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 § 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)(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.
Claims 1, 8, 9, 11, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hsu, US Patent No. 11,861,452.
Regarding claim 1, Hsu teaches A method performed by at least one processor configured to implement an accelerator for processing a softmax function (fig. 4), the method comprising: obtaining, using an input module included in the accelerator, input data comprising a plurality of quantized input values (fig. 4, operation 402); generating, using a distribution information processing module included in the accelerator, input data distribution information indicating a plurality of frequencies corresponding to the plurality of quantized input values included in the input data (fig. 4, intermediate network decision values); identifying, using the distribution information processing module, a largest value from among the plurality of quantized input values as a first maximum value, based on the input data distribution information (fig. 4, operation 404, maximum network decision value); determining, using the distribution information processing module, an offset value based on a difference between the first maximum value and a second maximum value, wherein the second maximum value indicates a maximum quantization value that is representable by an input value of the input data (fig. 4, operation 404, difference between the intermediate network decision value and a maximum network decision value); determining, using a look-up table (LUT) module included in the accelerator, a plurality of index values by applying the offset value to each quantized input value of the plurality of quantized input values (fig. 4, operation 406); and outputting, using the LUT module, a value of the softmax function corresponding to each of the plurality of quantized input values based on the plurality of index values (fig. 4, operation 410).
Regarding claim 8, Hsu teaches sequentially storing the value of the softmax function in a memory corresponding to the accelerator using an output buffer included in an output module included in the accelerator; and sequentially obtaining input data from the memory using an input buffer included in the input module (figs. 7, 8, memory 830 and accompanying text).
Regarding claim 9, Hsu teaches using a control module included in the processor, the accelerator to output output data comprising the value of the softmax function, based on the input data (fig. 8, processors 810 and accompanying text).
Regarding claim 11, it is a processor of claim 1 and is rejected on the same grounds presented above.
Regarding claim 18, it has similar limitations to those of claim 8 and is rejected on the same grounds presented above.
Regarding claim 19, it has similar limitations to those of claim 9 and is rejected on the same grounds presented above.
Regarding claim 20, it is an electronic device of claim 1 and is rejected on the same grounds presented above.
Allowable Subject Matter
Claims 2-7, 10, and 12-17 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: None of the prior art, either singularly or in combination, teaches or fairly suggests “generating, using the distribution information processing module, modified input data distribution information by shifting the input data distribution information by the offset value; and calculating, using an operation module included in the accelerator, a denominator value of the softmax function using the modified input data distribution information and exponential function information, wherein the exponential function information comprises an exponential function value corresponding to a quantized value, wherein a plurality of values stored in the LUT module are determined based on the denominator value of the softmax function.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yu et al. (US Patent No. 12,205,005) teaches classifying inputs into one or more classes of a plurality of a classes based in part on a softmax distribution.
Desai (US Patent No. 12,204,488) teaches high performance softmax for large models.
Rosing et al. (US Pub. No. 2022/0019441) teaches softmax functionality and lookup tables.
Diamant et al. (US Patent No. 11,188,302) teaches utilizing softmax functionality to generate a probability distribution.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH B LEE JR whose telephone number is (571)270-3147. The examiner can normally be reached Mon - Fri 9am-5pm.
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/KENNETH B LEE JR/Primary Examiner, Art Unit 2625
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