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 .
Contents of this Office Action:
35 U.S.C. 101 rejections. Please note the last paragraph of the 35 U.S.C. 101 rejection which discusses why claims 6-8 would overcome the rejection if they were integrated into the independent claim.
35 U.S.C. 112 rejections
Prior Art rejections
Prior Art relevant but not used in rejection
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. Based upon consideration of all of the relevant factors with respect to the claims as a whole, claims 1-20 are held to claim an unpatentable abstract idea, and are therefore rejected as ineligible subject matter under 35 U.S.C. § 101.
The limitations of the independent claims of receiving data related to the software system of the vehicle; identifying an anomalous event based on a pattern of the received data; collecting contextual information related to the anomalous event; inputting the anomalous event and the contextual information to a model; determining a root cause of the anomalous event by the model; and based on determining that the anomalous event corresponds to a malfunction, performing a mitigating action covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting the application of the steps by a generic processing device, nothing is being recited that could not be performed mentally. Specifically, claim 17 is the only independent claim that recites any device at all (the processing device). Claim 1 recites a method without a corresponding device and claim 11 recites a system in terms of a collection “module” and a root cause analysis “tool.” It is unclear if module and tool are software or hardware and this forms the basis of the 35 U.S.C. 112(b) rejection below. Even were these claims considered to be using a processor like claim 17, this is just a generic computing device where the steps are applied on it.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the ‘Mental Processes’ grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites the element of a processing device to perform the listed steps. The processing device in all steps is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. Moreover, the use of a machine learning model could be considered a technical feature and practical application, but it too is written at such a high level of generality that it does not constitute a practical application. There is no recitation of how the machine learning model is trained or what the specific machine learning model is. It is insufficient to merely state that a limitation is done by machine learning without the specific technical features that show how it is used.
It is important to note that while the last step of the independent claim recites “performing a mitigating action,” given not only broadest reasonable interpretation, but the explicit recitation of what this mitigating step is in claim 10, this can amount to the extra-solution activity of displaying an alert. If claim 10 only stated updating the software or controlling operation of the vehicle, it would be 35 U.S.C. 101 compliant.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processing device to perform the listed steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Turning to the dependent claims 2 and 3 recite data processing. Claims 4, 5, and 9 again state the use of an LLM without any specifics. See above for analysis of claim 10. This holds true for the mirror claims from the other dependent claims.
Claims 6-8, however are compliant with 35 U.S.C. 101 because they do recite particulars of the machine learning model, either in how it is trained or how it is used.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim limitation a data collection module and a root cause analysis tool has been evaluated under the three-prong test set forth in MPEP § 2181, subsection I, but the result is inconclusive. Thus, it is unclear whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they are part of a system claim and written in means plus function format. It is unclear based on the Specification whether these are hardware, software, or both. The boundaries of this claim limitation are ambiguous; therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
In response to this rejection, applicant must clarify whether this limitation should be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Mere assertion regarding applicant’s intent to invoke or not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph is insufficient. Applicant may:
(a) Amend the claim to clearly invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by reciting “means” or a generic placeholder for means, or by reciting “step.” The “means,” generic placeholder, or “step” must be modified by functional language, and must not be modified by sufficient structure, material, or acts for performing the claimed function;
(b) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, should apply because the claim limitation recites a function to be performed and does not recite sufficient structure, material, or acts to perform that function;
(c) Amend the claim to clearly avoid invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, by deleting the function or by reciting sufficient structure, material or acts to perform the recited function; or
(d) Present a sufficient showing that 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, does not apply because the limitation does not recite a function or does recite a function along with sufficient structure, material or acts to perform that function.
This rejection applies to claim 11 and all its dependent claims.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 9-12, and 17-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang “Research on CNN-LSTM Brake Pad Wear Condition Monitoring Based on GTO Multi-Objective Optimization,” Actuators, 2023.
Regarding claims 1, 11, and 17, Wang discloses a method and system comprising:
a memory having computer readable instructions (See below); and
a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform a method including (See below):
Page 4 Section 2.2 states As shown in Figure 1, monitoring the wear state of brake pads mainly includes four main modules, which are the data pre-processing module, the artificial Gorilla Troops Optimization(GTO) algorithm multi-objective optimization module, the Convolutional Neural Network (CNN) module, and the Long Short-Term Memory (LSTM) network module.
