DETAILED ACTION
This communication is responsive to the application, filed May 29, 2025. Claims 1-15 are pending in this application.
Examined under the first inventor to file provisions of the AIA
The present application was filed on May 29, 2025 which is on or after March 16, 2013, and thus is being examined under the first inventor to file provisions of the AIA .
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, 5, 9, and 13-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 5, 9, and 13-15 are directed to the abstract idea of a mental process, as explained in detail below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Step 1: It is first noted that Claims 1, 5, 9, and 13 are directed to a method, which falls within the statutory category of a process; Claim 14 is directed to a non-transitory computer readable medium, which falls within the statutory category of manufactures; and Claim 15 is directed to an apparatus or a system, which falls within the statutory category of a machine.
Step 2A – Prong 1:
Claims 1, 14, and 15 recites limitations entering information subject to failure mode analysis and querying a conversational AI to obtain a failure mode answer based on that information, which is a form of mental process and information analysis that can be performed by a human with pen or paper or with conventional computer tools. Claim 5 recites limitations asking a conversational AI for a failure cause and receiving an answer indicating the failure cause, which is another instance of information gathering and analysis. Claim 9 recites limitations asking a failure cause to a conversational AI and obtaining an answer indicating a solution to the failure cause, which is a classic result-oriented information processing step. Claim 13 recites limitations updating the conversational AI by acquiring corrected data and retraining the AI using the corrected data as training data, which is a data management and model-training concept that can be performed with conventional machine learning techniques. These limitations, as drafted, are a process that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “processor”, “memory including instructions”, “a conversational AI” and “a memory”; nothing in the claims preclude these steps from practically being performed in the human mind or with conventional computer tools. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for recitation of generic computer components, then it falls within the “Mental Processes” grouping of an abstract ideas. These steps describe the concept of a mental process, which corresponds to concepts identified as abstract ideas by the courts. The concept described in claims 1, 5, 9, and 13-15 is not meaningfully different than those found by the courts to be abstract ideas. As such, the description in claims 1, 5, 9, and 13-15 of information gathering and analysis using conversational AI recite an abstract idea.
Step 2A- Prong 2: This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional elements of “a processor”, “a memory” storing instructions, “a conversational AI”, in claims 1, 5, 9, and 13-15. These are recited at a high-level of generality (i.e. as a generic computer system with generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claims are directed to an abstract idea.
Step 2B: As discussed with respect to Step 2A Prong Two, the additional elements in the claims amounts to no more than mere instructions to apply the exception using a generic computer components.
The claim(s) do/does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claims include additional elements that are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions amount to no more than implementing the abstract idea with a computerized system. The use of generic computer components does not impose any meaningful limit on the computer implementation of the abstract idea. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Mere instructions to apply an exception using a generic computer system cannot provide an inventive concept. Accordingly, the claims are not patent eligible.
The claims are not patent eligible.
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 of this title, 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, 4-7, 9-11, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Khamis et al. (US 2025/0363836 A1) in view of Dan Thomas (Revolutionizing Failure Modes and Effect Analysis with ChatGPT – May 16, 2023).
As per claim 1: A computer-implemented failure mode analysis support method comprising:
Khamis discloses [0003] a computer-implemented method of diagnosing a software system. The method includes receiving data related to the software system, identifying anomalous events, collecting contextual information, inputting anomalous events and contextual information to a machine learning model, and determining root causes of anomalous events.
an input process of entering at least one piece of information that is subject to a failure mode analysis; and
Khamis discloses [0037, 0065-0068] contextual information can include telemetry data, event logs, diagnostics, and user-provided context. Khamis further discloses [0059] the data collection module inputs collected data to a software platform for aggregating and visualizing telemetry data, identifying issues, recognizing root causes, and enabling troubleshooting. Khamis further discloses [0100] the active learning of the LLM may include other root-cause analysis, such as failure mode and effects analysis (FMEA). Such methods may be performed in conjunction with question-answering.
a process of asking a question to a conversational AI so that at least one failure mode that may occur in a product or a manufacturing process is answered based on the information entered in the input process, to obtain an answer indicating a failure mode that may occur in the product or the manufacturing process.
Khamis discloses [0100] a process of asking a question to a conversational AI, which can support FMEA process to identify common failure modes for specific software component to identify trends and patterns that could be helpful in understanding root causes. Khamis explicitly teaches a conversational AI that can detect product anomalies based on interaction with user-based questions, but fails to explicitly disclose asking question to conversational AI so that at least one failure mode that may occur is answered. Thomas discloses a similar method, which further teaches [Pages 911-913] failure modes tied to process steps in an FMEA table for a technical system. The common data set includes at least one set of failure modes and a set of process steps, with each process step associated with failure modes. The user may input data relating to failure mode, cause, effect, and process step. ChatGPT can recommend mitigation steps based on the identified failure modes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Khamis with that of Thomas. One would have been motivated to obtain an answer indicating a failure mode because it can reduce the likelihood or severity of the failure modes and their consequences [Thomas; page 913].
