recites
a quantification module configured to calculate a risk of thermal runaway in LIB (this limitation is a mental process as it encompasses a human mentally calculating the risk quantification if an algorithm was given).
Therefore, claim 5 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 5 further recites additional elements of
an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway (this element does not integrate the abstract idea into a practical application because it is merely data gathering, which is an insignificant extra-solution activity (see MPEP 2106.05(g))).
a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
a quantification module configured to calculate a risk of thermal runaway in LIB (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
Therefore, claim 5 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 5 do not provide significantly more than the abstract idea itself, taken alone and in combination because
an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway is well understood, routine, and conventional. The court has ruled that “Receiving or transmitting data over a network, e.g., using the Internet to gather data” is recognized as a computer function that is well‐understood, routine, and conventional (buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway is a generic component used to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model is a generic component used to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
a quantification module configured to calculate a risk of thermal runaway in LIB is a generic component used to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway is a generic component used to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
Therefore, claim 5 is subject-matter ineligible.
Regarding claim 6:Subject Matter of Eligibility Analysis Step 1:
Claim 6 recites an apparatus, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 6 recites
utilizing the fault tree to analyze a triggering process of an accident for thermal runaway in LIB from two aspects of human and material factors by taking thermal runaway in LIB as a top event to obtain a fault tree model (this limitation is a mental process as it encompasses a human mentally mapping the nodes from the fault tree to the nodes of the Bayesian network).
Therefore, claim 6 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 6 does not further recite any additional elements. Therefore, claim 6 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
Since there are not additional elements, claim 6 does not provide significantly more than the abstract idea itself, taken alone or in combination. Therefore, claim 6 is subject matter ineligible.
Regarding claim 7:
Subject Matter of Eligibility Analysis Step 1:
Claim 7 recites an apparatus, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 7 recites
the quantification module is configured to map a fault tree structure to a dynamic Bayesian network, which comprises graphic and numerical conversion: during graphic conversion, a top event, an intermediate event and a basic event of the fault tree are respectively mapped as a leaf node, an intermediate node and a root node of the Bayesian network, and the nodes are connected in the same way as the corresponding events (this limitation is a mental process as it encompasses a human mentally mapping the nodes from the fault tree to the nodes of the Bayesian network).
during numerical conversion, an occurrence probability of the basic event is taken as a prior probability of the corresponding root node, and a conditional probability table is adopted to represent a relationship between the nodes (this limitation is a mental process as it encompasses a human mentally mapping the probabilities of nodes).
Therefore, claim 7 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 7 further recites additional elements of
the quantification module is configured to map a fault tree structure to a dynamic Bayesian network, which comprises graphic and numerical conversion: during graphic conversion, a top event, an intermediate event and a basic event of the fault tree are respectively mapped as a leaf node, an intermediate node and a root node of the Bayesian network, and the nodes are connected in the same way as the corresponding events (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
acquiring the prior probability and dependency between nodes within normal life of a battery in the dynamic Bayesian network from various channels such as statistical data, an open data set, and expert knowledge, and outputting quantitative results of the risk of thermal runaway in LIB (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 7 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 7 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the quantification module is configured to map a fault tree structure to a dynamic Bayesian network, which comprises graphic and numerical conversion: during graphic conversion, a top event, an intermediate event and a basic event of the fault tree are respectively mapped as a leaf node, an intermediate node and a root node of the Bayesian network, and the nodes are connected in the same way as the corresponding events uses a generic computing tool to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
acquiring the prior probability and dependency between nodes within normal life of a battery in the dynamic Bayesian network from various channels such as statistical data, an open data set, and expert knowledge, and outputting quantitative results of the risk of thermal 12 runaway in LIB is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 7 is subject-matter ineligible.
Regarding claim 8:
Subject Matter of Eligibility Analysis Step 1:
Claim 8 recites an apparatus, which is directed to a machine, and thus is one of the four statutory categories of patentable subject matter.
Subject Matter of Eligibility Analysis Step 2A Prong 1:
Claim 8 recites
the prediction module is configured to divide the quantitative results of the risk of thermal runaway of the dynamic Bayesian network into a training set and a test set (this limitation is a mental process as it encompasses a human mentally splitting data into subsets).
Therefore, claim 8 recites an abstract idea.
