DETAILED ACTION
This office action is in response to the amendments filed on 07/29/2025.
Claims 1-7 are currently pending.
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
Specification Objections:
In light of applicant’s amendment to the title (pg. 2), the objections to the specification have been withdrawn.
Drawing Objections:
In light of applicant’s amendments to the drawings (pg. 5), the objections to the drawings have been withdrawn.
Prior Art Rejections:
Applicant's arguments regarding the prior art rejections (pg. 7-8) have been fully considered but they are not persuasive.
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, Bodyanskiy teaches an encoder with connections provided by neo-fuzzy neurons, where “The neo-fuzzy neuron is the Wang-Mendel neuro-fuzzy system of zero order” (Bodyanskiy, pg. 6, section II). Yao teaches the benefits of an Interval Type-2 Fuzzy Logic System trained by an optimization algorithm. Keneni teaches an explanatory rule view window for Mamdani and Sugeno-type fuzzy logic systems. Therefore, the combination of Bodyanskiy, Yao, and Keneni amounts to a simple substitution of one known fuzzy logic system for another, where the introduction of Yao’s fuzzy system is motivated by improved performance (Yao, pg. 2886, section VI), and the introduction of Keneni’s rule viewer is motivated by explanatory power (Keneni, pg. 17014, section XI). MPEP 2143(I)(B) notes that express suggestion to substitute one equivalent for another need not be present to render such substitution obvious.
In response to applicant's argument that Bodyanskiy, Yao, and Keneni each use different models/systems, and thus their combination can have no reasonable expectation of success, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
The prior art rejections under 35 U.S.C. 103 are maintained for claims 1-7.
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.
Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over
Bodyanskiy et al. (hereinafter Bodyanskiy), “Neo-Fuzzy Encoder and Its Adaptive Learning for Big Data Processing” in view of
Yao et al. (hereinafter Yao), "A Big Bang-Big Crunch Optimization for a Type-2 Fuzzy Logic Based Human Behaviour Recognition System in Intelligent Environments" and
B. M. Keneni et al. (hereinafter Keneni), "Evolving Rule-Based Explainable Artificial Intelligence for Unmanned Aerial Vehicles."
Regarding Claim 1, A computer implemented method for machine learning comprising:
Bodyanskiy teaches training an autoencoder having a set of input units, a set of output units and at least one set of hidden units, (Figure 1 shows the architecture of a “neo-fuzzy autoencoder,” where x1, x2,…xn represent the set of input units, y1, y2,…ym represent the set of hidden units, and x̂1, x̂2,… x̂n represent the set of output units (pg. 7). Section III describes “the learning process of the neo-fuzzy autoencoder” (i.e., training the autoencoder) (pg. 8-9). Examiner notes that ‘unit’ is interpreted as referring to a node of the autoencoder.)
Bodyanskiy teaches wherein connections between the set of input units, the set of output units, and the at least one set of hidden units are provided by [interval type-2] fuzzy logic systems [each including one or more rules], and (Figure 1 shows the architecture of the neo-fuzzy autoencoder, where symbols NFN1[1], NFN2[1],… NFNm[1] represent the “neo-fuzzy neurons” comprising the fuzzy logic system which connects the input layer to the hidden layer, and symbols NFN1[2], NFN2[2],… NFNn[2] represent the “neo-fuzzy neurons” comprising the fuzzy logic system which connects the hidden layer to the output layer (pg. 7). "The neo-fuzzy neuron is the Wang-Mendel neuro-fuzzy system of zero order" (pg. 6, section II).)
Bodyanskiy does not appear to explicitly disclose
interval type-2 fuzzy logic systems each including one or more rules, and the interval type-2 fuzzy logic systems are trained using an optimization algorithm; and
generating a representation of rules in each of the interval type-2 fuzzy logic systems triggered beyond a threshold by input data provided to the set of input units so as to indicate rules involved in generating an output at the set of output units in response to the input data provided to the set of input units.
However, Yao teaches interval type-2 fuzzy logic systems each including one or more rules, and (Section II provides “a brief overview on the IT2FLS [interval type-2 fuzzy logic system]”, their functionality, and their components, including a “rule base” (pg. 2881).)
