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 .
Information Disclosure Statement
The Information Disclosure Statements (IDS) filed on 11/26/2025 has been acknowledged
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202510335738.1, filed on 03/20/2025.
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.
Claims 7 and 14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 7 and 14 recite the term “performing system construction”. The term is not reasonably understood in view of the language of the claim and the specification. While claims 7 and 14 attempt to define “performing system construction”, part of the definition recites “performing system construction” itself and fails to provide an objective or otherwise understandable meaning for the term. Thus, one of ordinary skill in the art would not be able to determine the metes and bounds of “performing system construction” with reasonable certainty.
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 an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a process. Therefore, claim 1 is within at least one of the four statutory categories.
Claim 8 is directed to an apparatus (device). Therefore, claim 8 is within at least one of the four statutory categories.
Claim 15 is directed to an apparatus (medium). Therefore, claim 15 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Claims 1, 8 and 15 include limitations that recite an abstract idea (emphasized below) and Claim 8 will be used as a representative claim for the remainder of the 101 rejections.
Claim 8 recites: An electronic device, comprising: at least one processor; and a memory coupled to the at least one processor and storing at least one computer executable instruction thereon which, when executed by the at least one processor, causes the at least one processor to:
obtain a target diagnostic requirement and historical diagnostic data of a target vehicle;
annotate the historical diagnostic data according to a preset dictionary format, to obtain reference data;
divide, according to a preset ratio, the reference data into a training set and a validation set;
obtain a pre-trained model and model feature information of the pre-trained model;
adjust the pre-trained model according to the target diagnostic requirement, the model feature information, the training set, and the validation set, to obtain a target model;
perform system construction according to the target model, to obtain a target diagnostic system;
determine a hardware facility corresponding to the target model;
determine a deep learning framework compatible with the hardware facility;
deploy the target model according to the deep learning framework, to obtain a large model layer;
integrate the reference data, to obtain a data layer;
obtain a cache layer, a startup layer, and a functional layer that are preset, wherein the cache layer is used to store cache information, the startup layer is used to provide a system interface, and the functional layer comprises a user management module, a session management module, a knowledge base management module, a database management module, and a tool block management module; and
perform system construction according to the large model layer, the data layer, the cache layer, the startup layer, and the functional layer, to obtain the target diagnostic system.
and diagnose the target vehicle according to the target diagnostic system, to obtain a target diagnostic result.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. Specifically, the “annotating, dividing, adjusting, and diagnosing” steps encompass a user to draw conclusions from the data. Annotating data to obtain different data is something that can be done mentally with pen and paper. Adjusting a model based on data is a process that can be done mentally with pen and paper. Dividing data into groups is a process that can be done mentally. Diagnosing/analyzing an object to make a conclusion is a process that can be done mentally.
Performing system construction is a limitation that is further defined by claim 14 and its parallel claims, which contain mental processes (Hence why that limitation is bolded and underlined). Determining a hardware facility and learning framework is a process that can be done mentally. Performing system construction according to multiple layers in order to obtain data, is a process that can be done mentally. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “processor…and memory”, the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the processor and memory are recited at a high level of generality and merely automates the obtaining, annotating, dividing, adjusting, performing, and diagnosing steps, therefore acting as a generic computer to perform the abstract idea. Additionally, the processor and memory are claimed generically and operate in their ordinary capacity and do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitations are no more than mere instructions to apply the exception using a processor and memory.
Furthermore, the examiner submits that the recitations of dividing data is a mere definition that does not necessarily impose any meaningful limits on performing the steps in the human mind, as it only compares data where a user could in fact perform this mentally or using paper and pencil.
In addition to that, the examiner submits that obtaining data, deploying models, and integrating data, using a processor and memory, are insignificant extra-solution activities that merely use a processor and memory to perform the process. In particular, the obtaining, deploying, and integrating steps are recited at a high level of generality (i.e. as a general means of gathering/transferring data for use in the annotating, adjusting, and performing steps), and amounts to mere data transfer, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a processor and memory or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 8 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the device, the processor and memory amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of obtaining data, dividing data, deploying data, and integrating data, the examiner submits that these limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of obtaining data are well-understood, routine, and conventional activities because the background recites that the sensors from which the data is acquired/received are all conventional sensors. 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 collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, claim 8 is not patent eligible.
