DETAILED ACTION
This non-final action is in response to the application filed 22 April 2025.
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 .
Priority
Claims 1-6 are pending having a filing date of 22 April 2025 and claiming foreign priority to Japanese Patent Application Number JP 2024-086303, filed 28 May 2024.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted 22 April 2025 complies with 37 C.F.R. 1.97. Accordingly, the IDS has been considered by the examiner. An initialed copy of the 1449 form is enclosed herewith.
Drawings
The drawing, filed 22 April 2025, are accepted by the examiner.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
• STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
• STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
o STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
o STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
o STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1 and 6 are directed toward non-statutory subject matter as shown below.
STEP 1: Do claims 1 and 6 fall within one of the statutory categories? Yes, because claim 1 is directed toward a system and claim 6 is directed to a method or process both of which fall within one of the statutory categories.
STEP 2A (PRONG 1): Are the claims directed to a law of nature, a natural phenomenon or an abstract idea? Yes, claims 1 and 6 are directed to abstract ideas.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
1. Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
2. Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
3. Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
As per claims 1 and 6, the system is a mental process that can be performed in the mind and, therefore, an abstract idea. In particular, claims 1 and 6 recite the abstract ideas:
“us[ing] a malfunction code ... , “
“infer the abnormal part in which the abnormality is occurring and the category of the abnormality corresponding to the input malfunction code.”
These recitations merely consist of using a malfunction code to infer which part corresponds to the malfunction. This is equivalent to a person using the malfunction code to infer which part corresponds to the malfunction. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, a person, a person uses the malfunction code to infer which part corresponds to the malfunction. The mere nominal recitations that the using and the inference are accomplished by “an artificial intelligence processor” (claim 1) and “a machine learning for artificial intelligence,” (claim 6), do not take the limitations out of the mental process grouping.
STEP 2A (PRONG 2): Do the claims recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
• an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
• an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
• an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
• an additional element effects a transformation or reduction of a particular article to a different state or thing; and
• an additional element applies or uses 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 more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
• an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
• an additional element adds insignificant extra-solution activity to the judicial exception; and
• an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1 and 6 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into practical application. Claims 1 and 6 further recites the additional element
“output[ing] [a malfunction code] from a vehicle ... “ (claims 1 and 6),
“inputting the malfunction code output from the vehicle into the artificial intelligence processor … ” (claim 1), and
“input data for learning ... (claim 6)”.
These additional element further limits the abstract idea without integrating the abstract idea into practical application or significantly more. In particular, the “output … “ and “input ... “ steps are recited at a high level of generality (i.e., as a general means of gathering an electronic representation of data relate to failed parts ) and amount to mere data gathering, a form of insignificant extra-solution activity added to the judicial exception per MPEP 2106.05(g), because the steps characterize pre solution activity, such as an individual observing the failure codes.
Claim 1 still further includes the additional “an artificial intelligence processor” (claim 1) and “a machine learning for artificial intelligence,” (claim 6). These elements are not sufficient to amount to significantly more than the judicial exception because they fail to integrate the exception into practical application. The mere inclusion of instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. In the instant case, the system and method accomplishes the input and output by “an artificial intelligence processor” (claim 1), and “a machine learning for artificial intelligence,” (claim 6), i.e. via computers. Thus, it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated in the practical application. The “artificial intelligence processor,” and the “machine learning for artificial intelligence” merely describes how to generally “apply” the otherwise metal judgements in a generic or general purpose computing environment. The artificial intelligence processor and the “machine learning for artificial intelligence” are recited at a high level of generality and merely automate the output and input steps.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, claims 1 and 6 do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
• adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
• simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claims 1 and 6 does not recite any specific limitation or combination of limitations that are well-understood, routine, conventional (WURC) activity in the field. Outputting and inputting data are fundamental, i.e. WURC, activities performed by processors, such as computers operating on data such as recited in claims 1 and 6. Further, applicant’s specification does not provide any indication that the outputting and inputting activities of the system are performed using anything other than a conventional computer. 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 performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017).
Thus, since claims 1 and 6 are: (a) directed toward abstract ideas; (b) do not recite additional elements that integrate the judicial exception into practical application; and (c) do not recites additional elements that amount to significantly more than the judicial exception, it is clear that claims 1 and 6 are directed to non-statutory subject matter.