Subject Matter of Eligibility Analysis Step 2A Prong 2:
Claim 8 further recites additional elements of
the prediction module is configured to divide the quantitative results of the risk of thermal runaway of the dynamic Bayesian network into a training set and a test set (this element does not integrate the abstract idea into a practical application because it recites a generic computing component on which to perform the abstract idea (see MPEP 2106.05(b))).
inputting the training set and the test set into a support vector regression model including parameter grid search to obtain the prediction results of the risk of thermal runaway (this element does not integrate the abstract idea into a practical application because it amounts to mere instructions to apply (see MPEP 2106.05(f))).
Therefore, claim 8 is not integrated into a practical application.
Subject Matter of Eligibility Analysis Step 2B:
The additional elements of claim 8 do not provide significantly more than the abstract idea itself, taken alone and in combination because
the prediction module is configured to divide the quantitative results of the risk of thermal runaway of the dynamic Bayesian network into a training set and a test set is a generic component used to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(b)).
inputting the training set and the test set into a support vector regression model including parameter grid search to obtain the prediction results of the risk of thermal runaway is an instruction to perform the abstract idea and cannot provide significantly more (see MPEP 2106.05(f)).
Therefore, claim 8 is subject-matter ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable in view of Wang et al. (CN 110489898 B) (hereafter referred to as Wang) in view of Huang et al. (Fire risk assessment of battery transportation and storage by combining fault tree analysis and fuzzy logic) (hereafter referred to as Huang and Yang et al. (State-of-health estimation for the lithium-ion battery based on support vector regression) (hereafter referred to as Yang).
Regarding claim 1, Wang teaches
mapping a fault tree structure to a dynamic Bayesian network model for thermal runaway in LIB to obtain quantitative results of a risk of thermal runaway in LIB (Wang, claim 1, “The dynamic multi-level system FTA [Faulty Tree Analysis] is converted into a static Bayesian network B 0 , which is used to describe the influence relationship between the internal components, equipment, subsystems and systems of the multi-level system” and “the static Bayesian network is extended to a dynamic Bayesian network to form a system state prediction model” (Wang, abstract)).
taking the quantitative results of a dynamic Bayesian network as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway in LIB (Wang, claim 1, “The dynamic multi-level system FTA [Faulty Tree Analysis] is converted into a static Bayesian network B 0 , which is used to describe the influence relationship between the internal components, equipment, subsystems and systems of the multi-level system” and “the static Bayesian network is extended to a dynamic Bayesian network to form a system state prediction model” (Wang, abstract)).
the mapping being configured to convert the fault tree structure and parameters into the corresponding dynamic Bayesian network to represent a more complex node relationship, and perform risk quantification by using a Bayesian algorithm (Wang, paragraph 0036, “With the help of the data set of the base-level components, the EM algorithm (expectation maximization algorithm) is used to determine the transition probability of the Bayesian network of two time slices, and the construction of the multi-level state prediction model is completed. The implementation tool is GeNIe software”).
Wang does not teach, but Huang does teach
mapping a fault tree structure to a dynamic Bayesian network model for thermal runaway in LIB to obtain quantitative results of a risk in thermal runaway in LIB (Huang, section 3, “The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell)”).
acquiring knowledge of a mechanism for thermal runaway in LIB (Huang, section 1, “As mentioned above, battery failure is induced by many factors. To comprehensively assess the fire risk during battery transportation and storage, all possible failure paths and corresponding factors need to be considered. Fault tree analysis (FTA) method is a backward reasoning method that can deduce all possible paths and basic events from the result” and “The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell)” (Huang, section 3). Examiner notes that the Fault Tree Analysis provides a structured knowledge on thermal runaway in LIBs).
describing an evolution process of thermal runaway in LIB by adopting a fault tree (Huang, section 3, “Here, we build a fault tree model for battery fire accident during transportation and storage as shown in Fig. 3. The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell). The occurrence of M1 is caused by one of the sub-middle events M2 (The external heat causes the internal decomposition for the battery) and M3 (Local high temperature of battery) along with the condition event X1 (The battery has thermal instability). Overall, nine basic events X1 ∼X9 are derived from the top event by FTA.”).
