DETAILED ACTION
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 § 112
Claims 1-18 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 per claim 1:
On line 1, “AI” should be changed to - -Artificial intelligence (AI) - -
On line 10, It is unclear of what is meant by “a set of relevant data providers”. Also on line 10 “the task instructions” lacks clear antecedent basis.
As per claim 8, line 3, the acronym “URI” should correctly be spelled out.
As per claim 11, line 3, the acronym “URI” should correctly be spelled out.
As per claim 12, lines 2-3, the phrase “”user’s digital asset portfolio data” lacks clear antecedent basis.
As per claim 16:
On line 1, “AI” should be changed to - -Artificial intelligence (AI) - -
On line 6, it is unclear of what is meant by “a set of relevant data providers”. Also on line 6 “the task instructions” lacks clear antecedent basis.
As per claim 17:
On line 1, “AI” should be changed to - -Artificial intelligence (AI) - -
On line 8, It is unclear of what is meant by “a set of relevant data providers”.
Claims not specifically addressed are rejected based on their dependencies.
The prior art taken alone or in combination failed to teach or suggest:
“obtain via the orchestration artificial intelligence engine, for each respective relevant data provider in the set of relevant data providers, relevant on-demand data from the respective data provider via the function corresponding to the respective relevant data provider, verify that a subtask execution generative AI engine for the task is authorized to use entity data associated with an entity or a user specified via the AI data determining request datastructure, obtain via the orchestration artificial intelligence engine, relevant entity data accessible by the user, and composite via the orchestration artificial intelligence engine, execution context data for the task from the retrieved relevant historical data, the obtained relevant on-demand data, and the obtained relevant entity data” as recited”, as recited in independent claim 1.
“obtain via the orchestration artificial intelligence engine, for each respective relevant data provider in the set of relevant data providers, relevant on-demand data from the respective data provider via the function corresponding to the respective relevant data provider, verify that a subtask execution generative AI engine for the task is authorized to use entity data associated with an entity or a user specified via the AI data determining request datastructure, obtain via the orchestration artificial intelligence engine, relevant entity data accessible by the user, and composite via the orchestration artificial intelligence engine, execution context data for the task from the retrieved relevant historical data, the obtained relevant on-demand data, and the obtained relevant entity data”, as recited in independent claims 16 and 17.
The above recited limitations provide meaningful limitations that transforms the abstract idea into patent eligible. The claim as a whole effects an improvement to another technology or technical field. These limitations in combination provide meaningful limitations beyond generally linking the use of the abstract idea to a practical application.
Bathwal et al (US Pub. No. 20240281446 A1) disclose an advanced search system which leverages a pre-trained large language model to enhance user query responses. The system, equipped with hardware processors, a search query via an interface and accesses a pre-trained large language model designed to respond to the search query. The system fine-tunes the model to generate a task-specific generative model. The system employs the task-specific generative model to generate a search result to the search query and analyzes the search result based on a performance metric associated with the task-specific generative model. The system refines the task-specific generative model based on the analyzing of the search result.
McCoy (US Pub. No. 20250217772 A1) discloses a system includes a memory and one or more processors coupled to the memory, the one or more processors are configured to: generate, by a first artificial intelligence model receiving as an input a first object including a first textual description of an entity, one or more first metrics descriptive of the entity; generate, by a second artificial intelligence model receiving as an input one or more of the first metrics, a second object including a second textual description of the entity and a first metric; identify, by the first artificial intelligence model, one or more third objects each having at least one first property satisfying a first metric; and cause a presentation of a portion of the second object at a first portion and a portion of the third object at a second portion of a user interface.
Conclusion
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/fp/
/FRANTZY POINVIL/Primary Examiner, Art Unit 3693
June 8, 2026
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