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 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:
At step 1:
Claims 1-20 is directed to a “hybrid Architecture for Artificial intelligence with iterative ” and thus directed to a statutory category.
At step 2A, Prong One:
The claims 1 and 19-20 recite the following limitation directed to an abstract ideas:
“receive an input” recites a mental process as receiving an input.
“apply a local Al model to the input to generate a response to the input” recites a mental process as apply schema or model to the input to generate a response to the input.
“when a confidence of the response satisfies one or more criteria, output the response; and when the confidence of the response does not satisfy the one or more criteria, escalate the input to a global Al model that is remote from the Al application, receive a global insight from the global Al model, Integrate the global insight into the response to produce a refined response, update a local knowledge base of the local Al model based on the global insight, and output the refined response” recites a mental process as when comparing a confidence of the response satisfies one or more criteria, output the response; and when the confidence of the response does not satisfy the one or more criteria, escalate the input to a global Al model that is remote from the Al application, receive a global insight from the global Al model, integrate the global insight into the response to produce a refined response, update a local knowledge base of the local Al model based on the global insight, and output the refined response.
The claims 2-5, 7, and 10-11 recite the following limitation directed to an abstract ideas:
With respect to claim 2, the claim recites “: receive feedback for the refined response; and provide the feedback to the global Al model.” It is abstract ideas. The claims recites a mental process receiving feedback for the refined response; and provide the feedback to the global Al model.
With respect to claim 3, the claim recites “wherein the global Al model is a large language model, and wherein the local Al model is a small language model”. The claim recites mental process the global Al model or model or schema or map are a large language model, and wherein the local Al model or schema or map is a small language model.
With respect to claim 4, the claim recites “wherein the small language model is distilled from the large language model”. The claim recites mental processing listing the small language model is distilled from the large language model.
With respect to claim 5, the claim recites “the Al application is an Al agent that comprises the local Al model.”. The claim recites mental process considering the Al application is an Al agent that comprises the local Al model..
With respect to claim 7, the claim recites “the local AI model outputs a confidence value for the response to the input, and wherein the confidence of the response satisfies the one or more criteria when the confidence value satisfies a confidence threshold, and does not satisfy the one or more criteria when the confidence value does not satisfy the confidence threshold”. The claim recites mental process reading the local AI model outputs a confidence value for the response to the input, and wherein the confidence of the response satisfies the one or more criteria when the confidence value satisfies a confidence threshold, and does not satisfy the one or more criteria when the confidence value does not satisfy the confidence threshold.
With respect to claim 10, the claim recites “escalating the input to the global AI model comprises sending a request to the global AI model, wherein the request comprises the input, a context window of the local AI model, and the response generated by the local AI model. The claim recites mental process escalating the input to the global AI model comprises sending a request to the global AI model, wherein the request comprises the input, a context window of the local AI model, and the response generated by the local AI model.
With respect to claim 11, the claim recites “integrating the global insight into the response comprises, in each of one or more sub-iterations, applying the local AI model to relevant data, comprising the input and the global insight, to generate a new response; when the confidence of the new response does not satisfy the one or more criteria, escalating the input to the global AI model, and receiving a new global insight from the global AI model; and when the confidence of the new response satisfies the one or more criteria, ending the one or more sub-iterations, and outputting the new response as the refined response.”. It is mental processing integrating the global insight into the response comprises, in each of one or more sub-iterations, applying the local AI model to relevant data, comprising the input and the global insight, to generate a new response; when the confidence of the new response does not satisfy the one or more criteria, escalating the input to the global AI model, and receiving a new global insight from the global AI model; and when the confidence of the new response satisfies the one or more criteria, ending the one or more sub-iterations, and outputting the new response as the refined response.
At step 2A, Prong Two:
The claims recite the following additional elements:
That the content management system includes “server” “resource”, which are high level recitation of generic computer component s and functions and represent mere instruction to apply to a computer as in MPEP 2106.05 (f) which does not provide integration into a practical application.
At step 2B
The conclusions for the mere implementation using a generic computer and mere field of use are carried over and to not provide significantly more.
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.