As per claim 2: The failure mode analysis support method according to claim 1, wherein the information entered in the input process includes at least one item of information among information on a structure of the product, information on functions of the product, and information on a manufacturing process.
Thomas discloses [Pages 911-913] that ChatGPT is trained on existing FMEA data and then used to generate failure modes for a given system or process. ChatGPT can identify failure modes, assess risk, and recommend mitigation strategies for the system or process under analysis. FMEA, by its nature, is a structured analysis of a system or process, and Thomas explicitly places the AI in that FMEA workflow.
As per claim 4: The failure mode analysis support method according to claim 1, wherein the conversational AI is a pre-trained conversational AI that is trained using, as training data, data recording keywords obtained from information on the product or information on the manufacturing process in association with failure modes.
Thomas discloses [Page 911] in terms of reinforcement data, GPT can be fine-tuned on specific tasks using supervised learning techniques. For example, to generate text that is more relevant to a specific domain, GPT can be fine-tuned on a smaller dataset of text from that domain. The fine-tuned smaller dataset of text from a product or domain are the data keywords obtained for that product or domain in association with failure modes.
As per claim 5: A computer-implemented failure mode analysis support method comprising: a process of asking a question to a conversational Al so that at least one failure cause is answered for a failure mode of product information or a manufacturing process, to obtain an answer indicating the failure cause.
Khamis discloses [0100] a process of asking a question to a conversational AI, which can support FMEA process to identify common failure modes for specific software component to identify trends and patterns that could be helpful in understanding root causes. Khamis explicitly teaches a conversational AI that can detect product anomalies based on interaction with user-based questions, but fails to explicitly disclose asking question to conversational AI so that at least one failure mode that may occur is answered. Thomas discloses a similar method, which further teaches [Pages 911-913] failure modes tied to process steps in an FMEA table for a technical system. The common data set includes at least one set of failure modes and a set of process steps, with each process step associated with failure modes. The user may input data relating to failure mode, cause, effect, and process step. ChatGPT can recommend mitigation steps based on the identified failure modes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Khamis with that of Thomas. One would have been motivated to obtain an answer indicating a failure mode because it can reduce the likelihood or severity of the failure modes and their consequences [Thomas; page 913].
As per claim 6: The failure mode analysis support method according to claim 1, further comprising: a process of asking a question to the conversational Al configured to respond with at least one failure cause based on the failure mode obtained as the answer in the process, to obtain an answer indicating the failure cause.
Thomas discloses [Pages 911-913] that ChatGPT can be trained on existing FMEA data and then used to generate failure modes for a given system or process. The AI can be used in an FMEA workflow to identify common failure modes and that question-answering can support FMEA analysis. FMEA requires identifying the cause associated with each failure mode. Therefore, it is a predictable extension to query the same AI for the cause associated with the identified failure mode during the question-answering, which is supported by the FMEA analysis.
As per claim 7: The failure mode analysis support method according to claim 6, wherein the conversational AI is a pre-trained conversational AI that is trained using, as training data, data recording failure modes and failure causes in association with each other.
Thomas discloses [Pages 911-913] the AI model is trained on existing FMEA data, and FMEA data by definition includes failure modes and their causes. The AI model learns from previous experiences to generate more accurate and relevant results for failure modes and their causes. It is clear that the AI model during the question-answering can be further queried for failure causes.
As per claim 9: A computer-implemented failure mode analysis support method comprising:
a process of asking a failure cause of product information or a manufacturing process to a conversational AI, to obtain an answer indicating a solution to the failure cause.
Khamis discloses [0100] a process of asking a question to a conversational AI, which can support FMEA process to identify common failure modes for specific software component to identify trends and patterns that could be helpful in understanding root causes. Khamis explicitly teaches a conversational AI that can detect product anomalies based on interaction with user-based questions, but fails to explicitly disclose asking question to conversational AI so that at least one failure mode that may occur is answered. Thomas discloses a similar method, which further teaches [Pages 911-913] the AI model is trained on existing FMEA data, and FMEA data by definition includes failure modes and their causes. The AI model learns from previous experiences to generate more accurate and relevant results for failure modes and their causes. It is clear that the AI model during the question-answering can be further queried for failure causes.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Khamis with that of Thomas. One would have been motivated to obtain an answer indicating a solution to the failure cause because it can reduce the likelihood or severity of the failure modes and their consequences [Thomas; page 913].
As per claim 10: The failure mode analysis support method according to claim 6, further comprising a process of asking the failure cause obtained as the answer in the process (sb) to the conversational AI, to obtain an answer indicating a solution to the failure cause.
Thomas discloses [Pages 911-913] the AI model is trained on existing FMEA data, and FMEA data by definition includes failure modes and their causes. The AI model learns from previous experiences to generate more accurate and relevant results for failure modes and their causes. It is clear that the AI model during the question-answering can be further queried for failure causes.
