DETAILED ACTION
Claims 1-20 are pending in the current application.
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 .
Claim Objections
Claims 1 and 11 are objected to because of the following informalities: These claims switch to LLM abbreviation without clearing indicating that LLM in the abbreviation for Large Language Model. Appropriate correction is required.
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 8-9 and 18-19 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.
As to claims 8 and 18 they recite “…embedding at least one vector representation of at least one of the concept data, the computational logic data and the workflow data into a model architecture of the first large language model” and it is unclear what in meant by embedding…into a model architecture, is the embedding the feeding the training data in vector form as input/embedding vectors into the model/model architecture which is how it is being interpreted for claim analysis or is the embedding vector representation of the training data directly embedded/incorporated into the model architecture, stored/changing/adjusting the model architecture to include this vector representation of training data as part of the model itself further clarification is required.
As to claims 9 and 19 they depend from claims 8 and 18 above and do not overcome the issue and thus rejected under the same reasoning.
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. Examiner has evaluated the claims under the framework provided in the 2019 Patent Eligibility Guidance published in the Federal Register 01/07/2019 and has provided such analysis below.
Step 1: Claims 1-20 are claims that are directed to a process, machine, manufacture or composition of matter.
In order to evaluate the Step 2A inquiry “Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?” we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Claims 1 and 11: The limitation of “ analyzing… the at least one IT infrastructure data” and “generating…at least one IT specification data based on the analyzing, wherein the at least one IT specification data comprises a functionality data representing at least one functionality provided by the at least one IT infrastructure implemented according to the at least one IT specification data” as drafted, are process that, but for the recitation of generic computing components, under its broadest reasonable interpretation, recite the abstract idea of a mental process. The limitations encompass a human mind carrying out the function of analyzing IT infrastructure data and based on that analysis determining/generating IT specification data through observation, evaluation judgment and /or opinion, or even with the aid of pen and paper. Thus, this limitation recites and falls within the “Mental Processes” grouping of abstract ideas under Prong 1.
Step 2A Prong 2:
Claims 1 and 11: The abstract idea is not integrated into a practical application. In particular the claims recite the following additional element “using a communication device,” “a client device”, “using a processing device,” “using a first Large Language Model” and “A system for facilitating provisioning of Information Technology (IT) infrastructure data, the system comprising” are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components, which does not integrate the abstract idea into a practical application. Additionally, the claim recites additional elements of “receiving, using a communication device, at least one IT infrastructure data associated with at least one IT infrastructure from a client device” and “transmitting, using the communication device, the at least one IT specification data to the client device” do nothing more than add insignificant extra solution activity to the judicial exception of merely receiving or transmitting data which does not integrate the abstract idea into a practical application. Furthermore, the claims recite additional elements of “wherein the first LLM is trained on a training data comprising an association of a plurality of IT infrastructure data and a plurality of IT specification data” which fails to meaningfully limit the claim because it does not apply the judicial exception in some other meaningful way and is at best the equivalent of merely adding the words “apply it” to the abstract idea as it does not require any particular application of the recited “training” and viewed as mere instructions in the form of an algorithm to implement the judicial exception on a computer. Accordingly, the additional elements do not integrate the recited judicial exception into a practical application, and the claim is therefore directed to the judicial exception. See MPEP 2106.05(g).
Step 2B:
Claims 1 and 11: The claims do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the abstract idea. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “using a communication device,” “a client device”, “using a processing device,” “using a first Large Language Model” and “A system for facilitating provisioning of Information Technology (IT) infrastructure data, the system comprising” amount to no more than mere instructions, or generic computer/computer components to carry out the exception. Additionally, the additional elements of “receiving, using a communication device, at least one IT infrastructure data associated with at least one IT infrastructure from a client device” and “transmitting, using the communication device, the at least one IT specification data to the client device” are merely insignificant extra-solution activity information of receiving or transmitting data which does not integrate the abstract idea into a practical application. Further, the insignificant extra solution data activity is also WURC, see MPEP 2106.05(d)(II), where “the courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity” i. receiving or transmitting data over a network where the receiving limitation is akin to receiving data and the transmitting limitation is akin to transmitting data. Furthermore, the addition element of “wherein the first LLM is trained on a training data comprising an association of a plurality of IT infrastructure data and a plurality of IT specification data” do not require any particular application of the recited “trained” and at best is equivalent to merely adding the words “apply it” to the abstract idea. Mere instruction to apply an abstract idea cannot provide an inventive concept. The recitation of generic computer instruction and computer components to apply the judicial exception and merely receiving data information and mere instruction to apply an abstract idea do not amount to significantly more, thus, cannot provide an inventive concept. Accordingly, the claims are not patent eligible under 35 USC 101.