Yao teaches the interval type-2 fuzzy logic systems are trained using an optimization algorithm; and (Section IV states “the BB-BC is utilized to calculate the optimized parameters for the MFs and the rules of our IT2FLS [interval type-2 fuzzy logic system]” (pg. 2884). BB-BC refers to the Big-Bang Big-Crunch optimization algorithm, which is described in detail in this section.)
Keneni teaches generating a representation of rules in each of the interval type-2 fuzzy logic systems triggered beyond a threshold by input data provided to the set of input units so as to indicate rules involved in generating an output at the set of output units in response to the input data provided to the set of input units. (Keneni teaches generating a representation of fuzzy rules using the Rule Viewer window in MATLAB, where each row corresponds to a rule in the FLS, along with an indication of whether it was triggered. Examples are shown in figures 20-25 (pg. 17012-17013). “For every event, the input variables are listed as the first seven columns in rule view, whereas the output is the last column in the rule view. The rules that fired are studied to give an explanation as to why an event has occurred.” (pg. 17012, section IX).)
Bodyanskiy, Yao, and Keneni are analogous art because they are concerned with the optimization and application of fuzzy logic systems. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the training an autoencoder with connections provided by fuzzy logic systems as taught by Bodyanskiy with the interval type-2 fuzzy logic systems and optimization algorithm as taught by Yao and the generating a representation of rules as taught by Keneni. One of ordinary skill would have motivation to combine Bodyanskiy, Yao, and Keneni because, according to Yao, “The use of interval type-2 FLS helps to simplify the computation” (Yao, pg. 2881, section II) while still “outperform[ing] the equivalent T1FLS and the state-of-the-art non-fuzzy methods regarding recognition accuracy and analysis performance” (Yao, pg. 2886, section VI). The optimization algorithm has the benefit of optimizing parameters of the IT2FLS “in the direction of having higher accuracy” (Yao, pg. 2884, section IV). Additionally, Keneni’s representation of rules helps to make the system “more transparent, easily understandable, and trustworthy” (Keneni, pg. 17014, section XI).
Regarding Claim 2, Bodyanskiy, Yao, and Keneni teach The method of claim 1, as shown above, wherein
Yao also teaches the optimization algorithm is a Big-Bang Big-Crunch algorithm. (Section IV states “the BB-BC is utilized to calculate the optimized parameters for the MFs and the rules of our IT2FLS [interval type-2 fuzzy logic system]” (pg. 2884). BB-BC refers to the Big-Bang Big-Crunch optimization algorithm, which is described in detail in this section.)
Bodyanskiy, Yao, and Keneni are analogous art because they are concerned with the optimization and application of fuzzy logic systems. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 1 as taught by Bodyanskiy, Yao, and Keneni with the Big-Bang Big-Crunch algorithm as taught by Yao because, according to Yao, “The key advantages of BB-BC are its low computational cost, ease of implementation, and fast convergence” (pg. 2884, section IV).
Regarding Claim 3, Bodyanskiy, Yao, and Keneni teach The method of claim 2, as shown above, wherein
Yao also teaches each interval type-2 fuzzy logic system is generated based on a type-1 fuzzy logic system adapted to include a degree of uncertainty to a membership function of the type-1 fuzzy logic system. (“The type-2 fuzzy MFs [membership functions] are then produced by using the obtained type-1 fuzzy sets as the principal membership functions which are then blurred by a certain percentage to create an initial Footprint of Uncertainty (FOU)” (pg. 2881, section III).)
Bodyanskiy, Yao, and Keneni are analogous art because they are concerned with the optimization and application of fuzzy logic systems. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 2 as taught by Bodyanskiy, Yao, and Keneni with the each interval type-2 fuzzy logic system is generated based on a type-1 fuzzy logic system adapted to include a degree of uncertainty to a membership function of the type-1 fuzzy logic system as taught by Yao because IT2FLSs can be straightforwardly generated based on the membership functions of T1FLSs with the benefit of “improv[ing] the robustness of T1FLSs in handling uncertainties” (pg. 2880, section I). Yao’s experiment quantifies this benefit: “the BB-BC optimized IT2FLS achieves 4.55% higher average per-frame accuracy than the BB-BC optimized T1FLS” (pg. 2885, section V).
