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
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 7 and 9 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.
In particular the claims recite the limitation "the adaptation information”. There is insufficient antecedent basis for this limitation in the claims. The claims are interpreted as claimed. Appropriate correction is required
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)(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.
1. Claims 1-4, 7-11 and 14-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ouyang US 2025/0238638 A1 (“Ouyang”)
Per claim 1, Ouyang discloses a computer-implemented method of adapting output generated by a first generative model, the method comprising:
providing input for processing by the first generative model to generate provisional output responsive to said input (In FIG. 1B, a short sequence of tokens 56 corresponding to the text sequence “Come here, look!” 55 is illustrated as input to the transformer 50. …, para. [0059]; The input data subregion 582 may be configured to receive user input (originating from a particular one of the user devices 504) comprising the input data…., para. [0084]; The candidate output region 566 may generally be configured to iteratively receive different candidate outputs generated by the LLM stored/hosted on the language model servers 502 and in response to the task prompt (e.g., including the instructions received in the instructions subregion 580 and the input data received in the input data subregion 582). …, para. [0086]);
in response to determining that the provisional output includes an instruction to adapt at least part of the provisional output, providing said at least part of the provisional output for processing by a second generative model to generate information for adapting the provisional output (para. [0004]; a first LLM may be selected to process the candidate tasks prompts to generate the candidate outputs and a different second LLM may be selected to process the modification prompts to generate the subsequent candidate task prompts. Such embodiments may be utilized when the first LLM is more adapted to a particular processing task but the second LLM is more adapted to the prompt modification task …, para. [0083]; para. [0100]-[0101]; para. [0115]); and
providing the information for adapting the provisional output for processing by the first generative model to generate adapted output (Abstract; fig. 7; para. [0081]-[0084]; para. [0100]-[0101]).
Per claim 2, Ouyang discloses the method of claim 1, wherein the first generative model and the second generative model are configured to use the same tokenization scheme (a particular LLM may be used to generate further candidate prompts based on user input directed to a previous prompt and/or previous outputs generated by the same LLM (e.g., based on that previous prompt). This can allow both candidate prompts and candidate outputs to be generated by the same LLM …, para. [0004]; para. [0058]-[0059]; Inputs to an LLM may be referred to as a prompt, which is a natural language input that includes instructions to the LLM to generate a desired output. A computing system may generate a prompt that is provided as input to the LLM via its API. As described above, the prompt may optionally be processed or pre-processed into a token sequence prior to being provided as input to the LLM …, para. [0065]; para. [0082]).
Per claim 3, Ouyang discloses the method of claim 2, wherein the provisional output comprises a plurality of first tokens decoded by the first generative model (the prompt modification server 506 in communication with the language model servers 502 and the user devices 504 in accordance with one embodiment is shown. The prompt modification server 506 may be configured to one or more of: (a) input candidate task prompts including instructions, context and input data into the LLMs stored on the language model servers 502 to generate corresponding candidate outputs in response to the candidate the task prompts …, para. [0075]).
Per claim 4, Ouyang discloses the method of claim 3, wherein the information for adapting the provisional output is indicative of whether each of the one or more first tokens is accepted or rejected (A user interface (UI) can be used to display a current task prompt into the LLM and a current output generated by the LLM (e.g., based on the current task prompt) and to receive the user input directed to the current task prompt or the current output …, para. [0005]; para. [0039]; para. [0075]).
Per claim 7, Ouyang discloses the method of claim 3, wherein the adaptation information is indicative of a new score and/or ranking assigned to at least one of the one or more first tokens by the second generative mode (para. [0090]).
Per claim 8, Ouyang discloses the method of claim 3, wherein said one or more first tokens include top-k tokens generated by the first generative model (fig. 5; para. [0109]).
Per claim 9, Ouyang discloses the method of claim 1, wherein the adaptation information includes an end of sequence (EOS) token to cause a sequence in the adapted output to be terminated (para. [0061]; para. [0086]-[0087]).