receiving data from a software system of a vehicle (Page 5 Step 1: The data preprocessing module performs information fusion and batch normalization on raw data such as the monitored brake disc speed, brake pressure, brake disc temperature, and the characterized brake disc wear values to form a spatiotemporal correlation sample data set);
identifying an anomalous event based on a pattern of the received data (Step 8 discloses brake pad wear values and prediction effects of the algorithm);
collecting contextual information related to the anomalous event (Step 2 is contextual information);
inputting the anomalous event and the contextual information to a machine learning model (Page 5 Step 3: The brake pad wear sample data set formed in step 1 is divided into training set, validation set, and test set, and the division ratio is 7:1:2. At the same time, the initialized hyperparameters are input into the CNN-LSTM model. See also Steps 4 and 5); and
determining a root cause of the anomalous event by the machine learning model based on the contextual information (The result of these steps is determining the root cause is brake pad wear);
based on determining that the anomalous event corresponds to a malfunction, performing a mitigation action (Page 19, P1 disclose providing an early warning, which is a mitigation action).
Regarding claims 2 and 18, Wang discloses wherein identifying an anomalous event includes clustering a plurality of similar events, and associating the anomalous event with the cluster (As above Page 5 Step 1: The data preprocessing module performs information fusion and batch normalization on raw data such as the monitored brake disc speed, brake pressure, brake disc temperature, and the characterized brake disc wear values to form a spatiotemporal correlation sample data set).
Regarding claims 3, 12, and 19, Wang discloses wherein the contextual information includes at least one of an identity context, a temporal context, a location context, and a situation context (The brake events are identity contexts).
Regarding claim 9, Wang discloses identifying the anomalous event is performed using an anomaly detection machine learning model (this is discussed in the rejection to claim 1).
Regarding claim 10, Wang discloses that the mitigating action is presenting an alert (the warning discussed in claim 1 is equivalent to an alert).
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.
Claim(s) 4-6, 13-15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang “Research on CNN-LSTM Brake Pad Wear Condition Monitoring Based on GTO Multi-Objective Optimization,” Actuators, 2023, in view of Yuan, “Rag-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model,” Feb 2024.
Regarding claims 4-6, 13-15, and 20, Wang does not disclose:
(for claim 4) wherein the machine learning model is a domain-specific large language model configured to output a diagnostic report including a plain language description of the anomalous event and the root cause;
(for claim 5) wherein the large language model is configured to interact with a user and provide diagnostic information in response to questions posed by the user using retrieval-augmented generation (RAG); and
(for claim 6) actively training the large language model based on identified anomalous events and associated contextual information, wherein the training includes iteratively presenting questions to machine learning model.
Note, however, that the figures on Page 12 of Wang disclose “output a diagnostic report including a plain language description of the anomalous event and the root cause.” The only difference is the use of a large language model, not the particular output. Further, as disclosed above, Wang also teaches “training the model based on identified anomalous events and associated contextual information.” The difference is the use of the large language model and the questions, not the training part.
Given that, Yuan, which is in the same field and discusses using LLMs with RAG in the context of autonomous vehicles, does teach the above-mentioned limitations. See at least Fig. 4 which shows all of the use of LLMs, the RAG-Driver model, and the specific questions being fed into it.
Therefore, it would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to modify Wang with the teachings of Yuan so that a user can more easily understand the results of the ML model. While the graph in Wang does represent a human readable output, it is much easier to just ask a question and have an LLM tell the person the results as opposed to reading a graph.
Allowable Subject Matter
Claims 7, 8, and 16 recite allowable subject matter should they be included in the independent claim. The two references cited above are the closest pieces of prior art and they do not teach the specifics of these limitations.
Relevant Prior Art
He US 20220111732A1 which discloses that invention relates to the technical field of battery safety, and in particular to a safety monitoring method and system for a vehicle. The invention is intended to solve the problem that a battery cannot be continuously and safely monitored when a new energy vehicle is parked. To this end, the safety monitoring method in the invention includes: when a vehicle is in a parked state, waking up a battery management controller at specific time intervals, and obtaining status data of the battery by means of a battery management controller; determining, based on the status data, whether the battery is in an anomalous state; and when the battery is not in the anomalous state, controlling the battery management controller to enter hibernation state again; or when the battery is in the anomalous state, further waking up a wireless communications network, and uploading the status data and/or an alarm signal to a remote monitoring platform by means of the wireless communications network. According to the present application, a status of a traction battery in a parked state can be continuously monitored, and in addition, data can be remotely uploaded purposefully to reduce energy consumption.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARYAN E WEISENFELD whose telephone number is (571)272-6602. The examiner can normally be reached M-F 9-5.
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ARYAN E. WEISENFELD
Primary Examiner
Art Unit 3689
/ARYAN E WEISENFELD/Primary Examiner, Art Unit 3663