Claims 1, 2, 5, 7-11, 19-20 are rejected under 35 U.S.C 103 as being unpatentable over Zhang et al. (U.S. pub. 2023/0336436 A1) in view of Pagan et al.(U.S. Pub. 2016/0104016 A1)
With respect to claim 1 and 19-20, Zhang et al. discloses a method comprising using at least one hardware processor to, during a real-time chat session between a user and an artificial intelligence (Al) application, by the Al application, in each of one or more iterations (i.e., “A computing node sends a first parameter to some or all of K subnodes in a t.sup.th round of iteration, where the first parameter includes a first global model and a first timestamp t−1, the first global model is a global model generated by the computing node in a (t−1).sup.th round of iteration , t is an integer greater than or equal to 1, and the K subnodes are all subnodes that participate in model training. The computing node receives, in the t.sup.th round of iteration, a second parameter sent by at least one subnode, where the second parameter includes a first local model and a first version number t′, the first version number indicates that the first local model is generated by the subnode through training, based on a local dataset, a global model received in a (t′+1).sup.th round of iteration, the first version number is determined by the subnode based on a timestamp received in the (t′+1).sup.th round of iteration, 1≤t′+1≤t, and t′ is a natural number”(0006)):
receive an input (i.e., “, including an input circuit, an output circuit, and a processing circuit. The processing circuit is configured to receive a signal through the input circuit, ”(0034) and “the input circuit may be an input pin, the output circuit may be an output pin, and the processing circuit may be a transistor, a gate circuit, a trigger, various logic circuits, or the like. The input signal received by the input circuit may be received and i input by, for example, but not limited to, a receiver, the signal output by the output circuit may be output to, for example, but not limited to, a transmitter and transmitted by the transmitter, and the input circuit and the output circuit may be a same circuit, where the circuit is used as the input circuit and the output circuit at different moments.”(0035));
apply a local Al model to the input to generate a response to the input (fig. 3 shows step S320 train, based on a local dataset, the first global model or a global model received before the first global , to generate a first local model);
when a confidence of the response satisfies one or more criteria, output the response (i.e., “A communication apparatus triggers, by setting a threshold (a time threshold and/or a count threshold), fusion of a local model sent by a terminal device, to generate a global model, and when a fusion weight of the local model is designed, a data feature included in the local model of the terminal device, a lag degree, and a utilization degree of a data feature of a sample set of the corresponding terminal device are comprehensively considered, so that a problem of low training efficiency caused by a synchronization requirement for model uploading versions in a synchronous system can be avoided, and a problem of unstable convergence and a poor generalization capability caused by an “update upon reception” principle of an asynchronous system can be avoided”(abstract) and triggers is output as claimed invention); and
when the confidence of the response does not satisfy the one or more criteria, escalate the input to a global Al model that is remote from the Al application, receive a global insight from the global Al model, integrate the global insight into the response to produce a refined response (i.e., “A communication apparatus triggers, by setting a threshold (a time threshold and/or a count threshold), fusion of a local model sent by a terminal device, to generate a global model, and when a fusion weight of the local model is designed, a data feature included in the local model of the terminal device, a lag degree, and a utilization degree of a data feature of a sample set of the corresponding terminal device are comprehensively considered, so that a problem of low training efficiency caused by a synchronization requirement for model uploading versions in a synchronous system can be avoided, and a problem of unstable convergence and a poor generalization capability caused by an “update upon reception” principle of an asynchronous system can be avoided”(abstract), update a local knowledge base of the local Al model based on the global insight (i.e., “It should be understood that, the updated first version number corresponding to the first local model generated by the client k using the first global model is different from that corresponding to the first local model generated by the client k using the third model. Details are not described herein again”(0105) or “When the central server is triggered to perform model fusion, the central server fuses m received first local models according to the model fusion algorithm, to generate the second global model, and updates the timestamp to =t (that is, a second timestamp), 1≤m≤K, and m is an integer.”(0115)), and
output the refined response (i.e., “ S340: The central end executes a central-end model fusion algorithm based on the received second parameter (that is, a local training result of each client) that is uploaded by the at least one client, to generate a second global model.”(0114)).
But Zhang et al. does not discloses , during a real-time chat session between a user and an artificial intelligence (Al) application, by the Al application. However, Pagan et al. discloses , during a real-time chat session between a user and an artificial intelligence (Al) application, by the Al application (i.e., “a user may open a content management service and input a prompt in an input or chat box to obtain corresponding intermediate response data and an AI response.”(0029) and “in instances in which a user more readily understands the intermediate response data used in facilitating an AI response, the need to generate additional AI queries and/or responses may be reduced as the user may be more confident in the initial AI response presented. As such, embodiments described herein reduce utilization of computer resources that would otherwise be used to repetitively generate AI responses (e.g., in an effort to ensure the user trusts the response).”(0018)).