Per claim 10, Ouyang discloses the method of claim 1, wherein the second generative model is configured to use a different tokenization scheme to the first generative model, and wherein providing said at least part of the provisional output includes providing a complete sequence decoded by the first generative model for processing by the second generative model (a particular LLM may be used to generate further candidate prompts based on user input directed to a previous prompt and/or previous outputs generated by the same LLM (e.g., based on that previous prompt). This can allow both candidate prompts and candidate outputs to be generated by the same LLM … In some embodiments, a first LLM may be used to perform the processing task and a second LLM may be used to generate further candidate prompts for the processing task performed by the first LLM …, para. [0004]; para. [0058]-[0059]; Inputs to an LLM may be referred to as a prompt, which is a natural language input that includes instructions to the LLM to generate a desired output. A computing system may generate a prompt that is provided as input to the LLM via its API. As described above, the prompt may optionally be processed or pre-processed into a token sequence prior to being provided as input to the LLM …, para. [0065]; para. [0082]).
Per claim 11, Ouyang discloses the method of claim 1, wherein the first generative model has been trained to generate output based on a first probability distribution, and wherein the second generative model has been trained to generate output based on a second probability distribution different from the first probability distribution (para. [0001]; para. [0004]; para. [0082]).
Per claim 14, Ouyang discloses the method of claim 1, wherein the first generative model has been trained to generate an adaptive output start token and an adaptive output end token indicative of a start point and an end point, respectively, of the at least part of the provisional output that is to be adapted based on said information generated by the second generative model (para. [0058]; para. [0086]-[0087], candidate output as including start and ending tokens).
Per claim 15, Ouyang discloses the method of claim 1, wherein the first generative model is executed on a first device, and the second generative model is executed on a second device (fig. 3; fig. 4; para. [0075]; para. [0095]; para. [0101]).
Per claim 16, Ouyang discloses the method of claim 15, wherein the second device is a user device and/or wherein the first device is a server (fig. 3; fig. 4).
Per claim 17, Ouyang discloses the method of claim 1, wherein providing said at least part of the provisional output for processing by the second generative model comprises providing contextual information for processing by the second language model, wherein the contextual information is indicative of a context associated with the input provided to the first generative model (para. [0106]).
Per claim 18, Ouyang discloses a system comprising:
one or more processors (para. [0067]; para. [0148]); and
memory storing computer readable instructions that, when executed by the one or more processors, cause the one or more processors to be operable to: provide input for processing by the first generative model to generate provisional output responsive to said input (In FIG. 1B, a short sequence of tokens 56 corresponding to the text sequence “Come here, look!” 55 is illustrated as input to the transformer 50. …, para. [0059]; para. [0067]; The input data subregion 582 may be configured to receive user input (originating from a particular one of the user devices 504) comprising the input data…., para. [0084]; The candidate output region 566 may generally be configured to iteratively receive different candidate outputs generated by the LLM stored/hosted on the language model servers 502 and in response to the task prompt (e.g., including the instructions received in the instructions subregion 580 and the input data received in the input data subregion 582). …, para. [0086]; para. [0148]);
in response to determining that the provisional output includes an instruction to adapt at least part of the provisional output, provide said at least part of the provisional output for processing by a second generative model to generate information for adapting the provisional output (para. [0004]; a first LLM may be selected to process the candidate tasks prompts to generate the candidate outputs and a different second LLM may be selected to process the modification prompts to generate the subsequent candidate task prompts. Such embodiments may be utilized when the first LLM is more adapted to a particular processing task but the second LLM is more adapted to the prompt modification task …, para. [0083]; para. [0100]-[0101]; para. [0115]); and
provide the information for adapting the provisional output for processing by the first generative model to generate adapted output (Abstract; fig. 7; para. [0083]-[0084]; para. [0100]-[0101]).
Per claim 19, Ouyang discloses a non-transitory computer readable medium containing computer-readable instructions that, when executed by a computer, cause the computer to:
provide input for processing by the first generative model to generate provisional output responsive to said input (In FIG. 1B, a short sequence of tokens 56 corresponding to the text sequence “Come here, look!” 55 is illustrated as input to the transformer 50. …, para. [0059]; The input data subregion 582 may be configured to receive user input (originating from a particular one of the user devices 504) comprising the input data…., para. [0084]; The candidate output region 566 may generally be configured to iteratively receive different candidate outputs generated by the LLM stored/hosted on the language model servers 502 and in response to the task prompt (e.g., including the instructions received in the instructions subregion 580 and the input data received in the input data subregion 582). …, para. [0086]);
in response to determining that the provisional output includes an instruction to adapt at least part of the provisional output, provide said at least part of the provisional output for processing by a second generative model to generate information for adapting the provisional output (para. [0004]; a first LLM may be selected to process the candidate tasks prompts to generate the candidate outputs and a different second LLM may be selected to process the modification prompts to generate the subsequent candidate task prompts. Such embodiments may be utilized when the first LLM is more adapted to a particular processing task but the second LLM is more adapted to the prompt modification task …, para. [0083]; para. [0100]-[0101]; para. [0115]); and
provide the information for adapting the provisional output for processing by the first generative model to generate adapted output (Abstract; fig. 7; para. [0083]-[0084]; para. [0100]-[0101]).