Dependent claims 2-5 further limit the abstract idea without integrating the abstract idea into practical application or addition significantly more. For example, the additional elements in claims 2-5 are further limitations that under their broadest reasonable interpretation are abstract using the analysis for independent claims 1 and 6.
Conclusion:
As such, claims 1-6 are rejected as being drawn to an abstract idea without significantly more, and thus are ineligible.
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.
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 non-obviousness.
Claims 1, 2 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication Number 2021/0357766 to Paul et al. (hereafter Paul) and U.S. Patent Publication Number 2025/0111715 to Cossa et al. (hereafter Cossa).
As per claim 1, Paul discloses [a]n abnormality identification assist system (see at least Paul, Abstract) comprising:
an artificial intelligence processor configured to use a malfunction code output from a vehicle as input (see at least Paul, [0013] disclosing that modular industrial equipment is intended to include at least commercial and military vehicles, such as aircraft, heavy equipment, and machines used for industrial production; [0021] disclosing that maintenance reports can be extracted from the database (not shown) or a user terminal (not shown) via the network interface 112 and provided to a text preprocessor 113. One example of a maintenance report 200 is shown as FIG. 2. Along with information about the specific equipment serviced, the time taken, and the identities of the personnel performing the maintenance, the report 200 includes an action code 202 that represents a category of action taken to maintain the equipment, and a malfunction code 203 that identifies a category into which the issue with the equipment addressed by the action falls) and
to infer ... (1) ... and a category of the abnormality (see at least Paul, [0021]; );
at least one processor (see at least Paul, [0020] disclosing that the system 100 includes a processor 102 and a non-transitory computer readable medium 110 storing computer readable instructions, executed by the processor 102. The executable instructions stored on the non-transitory computer readable medium 110 include a network interface 112 via which the system 100 communicates with other systems (not shown) via a network connection,); and
a storage medium configured to store a program configured to be executed by the at least one processor (see at least Paul, [0020]),
wherein the program includes at least one command configured to cause the at least one processor to execute processing for inputting the malfunction code output from the vehicle into the artificial intelligence processor (see at least Paul, [0019] disclosing that the systems and methods described herein provide a multi-faceted strategy to automate domain specific, multi-class text classification. This strategy includes various preprocessing approaches, feature extraction approaches, and supervised learning methods applied in a one-versus-all classification. The preprocessing methodologies are specifically created for domain-specific classification. The feature extraction methodology provides multiple approaches, with each model in the one-versus-all classification that can utilize a selected one of a plurality of available preprocessing techniques as well as one of the feature extraction techniques; [0021] ; [0023] disclosing that a feature extractor 114 receives and extracts a plurality of features for use at an expert system 116. The feature extractor 114 extracts the features from one or more free text regions on the maintenance report. To this end, the feature extractor 114 can compute the frequencies of various terms within the extracted text; [0029] disclosing that the expert system 116 uses the extracted features to classify a novel maintenance report, that is, an event report that was not presented in a training set for the model, into one or more of a plurality of categories. The machine learning model 116 can utilize one or more pattern recognition algorithms, implemented, for example, as classification and regression models, each of which analyze the extracted features or a subset of the extracted features to classify the reports into one of the categories) and
instructing the artificial intelligence processor to infer ... (2) ... and the category of the abnormality corresponding to the input malfunction code (see at least Paul, [0030] disclosing that each of a plurality of machine learning models are trained as a binary classifier that distinguishes between a code category associated with the machine learning model and all other classes. In this example, the output of the machine learning model is a categorical or continuous parameter that reflects a likelihood that the maintenance report is properly categorized with the code represented by the machine learning model. An arbitration element can be utilized to provide a coherent result from the plurality of machine learning models, for example, as the class having a highest continuous output or a highest confidence in a categorical output;[0042]; [0043] disclosing that the output from each of the plurality of machine learning models 352-354 is provided to an arbitrator 356 that selects a maintenance-related code for the maintenance report according to the outputs of the machine learning models. In one example, the arbitrator 356 can include one or more functions or look-up tables to translate an output of each machine learning model 352-354 into a standard value, for example, a value representing a likelihood that the maintenance-related code represented by the model should be assigned to the maintenance report. The arbitrator 356 can select the code associated with the machine learning model 352-354 providing a maximum or minimum standard value as the maintenance-related code that should be assigned to the maintenance report. A database storing maintenance reports can then be updated with the selected maintenance-related code via the network interface 302). But, the difference between Paul and the claimed invention is that Paul does not explicitly teach the following limitations taught in Cossa, a comparable method where it is known to:
(1), (2) infer an abnormal part of the vehicle in which an abnormality is occurring (see at least Cossa, [0064] disclosing that determining the presence of anomalies, the electronic operating device 40 is configured to indicate defective components in a vehicle being scanned).
