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 .
Status of Claims
This communication is in response to application No. 18/947,672 filed on November 14, 2024. Claims 1-12 are currently pending and have been examined. Claims 1-12 have been rejected as follows.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on November 14, 2024 is being considered by the examiner.
Priority
Acknowledgment is made of applicant's claim priority for foreign applications CN202411562719.4, filed on 11/04/2024.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
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.
Claim 1 and 12 are 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. “Optimal” is a relative term used in claim 1. Claim 12 states “an environment-adaptive Bayesian network”; it is not clear if it is the same environment-adaptive Bayesian network as claim 1 OR is a different environment-adaptive Bayesian network.
Claims 2-12 are rejected by virtue of their dependency on claim 1.
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- 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
. Claim 1 is directed to a method; therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claims 1 includes limitations that recite an abstract idea (emphasized below) and claim 1 will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
A method for diagnosing vehicle faults based on an environment-adaptive Bayesian network, the method comprising:
(S1) receiving an input of a faulty symptom of a vehicle into a vehicle fault detection system;
(S2) selecting an optimal weighted association fault tree model from a library comprising a plurality of weighted association fault tree models, based on the faulty symptom;
(S3) mapping the optimal weighted association fault tree model to an environment-adaptive Bayesian network;
(S4) calculating a credibility of each leaf node within the environment-adaptive Bayesian network; and
(S5) generating a fault ranking and a fault status information based on the credibility of each leaf node within the environment-adaptive Bayesian network and initiating a multimedia fault alert.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “mapping...” in the context of this claim encompasses a person looking at data collected and forming a simple judgement by matching. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
Claim 1 recites:
A method for diagnosing vehicle faults based on an environment-adaptive Bayesian network, the method comprising:
(S1) receiving an input of a faulty symptom of a vehicle into a vehicle fault detection system;
(S2) selecting an optimal weighted association fault tree model from a library comprising a plurality of weighted association fault tree models, based on the faulty symptom;
(S3) mapping the optimal weighted association fault tree model to an environment-adaptive Bayesian network;
(S4) calculating a credibility of each leaf node within the environment-adaptive Bayesian network; and
(S5) generating a fault ranking and a fault status information based on the credibility of each leaf node within the environment-adaptive Bayesian network and initiating a multimedia fault alert.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “receiving an input...,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the receiving steps are recited at a high level of generality and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional element does not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above, the additional limitations of “receiving an input...,” these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “receiving an input...,” are well-understood, routine, and conventional activities. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner
Hence, the claim is not patent eligible.
Dependent claim(s) 2-12 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception as mathematical concepts and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-12 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claim(s) 1-12 are ineligible under 35 USC §101.
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, 2, 3, 9 and 10 are rejected under 35 U.S.C 103 as being unpatentable over Li (CN 113592012 A) in view of Hou (CN 113780566 A).
Regarding claim 1, Li discloses A method for diagnosing vehicle faults based on an environment-adaptive Bayesian network, the method comprising: (S1) receiving an input of a faulty symptom of a vehicle into a vehicle fault detection system; (see at least [0012]; " Determine the credibility of the fault symptoms for each case in the case library;") Li describes an input of a faulty symptom into a fault detection system.
(S2) selecting an optimal weighted association fault tree model from a library comprising a plurality of weighted association fault tree models, based on the faulty symptom; (see at least [0159-163]; " (3) Calculate the similarity between the equipment to be diagnosed and case C<sub>p</sub> based on the similarity of all fault symptoms. The specific calculation formula is as follows: … Where D<sub>s</sub>(C<sub>n</sub>,C<sub>p</sub>) is the similarity between the device to be diagnosed C<sub>n</sub> and the case C<sub>p</sub>; W<sub>i</sub> is the weighting factor of the i-th fault symptom in the device to be diagnosed C<sub>n</sub>;… Steps 103 and 104 are the process of matching the equipment to be diagnosed with the case library (fault case library), that is, finding cases in the case library that are similar to the equipment to be diagnosed.") Li describes selecting an optimal weighted association fault tree model from a library based on the faulty symptom.
(S3) mapping the optimal weighted association fault tree model to an environment-adaptive Bayesian network; (see at least [0970]; " Finally, using the collected sample data, a Bayesian network model is established according to the corresponding judgment criteria.") Li describes mapping the optimal weighted association to a fault tree model.
(S4) calculating a credibility of each leaf node within the environment-adaptive Bayesian network; and (see at least [0109]; "obtaining the credibility of each fault symptom according to the inference order based on the rules.") Li describes calculating a credibility of each node within the Bayesian network.
Li does not explicitly disclose (S5) generating a fault ranking and a fault status information based on the credibility of each leaf node within the environment-adaptive Bayesian network and initiating a multimedia fault alert.
However, Hou teaches (S5) generating a fault ranking and a fault status information based on the credibility of each leaf node within the environment-adaptive Bayesian network and initiating a multimedia fault alert. (see at least [0071]; "then set the prior probability set B according to the ranking of the probability of faults occurring.") Hou describes generating a ranking based on the credibility of each node in the network.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Li to incorporate teachings of Hou which teaches generating a ranking based on the credibility of each node in the network in order to determine which node’s to match a fault tree too to determine which solution to use.
Regarding claim 2, Li and Hou, in combination, disclose limitations of claim 1 as discussed above, furthermore, Li discloses The method of claim 1, wherein step (S2) comprises: (S201) extracting and analyzing a feature of the faulty symptom; and (see at least [63]; "By classifying and processing the collected raw data, and using expert system knowledge to customize corresponding feature extraction rules, feature vectors are extracted from the collected data to generate a corresponding feature sample library.")