Having concluded analysis within the provided framework, claims 1 and 11 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 2 and 12 they recite additional abstract idea elements of “generating… an updated IT infrastructure data based on the at least one IT specification data” is an additional mental process under prong 1. Further, the claims recite additional elements of “using the processing device” and “wherein the generating of the updated IT infrastructure data is performed using a second LLM” which is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using generic computer, and/or mere computer components, which does not integrate the abstract idea into a practical application. Moreover, claims 2 and 12 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 2 and 12 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 2 and 12 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 3 and 13 they recite additional elements of “training, using the processing device, the first LLM based on the training data” fails to meaningfully limit the claim because it does not require any particular application of the recited “training” and is at best the equivalent of merely adding the words “apply it” to the judicial exception, which does not integrate the abstract idea into a practical application. Moreover, claims 3 and 13 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 3 and 13 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 3 and 13 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 4 and 14 they recite additional elements of “wherein the training data comprises at least one of a concept data, a workflow data and a computational logic data” which is merely field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 4 and 14 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 4 and 14 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 4 and 14 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 5 and 15 they recite additional elements of “wherein the training data corresponds to a domain” which is merely field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 5 and 15 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 5 and 15 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 5 and 15 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 6 and 16 they recite additional elements of “deploying, using the processing device, an updated IT infrastructure based on the updated IT infrastructure data” fails to meaningfully limit the claim because it does not require any particular application of the recited “deploying” and is at best the equivalent of merely adding the words “apply it” to the judicial exception, which does not integrate the abstract idea into a practical application. Moreover, claims 6 and 16 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 6 and 16 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 6 and 16 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 7 and 17 they recite additional abstract idea elements of “wherein the analyzing of the at least one IT infrastructure data is based on the training of the first LLM using the at least one feedback data” is an additional mental process under prong 1. Further, the claims recite additional elements of “receiving, using the communication device, at least one feedback data from the client device, wherein the feedback data comprises at least one feedback based on the at least one IT specification data” which merely insignificant extra-solution activity information of receiving or transmitting data which does not integrate the abstract idea into a practical application. Further, the insignificant extra solution data activity is also WURC, see MPEP 2106.05(d)(II), where “the courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity” i. receiving or transmitting data over a network where the receiving limitation is akin to receiving data. Moreover, claims 7 and 17 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 7 and 17 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 7 and 17 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 8 and 18 they recite additional elements of “wherein the training of the first LLM comprises embedding at least one vector representation of at least one of the concept data, the computational logic data and the workflow data into a model architecture of the first large language model” fails to meaningfully limit the claim because it does not require any particular application of the recited “embedding” and is at best the equivalent of merely adding the words “apply it” to the judicial exception, which does not integrate the abstract idea into a practical application. Moreover, claims 8 and 18 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 8 and 18 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 8 and 18 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 9 and 19 they recite additional elements of “wherein the training further comprises fine-tuning of the first LLM based on the embedding” which is merely field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 9 and 19 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 9 and 19 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 9 and 19 do not recite patent eligible subject matter under 35 USC 101.
With regard to claims 10 and 20 they recite additional abstract idea elements of “wherein the method further comprises: generating, using the processing device, a user interface data corresponding to a user interface configured to receive indication of the training data” is an additional mental process under prong 1. Further, the claims recite additional elements of “ transmitting, using the communication device, the user interface data to the client device” which merely insignificant extra-solution activity information of receiving or transmitting data which does not integrate the abstract idea into a practical application. Further, the insignificant extra solution data activity is also WURC, see MPEP 2106.05(d)(II), where “the courts have recognized the following computer functions as well-understood, routine and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity” i. receiving or transmitting data over a network where the transmitting limitation is akin to transmitting data. Additionally, the claims recite additional elements of “wherein the training data corresponds to a domain” which is merely field of use/technological environment which does not integrate the judicial exception into a practical application. Moreover, claims 10 and 29 do not recite any other additional elements and for the same reasons as above with regard to the integration into a practical application and whether the additional elements amount to significantly more, claims 10 and 20 also fail both Step 2A prong 2, thus the claims are directed to the abstract idea as it has not been integrated into practical application, and fails Step 2B as not amounting to significantly more. Therefore, claims 10 and 20 do not recite patent eligible subject matter under 35 USC 101.
