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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7, 9-17 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ma et al. CN-117556864-A.
As per claim 1, Ma et al teaches a computing system comprising:
a processor; and memory storing instructions that, when executed by the processor, cause the processor to perform acts comprising: providing input to a first machine learning model, where the first machine learning model outputs a directed acyclic graph that includes nodes and edges based upon the input, where the nodes represent steps of a multi-step task to be performed by a computer-executable agent and the edges represent relationships between the steps large language model used to take again, directed graph (page 4); intelligent body driven by the large model simulates that interaction between human and external environment (page 5); flow chart may be a directed graph (page 6 ,paragraph 2)];
providing a step in the multi-step task to a second machine learning model, where the step is represented by a node in the acyclic graph, where the second machine learning model outputs an action based upon the step, where the action is to be performed by the computer-executable agent to complete the step [after obtaining the directed graph, converted into structured graph instruction input to large model for processing (page 6 paragraph 2); process the task (page 6 pp 5, page 11); directed graph (page 11-12 – Figures 3-4); ID of node not repeated (page 22)];
transforming the action into computer-executable code that is to be executed by the computer-executable agent [process the task (page 6); after obtaining flow chart, flow chart can be processed (page 11); calling the A (page 16-17, page 25)]; and
performing, by the computer-executable agent, the action based upon the computer-executable code, where the computer-executable agent completes the multi-step task based upon performance of the action [finish ETC transaction (page 12-13); finish the task (page 19-20); successful completion of the task (page 21)].
As per claim 2, Ma teaches the computing system of claim 1, where the computing system is a client computing device, and further where providing the input to the first machine learning model comprises transmitting the input to a server computing system that is in network communication with the client computing device [framework of system between client and system (Figure 5, page 23)].
As per claim 3, Ma teaches the computing system of claim 2, where the second machine learning model executes on the client computing device [client in the system (page 6, page 8, page 13)].
As per claim 4, Ma teaches the computing system of claim 1, where providing the input to the first machine learning model comprises constructing a prompt, where the prompt includes an instruction for the first machine learning model to output the directed acyclic graph [input prompt information includes node sequence information (page 12) API information (page 14); page 17].
As per claim 5, Ma teaches the computing system of claim 4, where the first machine learning model is a generative model [large language model (page 4)].
As per claim 6, Ma teaches the computing system of claim 1, where providing the step in the multi-step task to the second machine learning model comprises constructing a prompt, where the prompt instructs the second machine learning model to output a sequence of actions that complete the step [output prompt information (paged 16-17), prompt information (page 19)].
As per claim 7, Ma teaches the computing system of claim 6, where the prompt includes identities of functions that are available to the computer-executable agent to complete at least one action in the sequence of actions [information of each API input as prompt information (page 15)].
As per claim 9, Ma teaches the computing system of claim 8, where the prompt additionally includes identities of previous actions performed by the computer-executable agent in connection with completing the multi-step task [determining whether previous round is executed according to the interaction information of the previous round (page 8, finish processing task through interaction of multiple turns (page 8; page 9, pp 5-8), page 9].
As per claim 10, Ma teaches the computing system of claim 1, where the computer-executable agent fails to complete the multi-step task subsequent to performing the action, the acts further comprising: providing a prompt to the first machine learning model, where the first machine learning model outputs a second directed acyclic graph based upon the prompt, where the second directed acyclic graph includes second nodes and second edges, where the second nodes represent second steps of the multi-step task to be performed by the computer-executable agent, where the second steps are non-identical to the steps [in each round agent interacts and has multiple rounds of interaction finishing the task processing (page 9, pp 5-8); finish processing task through interaction of multiple turns (page 8)].
Claims 11-17 and 19-20 are rejected, mutatis mutandis, under the same rationale as claims 1-7 and 10 as they do not further limit or define over the claims.
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 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. CN-117556864-A in view of Nychis et al. United States Patent Application Publication No. 20250335219.
As per claim 8, Ma teaches he computing system of claim 7. Ma does not explicitly teach the acts further comprising: obtaining an image of a graphical user interface (GUI) of an application being executed by the computing system; and providing the image to a third machine learning model, where the third machine learning model is configured to identify elements in the GUI that are interactive, where the prompt includes the elements in the GUI that are identified as being interactive by the third machine learning model.
However, in analogous art, Nychis teaches obtaining an image of a graphical user interface (GUI) of an application being executed by the computing system; and providing the image to a third machine learning model, where the third machine learning model is configured to identify elements in the GUI that are interactive, where the prompt includes the elements in the GUI that are identified as being interactive by the third machine learning model [object detection model (pp 0036); performing the process includes gathering screenshots (pp 0060-63, 112-117); performing the task (pp 0079-0080, 105-107)].
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify acts of Ma with identify GUI elements of Nychis. A person of ordinary skill in the art would have been motivated to do this to visualize user interaction with the screen.
Claim 18 is rejected, mutatis mutandis, under the same rationale as claim 8 as it does not further limit or define over the claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yusuf et al. United States Patent Application Publication No. 2023/0036167 teaches a concierge network.
Rao et al. United States Patent Application Publication No. 2024/0403652 teaches controlling an agent to perform a task.
Tan et al. United States Patent Application Publication No. 2025/0285055 teaches generating and executing plans for tasks.
Kannaiyan United States Patent Application Publication No. 2025/0203669 teaches synchronizing intent and execution across distributed interfaces.
Galvin et al. United States Patent Application Publication No. 2026/0212130 teaches natural human control of DCE.
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UZMA . ALAM
Supervisory Patent Examiner
Art Unit 2877
/UZMA ALAM/Supervisory Patent Examiner, Art Unit 2884
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