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 .
Examiner’s Note
Examiner has cited particular columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to
fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 4-5, 8, 11-12, 15 and 18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ajmera et al. (US 2025/0181899).
As to claim 1, Ajmera teaches a method (Figs.1, 3) comprising: receiving, via a conversational interface 102, user input prompt 340 from a user of an online system;
Generating/converting a user input to embedding 345/122 using the user input (The prompt embedder 122 generates one or more embeddings for the prompt, such as by generating a vector representation of a query, data, and metadata associated with the prompt);
retrieving a vector store (vector embedding database130) comprising a plurality of tool/entity description embeddings (Pars.18-20);
performing a similarity search 350/124 using the user input embedding 122 and the plurality of tool/entity description embeddings 130 ([0021] prompts/inputs received by a user or client device are augmented by using a similarity search of embeddings in the vector embedding database to retrieve embeddings that are relevant to the prompt, such as based on similarity between the embeddings and the prompt); and determining/selecting a set of tool descriptions (embeddings) using the similarity search; (Par.0025 the one or more embeddings are provided to the embedding selector 124, that searches for and retrieves one or more stored embeddings from an embedding database 130, based on the one or more embeddings that were generated for the prompt .The stored embeddings can be selected based on a similarity to the one or more embeddings for the prompt);
generating augmented prompt 355/26 using the set of tool descriptions and the user input; and applying one or more machine learning agents to the prompt to cause the one or more machine learning agents to use one or more tools associated with the set of tool descriptions ([0030] the prompt generator 126 receives the embeddings from the embedding selector 124 and generates an augmented prompt based on the input prompt and the stored embeddings that are retrieved by the embedding selector 126); receiving a response 128 to the prompt, from the one or more machine learning agents (150), in response to the machine learning agents using the one or more tools; and sending 360-365, via the conversational interface, an output to the user input based on the response (Figs.1, 3; Pars.1, 20-32, 51-54).
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As to claim 4, Ajmera teaches wherein the user input embedding includes metadata for the user of the online system and wherein each of the plurality of tool description embeddings includes tool description metadata, the method further comprising: comparing/matching metadata of the user input embedding with tool description metadata for the plurality of tool description embeddings, wherein determining the set of tool descriptions further uses the metadata comparison (Pars.50, 68, 71, 29; Fig.3)
As to claim 5, Ajmer teaches generating output (360, 365) for the user, therefore outputting the status/result of the for the user input, will be inherent.
Regarding claims 8, 11-12, 15 and 18, the corresponding system and instructions comprising the steps similar to the claims above are analogous, therefore rejected as being anticipated by Ajmer for the foregoing reasons.
Allowable Subject Matter
Claims 2-3, 6-7, 9-10, 13-14, 16-17 and 19-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: Claims 2-3, 9-10 and 16-17 are allowable because Ajmer doesn’t teach wherein the set of tool descriptions comprise an administrator communication tool description for interacting with an administrator system and wherein applying the one or more machine learning agents to the prompt causes the one or more machine learning agents to use an administrator communication tool associated with the administrator communication tool description by causing the one or more machine learning agents to: generate a question for an administrator system using the prompt; send the question to the administrator system; receive a reply from the administrator system; and generate the output using the reply from the administrator system.
Claims 6, 13 and 19 are allowable because Ajmer doesn’t teach wherein the set of tool descriptions comprise a code generation tool description for generating application programming interface code and wherein applying the one or more machine learning agents to the prompt causes the one or more machine learning agents to use a code generation tool associated with the code generation tool description by causing the one or more machine learning agents to: generate code to access an application programming interface; and generating the output by using the generated code to access the application programming interface.
Claims 7, 14 and 20 are allowable because Ajmer doesn’t teach wherein the set of tool descriptions comprise a tool description editing tool description for editing tool descriptions of the plurality of tool description embeddings and wherein applying the one or more machine learning agents to the prompt causes the one or more machine learning agents to use a tool description editing tool associated with the tool description editing tool description by causing the one or more machine learning agents to: evaluate the output; edit a tool description of the plurality of tool description embeddings in response to the evaluation; and regenerate the output using the edited tool description.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DEMELASH ABEBE whose telephone number is (571)272-7615. The examiner can normally be reached monday-friday 7-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571-272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL ABEBE/Primary Examiner, Art Unit 2657