Paul and Cossa are analogous art to claim 1 because they relate to identifying anomalies that occur in vehicles. Paul relates to classification of maintenance reports for modular industrial equipment from free-text descriptions (see at least Paul, [0001]). Cossa relates to the detection of failure of a vehicle component of a vehicle by determining anomalies in measurements using a trained artificial intelligence model (see Cossa, [0004]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Paul, to provide the benefit of inferring an abnormal part of the vehicle in which an abnormality is occurring, as disclosed in Cossa, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
As per claim 2, the combination of Paul and Cossa discloses all of the limitations of claim 1, as shown above. Paul further discloses the following limitation:
wherein the at least one command is configured to cause the at least one processor to execute inference result display processing for causing a display to display information indicating an inference result obtained by the artificial intelligence processor (see at least Paul, [0029] disclosing that the machine learning model 116 can utilize one or more pattern recognition algorithms, implemented, for example, as classification and regression models, each of which analyze the extracted features or a subset of the extracted features to classify the reports into one of the categories. The selected category can be provided to a user at an associated display (not shown) or stored on the non-transitory computer readable medium 110, for example, in a record associated with the maintenance report).
As per claim 6, similar to claim 1, Paul discloses [a] learning method (see at least Paul, Abstract ) comprising:
conducting machine learning for artificial intelligence by using a malfunction code output from a vehicle (see at least Paul, [0013]; [0021]; [0029] )
as input data for learning and by using an abnormal part of the vehicle in which an abnormality has occurred and a category of the abnormality as supervisor data (see at least Paul, [0019] disclosing that the systems and methods described herein provide a multi-faceted strategy to automate domain specific, multi-class text classification. This strategy includes various preprocessing approaches, feature extraction approaches, and supervised learning methods applied in a one-versus-all classification. The preprocessing methodologies are specifically created for domain-specific classification. The feature extraction methodology provides multiple approaches, with each model in the one-versus-all classification that can utilize a selected one of a plurality of available preprocessing techniques as well as one of the feature extraction techniques.; [0030] ); and
generating artificial intelligence configured to infer ... (1) ... the abnormal part and the category of the abnormality corresponding to the input malfunction code (see at least Paul, [0030]; [0042];[0043]). But, the difference between Paul and the claimed invention is that Paul does not explicitly teach the following limitations taught in Cossa, a comparable method where it is known to:
(1) infer the abnormal part ... corresponding to the input malfunction code (see at least Cossa, [0064]).
Paul and Cossa are analogous art to claim 6 because they relate to identifying anomalies that occur in vehicles. Paul relates to classification of maintenance reports for modular industrial equipment from free-text descriptions (see at least Paul, [0001]). Cossa relates to the detection of failure of a vehicle component of a vehicle by determining anomalies in measurements using a trained artificial intelligence model (see Cossa, [0004]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Paul, to provide the benefit of inferring infer the abnormal part corresponding to the input malfunction code, as disclosed in Cossa, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Paul and Cossa as applied to claim 1 above, and further in view of World Intellectual Property Organization Publication Number WO 2023002897 A1 to Nakano et al. (hereafter Nakano).
As per claim 3, Paul discloses all of the limitations of claim 1, as shown above. But, the difference between Paul and the claimed invention is that Paul does not explicitly teach the following limitations taught in Nakano, a comparable method where it is known to add:
wherein, in the inference result display processing, the at least one processor is configured to cause the display to display information indicating candidates for each of the abnormal part and the category of the abnormality which are obtained by the artificial intelligence processor as the inference result (see at least Nakano, Pg. 12, para. 8, disclosing that terminal device 2 has a failure status input section 21 for inputting customer information of the customer who delivered the equipment, the status of the equipment, etc., a failure part display part 22 for displaying the failure part of the equipment, and a part category of replacement parts. , a replacement part display section 24 that displays candidates for replacement parts, and a past case display section 25 that displays past failure cases, and a part name of a recommended part).