As per claim 11: The failure mode analysis support method according to claim 10, wherein the
conversational AI is a pre-trained conversational AI that is trained using, as training data, data recording a correlation between failure causes and solutions to the failure causes.
Thomas discloses [Pages 911-913] ChatGPT can be trained on existing FMEA data and that the model can learn from previous experiences to generate more accurate results. ChatGPT can recommend mitigation strategies in the FMEA context and generate more accurate results based on previous learning data. It is clear from the teachings that cause-to-solution associations are included, since it provides causes and mitigation actions for failure modes.
As per claim 13: The failure mode analysis support method according to claim 1, further comprising:
a process of updating the conversational AI; wherein the process of updating the conversational AI includes:
a process of acquiring corrected data in which a relationship between input information to and output information from the conversational AI; and
a process of training the conversational AI using the corrected data as training data.
Thomas discloses [Pages 911-913] ChatGPT can be trained on existing FMEA data and that the model can learn from previous experiences to generate more accurate results. The model can also be used in a domain-specific FMEA workflow and its outputs can be improved over time through exposure of new training/corrected data. Since the model improves by retraining it on corrected examples of input information, it teaches the feedback loop of acquiring corrected input-output pairs and retraining on that corrected data.
As per claim 14: Although claim 14 is directed towards a medium claim, it is rejected under the same rationale as the method claim 1 above.
As per claim 15: Although claim 15 is directed towards a system claim, it is rejected under the same rationale as the method claim 1 above.
Claims 3, 8, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Khamis in view of Dan Thomas and further in view of Menichelli et al. (US 2023/0244563 A1).
As per claim 3: The failure mode analysis support method according to claim 2, wherein the information entered in the input process includes character information extracted by a computer from text information relating to the structure of the product, the functions of the product, and the manufacturing process.
Khamis and Thomas disclose the method of claim 2, but fail to explicitly disclose extracting information relating to the structure, function, and manufacturing process from text information. Menichelli discloses a similar method, which further teaches [Fig. 3; 0093-0096; 0130] that the common data set is typically input by the user through a GUI (information entered in the input process), and that the user may input data relating to a failure mode, an associated cause, an associated effect, and the corresponding process step. It also shows that the FMEA table is generated from that data (extracting by a computer from user text information) and that the process steps and failure modes are represented in the table.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Khamis and Thomas with that of Menichelli. One would have been motivated to extract information by a computer in the input process because it allows to generate an FMEA table [Menichelli; 0094].
As per claim 8: The failure mode analysis support method according to claim 6, further comprising a process of creating a data table summarizing the information on the product or the manufacturing process entered in the input process, the failure mode obtained in the process, and the failure cause obtained in the process in a table format.
Khamis and Thomas disclose the method of claim 6, but fail to explicitly disclose creating a data table summarizing the information on the product, the failure mode obtained, and the failure cause in a table format. Menichelli discloses a similar method, which further teaches [Fig. 3; 0093-0096] the FMEA table is generated from the common data set and the table shows the failure modes, the causes associated with those failure modes, and the corresponding process steps. It also shows the data of the common data set are typically input by the user and that the failure modes are grouped according to the process step they correspond to.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Khamis and Thomas with that of Menichelli. One would have been motivated to create a data table format because it allows to group failure modes to the corresponding process steps [Menichelli; 0093].
As per claim 12: The failure mode analysis support method according to claim 10, further comprising:
a process of creating a data table summarizing the information on the product or the manufacturing process entered in the input process, the failure mode obtained in the process, the failure cause obtained in the process, and the solution to the failure cause obtained in the process, in a table format.
Khamis and Thomas disclose the method of claim 10, but fail to explicitly disclose creating a data table summarizing the information on the product, the failure mode obtained, and the solution to the failure cause in a table format. Menichelli discloses a similar method, which further teaches [Fig. 3; 0093-0096] the FMEA table is generated from the common data set and the table shows the failure modes, the causes associated with those failure modes, and the corresponding process steps. It also shows the data of the common data set are typically input by the user and that the failure modes are grouped according to the process step they correspond to. The FMEA table also shows the risk mitigation measures associated with each failure mode, including preventions and barriers, and that those mitigation measures are represented in the table itself.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Khamis and Thomas with that of Menichelli. One would have been motivated to create a data table format because it allows to group failure modes to the corresponding process steps [Menichelli; 0093].
Conclusion
The following prior art made of record and not relied upon is cited to establish the level of skill in the applicant’s art and those arts considered reasonably pertinent to applicant’s disclosure. See MPEP 707.05(c).
· US 2023/0315954 A1 – Das discloses training the FMEA engine, metadata from the hierarchy of devices may be input to the FMEA engine to identify a failure mode that may have occurred, and the FMEA engine may select a recovery process.
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/JIGAR P PATEL/Primary Examiner, Art Unit 2114
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