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the fault tree being configured to systemically induce the knowledge of the mechanism for thermal runaway and graphically represent evolution of thermal runaway in LIB by virtue of events and a logic relationship therebetween (Huang, section 3, “Here, we build a fault tree model for battery fire accident during transportation and storage as shown in Fig. 3. The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell). The occurrence of M1 is caused by one of the sub-middle events M2 (The external heat causes the internal decomposition for the battery) and M3 (Local high temperature of battery) along with the condition event X1 (The battery has thermal instability). Overall, nine basic events X1 ∼X9 are derived from the top event by FTA.”).
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Wang and Huang are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang to substitute a fault tree for an energy storage battery system with a fault tree for thermal runaway in LIBs from Huang. One of the ordinary skill in the art would have known that both fault trees determine the cause of battery failure. Therefore, substituting Wang’s technique with Huang’s technique would yield the predictable result of creating a robust risk assessment framework for thermal runaway in LIBs (See MPEP 2141 (III)(B) Simple substitution of one known element for another to obtain predictable results.
Wang and Huang do not teach, but Yang does teach
taking the quantitative results of a dynamic Bayesian network as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway in LIB (Yang, abstract, “An improved battery model ... is proposed to improve the model accuracy and study the relation between internal parameters and states of the battery” and “Based on these aforementioned SVM methods, we propose a novel least square support vector regression (LSSVR) based method to estimate the SOH” (Yang, section 1)).
the machine learning model referring to a support vector regression model configured to predict a trend of the risk of thermal runaway (Yang, section 1, “Based on these aforementioned SVM methods, we propose a novel least square support vector regression (LSSVR) based method to estimate the SOH. Compared to the basic SVR method, LSSVR has faster solving speed and simpler solving process. In this paper, a novel state of-health estimation approach is proposed for lithium-ion batteries based on statistical knowledge”).
Wang, Huang, and Yang are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang and Huang to put the output of the Dynamic Bayesian Model into a least square support vector regression (LSSVR) model from Yang. Yang teaches that “Compared to the basic SVR method, LSSVR has faster solving speed and simpler solving process” (Yang, section 1) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
Regarding claim 3, Wang, Huang, and Yang teach the method of claim 1, Wang further teaches
mapping the fault tree structure to the dynamic Bayesian network, which comprises graphic and numerical conversion: during graphic conversion, a top event, an intermediate event and a basic event of the fault tree are respectively mapped as a leaf node, an intermediate node and a root node of the Bayesian network, and the nodes are connected in the same way as the corresponding events (Wang, table 1,
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during numerical conversion, an occurrence probability of the basic event is taken as a prior probability of the corresponding root node, and a conditional probability table is adopted to represent a relationship between the nodes (Wang, paragraph, 0030, “Input the data set D(C, t ) of the base-level components into the B1 network for training, obtain the probability relationship θ between the B1 network nodes, realize the quantitative description of the network node relationship”).
acquiring the prior probability and dependency between nodes of the dynamic Bayesian network according to multi-source information such as statistical data, an open data set, and expert knowledge to obtain Bayesian-inference-based quantitative results of the risk of thermal runaway in LIB (Wang, paragraph 0045, “300 groups of single battery state data are used to train the input into the parent node of the Bayesian network in Figure 6, and the network parameters are obtained through training”).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Huang, and Yang in view of Jafari et al. (Reliability evaluation of fire alarm systems using dynamic Bayesian networks and fuzzy fault tree analysis) (hereafter referred to as Jafari).
Regarding claim 2, Wang, Huang, and Yang teach the method of claim 1, Huang teaches
utilizing the fault tree to analyze a triggering process of an accident for thermal runaway in LIB from two aspects of human and material factors by taking thermal runaway in LIB as a top event to obtain a fault tree model, wherein the human factor refers to emergency response failure for early abnormal heating-up, and the material factor refers to abnormal heating-up caused by mechanical abuse, electrical abuse, thermal abuse, etc. (Huang, section 3, “Battery fire is mainly caused by three modes of failure: mechanical, electric and thermal. Here, we build a fault tree model for battery fire accident during transportation and storage as shown in Fig. 3. The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell)”).