Further Claims 1 and 15 are not patent eligible for the same reasons.
Dependent Claims 2-7, 9-14, and 16-20 when analyzed as a whole, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. The additional elements, if any, in the dependent claims are not sufficient to amount to significantly more than the judicial exception for the same reasons as with Claims 1, 8, and 15.
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.
Claim(s) 1-2, 7-9, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over CN-119598345-A to Zhang et. al. (“Zhang”), in view of CN-114740815-B to Kang et. al. (“Kang”), further in view of US-20200320802-A1 to Yang et. al. (“Yang”) and CN-117332858-A to Hu et. al. (“Hu”).
Regarding claim 8, Zhang teaches
obtaining a pre-trained model and model feature information of the pre-trained model (Zhang Description “constructing a pre-training automobile fault identification model based on BERT model; the pre-training automobile fault identification model uses a large number of marked automobile fault related description entities and data coded automobile fault natural language description text to train the model through the method of monitoring learning, ensuring that the model can accurately identify the fault type from the description entity related to the vehicle fault and the natural language description text of the vehicle fault after data coding; the pre-training model is adjusted by the monitoring fine adjustment operation so as to make it more fit for the vehicle fault identification task;”);
adjusting the pre-trained model according to the target diagnostic requirement and sets, to obtain a target model (Zhang Description “the pre-training model is adjusted by the monitoring fine adjustment operation so as to make it more fit for the vehicle fault identification task; in order to further adjust the accuracy of the model, a manual feedback mechanism is introduced; the automobile fault identification expert puts forward feedback opinion according to the output result of the model, and guides the model to adjust; the automobile fault identification expert marks the identification result of the pre-training automobile fault identification model and points out the wrong or inaccurate classification; integrating the artificial feedback data into the model training for further fine adjustment, updating the deviation of the data set correction model on the specific fault type, so as to optimize the model expression; the model adjusts the strategy according to the artificial feedback so as to strengthen the classified expression in different situations;”);
performing system construction according to the target model, to obtain a target diagnostic system (Zhang Description “in S300, based on Neo4j database, vehicle fault knowledge map model is constructed through entity relational graph, node in the map represents various vehicle parts, fault type and related technical parameter entity, side represents relation between entities.”); and
diagnosing the target vehicle according to the target diagnostic system, to obtain a target diagnostic result (Zhang Claim 7 “using the automobile fault knowledge map model to query the language Cypher, inputting the fault description entity and the fault type to perform semantic similarity matching, using the automobile fault knowledge map model to query the most similar fault solution, The automobile fault knowledge map model inquires the matching result closest to the user description, including the possible fault reason and the solution result.”).
Zhang does not teach annotating the historical diagnostic data according to a preset dictionary format, to obtain reference data; and dividing, according to a preset ratio, the reference data into a training set and a validation set. However, Kang teaches annotating the historical diagnostic data according to a preset dictionary format, to obtain reference data (Kang Claim 2 “the preset fault diagnosis model is: taking the historical fault information as the sample data, taking the vehicle data of the pre-set days before and after the occurrence point of the historical fault information as the input data”); and dividing, according to a preset ratio, the reference data into a training set and a validation set (Kang Claim 2 “wherein the solution corresponding to the fault reason and the fault code is obtained by dividing the data into a training set and a test set by the tag, and training and testing according to the pre-set algorithm.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to have modified the system of Zhang to incorporate the teachings of Kang such that the system comprises annotating the historical diagnostic data according to a preset dictionary format, to obtain reference data; and dividing, according to a preset ratio, the reference data into a training set and a validation set. Doing so would ensure that the system can diagnose a vehicle fault based on a neural network in a full dimension (Kang Description).