It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Pagan et al.’s feature in order to efficiently and effectively provide intermediate response data for presenting via a user interface for the stated purpose has been well known in the art as evidenced by teaching of Pagan et al. (0003).
With respect to claim 2, Pagan et al. discloses further comprising using the at least one hardware processor, during the real-time chat session, by the Al application, in each of the one or more iterations (i.e., “the context manager 222 (e.g., via AI) may use previous interactions or additional contextual information provided in the session to refine its skill selection”(0049)), and when the confidence of the response does not satisfy the one or more criteria: receive feedback for the refined response; and provide the feedback to the global Al model. (i.e., “Such feedback may be particular to a current user input prompt and corresponding AI response, a current session, or any session for the user”(0075)) , further Zhang et al. discloses when the confidence of the response does not satisfy the one or more criteria: receive feedback for the refined response; and provide the feedback to the global Al model (fig. 3 shows step S330 send a second parameter, where the second parameter includes first local model and a a first version number and S340 execute a central end model fusion algorithm based on received second parameter (feedback as claimed invention) that is uploaded by at leat one client to generate a second global model including the 1th round of interaction)(with motivation above).
With respect to claim 5, Zhang et al. discloses The method of Claim 1, wherein the Al application is an Al agent that comprises the local Al model (i.e., “In this embodiment of this application, the subnode may be user equipment (UE), an access terminal, a subscriber unit, a subscriber station, a mobile station, a mobile console, a remote station, a remote terminal, a mobile device, a user terminal, a terminal, a wireless communication device, a user agent, or a user apparatus”(0062) and “2) In a t.sup.th∈[1, T] round, a client k∈[1, K] trains a received global model w.sub.g.sup.t−1 based on a local dataset [AltContent: rect].sub.k for E epochs, to obtain a local training result w.sub.k.sup.t. [0070] (3) A server of the central end collects and summarizes local training results from all (or some) clients.”(0069)).
With respect to claim 7, Zhang et al. discloses wherein the local AI model outputs a confidence value for the response to the input, and wherein the confidence of the response satisfies the one or more criteria when the confidence value satisfies a confidence threshold, and does not satisfy the one or more criteria when the confidence value does not satisfy the confidence threshold i.e., “A communication apparatus triggers, by setting a threshold (a time threshold and/or a count threshold), fusion of a local model sent by a terminal device, to generate a global model, and when a fusion weight of the local model is designed, a data feature included in the local model of the terminal device, a lag degree, and a utilization degree of a data feature of a sample set of the corresponding terminal device are comprehensively considered, so that a problem of low training efficiency caused by a synchronization requirement for model uploading versions in a synchronous system can be avoided, and a problem of unstable convergence and a poor generalization capability caused by an “update upon reception” principle of an asynchronous system can be avoided”(abstract).
With respect to claim 8, Zhang et al. discloses wherein escalating the input to the global AI model comprises establishing a connection with a global AI application that comprises the global AI model, via an application programming interface of the global AI application (i.e.,” when the server delivers a global model by broadcasting, an available bandwidth may be set to B, and transmit powers of the server and each client device are respectively P.sub.s and P.sub.c. It is assumed that an iteration period of each local training performed by the client is E epochs, each sample needs to consume C floating-point operations during training, and a CPU frequency of each client device is f.”(0083)).
With respect to claim 9, Zhang et al. discloses wherein the method of Claim 8, wherein the connection is an asynchronously coupled connection (i.e., “a method for semi-asynchronous federated learning is provided, may be applied to a subnode, or may be applied to a component (for example, a chip, a chip system, or a processor) in the subnode.”(0015)).
With respect to claim 10, Zhang et al. discloses wherein escalating the input to the global AI model comprises sending a request to the global AI model, wherein the request comprises the input, a context window of the local AI model (i.e., “The subnode trains, based on a local dataset, the first global model or a global model received before the first global model, to generate a first local model.”(0015) and local data set is context window of the local ai model as claimed invention), and the response generated by the local AI model (S320 to generate a first local model, fig. 3).
