DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is responsive to the communication received on 11/07/2023. The claims 1-20 are pending, of which the claim(s) 1, 9, & 18 is/are in independent form. The filing date of this application is 11/07/2023.
Specification
The disclosure is objected to because of the following informalities:
The para. 001 is missing application numbers for two applications. One of them appear to for 18/503,866 and another one appear to be for 18/503,898.
Similarly, the para. 046 is also missing application numbers.
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 2, 8 & 16 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.
Regarding claim 2, the claim recites “the response comprises a visualization generative by the generative AI system based on the package”. Here, the meaning of the phrase “visualization generative” is unclear in light of the specification. It appears that the para. [061] of the specification is the closest portion that elaborates this subject matter. Examiner notes that the phrase “visualization generative” is not the term of the art and is not being discussed elsewhere in the specification. In the claim, the phrase “visualization generative” is used in confusing manner and is not described as a part of the response provided by the generative AI. The para. 061 discusses it as “a suitable visualization” such as “bar graph, line graph” to be presented to a user. Therefore, the scope of the claim is indefinite/contradictory corresponding section of the specification.
For the examination purpose, response from the generative AI system and that can be presented to a user having the appropriate authority is interpreted as claimed “a visualization generative” as applied infra under art rejection section.
Regarding claims 8 & 16, these claims depend on claims 1 & 9 respectively but not with the claims 7 & 15. The claims 7 & 15 recite the limitation "the language framework" in line 1. However, there is insufficient antecedent basis for this limitation in the claim(s).
For the examination purpose, “the language framework” of these claims 8 & 16 is interpreted as “a language framework”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1- 2, 6, 9- 10, 14, & 17- 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ho (US 20250112768 A1, claims priority to US 63540938 filed on 2023-09-28) in view of Kattel (US 20250097022 A1, Foreign Priority Date: 2022-05-24), and in further view of SayyarRodsari et al. (US 20200103878 A1).
The combination of Ho, Kattel, and SayyarRodsari is referred as HKS hereinafter.
Regarding claim 1, Ho teaches a method, comprising:
receiving, via a processing system [computers used by the Ho], a request [“prompt” inputted by the user 118 to the “Role-Based Access Control (RBAC) enforcement component 116”. The user 118 can include any user including user managing/controlling an industrial automation system. When the user provides “prompt” while observing an industrial automation, such request/prompt can be called a request associated with an industrial automation system] for information item 118]; identifying, via the processing system, a prompt [“prompt” provided from RBAC 116 to the Retriever 112. The claim covers every possible types of the prompts] associated with the request (Fig. 1A, [040-041]);
identifying, via the processing system, one or more datasets [“encrypted and labeled data” as part of the contexts provided to the LLM 114] associated (Please note that the claim covers every possible type of association. “sending the encrypted and labeled data from the data stores 106 to the LLM 114 for context in prompts for inference”) with the request based on the prompt and the information; receiving [providing data from the primate data and index 110 to the retriever 112], via the processing system, the one or more datasets from one or more data sources [“data store 106 and private data 108”] (Fig. 1A, [035 - 038]);
formatting (the claim covers every possible formatting), via the processing system, the request and the one or more datasets The LLM 114 may generate responses based on the provided prompts and context”], via the processing system, the request and the one or more datasets 1“a generative AI model, such as the LLM 114”] (Fig. 1A, [035, 038, 047]); and
receiving, via the processing system, a response [response returned from LLM 114 to the RBAC 116, “generate responses based on the provided prompts and context”] from the generative AI system, wherein the response is configured to be presented via a display of a human machine interface (HMI) system [computer used by the RBAC enforcement 116] (Fig. 1A, [038, 040, 047, 061]).
Ho relates to performing data security while using a generative AI (LLM 114) and teaches of sending user’s request along with context data to the generative AI to receive a response in some format (Fig. 1A).
Ho teaches:
[0036] In some cases, the system 100 may include a retriever 112 that accesses the index 110 to obtain context information. The retriever 112 may be a software or hardware component that retrieves data from the data stores 106 based on the index 110. The retriever 112 may interact with the LLM 114, exchanging prompts, context, and responses. The LLM 114 may generate responses based on the provided prompts and context, which may include the encrypted and labeled data.
