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 .
Election/Restrictions
Applicant’s election without traverse of claims 10-13 in the reply filed on 3/11/2026 is acknowledged.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental concept without significantly more. The claims recite accepting text, inferring control text from the text, and post processing to convert control text to control data. This judicial exception is not integrated into a practical application because it is only linked to computers. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer readable media is generic computer part.
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.
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over US20080300053A1 to Muller and Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Raffel et al.
Muller teaches claims 10, 12 and 13. A system (Muller para 17) for supporting the creation of a game script including natural language data representing explanation text in a game and also including control data for controlling the game, the natural language data and the control data being associated in accordance with the content of the game, the system comprising: (Muller abs “Input from a text editor containing lines of text of a script is received, commands to control objects in a simulation are identified in the lines of text in the editor, a state of the simulation is updated in accordance with the input and the commands…”)
an input acceptance unit that accepts the input of explanation text in the game; and: (Muller abs “Input from a text editor containing lines of text of a script is received…”)
an inference unit that infers, (Muller abs “Input from a text editor containing lines of text of a script is received, commands to control objects in a simulation are identified in the lines of text in the editor, a state of the simulation is updated in accordance with the input and the commands…”) (Muller abs “text of a script”) and control explanation text in the form of natural language data created from control data corresponding to the explanation text. (Muller abs “commands to control objects in a simulation are identified in the lines of text in the editor…”) Muller doesn’t teach a trained NLP engine.
However, Raffel teaches using a trained model… the trained model being generated by causing a pre-trained natural language model to learn processed (Applicant’s “advance grammatical structures” is not a term of art and is not defined in a rigorous way to mean something other than generic NLP. This text-to-text training is taught by the drop-out training of Raffel sec. 3.1.4 and fig. 2. Using the trained model is taught in fig. 2 as a table of average and standard deviation scores of the trained models on different tasks and using different training methods.)
Muller, Raffel and the claims are all directed to processing text. It would have been obvious to a person having ordinary skill in the art, at the time of filing, Because Muller requires identifying commands in input text and Raffel “showed how this approach can be successfully applied to generative tasks like abstractive summarization, classification tasks like natural language inference…” Raffel sec. 4.1.
Muller teaches claim 11. The system according to claim 10, further comprising a data post-processing unit that creates, on the basis of conversion information indicating corresponding relationships between control data and control explanation text, control data from the control explanation text inferred by the inference unit. (Muller abs “Input from a text editor containing lines of text of a script is received, commands to control objects in a simulation are identified in the lines of text in the editor, a state of the simulation is updated in accordance with the input and the commands…” emphasis added.)
Conclusion
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/AUSTIN HICKS/ Primary Examiner, Art Unit 2142
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