Regarding Claim 4, Bodyanskiy, Yao, and Keneni teach The method of claim 3, as shown above, wherein
Yao also teaches the type-1 fuzzy logic system is trained using the Big-Bang Big-Crunch optimization algorithm. (Section V references “the BB-BC optimized T1FLS [type-1 fuzzy logic system]” (pg. 2885). As above, BB-BC refers to the Big-Bang Big-Crunch optimization algorithm, which is described in detail in section IV.)
Bodyanskiy, Yao, and Keneni are analogous art because they are concerned with the optimization and application of fuzzy logic systems. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 3 as taught by Bodyanskiy, Yao, and Keneni with the type-1 fuzzy logic system is trained using the Big-Bang Big-Crunch optimization algorithm as taught by Yao because, according to Yao, “BB-BC optimization improves the performance of the T1FLS and the IT2FLS where the per-frame accuracy is improved” (pg. 2885, section V).
Regarding Claim 5, Bodyanskiy, Yao, and Keneni teach The method of claim 1, as shown above, wherein
Keneni also teaches the representation is rendered for display as an explanation of an output of the method. (Section X discusses “develop[ing] an easily accessible rule view HCI [human-computer interaction] interface” based on the numerical representations in the Rule Viewer discussed with regard to claim 1 (pg. 17013). An example of the user interface is provided in figure 27, where each row represents an input to the system, and selecting a row displays an explanation of the associated output (pg. 17013).)
Bodyanskiy, Yao, and Keneni are analogous art because they are concerned with the optimization and application of fuzzy logic systems. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of claim 1 as taught by Bodyanskiy, Yao, and Keneni with the representation is rendered for display as an explanation of an output of the method as taught by Keneni because, according to Keneni, “it allows the user to understand the explanation of the UAV behavior in human-reasoning logic” (pg. 17013, section X), which makes the system “more transparent, easily understandable, and trustworthy” (pg. 17014, section XI).
Regarding Claim 6, Bodyanskiy, Yao, and Keneni teach the elements of claim 1, as shown above, which are substantially the same as the elements of claim 6.
Yao also teaches A computer system comprising: a processor and memory storing computer program code for machine learning. (Examiner notes this limitation is interpreted as a general-purpose computing environment. Yao teaches “a computationally efficient system based on interval type-2 fuzzy logic systems for the automatic recognition of human behaviour using machine vision for applications in intelligent environments” (pg. 2885, section VI). Intelligent environments necessarily include embedded computing systems.)
Bodyanskiy, Yao, and Keneni are analogous art because they are concerned with the optimization and application of fuzzy logic systems. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the training an autoencoder and generating a representation of rules as taught by Bodyanskiy, Yao, and Keneni with the computer system as taught by Yao because, according to Yao, this “significantly increases the quality of the user experience in intelligent environments” (pg. 2880, section I).
Regarding Claim 7, Bodyanskiy, Yao, and Keneni teach the method as claimed in claim 1, as shown above.
Yao also teaches A non-transitory computer-readable storage medium storing a computer program element comprising computer program code to, when loaded into a computer system and executed thereon, cause the computer system to perform the method. (Examiner notes this limitation is interpreted as a general-purpose computing environment. Yao teaches “a computationally efficient system based on interval type-2 fuzzy logic systems for the automatic recognition of human behaviour using machine vision for applications in intelligent environments” (pg. 2885, section VI). Intelligent environments necessarily include embedded computing systems.)
Bodyanskiy, Yao, and Keneni are analogous art because they are concerned with the optimization and application of fuzzy logic systems. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method as claimed in claim 1 as taught by Bodyanskiy, Yao, and Keneni with the non-transitory computer-readable storage medium as taught by Yao because, according to Yao, this “significantly increases the quality of the user experience in intelligent environments” (pg. 2880, section I).
Conclusion
Claims 1-7 are rejected.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/B.M.R./Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151