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However, Ho fails to teach the sent format for the “prompt” and the “context” is in the form of a package format as claimed. Thus, Ho fails to teach:
(1) its requested information from a user is “associated with an industrial automation system”
(2) its formatting of the request and the one or more datasets are into a package as claimed.
Kattel is in the field of data security that includes secure sending of communications comprising an encrypted data along with one or more prompt as a package across insecure communication channels (Abstract). Specifically, Kattel teaches a method comprising:
formatting, via the processing system, the request and the one or more datasets into a package [“the package comprises ciphertext 109 comprising the encrypted message 107 and plaintext 111 comprising the prompt text 102.”]; sending [“a package is transmitted across the insecure communication channel 110.”], via the processing system, the package to another computing device via an insecure communication channel ([015, 032]). Kattel cures the 2nd missing limitation of Ho.
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Kattel and Ho because they both related to sending encrypted dataset and user prompt(s) from a sending computer device to a data receiving computing device and (2) modify the steps of Ho to have its formatted the prompt and context sending to the LLM 114 in a package format as in Kattel. Doing so would allow sending of the prompt and context to the LLM 114 from retriever 112 even across an insecure communications channel so that the computer implementing the retriever 112 and the LLM 114 do not have to be located in the same location (Kattel [001] & Ho, Fig. 1A).
Ho in view of Kattel may not teach the received a request for information from a user is request for information associated with an industrial automation but this is cured by SayyarRodsari. That is, SayyarRodsari relates to utilizing generative Artificial intelligence (AI) by an operator of a processing system for monitoring and diagnostics one or more equipment of an industrial facility (Abstract, [002, 0135]). Specifically, SayyarRodsari teaches method, comprising:
receiving, via a processing system, a request for information [“receive a request for information regarding one or more particular industrial automation devices 20”] associated with an industrial automation system from a user ([091, 096]); and
receiving, via the processing system, a response from a generative AI system,
wherein the response is configured to be presented via a display of a human machine
interface (HMI) system ([099]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine SayyarRodsari and Ho in view of Kattel because they both related to a processing system to provide an answer/response from the AI module for a request/query made by an operator/user and (2) have the method of Ho in view of Kattel to provide request for information associated with an industrial automation system from a user as in SayyarRodsari. SayyarRodsari teaches missing details for Ho in view of Kattel about types of the environment where the responses from its LLM can be practically used to solve user 118’s real problems and the reasons (“root causes for detected issues (e.g., alarms)”) why the user 118 needs to send query to the generative AI (Ho, Fig. 1A & SayyarRodsari, [087, 0164]). Accordingly, HKS teaches each limitations of the claim and renders invention thereof obvious to PHOSITA.
Regarding claim 2, HKS further teaches/suggests method of claim 1, wherein the response comprises a visualization generative by the generative AI system based on the package [providing of the package with context and the request causes the response to be generated and returned] (Ho, [062] & SayyarRodsari [0100]).
Regarding claim 6, HKS teaches the method of claim 1, comprising:
generating one or more commands [“receive a command from the AI module 102”] based on the response; and sending [“send a command to the local control system 42 for the depositor 22 to adjust the operations”] the one or more commands to one or more industrial automation components of the industrial system, wherein the one or more commands are configured to cause the one or more industrial automation components to adjust one or more operations (SayyarRodsari 099-0101, 0109]).
Regarding claim 9, HKS teaches invention of this non-transitory computer-readable medium claim for the similar reasons set forth above in method claim 1.
Regarding claim 10, HKS further teaches the non-transitory computer-readable medium of claim 9, wherein the generative AI system [LLM 114 which is “trained on the encrypted and labeled data”] comprises a generative pre-trained model (Ho [036-037]).
Regarding claim 14, HKS further teaches the invention of this claim for the similar reasons set forth above in claim 6.
Regarding claim 17, HKS further teaches the non-transitory computer-readable medium of claim 9, wherein the one or more datasets comprises real-time data (Ho [029] & SayyarRodsari [0125]).
Regarding claim 18, the rejection of claim 1 is incorporated. Thus, Ho in view of Kattel teaches all limitations of the claimed system except its system comprising:
one or more industrial automation components of an industrial system configured to perform a batch operation. Ho in view of Kattel is silent about what types of environment its system is being utilized and what other types of the actions the user can perform after receiving the response from the LLM 114.