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.
Claims 1, 3-5, 7, 10-11, 13-15, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran et al. (Pub. No. US 2021/0021479 A1) and further in view of Knox et al. (Patent No. US 12,730,978 B1).
As to claims 1 and 11 Chandran discloses a system for facilitating provisioning of Information Technology (IT) infrastructure data, the system comprising: a communication device configured for (Chandran [0060] lines 1-8):
receiving at least one IT infrastructure data associated with at least one IT infrastructure (Chandran [0002] lines 1-4 and [0066] lines 1-5; which shows the system being able to receive infrastructure architecture diagram/infrastructure data representing a diverse array of infrastructure resources/infrastructure where the infrastructure data is associated with information technology resources and thus viewed as IT infrastructure data); and
transmitting at least one IT specification data to the device (Chandran [0002] lines 1-4, [0050] lines 7-15, [0068[ lines 1-5, [0070] lines 1-8, [0072] lines 1-12[0097] lines 1-4; which shows the output of the information generated by the deep learning/neural network models, where the output generated includes the associated IT infrastructure specification data, where output can include the sending and/or transmitting the data to another system/device);
a processing device configured for (Chandran [0054] lines 1-6):
analyzing the at least one infrastructure data using a first model, wherein the first model is trained on a training data comprising an association of a plurality of IT infrastructure data and a plurality of IT specification data (Chandran [0002] lines 1-4, [0048] lines 1-16, [0050] lines 7-15, [0068] lines 1-5, and [0070] lines 1-8; which should using trained machine learning models that analyzes the received infrastructure architecture diagram/infrastructure data to generate associated specification data, where the machine learning model is trained on a set of training data that includes pre-labeled infrastructure architecture diagrams where the infrastructure architecture diagram/infrastructure data includes regions, object and text indicating properties that determine functioning of an infrastructure resource, viewed as specification type data thus viewed that the training data comprising an association of a plurality of infrastructure data and plurality of specification data associated with resources that are viewed as being associated with information technology resources/infrastructure getting used ); and
generating the at least one IT specification data based on the analyzing, wherein the at least one IT specification data comprises a functionality data representing at least one functionality provided by the at least one IT infrastructure implemented according to the at least one IT specification data (Chandran [0002] lines 1-4, [0048] lines 1-16 and [0050] lines 7-15; which shows through the use of machine learning models being able to generate infrastructure architectures specifications, viewed as specification data, including infrastructure resources and properties of the resources, where the resource/specification/infrastructure are associated with IT resources/infrastructure, where the properties identified can be fixed or variables parameters that determine the functioning of the infrastructure resource, thus viewed as specification data that comprises a type of functionality data representing functioning/functionality provided by an infrastructure resource specified in the generated specification data).
Chandran does not specifically disclose the specifics of receiving at least one IT infrastructure data associated with at least one IT infrastructure from a client device; transmitting at least one IT specification data to the client device; and analyzing data using analyzing the at least one infrastructure data using a first large language model, wherein the first LLM is trained on a training data.
However, Knox discloses the specifics of receiving at least one IT infrastructure data associated with at least one IT infrastructure from a client device (Knox Col. 4 Col. 4 lines 36-52; which shows receiving data from a client device input data for a machine learning model, where the specifics of the IT infrastructure data being received as used as input into a model is seen specifically disclosed in the teachings of Chandran above);
transmitting at least one IT specification data to the client device (Knox Col. 4 Col. 4 lines 36-52; which shows that the output/results/data generated from a machine learning model is output/transmitted back to the same client device, where the specifics of the output of the model being IT specification data is seen in the teachings of Chandran above);
analyzing the at least one infrastructure data using a first large language model, wherein the first LLM is trained on a training data (Knox Col. 23 lines 1-11; which shows the specifics of the trained machine learning models used can include large language models that in light of the teachings of Chandran above showing the use of a trained machine learning/neural network model used to analyzed IT infrastructure data where the model is training of an associated of a plurality of IT infrastructure data and plurality of IT specification data and thus together show the specifics of analyzing the at least one infrastructure data using a first large language model, wherein the first LLM is trained on a training data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Knox showing the specifics of using large language machine learning models for analyzing data, into the machine learning model to analyze IT infrastructure data of Chandran for the purpose of increasing the adaptability with different machine learning models, as taught by Knox Col. 23 lines 1-11.