Zhang as modified by Kang does not teach an electronic device, comprising: at least one processor; and a memory coupled to the at least one processor and storing at least one computer executable instruction thereon which, when executed by the at least one processor, causes the at least one processor to: obtain a target diagnostic requirement and historical diagnostic data of a target vehicle. However, Yang teaches an electronic device, comprising: at least one processor; and a memory coupled to the at least one processor and storing at least one computer executable instruction thereon which, when executed by the at least one processor, causes the at least one processor to (Yang Claim 1 “An autonomous vehicle, comprising: a memory configured to store a driving record of the vehicle; and a processor” and [0023]): obtain a target diagnostic requirement and historical diagnostic data of a target vehicle (Yang Fig. 3, [0049] “The processor 270 may be configured to store a driving record of the vehicle 200 (e.g., vehicle startup date and time and vehicle shutdown date and time) in the memory 240 by using a clock (not illustrated) or a tachograph (not illustrated). The processor 270 may be configured to calculate a stopping period (non-driving period) of the vehicle 200, based on the driving record.”, and Claim 1 “a memory configured to store a driving record of the vehicle; and a processor configured to generate a diagnosis plan”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of Yang to Zhang as modified by Kang such that the system is an electronic device, comprising: at least one processor; and a memory coupled to the at least one processor and storing at least one computer executable instruction thereon which, when executed by the at least one processor, causes the at least one processor to: obtain a target diagnostic requirement and historical diagnostic data of a target vehicle. Doing so would provide a device for diagnosing states of parts of an autonomous vehicle (Yang [0005]).
Zhang as modified by Kang and Yang does not teach adjusting the pre-trained model according to the target diagnostic requirement, the model feature information, the training set, and the validation set, to obtain a target model. However, Hu teaches adjusting the pre-trained model according to the target diagnostic requirement, the model feature information, the training set, and the validation set, to obtain a target model (Hu Description “based on the training set, the verification set and the test set, finishing the training, parameter optimization and model test task of the entity extraction model: Each text in the training set is used as the input, and the BIOS tag corresponding to each character in the sentence is used as the output. The built BERT-BiLSTM-CRF model is trained, and the specific model structure is shown in FIG. 3. by using BERT pre-training model as embedded layer to realize better input semantic representation, using BiLSTM as coding layer to extract features, finally using conditional random field CRF as decoding layer to decode the output of BiLSTM, so as to obtain the BIOS label corresponding to each character.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of Hu to Zhang as modified by Kang and Yang such that the device comprises adjusting the pre-trained model according to the target diagnostic requirement, the model feature information, the training set, and the validation set, to obtain a target model. Doing so would improve the accuracy of automobile fault diagnosis (Hu Description).
Regarding claim 9, Zhang as modified by Kang, Yang, and Hu teaches all of the elements of the current invention in claim 8. Kang further discloses determining the reference data according to the first reference data and the second reference data (Kang Claim 7 “analyzing the vehicle data to obtain the fault information corresponding to the first fault code of the vehicle-mounted terminal, and the non-fault data of the pre-set days before and after the fault information time point; an input module for inputting the non-fault data of the pre-set days before and after the fault information time generation point into the pre-set fault diagnosis model to obtain the fault information corresponding to the second fault code and the fault reason; an output module for comparing the accuracy of the fault information corresponding to the first fault code and the fault information corresponding to the second fault code”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of Kang to Zhang as modified by Kang, Yang, and Hu such that the device comprises determining the reference data according to the first reference data and the second reference data. Doing so would ensure that the system can diagnose a vehicle fault based on a neural network in a full dimension (Kang Description).