Paul, Cossa and Nakano are analogous art to claim 3 because they relate to identifying anomalies that occur in vehicles. Paul relates to classification of maintenance reports for modular industrial equipment from free-text descriptions (see at least Paul, [0001]). Cossa relates to the detection of failure of a vehicle component of a vehicle by determining anomalies in measurements using a trained artificial intelligence model (see Cossa, [0004]). Nakano relates to a failure site/replacement part estimation system, method, and program (see Nakano, pg. 1, Description, Technical-Field).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Paul, as modified by Cossa to provide the benefit of displaying information indicating candidates for each of the abnormal part and the category of the abnormality which are obtained by the artificial intelligence processor as the inference result, as disclosed in Nakano, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Paul and Cossa as applied to claim 1 above, and further in view of U.S. Patent Publication Number 2022/0055557 to Schweickhardt et al. (hereafter Schweickhardt).
As per claim 4, the combination of Paul and Cossa discloses all of the limitations of claim 1, as shown above. Paul further discloses the following limitations:
a first artificial intelligence model configured to learn to infer the category of the abnormality from the malfunction code output from the vehicle as a result of conducting machine learning by using the malfunction code as input data and the category of the abnormality (as cited in claim 1, see at least Paul, [0030]; see at least Paul, [0006] disclosing that the expert system includes a first machine learning model and a second machine learning model. The first machine learning model uses a first proper subset of the plurality of features to determine if the maintenance-related code associated with the maintenance report should be assigned as a first code. The second machine learning model uses a second proper subset of the plurality of features to determine if the maintenance-related code associated with the maintenance report should be assigned as a second code. The first proper subset of the plurality of features is different from the second proper of the plurality of features; [0042] ; [0047] disclosing that at 406, a new code from the defined library of codes for the item of modular industrial equipment is determined at an expert system from the plurality of features. In one implementation, the expert system includes a plurality of machine learning models, with each of the machine learning models representing a code from the library of codes.) ... (1) ... ,
the first artificial intelligence model infers the category of the abnormality corresponding to the input malfunction code, which is a malfunction code input as the input data (see at least Paul, [0030] ; [0006]; [0042]; [0044]; [0047]), and
a second artificial intelligence model configured to learn to infer (see at least Paul, [0006]) ... . Cossa further discloses the following limitations:
infer the abnormal part from a combination of the malfunction code output from the vehicle and the category of the abnormality as a result of conducting machine learning by using the malfunction code and the category of the abnormality as the input data and the abnormal part (see at least Cossa, [0030] disclosing that disclosing that he anomalies are aggregated by an aggregator 84 that counts and stores the times and the occurrences or number of the anomalies. In one arrangement, the aggregated anomalies are further processed by the aggregator 84 to determine a vehicle component, system, or sensor failure/error for display. Vehicle component failures include sensor failures/faults or system failures/faults <interpreted as the category of the anomaly> ; [0036] disclosing that the comparison device 80 and the aggregator 84 are integrated into the electronic processor 44 and the memory 48 shown in FIG. 2A, and are provided as an anomaly detection software algorithm and a vehicle component failure indication arrangement. The memory 48 store the anomalies and times for occurrence of the anomalies received from the vehicle. The electronic processor 44 operates to indicate failure of a vehicle component depending on a number of anomalies over a period of time. The vehicle component and type of failure can be indicated on visual display 58) ... (2) ...; and
while the second artificial intelligence model infers the abnormal part by using the input malfunction code and the category of the abnormality inferred by the first artificial intelligence model as the input data (see at least Cossa, [0030]; [0036]).
(1) a result of conducting machine learning by using the malfunction code as input data and the category of the abnormality as supervisor data (see at least Schweickhardt, [0036] disclosing that mobile measurement device, for example, is for example interlinked with online state and/or surroundings data as well as with databases with information on the structure of the vehicle, wherein these data are assigned to the measured data, in particular via the application. Furthermore, for example, an artificial intelligence is trained using the transmitted and received measured data along with the labelling, for example in the context of supervised learning)
(2) using the malfunction code and the category of the abnormality as the input data and the abnormal part as the supervisor data (see at least Schweickhardt, [0036] ) ... (2) ... .
