DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The rejection under 35 USC 101 is withdrawn in view of Applicant’s response in view of the updates to the MPEP in the memo Advance notice of change to the MPEP in light of Ex Parte Desjardins.
Applicant’s arguments filed 11/21/2025 have been fully considered and are persuasive. In particular, Applicant argues that the cited references do not teach the transforming step as recited in the amendment. Examiner agrees. However, upon further consideration, new grounds of rejection under 35 USC 103 necessitated by amendment are presented herein.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/21/2025 has been entered.
Examiner Remarks
The “Barkus” reference US 2019/0278647 A1 previously cited, is actually to Peter Bakucz. The use of “Barkus” was apparently a typographical error. Nevertheless, the reference will continue to be referred to as “Barkus” to avoid confusing the record any further.
Claim Objections
Claim 6 is objected to because of the following informalities: claim 6 includes a stray “I” that is being treated as an unintentional insertion. Appropriate correction is required.
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 1-5, 7-10, and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Barkus (US 2019/0278647 A1) in view of Tsang (Improving Learning Accuracy of Fuzzy Decision Trees by Hybrid Neural Networks).
Regarding claim 1, Barkus discloses a computer-implemented method for determining criticality values (Barkus:¶[0014]: probability of an undesirable event) of a technical system (Barkus:¶[0028]: The technical system may be, for example, a surroundings detection system and/or a control system of an at least partially automated driving vehicle.), the method comprising the following steps:
specifying a reliability (Barkus:¶[0015]: maximum admissible probability of an undesirable event) of the technical system that is to be satisfied; (Barkus:¶[0014]: the specification frequently includes a maximum admissible probability of an undesirable event)
providing a fuzzy fault tree (Barkus:¶[0007] tree-like logical linkage) for the technical system, the fuzzy fault tree including a fuzzy top event (Barkus:¶[0007] causative event) , multiple fuzzy basic events ( Barkus:¶[0007] lower order events) , and logical, programmable fuzzy AND/OR operators; (Barkus:¶[0007] : In the tree-like logical linkage, a causative event may in particular be an arbitrary logical linkage of lower order events. If, for example, in an electronic stability program (ESP) for acquiring a measured value, five redundant sensors are provided, the event "measured value not acquired or acquired incorrectly" may occur if the event "sensor faulty" occurs at least three of the sensor) (Barkus:¶[0008]: The logical linkage of the causative events may be carried out using any arbitrary logical operators, i.e., for example, AND, OR, XOR, NOR, NAND, and NOT.)
transforming the fuzzy fault tree into a flexible (neural) network including a tree structure (Barkus:¶[0049] To make this effort manageable at all, tree-like logical linkage 2 generally has to be transformed (for example, using the Kohda-Henley-Inous comprehensive method or the Yllera method), to decompose linkage 2 into modules and to find "minimal cut sets" in which redundancies are eliminated.) (Barkus:¶[0051]: The conversion of non-self-similar tree-like logical linkage 2 into self-similar version 2a is not unique. Another self-similar structure could thus instead also be used, as long as there is an area which accurately depicts the cascading interactions between causative events 21 through 27 and undesirable event 28.
...determining an optimized flexible (neural) network by carrying out a learning method for optimizing the flexible (neural) network, the optimized flexible neural network achieving the reliability of the technical system that is to be satisfied; (Barkus:¶[0043]: The probability is sought that an undesirable event 28 will occur, and/or an effort is made to keep this probability below a predefined level)) (The examiner interprets ¶[0056] as describing ‘a learning method’ as claimed)
Barkus: ¶[0056]:In the example shown in FIG. 3, especially for application in an at least partially automated driving vehicle, the correct function of functional units 11 through 15 in system 1 is progressively monitored by an onboard diagnosis unit of the vehicle according to block 130. If a malfunction 11a through 15a is established, according to block 135, the probability of malfunction 11a through 15a is accordingly modified in self-similar tree-like logical linkage 2a. Alternatively or also in combination therewith, according to block 140, the probability of malfunction 11a through 15a is incremented with increasing age and/or with increasing use of particular functional unit 11 through 15.