Huang does not teach, but Jafari does teach
utilizing the fault tree to analyze a triggering process of an accident for thermal runaway in LIB from two aspects of human and material factors by taking thermal runaway in LIB as a top event to obtain a fault tree model, wherein the human factor refers to emergency response failure for early abnormal heating-up, and the material factor refers to abnormal heating-up caused by mechanical abuse, electrical abuse, thermal abuse, etc. (Jafari, figure 4
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Examiner notes that according to table 4, X1 and X2 depicts the faults within the system itself and administrative error, respectively, which maps to a human and material factor. Also, X6 depicts faults in managerial levels, which maps to emergency response failures).
Wang, Huang, Yang, and Jafari are considered analogous to the claimed invention because all deal with fire safety systems. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huang to use the fault tree structure from Jafari and add the human factors for thermal runaway. One of the ordinary skill in the art would have known to apply the known technique of adding human factors into a fault tree. Therefore, applying Huang’s technique would yield the predictable result of increasing the accuracy of fault trees by accounting for both technical and human factors (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Huang, and Yang in view of Yao et al. (An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine) (hereafter referred to as Yao).
Regarding claim 4, Wang, Huang, and Yang teach the method of claim 1, Wang teaches
dividing the quantitative results of the risk of thermal runaway of the dynamic Bayesian network into a training set and a test set (Wang, paragraph 0045, “300 groups of single battery state data are used to train the input into the parent node of the Bayesian network in Figure 6, and the network parameters are obtained through training” and “The remaining 132 sets of data are input into the two network structures respectively, and the system state is inferred” (Wang, paragraph 0048)).
Wang does not teach, but Yang does teach
taking the training set and the test set as inputs of the support vector regression model supporting parameter grid search to obtain the prediction results of the risk of thermal runaway (Yang, section 1, “Based on these aforementioned SVM methods, we propose a novel least square support vector regression (LSSVR) based method to estimate the SOH. Compared to the basic SVR method, LSSVR has faster solving speed and simpler solving process. In this paper, a novel state of-health estimation approach is proposed for lithium-ion batteries based on statistical knowledge”).
Wang, Huang, and Yang are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang and Huang to put the output of the Dynamic Bayesian Model into a least square support vector regression (LSSVR) model from Yang. Yang teaches that “Compared to the basic SVR method, LSSVR has faster solving speed and simpler solving process” (Yang, section 1) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
Wang does not teach, but Yao does teach
taking the training set and the test set as inputs of the support vector regression model supporting parameter grid search to obtain the prediction results of the risk of thermal runaway (Yao, section 1, “the grid search method is adopted to optimize the kernel function parameter and penalty factor to ensure the model’s accuracy and robustness”).
Wang, Huang, Yang, and Yao are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang, Hu, and Yang to use parameter grid search method from Yao into the support vector regression model. Yao teaches that “To improve the accuracy and applicability of the SVM classifier, this section uses a cross-validation (CV) and grid search (GS) method to optimize its parameters” (Yao, section 5.1) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
Claim(s) 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Huang, and Yang in view of Zang et al. (CN 115372830 A) (hereafter referred to as Zang).
Regarding claim 5,
Wang teaches
a quantification module configured to calculate a risk of thermal runaway in LIB (Wang, paragraph 0003, “In order to propose a more reasonable system analysis method, STAMP analysis is introduced from the perspective of safety and risk analysis on the basis of classical analysis methods” and “With the help of the data set of the base-level components, the EM algorithm (expectation maximization algorithm) is used to determine the transition probability of the Bayesian network of two time slices, and the construction of the multi-level state prediction model is completed. The implementation tool is GeNIe software.” (Wang, paragraph 0036). Examiner notes that since GeNIe is a software, there must be a module that executes it to perform the risk analysis).
Wang does not teach, but Huang does teach
an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway (Huang, section 1, “As mentioned above, battery failure is induced by many factors. To comprehensively assess the fire risk during battery transportation and storage, all possible failure paths and corresponding factors need to be considered. Fault tree analysis (FTA) method is a backward reasoning method that can deduce all possible paths and basic events from the result” and “The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell)” (Huang, section 3). Examiner notes that the Fault Tree Analysis provides a structured knowledge on thermal runaway in LIBs).
a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model(Huang, section 3, “Here, we build a fault tree model for battery fire accident during transportation and storage as shown in Fig. 3. The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell). The occurrence of M1 is caused by one of the sub-middle events M2 (The external heat causes the internal decomposition for the battery) and M3 (Local high temperature of battery) along with the condition event X1 (The battery has thermal instability). Overall, nine basic events X1 ∼X9 are derived from the top event by FTA.”).