Hu further discloses that to annotate the historical diagnostic data according to the preset dictionary format, to obtain the reference data, the at least one computer executable instruction, when executed by the at least one processor, causes the at least one processor to:
determine first data and second data in the historical diagnostic data, wherein a data type of the first data is structured data and a data type of the second data is unstructured data (Hu Claim 2 “the step b comprises the following contents: firstly judging whether the field of the maintenance history data is structured, for the non-structured data,”);
annotate the first data according to the preset dictionary format, to obtain first reference data (Hu Claim 2 “respectively extracting the fault phenomenon entity and the fault reason entity, extracting the data from the maintenance history data”);
perform text extraction on the second data, to obtain a text content (Hu Claim 2 “using BIOS labelling method to label the training set, verifying set and testing set, that is to cut the text in the language material based on single character, then adding corresponding label for each character, the label can represent the position and entity type of the entity of the character.” and Claim 3);
integrate the text content, to obtain third data, wherein a data type of the third data is structured data (Hu Claim 2 “firstly judging whether the field of the maintenance history data is structured” and Claim 3 “taking each text in the training set as input, taking the BIOS tag corresponding to each character in the sentence as output, training the built BERT-BiLSTM-CRF model, realizing better input semantic representation by using the BERT pre-training model as embedded layer, the BiLSTM is used as the coding layer to extract the feature, and the conditional random field CRF is used as the decoding layer to decode the output of the BiLSTM so as to obtain the BIOS label corresponding to each character.”); and
annotate the third data according to the preset dictionary format, to obtain second reference data (Hu Claim 3-5);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of Hu to Zhang as modified by Kang, Yang, and Hu such that that to annotate the historical diagnostic data according to the preset dictionary format, to obtain the reference data, the at least one computer executable instruction, when executed by the at least one processor, causes the at least one processor to: determine first data and second data in the historical diagnostic data, wherein a data type of the first data is structured data and a data type of the second data is unstructured data; annotate the first data according to the preset dictionary format, to obtain first reference data; perform text extraction on the second data, to obtain a text content; integrate the text content, to obtain third data, wherein a data type of the third data is structured data; and annotate the third data according to the preset dictionary format, to obtain second reference data. Doing so would improve the accuracy of automobile fault diagnosis (Hu Description).
Regarding claim 14, Zhang as modified by Kang, Yang, and Hu teaches all of the elements of the current invention in claim 8. Zhang further discloses that to perform system construction according to the target model, to obtain the target diagnostic system, the at least one computer executable instruction, when executed by the at least one processor, causes the at least one processor to:
determine a hardware facility corresponding to the target model; determine a deep learning framework compatible with the hardware facility; deploy the target model according to the deep learning framework, to obtain a large model layer (Zhang Description “a vehicle fault identification model is constructed by using a deep learning model, a description entity related to the vehicle fault is input to the vehicle fault identification model, and a fault type possibly existing in the current vehicle is predicted in combination with historical fault data.”);
integrate the reference data, to obtain a data layer (Zhang Description “through natural language processing, obtaining and pre-processing the automobile fault description text provided by the user, and using NER model to extract the description entity related to the fault”);
obtain a cache layer, a startup layer, and a functional layer that are preset, wherein the cache layer is used to store cache information, the startup layer is used to provide a system interface, and the functional layer comprises a user management module, a session management module, a knowledge base management module, a database management module, and a tool block management module (Zhang Description “An automobile fault diagnosis system comprises an information retrieval module, a fault reasoning diagnosis module, a data integration module and a human-computer interaction module … BERT model comprises three main components: an input layer, a coding layer and an output layer; the input layer is obtained by adding three feature embedding vectors, including Token embedding, paragraph embedding and position embedding; the Token embedding vector expression of each word or sub-word sequence of the input sequence is obtained by using the Tokenizer, and the segment coding vector expression Segment Embedding operation and the position coding vector expression Embedding operation are carried out; constructing a coding layer based on the magnet encoder; The coding layer can keep long-distance information similar to RNN, and also can perform parallel calculation similar to CNN; constructing an output layer based on a mask (MASK) language model; the output layer covers about 15 % of the words and predicts the covered words, and trains and learns the text knowledge through the form similar to the complete filling;”); and
perform system construction according to the large model layer, the data layer, the cache layer, the startup layer, and the functional layer, to obtain the target diagnostic system (Zhang Description “in S300, based on Neo4j database, vehicle fault knowledge map model is constructed through entity relational graph, node in the map represents various vehicle parts, fault type and related technical parameter entity, side represents relation between entities.”).
With respect to claims 1-2 and 7, all limitations have been examined with respect to the device in claims 8-9 and 14. The device taught/disclosed in claims 8-9 and 14 can clearly perform the method of claims 1-2 and 7. Therefore claims 1-2 and 7 are rejected under the same rationale.