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.
2. Claims 5, 6, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Ouyang in view of De Wynter et al US 2025/0315629 A1 (“De Wynter”)
Per claim 5, Ouyang discloses the method of claim 4,
Ouyang does not explicitly disclose wherein in dependence on the information for adapting the provisional output indicating that at least one token among the one or more first tokens is accepted, said at least one token is included in the adapted output
However, this feature is taught by De Wynter (fig. 2; para. [0024]; para. [0026]; para. [0065]; Review pane 611 also includes buttons by which the user can replace the selected content with the modified version (or the modified version as further edited by the user) or to add the modified version into document 601 without replacing the selected content, para. [0071])
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to combine the teachings of De Wynter with the method of Ouyang in arriving at the missing features of Ouyang, because such combination would have resulted in improving user experience in terms of minimizing user effort leading to more rapid convergence to satisfactory outcomes and reducing latency (De Wynter, para. [0035]-[0036]).
Per claim 6, Ouyang discloses the method of claim 4,
Ouyang does not explicitly disclose wherein for each of the one or more first tokens that is indicated as rejected in the information for adapting the provisional output, said information comprises one or more second tokens to replace respective rejected tokens among the one or more first tokens, the second tokens having been decoded by the second generative model.
However, this feature is taught by De Wynter (fig. 2; para. [0024]; para. [0026]; para. [0050]; para. [0065]; Review pane 611 also includes buttons by which the user can replace the selected content with the modified version (or the modified version as further edited by the user) …, para. [0071])
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to combine the teachings of De Wynter with the method of Ouyang in arriving at the missing features of Ouyang, because such combination would have resulted in improving user experience in terms of minimizing user effort leading to more rapid convergence to satisfactory outcomes and reducing latency (De Wynter, para. [0035]-[0036]).
Per claim 12, Ouyang discloses the method of claim 11, wherein the second generative model is a language model (LM) (para. [0001]; para. [0004]; para. [0071]-[0072]; para. [0082])
Ouyang does not explicitly disclose wherein the second probability distribution is indicative of a writing or speaking style associated with a user profile
However, this feature is taught by De Wynter (para. [0050], LLM as having probability distribution)
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to combine the teachings of De Wynter with the method of Ouyang in arriving at the missing features of Ouyang, because such combination would have resulted in improving user experience in terms of minimizing user effort leading to more rapid convergence to satisfactory outcomes and reducing latency (De Wynter, para. [0035]-[0036]).
Per claim 13, Ouyang discloses the method of claim 12, wherein the second generative model is one of a plurality of second generative models and the user profile is one of a plurality of user profiles, each of the plurality of second generative models being associated with a respective one of the plurality of user profiles (fig. 3; fig. 4; user devices as including user profiles),
Ouyang does not explicitly disclose the method comprising: selecting a respective one of the plurality of second generative models as the second generative model to be used to generate said information for adapting the provisional output, in dependence on the user profile associated with said respective one of the plurality of second generative models, thereby to adapt the at least part of the provisional output to the writing or speaking style associated with said user profile
However, this feature is taught by De Wynter (fig. 3; para. [0061])
It would have been obvious to one of ordinary skill in the art before the effective filing of the instant invention to combine the teachings of De Wynter with the method of Ouyang in arriving at the missing features of Ouyang, because such combination would have resulted in improving user experience in terms of minimizing user effort leading to more rapid convergence to satisfactory outcomes and reducing latency (De Wynter, para. [0035]-[0036]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUJIMI A ADESANYA whose telephone number is (571)270-3307. The examiner can normally be reached Monday-Friday 8:30-5:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at 571-272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUJIMI A ADESANYA/Primary Examiner, Art Unit 2658
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