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Wang and Huang are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang to substitute a fault tree for an energy storage battery system with a fault tree for thermal runaway in LIBs from Huang. One of the ordinary skill in the art would have known that both fault trees determine the cause of battery failure. Therefore, substituting Wang’s technique with Huang’s technique would yield the predictable result of creating a robust risk assessment framework for thermal runaway in LIBs (See MPEP 2141 (III)(B) Simple substitution of one known element for another to obtain predictable results.
Wang and Huang do not teach, but Yang does teach
a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway (Yang, abstract, “An improved battery model ... is proposed to improve the model accuracy and study the relation between internal parameters and states of the battery” and “Based on these aforementioned SVM methods, we propose a novel least square support vector regression (LSSVR) based method to estimate the SOH” (Yang, section 1)).
Wang, Huang, and Yang are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang and Huang to put the output of the Dynamic Bayesian Model into a least square support vector regression (LSSVR) model from Yang. One of the ordinary skill in the art would have known to apply the known technique of using a support vector regression model for probabilistic predictions. Therefore, applying Yang’s technique would yield the predictable result of higher accuracy in predicting battery failure (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results.
Wang, Huang, and Yang do not teach, but Zang teaches
an acquisition module configured to acquire knowledge of a mechanism for LIB thermal runaway (Zang, “The invention further claims a power battery thermal runaway risk evaluation device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, the processor executes the computer program to realize any one of the power battery thermal runaway risk evaluation method of the step”).
a structurized module configured to decompose a triggering process of thermal runaway in LIB to obtain a structurized model (Zang, “The invention further claims a power battery thermal runaway risk evaluation device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, the processor executes the computer program to realize any one of the power battery thermal runaway risk evaluation method of the step”).
a quantification module configured to calculate a risk of thermal runaway in LIB (Zang, “The invention further claims a power battery thermal runaway risk evaluation device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, the processor executes the computer program to realize any one of the power battery thermal runaway risk evaluation method of the step”).
a prediction module configured to take results from the quantification module as inputs of a machine learning model to obtain prediction results of the risk of thermal runaway (Zang, “The invention further claims a power battery thermal runaway risk evaluation device, comprising a memory, a processor and a computer program stored on the memory and capable of running on the processor, the processor executes the computer program to realize any one of the power battery thermal runaway risk evaluation method of the step”).
Wang, Huang, Yang, and Zang are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang, Huang, Yang to apply their methods onto the thermal runaway risk evaluation device from Zang. One of the ordinary skill in the art would have known to apply the known technique of using a computer machine/module to perform instructions. Therefore, applying Zang’s technique would yield the predictable result of running instructions on computer modules(See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results.
Regarding claim 7, Wang, Huang, Yang, and Zang teach the system of claim 5 and the quantification module, Wang further teaches
map a fault tree structure to a dynamic Bayesian network, which comprises graphic and numerical conversion: during graphic conversion, a top event, an intermediate event and a basic event of the fault tree are respectively mapped as a leaf node, an intermediate node and a root node of the Bayesian network, and the nodes are connected in the same way as the corresponding events (Wang, table 1,
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during numerical conversion, an occurrence probability of the basic event is taken as a prior probability of the corresponding root node, and a conditional probability table is adopted to represent a relationship between the nodes; and acquiring the prior probability and dependency between nodes within normal life of a battery in the dynamic Bayesian network from various channels such as statistical data, an open data set, and expert knowledge, and outputting quantitative results of the risk of thermal 12 runaway in LIB (Wang, paragraph, 0030, “Input the data set D(C, t ) of the base-level components into the B1 network for training, obtain the probability relationship θ between the B1 network nodes, realize the quantitative description of the network node relationship”).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Huang, Yang and Zang in view of Jafari.