Paul, Cossa and Schweickhardt are analogous art to claim 4 because they relate to identifying anomalies that occur in vehicles. Paul relates to classification of maintenance reports for modular industrial equipment from free-text descriptions (see at least Paul, [0001]). Cossa relates to the detection of failure of a vehicle component of a vehicle by determining anomalies in measurements using a trained artificial intelligence model (see Cossa, [0004]). Schweickhardt relates to a method and an electronic computing device in order for at least one vehicle to be able to be checked (see Schweickhardt, [0003]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Paul, as modified by Cossa, to provide the benefit of (1) having a result of conducting machine learning by using the malfunction code as input data and the category of the abnormality as supervisor data and (2) using the malfunction code and the category of the abnormality as the input data and the abnormal part as the supervisor data, as disclosed in Schweickhardt, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
As per claim 5, the combination of Paul, Cossa and Schweickhardt discloses all of the limitations of claim 4, as shown above. Cossa further discloses the following limitations:
wherein the category of the abnormality comprises a plurality of categories of abnormalities to be inferred by the first artificial intelligence mode (see at least Cossa, [0030] disclosing that disclosing that he anomalies are aggregated by an aggregator 84 that counts and stores the times and the occurrences or number of the anomalies. In one arrangement, the aggregated anomalies are further processed by the aggregator 84 to determine a vehicle component, system, or sensor failure/error for display. Vehicle component failures include sensor failures/faults or system failures/faults <interpreted as the category of the anomaly> ), and
the abnormal part comprises a plurality of abnormal parts to be inferred by the second artificial intelligence model, (see at least Cossa, [0036]; [0038] disclosing that the electronic processor 44 shown in FIG. 2A is configured to retrieve from the memory 48 and execute, among other things, software including a trained AI algorithm for performing methods as described herein. The input/output interface 52 retrieves information originally from the vehicle electronic operating unit 200 external to the electronic operating unit 40; [0040] disclosing that FIG. 5 shows sensors that measure one or more attributes of a vehicle/vehicle internal combustion engine and the environment around the vehicle and communicate information regarding those attributes to the other components of the vehicle using, for example, messages transmitted on the communication bus 216. The sensors shown in FIG. 5 include, for example, an ambient or environmental temperature sensor 220, an ambient atmospheric or environmental pressure sensor 222, an engine speed sensor 224, a Trb CH actuator sensor 226, and a boost pressure sensor 228. In some instances, the listed sensors are similar to or the same as sensor sets used in an electronic stability control (ESC) system and/or a vehicle engine control system. In some arrangements the internal combustion engine is for a gasoline or flex fuel powered vehicle) and
wherein the categories of abnormalities are smaller in number than the abnormal parts (see at least Cossa, [0030] Vehicle component failures include sensor failures/faults or system failures/faults <interpreted as the category of the anomaly>; [0060] disclosing an arrangement for a fuel cell vehicle, when the measured fuel cell temperature signal is not within the tolerance band for the predicted fuel cell temperature signal as shown by a number of detected anomalies, a fuel cell temperature sensor warning indication is provided on a visual display 58 of the electronic operating device 40; [0064] disclosing determining the presence of anomalies, the electronic operating device 40 is configured to indicate defective components in a vehicle being scanned. For instance, in one arrangement, when the measured and stored exhaust gas recirculation signal is not within the tolerance band for the predicted EGR signal as shown by a number of detected anomalies, an indication of an EGR system failure is provided as an indication on a visual display of the electronic operating device 40; Fig. 10 showing fuel cell component sensors; <based on the disclosed sensor, engine, exhaust, and fuel <interpreted as categories>, one of ordinary skill in the art would know that there are many components <interpreted as parts> in each of these categories, and thus that the categories are smaller in number than the number of abnormal parts>).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICK M. BRADY III whose telephone number is (571)272-7458. The examiner can normally be reached Monday - Friday 7:00 am - 4;30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 571-270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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PATRICK M. BRADY III
Examiner
Art Unit 3665
/PATRICK M BRADY/Examiner, Art Unit 3665 /Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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