With respect to claim 11, Zhang et al. discloses wherein integrating the global insight into the response comprises, in each of one or more sub-iterations (i.e., “The computing node sends the second contribution vector to some or all subnodes of the K subnodes in the (t+1).sup.th round of iteration.”(0011)), applying the local AI model to relevant data, comprising the input and the global insight, to generate a new response (fig. 3 shows step S320 including train, based on a local dataset and the first global model or global model received before the first global model to generate a first local model); when the confidence of the new response does not satisfy the one or more criteria, escalating the input to the global AI model, and receiving a new global insight from the global AI model (i.e., “A communication apparatus triggers, by setting a threshold (a time threshold and/or a count threshold), fusion of a local model sent by a terminal device, to generate a global model, and when a fusion weight of the local model is designed, a data feature included in the local model of the terminal device, a lag degree, and a utilization degree of a data feature of a sample set of the corresponding terminal device are comprehensively considered, so that a problem of low training efficiency caused by a synchronization requirement for model uploading versions in a synchronous system can be avoided, and a problem of unstable convergence and a poor generalization capability caused by an “update upon reception” principle of an asynchronous system can be avoided”(abstract); and when the confidence of the new response satisfies the one or more criteria, ending the one or more sub-iterations, and outputting the new response as the refined response (i.e., “It should be understood that, the updated first version number corresponding to the first local model generated by the client k using the first global model is different from that corresponding to the first local model generated by the client k using the third model. Details are not described herein again”(0105) or “When the central server is triggered to perform model fusion, the central server fuses m received first local models according to the model fusion algorithm, to generate the second global model, and updates the timestamp to =t (that is, a second timestamp), 1≤m≤K, and m is an integer.”(0115)), .
Claims 3-4 are rejected under 35 U.S.C 103 as being unpatentable over Zhang et al. (U.S. pub. 2023/0336436 A1) , Pagan et al.(U.S. Pub. 2016/0104016 A1) and further in view of Angeles et al.(U.S. Pub. 2026/0037863 A1)
With respect to claim 3, Zhang and Pagan et al. disclose all limitation recited in claim 1 except for wherein the global Al model is a large language model, and wherein the local Al model is a small language model. However, Angeles et al. discloses wherein the global Al model is a large language model (i.e., “The architecture of such a personalized digital twin LLM chatbot assistant is schematically illustrated in FIG. 3A. The digital twin assistant includes the following components. User data collection layer, which gathers and processes user data to create detailed and accurate personas. Multiple persona modules, including separate and dedicated modules for each persona, including the game developer. Each module is trained with persona-specific data and objectives. LLM core engine, which is the central AI engine capable of language understanding, generation, and creative thinking”(0042)), and wherein the local Al model is a small language model. (i.e., “ Running an LLM (Large Language Model) or SLM (Small Language Model) on a user's local computer has several benefits that can significantly enhance the user's control, privacy, and responsiveness when interacting with the chatbot. Here are some of the key advantages”(0047)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Angeles et al.’s feature in order to have both AI and real in achieving the goals efficiently and seamless
for the stated purpose has been well known in the art as evidenced by teaching of Angeles et al. (0009).
With respect to claim 4, Angeles et al. discloses the method of Claim 3, wherein the small language model is distilled from the large language model (i.e., “Here are a few SLM options that are often considered affordable to use: 1. GPT-Neo and GPT-J by EleutherAI: These models are open-source and provide various sizes, from smaller models like GPT-2 (124M parameters) to larger ones like GPT-J (6B parameters). They are known for being cost-effective and suitable for many applications. 2. BERT and , DistilBERT by Hugging Face: BERT (Base and Large versions) and its distilled version, DistilBERT, are widely used for NLP tasks. , DistilBERT, in particular, is designed to be more efficient and less resource-intensiv”(0438)).
Claims 6 and 12 are rejected under 35 U.S.C 103 as being unpatentable over Zhang et al. (U.S. pub. 2023/0336436 A1) , Pagan et al.(U.S. Pub. 2016/0104016 A1) and further in view of Angeles et al.(U.S. Pub. 2026/0037863 A1)
With respect to claim 6, Zhang and Pagan discloses all limitations recited in claim 5 except for wherein the AI agent is executed within a runtime engine on an on-premise system, and wherein the global AI model is executed within a computing cloud that is remote from the on-premise system , however, Angeles et al. discloses wherein the AI agent is executed within a runtime engine on an on-premise system, and wherein the global AI model is executed within a computing cloud that is remote from the on-premise system (i.e., “By running the LLM or SLM locally, all data processing occurs on the user's own device. This setup reduces the risk of data breaches that are more prevalent with cloud-based solutions, where data must be transmitted over the internet”(0487) and “ Default-game scenes and customizable game environments created by Personalized Digital Twin LLM Chatbot Assistants or AI agents.’(0008)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Angeles et al.’s feature in order to have both AI and real in achieving the goals efficiently and seamless with fast and instant transfer for the stated purpose has been well known in the art as evidenced by teaching of Angeles et al. (0009).