As to claims 3 and 13, Chandran discloses wherein the processing device is further configured for training the first LLM based on the training data (Chandran [0070] lines 1-8; which shows that the neural network/machine learning model/first model is trained based on the training data).
As to claims 4 and 14, Chandran discloses wherein the training data comprises at least one of a concept data, a workflow data and a computational logic data (Chandran [0021] lines 5-15 and [0070] lines 1-8; which shows that the training data includes pre-labeled infrastructure architecture diagrams where these depict schematically the provision of an infrastructure by identifying specific resources and configurating the arrangement or connections among the specified resources, viewed as type of use/concept data used for training).
As to claims 5 and 15, Chandran does not specifically disclose, however, Knox discloses wherein the training data corresponds to a domain (Knox Col. 11 lines 1-6, Col. 12 lines 49-53 and Col. 23 lines 29-36; which shows that the training data for machine learning models can include among other text sequence of document text where the document text can be processed to identify particular features include domains, thus viewed as the training data corresponds to the domain ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Knox showing the specifics of using large language machine learning models for analyzing data, into the machine learning model to analyze IT infrastructure data of Chandran for the purpose of increasing the adaptability with different machine learning models, as taught by Knox Col. 23 lines 1-11.
As to claims 7 and 17 Chandran discloses wherein the communication device is further configured for receiving at least one feedback data from the client device, wherein the feedback data comprises at least one feedback based on the at least one IT specification data (Chandran [0087] lines 1-8; which shows being able to receive feedback data from associated user/client where the feedback provided is based on the provided infrastructure specification data that is used to modify the infrastructure specification data and thus viewed as feedback based on the IT specification data), wherein the processing device is further configured for analyzing the at least one IT infrastructure data based on the training of the first LLM using the at least one feedback data (Chandran [0048] lines 1-16, [0050] lines 7-15, [0087] lines 1-8 and [0089] lines 1-6; which shows the feedback provided from the user associated with the infrastructure specification can be part of a feedback look used for updating/refining/training the associated neural network models that analyze the infrastructure data/ infrastructure architecture diagram where the specifics of the model being a LLM is seen specifically disclosed in the teachings of Knox above).
As to claims 10 and 20, Chandran does not specifically disclose, however, Knox discloses wherein the training data corresponds to a domain (Knox Col. 11 lines 1-6, Col. 12 lines 49-53 and Col. 23 lines 29-36; which shows that the training data for machine learning models can include among other text sequence of document text where the document text can be processed to identify particular features include domains, thus viewed as the training data corresponds to the domain),
wherein the processing device is further configured for generating a user interface data corresponding to a user interface configured to receive indication of the training data, wherein the communication device is further configured for transmitting the user interface data to the client device (Knox Col. 4 lines 36-52, Col. 18 lines 50-58 and Col. 19 lines 8-14; which shows being able to generate and transmit data/updated presentations instructions that are formatted for rendering at the client device and to cause updating the graphical user inface of the client device to display information, viewed as type of user interface data that is transmitted to the client device, where the graphical user interface at the client device can receive user input/selection/feedback of information message information delivered and based on feedback information received through the messaging delivered gui update one or more machine learning models based on feedback input, thus viewed as type of training data for updating machine learning model thus viewed that the graphical user interface is configured to receive indication of the training data ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Knox showing the specifics of using large language machine learning models for analyzing data, into the machine learning model to analyze IT infrastructure data of Chandran for the purpose of increasing the adaptability with different machine learning models, as taught by Knox Col. 23 lines 1-11.
Claims 2, 6, 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran and Knox as applied to claims 1 and 11 above, and further in view of Dhokane et al. (Pub. No. US 2025/0036398 A1).