Regarding claim 6, Wang, Huang, Yang, and Zang teach the system of claim 5 and the structurized module,
utilize a fault tree to decompose a triggering process of thermal runaway in LIB and analyze a triggering process of an accident for thermal runaway in LIB from two aspects of human and material factors by taking thermal runaway in LIB as a top event to obtain a fault tree model (Huang, section 3, “Battery fire is mainly caused by three modes of failure: mechanical, electric and thermal. Here, we build a fault tree model for battery fire accident during transportation and storage as shown in Fig. 3. The top event (Lithium-ion batteries catch fire during storage and transportation) is caused by the middle event M1 (Thermal runaway of cell)”).
utilize a fault tree to decompose a triggering process of thermal runaway in LIB and analyze a triggering process of an accident for thermal runaway in LIB from two aspects of human and material factors by taking thermal runaway in LIB as a top event to obtain a fault tree model (Jafari, figure 4
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Examiner notes that according to table 4, X1 and X2 depicts the faults within the system itself and administrative error, respectively, which maps to a human and material factor. Also, X6 depicts faults in managerial levels, which maps to emergency response failures).
Wang, Huang, Yang, Zang, and Jafari are considered analogous to the claimed invention because all deal with fire safety systems. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Huang to use the fault tree structure from Jafari and add the human factors for thermal runaway. One of the ordinary skill in the art would have known to apply the known technique of adding human factors into a fault tree. Therefore, applying Huang’s technique would yield the predictable result of increasing the accuracy of fault trees by accounting for both technical and human factors (See MPEP 2141 (III)(D) Applying a known technique to a known device ready for improvement to yield predicable results.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang, Huang, Yang and Zang in view of Yao et al. (An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine) (hereafter referred to as Yao).
Regarding claim 8, Wang, Huang, Yang, and Zang teach the system of claim 5 and the prediction module, Wang teaches
divide the quantitative results of the risk of thermal runaway of the dynamic Bayesian network into a training set and a test set (Wang, paragraph 0045, “300 groups of single battery state data are used to train the input into the parent node of the Bayesian network in Figure 6, and the network parameters are obtained through training” and “The remaining 132 sets of data are input into the two network structures respectively, and the system state is inferred” (Wang, paragraph 0048)).
Wang does not teach, but Yang does teach
inputting the training set and the test set as inputs of the support vector regression model supporting parameter grid search to obtain the prediction results of the risk of thermal runaway (Yang, section 1, “Based on these aforementioned SVM methods, we propose a novel least square support vector regression (LSSVR) based method to estimate the SOH. Compared to the basic SVR method, LSSVR has faster solving speed and simpler solving process. In this paper, a novel state of-health estimation approach is proposed for lithium-ion batteries based on statistical knowledge”).
Wang, Huang, and Yang are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang and Huang to put the output of the Dynamic Bayesian Model into a least square support vector regression (LSSVR) model from Yang. Yang teaches that “Compared to the basic SVR method, LSSVR has faster solving speed and simpler solving process” (Yang, section 1) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
Wang does not teach, but Yao does teach
inputting the training set and the test set as inputs of the support vector regression model supporting parameter grid search to obtain the prediction results of the risk of thermal runaway (Yao, section 1, “the grid search method is adopted to optimize the kernel function parameter and penalty factor to ensure the model’s accuracy and robustness”).
Wang, Huang, Yang, Zang, and Yao are considered analogous to the claimed invention because they both deal with the state of batteries. It would have been obvious to one having ordinary skill in the art prior to the effective filing date to have modified Wang, Hu, Yang, and Zang to use parameter grid search method from Yao into the support vector regression model. Yao teaches that “To improve the accuracy and applicability of the SVM classifier, this section uses a cross-validation (CV) and grid search (GS) method to optimize its parameters” (Yao, section 5.1) (See MPEP 2141 (III)(G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Hu et al. (Comprehensively analysis the failure evolution and safety evaluation of automotive lithium ion battery) discloses a map of lithium-ion battery failure evolution combining battery tests and forward development by Fault Tree Analysis. Wu et al. (Bayesian Network modelling for safety management of electric vehicles transported in RoPax ships) discloses a data-driven Bayesian Network to analyze the effects of the influencing factors on the consequences, and to propose appropriate countermeasures. Sarbayev et al. (Risk assessment of process systems by mapping fault tree into artificial neural network) discloses mapping Fault trees into Artificial Neural Networks to support the convenient and practical application of Artificial Neural Networks in risk assessment. Wang et al. (CN 110161414 B) discloses an online prediction model and system for thermal runaway of a power battery.
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/S.V./ Examiner, Art Unit 2148 /MICHELLE T BECHTOLD/Supervisory Patent Examiner, Art Unit 2148
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