SayyarRodsari teaches a system [system shown in figs. 1- 2] comprising a processing system [Fig. 2, “exemplary control and monitoring system 50” that includes items 54+ 52] configured to receive one or more user query/request for information [“a request from a user…to determine a cause for downtime”] about an industrial system comprising an automation device [equipment 56] and providing responses [“answer to the request and send the solution back to the user”] to the requested query, wherein the received response is presented via a display [“the requested information via the display 86”] of a human machine interface (HMI) system ([007,099]). Specifically, SayyarRodsari teaches a system comprising: ([054- 055, 087-088]);
one or more industrial automation components [“automation equipment 56”] of an industrial system [system 10] configured to perform a batch operation [“manufacturing, processing, batch processing”] ([055, 064]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine SayyarRodsari and Ho in view of Kattel because they both related to a processing system receiving a query inputted by an operator for an AI module and to provide the response from the AI module and (2) modify the processing system of Ho in view of Kattel to be used to diagnosis health of one or more industrial automation components configured to perform a batch operation as in SayyarRodsari. SayyarRodsari teaches missing details for Ho in view of Kattel about types of the environment where the responses from its LLM can be practically utilized to solve user 118’s real problem and the reasons (“root causes for detected issues (e.g., alarms)”) why the user 118 needs to send query to the generative AI (Ho, Fig. 1A & SayyarRodsari, [087, 0164]). Accordingly, HKS teaches each elements of the claim and renders invention of this claim obvious to PHOSITA.
Regarding claim 19, HKS further teaches the system of claim 18, wherein the request for information comprises a root cause [“determine a root cause”] analysis inquiry (SayyarRodsari [042, 0147, 0164]).
Regarding claim 20, HKS further teaches the system of claim 18, wherein the operations comprise: generating one or more commands based on the response; and sending the one or more commands to the one or more industrial automation components of the industrial system, wherein the one or more commands are configured to cause the one or more industrial automation components to adjust [“control signals or commands to adjust one or more operations of the industrial automation equipment 56”] one or more operations (SayyarRodsari [099-0101, 0109]).
Claim(s) 3 & 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over HKS (as in claims 1 & 9) in view of Gupta et al. (US 20140358940 A1).
Regarding claim 3, HKS teaches a method comprising receiving a request for information and identifying a prompt to be used to query the generative artificial
intelligence (Al) system as discussed above (Ho, Fig. 1A).
HKS is silent about the request (for information) is updated based on the prompt as claimed.
Gupta is directed to identifying a prompt (“query template”, analogous to “prompt” provided to LLM 114 in Ho) to be used against a database [“content database 130”] to return search results to information requesting user(s) so that the results are presented via a display of a human machine interface (HMI) system [computing device 105 having a browser 110] (Abstract, fig. 1, [011]). Specifically, Gupta teaches a method comprising:
receiving, via a processing system [computing device 105], a request [“a partial query entered by a user”] for information from a user; identifying, via the processing system, a prompt [“match a submitted query to a query 2template and utilize the query template to determine one or more query suggestion”] associated with the request ([035, 041]),
wherein the request is updated [“enable the user to choose one of the query suggestions as a basis for utilization in a search” from the partial query. Here, the suggestions to be chosen by the users (after they enter partial query) are provided based on the query template] based on the prompt ([023, 041-042]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Gupta and HKS because they both related to providing a response to the user’s request using prompt templates and (2) modify the method/system of HKS to update the user request for information based on the prompt as in Gupta. Doing so would allow the system to receive complete form of requests for information even when users enter only partial request so that appropriate responses can be returned from the LLM 114 (Gupta [039]).
Regarding claim 11, HKS in view of Gupta teaches invention of this claim for the similar reasons set forth above in claim 3.
Claim(s) 4- 5 & 12- 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over HKS (as in claims 1 & 9) in view of Chakhvadze et al. (US 20250045023 A1, filing Date: 2023-07-31).
Regarding claim 4, HKS teaches a system/method comprising: identifying, via the processing system, a prompt associated with the request and sending the prompt to a generative artificial intelligence (Al) system to receive a response and outputting the response via a display of a human machine interface (HMI) system as discussed above.