With respect to claims 15-16, all limitations have been examined with respect to the device in claims 8-9. The device taught/disclosed in claims 8-9 can clearly perform the medium of claims 15-16. Therefore claims 15-16 are rejected under the same rationale.
Claim(s) 3, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang, in view of Kang, further in view of Yang, Hu, and US-20190019356-A1 to Liu et. al. (“Liu”).
Regarding claim 10, Zhang as modified by Kang, Yang, and Hu teaches all of the elements of the current invention in claim 8. Kang further discloses that to adjust the pre-trained model according to the target diagnostic requirement, the model feature information, the training set, and the validation set, to obtain the target model, the at least one computer executable instruction, when executed by the at least one processor, causes the at least one processor to: configure the pre-trained model according to the initial parameter, to obtain a first reference model; fine-tune the first reference model according to the training set, to obtain a second reference model; evaluate the second reference model according to the validation set, to obtain an evaluation result; and adjust the second reference model according to the evaluation result, to obtain the target model (Kang Description “The model self-training module is used for forming a new data set by the final result processed by the user and the corresponding original data through the sample data collecting module, and training the existing model parameters, which comprises: collecting new fault information, combining the historical fault information to form a new sample data, according to the historical fault information and the vehicle data of the preset days before and after the new fault information generating point, as input data, performing iterative training according to the preset algorithm, specifically, when the accuracy of the training result reaches the requirement, the data intelligent diagnosis analysis module updates the new model parameter to analyze. The sample collecting module pre-processes the data obtained each time, realizes the automatic updating and iteration of the whole system, and can obtain more accurate algorithm model.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of Kang to Zhang as modified by Kang, Yang, and Hu such that to adjust the pre-trained model according to the target diagnostic requirement, the model feature information, the training set, and the validation set, to obtain the target model, the at least one computer executable instruction, when executed by the at least one processor, causes the at least one processor to: configure the pre-trained model according to the initial parameter, to obtain a first reference model; fine-tune the first reference model according to the training set, to obtain a second reference model; evaluate the second reference model according to the validation set, to obtain an evaluation result; and adjust the second reference model according to the evaluation result, to obtain the target model. Doing so would ensure that the system can diagnose a vehicle fault based on a neural network in a full dimension (Kang Description).
Zhang as modified by Kang, Yang, and Hu does not teach determining an initial parameter according to the target diagnostic requirement and the model feature information. However, Liu teaches determining an initial parameter according to the target diagnostic requirement and the model feature information (Liu [0026] “obtaining vehicle characteristic parameters”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable expectation of success to further incorporate the teachings of Liu to Zhang as modified by Kang, Yang, and Hu such that to adjust the pre-trained model according to the target diagnostic requirement, the model feature information, the training set, and the validation set, to obtain the target model, the at least one computer executable instruction, when executed by the at least one processor, causes the at least one processor to: determining an initial parameter according to the target diagnostic requirement and the model feature information. Doing so would ensure that a vehicle to be diagnosed can be automatically detected, reducing the cost of labor (Liu [0065]).
With respect to claim 3, Zhang as modified by Kang, Yang, and Hu teaches all of the elements of the current invention in claim 1. Additionally, the limitations recited in claim 3 mirror the limitations recited in claim 10, which were rejected above. See the rejection of claim 10 above.
With respect to claim 17, Zhang as modified by Kang, Yang, and Hu teaches all of the elements of the current invention in claim 15. Additionally, the limitations recited in claim 17 mirror the limitations recited in claim 10, which were rejected above. See the rejection of claim 10 above.
Office Note: Examiner notes that the cited prior art does not disclose the claimed subject matter of claims 4-6, 11-13, and 18-20, but also notes that the claims are currently rejected under 35 USC 101.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON TOAN NGUYEN whose telephone number is (571)272-6163. The examiner can normally be reached M-T: 8-5:30 F1:8-12 F2: Off.
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/J.N./Examiner, Art Unit 3666
/SCOTT A BROWNE/ Supervisory Patent Examiner, Art Unit 3666
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