¶[0057] After the probabilities for malfunctions 11a through 15a of functional units 11 through 15 have been modified in self-similar tree-like logical linkage 2a, in step 150, the probability of undesirable event 28 is reanalyzed on the basis of updated linkage 2a. It is subsequently checked in block 160 whether the reanalyzed probability meets a predefined criterion.)
and
deriving criticality values of the fuzzy basic events from the optimized flexible (neural) network. (Barkus: ¶[0056]: In the example shown in FIG. 3, especially for application in an at least partially automated driving vehicle, the correct function of functional units 11 through 15 in system 1 is progressively monitored by an onboard diagnosis unit of the vehicle according to block 130. If a malfunction 11a through 15a is established, according to block 135, the probability of malfunction 11a through 15a is accordingly modified in self-similar tree-like logical linkage 2a. Alternatively or also in combination therewith, according to block 140, the probability of malfunction 11a through 15a is incremented with increasing age and/or with increasing use of particular functional unit 11 through 15.)
Barkus, however, fails to disclose “transforming the fuzzy fault tree into a flexible neural network, wherein the transforming includes translating fuzzy membership functions of the basic events and linkages between the basic events into neurons of the flexible neural network; determining an optimized flexible neural network by carrying out a learning method for optimizing the flexible neural network, and deriving criticality values from the optimized flexible neural network.
However, Tsang—directed to analogous art--teaches
transforming the fuzzy fault tree into a flexible neural network, wherein the transforming includes translating fuzzy membership functions of the basic events and linkages between the basic events into neurons of the flexible neural network; determining an optimized flexible neural network by carrying out a learning method for optimizing the flexible neural network, and deriving criticality values from the optimized flexible neural network. (Tsang, Abstract describes using an HNN (hybrid neural network) to refine an FDT (fuzzy decision tree). Page 608, section IV.A describes the transformation of the Fuzzy Production Rules to the HNN. In particular, IV.A.2 Rule Layer indicates that each node of the hidden layer represents an extracted fuzzy rule from the weighted FDT. The Fuzzy Production Rules are being interpreted as fuzzy membership functions. The rules are described at least in section III.B-C, starting page 606. For example, Rule 1) on page 606, right hand column is “If Temperature = Hot AND Outlook = Cloudy THEN Swimming” indicates that the event determined by the intersection of the events “Temperature = Hot” and “Outlook = Cloud” is a subset of the event “Swimming”. In the combination with Barkus, the tree taught by Barkus would be modified to incorporate the hybrid neural network taught by Tsang.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barkus by Tsang because of the advantages of the technique of Tsang identified in the conclusion on page 613: “The main advantages of the proposed method are as follows: 1) the learning performance can be improved by refining these parameters without much computational effort. 2) since each knowledge parameter used in FDTs has the clear meaning, the comprehensibility of FDTs can be kept. 3) to determine these knowledge parameters, the training of HNN can replace the task of consultation with domain specialists to a great extent.”
Regarding claim 2, Barkus discloses the computer-implemented method as recited in claim 1, wherein the learning method includes a first level for optimizing the structure of the flexible (neural) network. (Barkus: ¶[0055] In step 210, system 1 is modeled with the aid of self-similar tree-like logical linkage 2a) (The examiner interprets a first learning level optimizing the structure of the flexible network as taught by mapping a non-self-similar tree to a self-similar tree).
Barkus does not explicitly describe that the flexible network is a neural network as claimed.
However, Tsang, in the same field of endeavor, teaches
optimizing the structure of a flexible neural network as claimed (Tsang, Abstract describes using an HNN (hybrid neural network) to refine an FDT (fuzzy decision tree). Page 608, section IV.A describes the transformation of the Fuzzy Production Rules to the HNN. In particular, IV.A.2 Rule Layer indicates that each node of the hidden layer represents an extracted fuzzy rule from the weighted FDT. The Fuzzy Production Rules are being interpreted as fuzzy membership functions. The rules are described at least in section III.B-C, starting page 606. For example, Rule 1) on page 606, right hand column is “If Temperature = Hot AND Outlook = Cloudy THEN Swimming” indicates that the event determined by the intersection of the events “Temperature = Hot” and “Outlook = Cloud” is a subset of the event “Swimming”. In the combination with Barkus, the tree taught by Barkus would be modified to incorporate the hybrid neural network taught by Tsang.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 1.
