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 § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (Publication No. 2024/0184763 filed July 31, 2023, hereinafter Li) and Tadmor (2024/0386885 filed May 13, 2024, priority to provisional application No. 63/502901 filed May 17, 2023, hereinafter Tadmor).
Regarding Claims 1, 8, 13, and 20, Li teaches generating a first input sequence for a first target model based on target data to be compressed the first target model being constructed based the first prompt indicating the first target model to perform a data compression task (see Abstract, dataset to be compressed as one index, sequentially building a new Huffman tree corresponding to each index, and then adding a separator to obtain an encoding list containing a target encoding value and length, adding the encoding list to the initial lookup table to obtain a target lookup table); obtaining a first output sequence of the first target model by providing the first input sequence to the first target model ([0029] This application provides a data compression method. Refer to FIG. 1, which is a schematic method flowchart of an embodiment of a data compression method according to this application. The method may be executed by an electronic device. The electronic device may be implemented by software and/or hardware. The data compression method includes: [0030] S110: establishing an initial lookup table by using data with a same numerical value in data to be compressed as one index and according to a number of indexes; [0031] S120: sequentially performing an encoding operation on the numerical value corresponding to each of the indexes to obtain an encoding result, and adding a separator to the encoding result to obtain an encoding list containing a target encoding value corresponding to each of the indexes and a target encoding length corresponding to each of the indexes); and extracting a compressed representation of the target data from the first output sequence ([0034] The data to be compressed is classified and counted, and the data with a same numerical value in the data to be compressed is used as one index. For example, in the data to be compressed, the numerical value 174 has appeared 248 times, the numerical value 176 has appeared 234 times, and the numerical value 175 has appeared 232 times, then 248 numerical values 174 is used as one index, 234 numerical values 176 is used as one index, and 232 numerical values 175 is used as one index. The initial lookup table is established according to the number of indexes, and further, the initial lookup table may also include the numerical values corresponding to the indexes and the frequency of appearance of numerical values.); and compressed representation being of the target data ([0063] the encoding list is added to the initial lookup table to obtain the target lookup table for compression of the data to be compressed…).
Li does not expressly teach constructing based on a language model and a vectorized representation.
Tadmor teaches constructing based on a language model and a vectorized representation ([0005] implementations and generation of language model decoder and [0032] fixed-length vector).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to incorporate the concept of receiving an input sequence of speech features characterizing a spoken prompt to further determine a generated output of sequence of speech features suggested in Tadmor’s method into Li and by incorporating Tadmor into Li because both methods Tadmor and Li are analogous art and because they are in the same field of endeavor, filtering compressed data, would improve in technologies related to the compression of data.
Regarding Claims 2, 9, and 14, Li teaches the first prompt further indicates at least one of type of the target data (Abstract, see target lookup table).
Regarding Claims 3. 10, and 15, Tadmor teaches the first input sequence the target data is located before the first prompt (Abstract sequence of speech features characterizing a spoken prompt).
Regarding Claims 4, 11, and 16, Li teaches the first input sequence further comprises a predetermined symbol corresponding to the compressed representation of the target data and extracting the compressed representation of the target data from the first output sequence comprises extracting the compressed representation of the target data from a position corresponding to the predetermined symbol in the first output sequence ([0034] The data to be compressed is classified and counted, and the data with a same numerical value in the data to be compressed is used as one index. For example, in the data to be compressed, the numerical value 174 has appeared 248 times, the numerical value 176 has appeared 234 times, and the numerical value 175 has appeared 232 times, then 248 numerical values 174 is used as one index, 234 numerical values 176 is used as one index, and 232 numerical values 175 is used as one index. The initial lookup table is established according to the number of indexes, and further, the initial lookup table may also include the numerical values corresponding to the indexes and the frequency of appearance of numerical values.).
Regarding Claims 5, 12, and 17, Li teaches the target data comprises text ([0082]).
Regarding Claims 6 and 18, Li teaches target data comprises data in a non-text modality and generating the first input sequence for the first target model comprises using a feature encoder corresponding to the modality of the target data at least one feature representation from the target data and the first input sequence for the first target model based on the at least one feature representation and the first prompt sequence ([0034] The data to be compressed is classified and counted, and the data with a same numerical value in the data to be compressed is used as one index. For example, in the data to be compressed, the numerical value 174 has appeared 248 times, the numerical value 176 has appeared 234 times, and the numerical value 175 has appeared 232 times, then 248 numerical values 174 is used as one index, 234 numerical values 176 is used as one index, and 232 numerical values 175 is used as one index. The initial lookup table is established according to the number of indexes, and further, the initial lookup table may also include the numerical values corresponding to the indexes and the frequency of appearance of numerical values.).
Regarding Claims 7 and 19, Tadmor teaches compressed representation comprises a vector representation at one predetermined dimension and wherein the first prompt indicated a number of vectorized representations of the predetermined dimension to be output (Abstract sequence of speech features characterizing a spoken prompt; [0005] implementations and generation of language model decoder and [0032] fixed-length vector).
Conclusion
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/CHERYL LEWIS/Primary Examiner, Art Unit 2166 January 10, 2026