With respect to claim 12, Angeles et al. discloses wherein applying the local AI model to the input comprises: processing the input to generate a search query; querying the local knowledge base using the search query to retrieve relevant data (i.e., “ Validating Knowledge Vector Database Status: Perform test queries to ensure that the vectors have been correctly indexed and are retrievable. This can involve querying with known vectors and checking if the database returns the expected results. Monitor the database's performance metrics, such as query latency and throughput, to ensure that it meets the application's requirements.”(0260)); generating a prompt based on the input and the relevant data (i.e., “ Validating Knowledge Vector Database Status: Perform test queries to ensure that the vectors have been correctly indexed and are retrievable. This can involve querying with known vectors and checking if the database returns the expected results. Monitor the database's performance metrics, such as query latency and throughput, to ensure that it meets the application's requirements.”(0260)); ; and inputting the prompt to the local AI model to generate the response to the input. (i.e., “Vector Search for Retrieval: When the chatbot receives a query, convert the query into a vector embedding using the same NLP model utilized for the knowledge base. Perform a vector similarity search within the indexed knowledge base to find the most relevant information. This process involves calculating the similarity between the query vector and the indexed embeddings, typically using cosine similarity or another appropriate metric.”(0264)) (with same motivation above)
Claims 13-15 and 17 are rejected under 35 U.S.C 103 as being unpatentable over Zhang et al. (U.S. pub. 2023/0336436 A1) , Pagan et al.(U.S. Pub. 2016/0104016 A1) and further in view of Wang et al.(U.S. Pub. 2025/0338239 A1)
With respect to claim 13, Zhang and Pagan discloses all limitations recited in claim 1 except for wherein the AI application is a local AI application; wherein escalating the input to the global AI model comprises establishing a connection with a global AI application that comprises the global AI model, via an application programming interface of the global AI application, and sending a request to the global AI application, and wherein the method further comprises, by the global AI application: receiving the request from the local AI application; applying the global AI model to the request to generate the global insight; and sending the global insight to the local AI application. However, Wang et al. discloses wherein the AI application is a local AI application (i.e., “ transmit, to a further device, a second request for second registration of an AI client on the device and of at least one AI entity including the AI entity.”(0002) and “study whether and how 5GS provides assistance to an AF and the UE for the AF and UE to manage the FL operations and model distribution/redistribution (e.g., FL members selection) to facilitate collaborative application AI based on Federated Learning operations between the application clients running on the UEs (i.e., FLCs) and the Application Servers (i.e., the FLS”(0110)); wherein escalating the input to the global AI model comprises establishing a connection with a global AI application that comprises the global AI model (fig. 4 shows s403 initial Global model from FLS (Federated Learning Server) is transmitted (connection) to local training on Federated Learning client), via an application programming interface of the global AI application and and “study whether and how 5GS provides assistance to an AF and the UE for the AF and UE to manage the FL operations and model distribution/redistribution (e.g., FL members selection) to facilitate collaborative application AI based on Federated Learning operations between the application clients running on the UEs (i.e., FLCs) and the Application Servers (i.e., the FLS”(0110)), and sending a request to the global AI application (i.e., “In step S908, the AIEC 903 sends a request to the AIES 905 to register itself and one or multiple AIA PPs”(0177) and fig. 9 shows AIEC 903 (client) sent request S908 to AIES 095), and wherein the method further comprises, by the global AI application: receiving the request from the local AI application (i.e., “In step S908, the AIEC 903 sends a request to the AIES 905 to register itself and one or multiple AIA PPs”(0177) and fig. 9 shows AIEC 903 (client) sent request S908 to AIES 095); applying the global AI model to the request to generate the global insight (fig. 4 shows global model update to FLC (Federated Learning Client)); and sending the global insight to the local AI application (fig. 4 shows global model update to FLC (Federated Learning Client)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Wang et al.’s feature in order to have speed and accuracy are paramount for the stated purpose has been well known in the art as evidenced by teaching of Wang et al. (0414).