Regarding claim 3, the rejection of claim 2 is incorporated herein. Furthermore, Barkus teaches
wherein an optimization is determined based on a fitness function of fuzzy redundancy functions of the fuzzy fault tree (Barkus: ¶[0057]: e.g., probability of undesirable event) . (¶[0057]: After the probabilities for malfunctions 11a through 15a of functional units 11 through 15 have been modified in self-similar tree-like logical linkage 2a, in step 150, the probability of undesirable event 28 is reanalyzed on the basis of updated linkage 2a. It is subsequently checked in block 160 whether the reanalyzed probability meets a predefined criterion.)( The examiner interprets determining whether probability of malfunction of the potential redundant functional units 11-15 (the probability increasing as taught at ¶[0056] with age and use) comprise fitness functions of the fuzzy redundancy functions of the tree which are used to suggest alternate linkages and considered during optimization by Barkus).
Regarding claim 4, the rejection of claim 1 is incorporated herein. Furthermore, Barkus teaches
wherein the learning method includes a second level for optimizing parameters (e.g., ¶[0056]: probabilities) of fuzzy membership functions. (Barkus: ¶[0056]: Alternatively or also in combination therewith, according to block 140, the probability of malfunction 11a through 15a is incremented with increasing age and/or with increasing use of particular functional unit 11 through 15.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Furthermore, Tsang teaches
wherein at least one step of the learning method, including steps for optimizing the structure of the neural network and/or steps for optimizing parameters of fuzzy membership functions, are based on a heuristic method. (Tsang, pages 603-604, sections II.B and section II.C describe determining the fuzzy decision tree based on heuristic methods.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 1.
Regarding claim 7, the rejection of claim 4 is incorporated herein. Furthermore, Barkus teaches
wherein steps for optimizing the parameters of the fuzzy membership functions include execution of an optimization algorithm (Barkus: e.g., the Kohda-Henly-Inous comprehensive method). (Barkus:¶[0049] To make this effort manageable at all, tree-like logical linkage 2 generally has to be transformed (for example, using the Kohda-Henley-Inous comprehensive method or the Yllera method), to decompose linkage 2 into modules and to find "minimal cut sets" in which redundancies are eliminated.)
Regarding claim 8, the rejection of claim 5 is incorporated herein. Furthermore, Barkus teaches
wherein the optimization of the structure of the flexible neural network and/or the optimization of the parameters of the membership function are executed repeatedly until a termination criterion is met. (Barkus: ¶[0057] After the probabilities for malfunctions 11a through 15a of functional units 11 through 15 have been modified in self-similar tree-like logical linkage 2a, in step 150, the probability of undesirable event 28 is reanalyzed on the basis of updated linkage 2a. It is subsequently checked in block 160 whether the reanalyzed probability meets a predefined criterion.)
Regarding claim 9, the rejection of claim 8 is incorporated herein. Furthermore, Barkus teaches
wherein a termination criterion is given by reaching or exceeding a specific number of iterations or by expiration of a specifiable period of time or by reaching an optimization. (Barkus: ¶[0057] After the probabilities for malfunctions 11a through 15a of functional units 11 through 15 have been modified in self-similar tree-like logical linkage 2a, in step 150, the probability of undesirable event 28 is reanalyzed on the basis of updated linkage 2a. It is subsequently checked in block 160 whether the reanalyzed probability meets a predefined criterion.)
Regarding claim 10, the rejection of claim 5 is incorporated herein. Furthermore, Barkus teaches
wherein the optimization of the structure of the flexible neural network and the optimization of the parameters of the fuzzy membership functions are executed repeatedly (Barkus: ¶[0056]: progressively monitored by onboard diagnosis) in alternation. (Barkus: ¶[0056]: In the example shown in FIG. 3, especially for application in an at least partially automated driving vehicle, the correct function of functional units 11 through 15 in system 1 is progressively monitored by an onboard diagnosis unit of the vehicle according to block 130. If a malfunction 11a through 15a is established, according to block 135, the probability of malfunction 11a through 15a is accordingly modified in self-similar tree-like logical linkage 2a. Alternatively or also in combination therewith, according to block 140, the probability of malfunction 11a through 15a is incremented with increasing age and/or with increasing use of particular functional unit 11 through 15.)