However, HKS fails to teach the prompt comprises metadata associated with an expected response format as claimed.
Chakhvadze relates to sending one or more user requests to a generative AI system and the AI system to returning results for the requests. Specifically, Chakhvadze teaches a method/system comprising:
receiving, via a processing system, a request [“input data”] for information from a user and identifying, via the processing system, a prompt [“generating a prompt that includes a schema and the input data”] associated with the request ([017, 060]);
wherein the prompt comprises metadata [“prompt prefix can refer to a piece of text with one or more instructions that guide the large language models to generate responses in a desired data format”] associated with an expected response format ([022-023, 061-063]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Chakhvadze and HKS because they both related to providing prompts into the generating AI system to receive model responses and (2) modify the method/system of HKS to have the provided prompts to the LLM 114 to include metadata associated with an expected response format as in Chakhvadze. Doing so would allow the LLM of the HKS to return its responses in the user desired/defined output format to make interpreting the responses easier for the user (Chakhvadze [0023, 029]).
Regarding claims 5, HKS in view of Chakhvadze teaches/suggests the method of claim 4, wherein the generative AI system is configured to generate the response [“Outputs of the models can be error-corrected, aggregated, and converted into data objects in a (e.g., user-defined) object-oriented programming language format”] based on the expected response format (Chakhvadze [015, 029]).
Regarding claims 12- 13, HKS in view of Chakhvadze teaches/suggests inventions of these claims for the similar reasons as set forth above in claims 4-5.
Claim(s) 7-8 & 15- 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over HKS (as in claims 1 & 9) in view of Nguyen et al. (US 20250110948 A1).
Regarding claim 7, HKS teaches formatting input provided to the generative AI into a package as discussed above.
However, HKS fails to teach such formatting is performed by a language framework system.
Nguyen relates to use of generative artificial intelligence [AI model 226] to generate one or more response. Nguyen teaches a method comprising:
formatting, via a processing system, a request for information and one or more datasets into a package, wherein the package is formatted [“the ensemble tool model 208 and the NLP model 212 may be parser tools to parse natural language inputs and may be implemented as a component in the LangChain framework. The LangChain framework allows outputs to be in a suitable format, such as query instructions in an SQL format. The other database query tools 214-218 may be implemented as tools accessible in the LangChain framework in order to generate outputs in a suitable format for either generating query instructions or generating context to be provided to the generative AI model 226.”] by a language framework system [AI model 226, analogous to Ho’s LLM] (Fig. 2, [044-045, 054-055]).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to (1) combine Nguyen and HKS because they both related to sending of request and context into a generative AI to generate responses and (2) modify the method/system of HKS to use a language framework system such as that of LangChain system as in Nguyen. Doing so would allow the sent request and the context would be in the suitable format to be properly used at the LLM of the HKS (Nguyen, [044]). Furthermore, LangChain is an example well-known tool to be used to generate inputs to the LLM for improving LLM training, tuning, or prompting to realize better LLM outputs (Duggal, US 20240354567 A, para. [012]).
Regarding claim 8, HKS in view of Nguyen teaches The method of claim 1, wherein the language framework system comprises a LangChain system (Nguyen [044]).
Regarding claims 15- 16, HKS in view of Nguyen teaches inventions of these claims for the similar reasons set forth above in claims 7- 8 respectively.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1) Kasap (US 20240414048 A1) teaches using a pre-trained generative model to determine the root cause for an incident as recorded in event data of a system log (Abstract).
2) Bailey et al. (US 20250068881 A1) teaches a computer generates a response to the prompt, based on the modified prompt and the datasets, where the context-aware chatbot responds to the prompt with the response (Abstract).
Contacts
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANTOSH R. POUDEL whose telephone number is (571)272-2347. The examiner can normally be reached Monday - Friday (8:30 am - 5:00 pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SANTOSH R POUDEL/ Primary Examiner, Art Unit 2115
1 Just like in applicant’s system, in order not to expose “proprietary datasets and processes” to the generative AI system (like ChatGPT), the LLM 114 of Ho are also provided only the encrypted datasets.
2 Examiner notes that applicant’s specification in para. 049 states that the prompt can include “template structure” in para. 049 as in Gupta.