With respect to claim 14, Wang et al. discloses the method of Claim 13, wherein the request comprises the response generated by the local AI model, and wherein the method further comprises, by the global AI application, analyzing the request to identify a local insight from the response (i.e., “The AIES 905 may approve one or multiple services as requested in Requested-Service-List. All approved services are contained in Approved-Service-List. The AIES 905 may (re) assign a unique identifier for the AIEC 903, referred to as AIEC-ID. The AIES 905 may create a new AIEC record for the registered AIEC 903, which may include AIEC-ID, Approved-Service-List, and/or other parameters received in the request in step S908. This new AIEC record will be added to the AIEC repository.”(0185)) (with same motivation above).
With respect to claim 15, Wang et al. discloses the method of Claim 14, further comprising, by the global AI application, updating a global knowledge base based on the local insight (i.e., “his parameter could indicate but is not limited to: 1) Computing resource budget for running AI tasks; 2) Storage resource budget for running AI tasks; 3) Training data properties such as the number of data samples and the number of features in a data sample; and 4) Input data properties (used for knowledge inference) such as the number of data samples and the number of features in a data sample. ”(0177)).
With respect to claim 17, Zhang et al. discloses the method of Claim 13, further comprising, by the global AI application: receiving feedback from the local AI application ; and updating one or both of the global AI model or a global knowledge base based on the feedback (fig. 3 shows step S330 send a second parameter, where the second parameter includes first local model and a a first version number and S340 execute a central end model fusion algorithm based on received second parameter (feedback as claimed invention) that is uploaded by at leat one client to generate a second global model including the 1th round of interaction).
Claim 16 is rejected under 35 U.S.C 103 as being unpatentable over Zhang et al. (U.S. pub. 2023/0336436 A1) , Pagan et al.(U.S. Pub. 2016/0104016 A1) , Wang et al.(U.S. Pub. 2025/0338239 A1) and further in view of Whitenack (U.S. Pub. 2025/0371192 A1)
With respect to claim 16, Wang et al. discloses wherein applying the global AI model to the request comprises: processing the request to generate a search query; querying a global knowledge base using the search query to retrieve relevant data (i.e., “Once an AIEC receives such DSS information, the AIEC can directly publish any information about itself, the AIA , the AI task hosted by the AIA , the AIA PP, any produced model, and/or any produced knowledge to the DSS. The AIEC can also directly look up and search the information about other AIECs from the DSS and those repositories. After the AIEC discovers such information, the AIEC can forward the discovered information to the AIES.”(0159)); but Zhang, Pagan and Wang does not disclose generating a prompt based on the request and the relevant data; and inputting the prompt to the global AI model to generate the global insight. However, Whitenack discloses generating a prompt based on the request and the relevant data (i.e., “It ensures that the prompts sent to model applications 114 are in a format that can be readily understood and acted upon by the AI models, such as JSON for structured data queries or plain text for language generation tasks..” (0074)); and inputting the prompt to the global AI model to generate the global insight (i.e., “Also not explicitly pictured, the model application 114 may include one or more modules or model implementations that allow the execution of models that is end-to-end encrypted. Specifically, these modules may allow the prompts sent from the client application 128 to be encrytped in the client application 128 and allow the models in the model application 114 to process the prompts without decrypting them. In this way, the data sent from the client application can be sent to the server device 100 without comprising the privacy of the data..”(0079))). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Whitenack’s feature in order to easy maintain API usage and compliance for the stated purpose has been well known in the art as evidenced by teaching of Whitenack. (0004).
Claim 18 is rejected under 35 U.S.C 103 as being unpatentable over Zhang et al. (U.S. pub. 2023/0336436 A1) , Pagan et al.(U.S. Pub. 2016/0104016 A1) , Wang et al.(U.S. Pub. 2025/0338239 A1) and further in view of Touati et al. (U.S. Pub. 2023/0393832 A1)
With respect to claim 18, Zhang, Pagan and Wang et al. disclose all limitations recites in claim 13 except for wherein the global AI application resides in a computing cloud that hosts an integration platform as a service (iPaaS) platform. However, Touati et al. discloses wherein the global AI application resides in a computing cloud that hosts an integration platform as a service (iPaaS) platform (i.e., “This dynamic QA can make use of the Integration Platform as a Service (IPaaS) and other standards for dynamically recording system status and integrations such as IT service management (ITSM) and others.”(0184)). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the claimed invention, to include Touat’s feature in order to have different dynamically recording system status and integration for the stated purpose has been well known in the art as evidenced by teaching of Touat (0184).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HUNG T VY whose telephone number is (571)272-1954. The examiner can normally be reached M-F 8-5.
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/HUNG T VY/Primary Examiner, Art Unit 2163 June 10, 2026
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