Regarding claim 12, Barkus teaches
a non-transitory computer-readable storage medium on which is stored a computer program including computer-readable instructions for determining criticality values of a technical system, the instructions, when executed by a computer, causing the computer to perform the following steps: (Barkus [0034] describes an embodiment as a computer program product. Barkus:¶[0014]: probability of an undesirable event. Barkus:¶[0028]: The technical system may be, for example, a surroundings detection system and/or a control system of an at least partially automated driving vehicle.)
The remainder of claim 12 is substantially similar to claim 1; claim 12 is rejected with the same rationale.
Regarding claim 13, the rejection of claim 1 is incorporated herein. Furthermore, Barkus teaches
herein the method is used in an embedded environment of the technical system for establishing and/or checking functionalities of the technical system, and/or is used for developing a technical system.
Barkus: ¶[0056]:In the example shown in FIG. 3, especially for application in an at least partially automated driving vehicle, the correct function of functional units 11 through 15 in system 1 is progressively monitored by an onboard diagnosis unit of the vehicle according to block 130. If a malfunction 11a through 15a is established, according to block 135, the probability of malfunction 11a through 15a is accordingly modified in self-similar tree-like logical linkage 2a. Alternatively or also in combination therewith, according to block 140, the probability of malfunction 11a through 15a is incremented with increasing age and/or with increasing use of particular functional unit 11 through 15.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Barkus (US 2019/0278647 A1) in view of Tsang (Improving Learning Accuracy of Fuzzy Decision Trees by Hybrid Neural Networks), and further in view of Ho (US 2018/0314930 A1).
Regarding claim 6, the rejection of claim 5 is incorporated herein. Barkus and Tsang does not appear to explicitly teach
wherein, the heuristic method is an embedded simulated annealing method.
However, Ho directed to analogous art--teaches
wherein, the heuristic method is an embedded simulated annealing method. (Ho, [0001, 0055] and Figure 9 describe a method for training a neural network using simulated annealing. The simulated annealing program is being interpreted as being “embedded” in whatever hardware is implementing it.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Barkus and Tsang by Ho because “simulated annealing is able to find the global minimum of the cost function, yielding a value of the cost function that is lower than the lowest value of the cost function found by gradient optimization alone” (Ho, [0043]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over the combination of Barkus and Chen as set forth with regard to independent claim 1 above, and further in view of US Patent 7,526,683 to Votta et al. (hereinafter “Votta”).
Regarding claim 11, the rejection of claim 1 is incorporated herein. The combination of Barkus and Tsang does not appear to explicitly teach
wherein the derivation of criticality values of the fuzzy basic events from the optimized flexible neural network occurs based on a number of the fuzzy basic events of the optimized flexible neural network normalized with the optimized parameter of a second level of the respective fuzzy basic event.
However, Votta, also in the field of error discrimination, discloses,
wherein the derivation of criticality values of the fuzzy basic events from the optimized flexible neural network occurs based on a number of the fuzzy basic events of the optimized flexible neural network normalized with the optimized parameter of a second level of the respective fuzzy basic event. (Col. 3, ¶[11], lines 51-57]: “… The BCF varies dynamically with the inferred cosmic flux activity. The BCF is used to normalize a mean value of a moving window of CE events for any given SRAM. The method of the present invention is used, in effect, to subtract out a "dynamic background" flux level. The method of the present invention is therefore an improvement of the leaky bucket algorithm, but with the contents of the bucket being continuously normalized by the local cosmic neutron flux. After normalizing for dynamic flux, the remaining residuals form a stationary Poisson process with time. To this a Poisson Sequential Probability Ratio Test (SPRT) is applied, which gives the shortest mathematically possible time to annunciation of a subtle hard-fault mechanism growing into the monitored device, with the lowest mathematically possible probability of false alarms.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the normalization of events of Votta with the system of Barkus because it is merely a combination of existing elements in known ways to produce predictable results (MPEP 2143). One would be motivated to make this combination because, as described at Col. 3, lines 64-65, it provides the lowest mathematically possible probability of false alarms.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Markus A Vasquez whose telephone number is (303)297-4432. The examiner can normally be reached Monday to Friday 10AM to 2PM PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARKUS A. VASQUEZ/Primary Examiner, Art Unit 2121
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