As to claims 2 and 12 Chandran as modified by Knox do not specifically disclose wherein the processing device is further configured for generating an updated IT infrastructure data based on the at least one IT specification data using a second LLM.
However, Dhokane discloses wherein the processing device is further configured for generating an updated IT infrastructure data based on the at least one IT specification data using a second LLM (Dhokane [0089] lines 1-25; which shows the use of its own machine learning models/second model that use input parameters that can include specifications associated with infrastructure to generate update/adjusted infrastructure and store updates in database thus as type of updated infrastructure data based on the input parameters/specification, that in light of the teachings of Chandran above can be the specifics of the IT specification data generated by its own machine learning/neural network model/first model and the teachings of Knox above shows the specifics of machine learning models can be large langue models).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Dhokane showing the specifics of a machine learning model to generate updated/modified infrastructure information based on input/specification data into the machine learning models used for determine IT infrastructure specification for updating IT infrastructure of Chandran as modified by Knox for the purpose of helping to maintain the accuracy of infrastructure data by updating it based on associated specification information, as taught by Dhokane [0089[ lines 23-28.
As to claims 6, and 16, Chandran as modified Knox does not specifically disclose, however Dhokane discloses wherein the processing device is further configured for deploying an updated IT infrastructure based on the updated IT infrastructure data (Dhokane [0084] lines 1-5, [0089] lines 1-25, [0090] lines 1-4, [0092] lines 1-4 and [0098] lines 1-6; which shows the initiations/deploying of the final build of the associated infrastructure based on infrastructure data/information stored in the database that can include the update/associated infrastructure information/data stored).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Dhokane showing the specifics of a machine learning model to generate updated/modified infrastructure information based on input/specification data into the machine learning models used for determine IT infrastructure specification for updating IT infrastructure of Chandran as modified by Knox for the purpose of helping to maintain the accuracy of infrastructure data by updating it based on associated specification information, as taught by Dhokane [0089[ lines 23-28.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran and Knox as applied to claims 4 and 14 above, and further in view of Eser et al. (Pub. No. US 2019/0370616 A1)
As to claims 8 and 18, Chandra as modified by Knox do not specifically disclose wherein the training of the first LLM comprises embedding at least one vector representation of at least one of the concept data, the computational logic data and the workflow data into a model architecture of the first large language model.
However, Eser discloses wherein the training of the first LLM comprises embedding at least one vector representation of at least one of the concept data, the computational logic data and the workflow data into a model architecture of the first large language model (Eser [0035] lines 14-18, [0070] lines 1-9 and [0077] lines 1-6; which shows that the training data used for the training of neural network/machine learning/statistical model is converted into embedding vectors that provided as input into the model, viewed as embedding the vector representation of training data into the model architecture of the learning model, where the specifics of the learning model being a large language model is seen in the specific teachings of Knox above and the specifics of the training data used for training the learning model being at least one of the concept data, the computational logic data and the workflow data is seen disclosed in the teachings of Chandran above).
Therefore, 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 Eser showing the specifics of incorporating training data in embedding vector representation into the use of training data to train machine learning models of Chandran as modified by Knox for the purpose of increase ease of comparison by representing data as vectors of real numbers to improve the ease of determine relationship between training variables as taught by Eser [0070] lines 1-9.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chandran, Knox and Eser as applied to claims 8 and 18 above, and further in view of Lin et al. (Pub. No. US 2026/0195583 A1)
As to claims 9 and 19, Chandran as modified by Knox and Eser do not specifically disclose wherein the training further comprises fine-tuning of the first LLM based on the embedding.
However, Lin discloses wherein the training further comprises fine-tuning of the first LLM based on the embedding (Lin [0040] lines 14-17 and [0046] lines 13-18; which shows that the specific distilled personalized training data can be a vector embedding of the training data where the model is further fine-tune trained using that distilled personalized training data thus viewed as based on the embedding of data where the specifics of the model being a large langue model is seen specifically disclosed in the teachings of Knox above).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate the teachings of Lin showing the specifics of using training data embedded in vector representation to further train the learning model into the training of the learning model of Chandran as modified by Knox and Eser for the purpose of improving the model for particular use by using additional training data to fine tune the model to be responsive to particular data, as taught by Lin [0004] lines 1-3.
Conclusion
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