(S202) selecting, through a similarity calculation, a weighted association fault tree model withthe highest similarity withthe feature of the faulty symptom from the library comprising a plurality of weighted association fault tree models as the optimal weighted association fault tree model. (see at least [0063]; "The test data information is processed by feature vector extraction and used as input data for various fault diagnosis models. The similarity of the input feature vector in different fault classification models is calculated.")
Regarding claim 3, Li and Hou, in combination, disclose limitations of claim 1 as discussed above, furthermore, Li discloses The method of claim 2, wherein the similarity calculation comprises using a cosine similarity or a Euclidean distance. (see at least [0063]; " The similarity of the input feature vector in different fault classification models is calculated.")
Regarding claim 9, Li and Hou, in combination, disclose limitations of claim 1 as discussed above, furthermore, Li discloses The method of claim 2, wherein step (S3) comprises mapping a top event, an intermediate event and a basic event of the weighted association fault tree model to a root node, (see at least [0098, 0097]; "The model consists of three parts: the component state layer, the fault layer, and the fault symptom layer. The upper layer C<sub>i</sub> is the component state layer, representing the fault case sample set, i.e., the fault cause. The middle layer S<sub>8</sub>～S<sub>11</sub> represents the fault state. The lower layer S<sub>12</sub>～S<sub>16</sub> is the fault symptom layer, representing the symptom sample set, i.e., the fault phenomenon…Nodes with dependencies between fault types and symptoms are connected by directed arcs.")
an intermediate node and a leaf node of the environment-adaptive Bayesian network, respectively, wherein the top event of the weighted association fault tree model denotes a system failure, and the basic event of the weighted association fault tree model denotes a fault that causes the system failure. (see at least [0099]; "For example, S<sub>9</sub>“Insufficient fuel injection” corresponds to four parent nodes: C<sub>2</sub>“Single cylinder injector damage”, C<sub>4</sub>“Fuel filter blockage”, C<sub>5</sub>“Fuel line leak”, and C<sub>7</sub>“Fuel pressure regulator spring weakness”. At the same time, problems with this child node will affect the next level child node, S<sub>14</sub>“Insufficient engine power” and S<sub>16</sub>“Difficulty starting the engine”.")
Regarding claim 10, Li and Hou, in combination, disclose limitations of claim 1 as discussed above, furthermore, Li discloses The method of claim 1, wherein step (S5) comprises presenting the fault ranking in a form of a list that includes a name of the leaf node, the credibility and a description of the fault mode. (see at least [0057, Fig. 5]; " The hardware framework of PMA includes: small-screen display terminal, voice and video encryption, network receiver, large-screen display terminal, display driver, computer motherboard, basic interfaces, adapter, etc., as shown in Figure 1.")
Claim 11 ais rejected under 35 U.S.C 103 as being unpatentable over Li (CN 113592012 A) in view of Hou (CN 113780566 A), in further view of Pan (CN 108957230 A).
Regarding claim 11, Li and Hou, in combination, disclose limitations of claim 1 as discussed above, furthermore, Li does not explicitly disclose The method of claim 1, wherein the multimedia fault alert in step (S5) comprises a multi-level alert.
However, Pan teaches The method of claim 1, wherein the multimedia fault alert in step (S5) comprises a multi-level alert. (see at least [0125]; "An alarm system that sounds when a malfunction occurs")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Li to incorporate teachings of Pan which teaches an alert for a fault in order to notify the user or system that a fault has occurred to be able to determine where or what could have caused it.
Claim 12 ais rejected under 35 U.S.C 103 as being unpatentable over Li (CN 113592012 A) in view of Hou (CN 113780566 A), in further view of Park (US 20240096142 A1).
Regarding claim 12, Li and Hou, in combination, disclose limitations of claim 1 as discussed above, furthermore, Li does not explicitly disclose A system comprising one or more computer processors and a computer readable memory, the computer readable memory comprising machine executable code, which when executed by the one or more computer processors implements the method for diagnosing vehicle faults based on an environment-adaptive Bayesian network of claim 1.
However, Park teaches A system comprising one or more computer processors and a computer readable memory, (see at least [0008];" the apparatus including: a processor; a memory ")
the computer readable memory comprising machine executable code, (see at least [0008]; "a memory storing one or more programs configured to be executed by the processor;")
which when executed by the one or more computer processors implements the method for diagnosing vehicle faults based on an environment-adaptive Bayesian network of claim 1. (see at least [0008]; "and the one or more programs include instructions for: an acceleration sensor for detecting an acceleration signal; a feature extraction unit for extracting features related to a fault of a vehicle component from the detected acceleration signal; and a machine learning model for diagnosing a fault of the vehicle component based on the extracted features,")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Li to incorporate teachings of Park which teaches a processor, a memory and instructions to execute the method in order to execute the desired functions to determine a fault using Bayesian Models.
Allowable Subject Matter
Claims 4, 5, 6, 7 and 8 are objected to as being dependent upon a rejected base claim, but it appears they would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and rewritten to overcome the 35 USC 101 and 112 rejections.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HANA VICTORIA HALL whose telephone number is (571)272-5289. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rachid Bendidi can be reached at 5712724896. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HANA VICTORIA HALL/ Examiner, Art Unit 3664
/RACHID BENDIDI/ Supervisory Patent Examiner, Art Unit 3664