DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
I. REJECTIONS BASED ON PRIOR ART
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Simpson (US Pub.: 2020/0311183) in view of Afzal (US Patent 11,520,561).
As per claim 1, Simpson teaches/suggest a neural network operation apparatus comprising: a receiver configured to receive data for a neural network operation of a neural network; and a processor configured to: operating accordingly based on a structure of the neural network; perform operation by multiplying the data to obtain data;
perform a rounding operation by truncating bits forming a result of preceding operation;
operation based on a result of the rounding operation; and perform the neural network operation by accumulating results of operation (Fig. 4; [0007]; [0022]-[0023]; and [0042]-[0045]).
Simpson does not teach the apparatus comprising:
determine a scaling constant;
perform a scaling by the scaling constant to obtain scaled data;
having a result of the scaling operation;
perform a scaling back operation; and
operating with results of the scaling back operation.
Afzal teaches/suggests an apparatus comprising: determine a scaling constant; perform a scaling by the scaling constant to obtain scaled data; having a result of the scaling operation; perform a scaling back operation; and operating with results of the scaling back operation (col. 2, ll. 6-31; col. 5, ll. 20-24; and col. 8, ll. 12-41).
It would have been obvious for one of ordinary skill in this art, before the effective filing date of the claimed invention, to include Afzal’s scaling operations into Simpson’s apparatus for the benefit of implementing a highly configurable architecture that can handle data elements of variable sizes (Afzal, Abstract) to obtain the invention as specified in claim 1.
As per claim 2, Simpson and Afzal teach/suggest all the claimed features of claim 1 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising: wherein the processor is configured to: perform a clipping operation on the result of the rounding operation based on a predetermined precision range; and perform the scaling back operation on a result of performing the clipping operation (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 3, Simpson and Afzal teach/suggest all the claimed features of claim 1 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising: wherein the processor is configured to: calculate a partial sum based on the data; and perform the scaling operation on the partial sum (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 4, Simpson and Afzal teach/suggest all the claimed features of claim 1 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising wherein the scaling operation and the scaling back operation are performed by a bit shifter (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 6, l. 62 to col. 7, l. 4; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 5, Simpson and Afzal teach/suggest all the claimed features of claim 1 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising wherein: the processor is configured to perform the scaling operation based on a scale factor that is a power of two (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 6, Simpson and Afzal teach/suggest all the claimed features of claim 5 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising: wherein the processor is configured to determine the scale factor based on precision of an accumulator (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 7, Simpson and Afzal teach/suggest all the claimed features of claim 1 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising: wherein the processor is configured to perform the rounding operation by performing truncation based on a most significant bit of the result of the scaling operation (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 8, Simpson and Afzal teach/suggest all the claimed features of claim 7 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising: wherein the processor is configured to perform the rounding operation by inputting the most significant bit to an accumulator (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 9, Simpson and Afzal teach/suggest all the claimed features of claim 1 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising: wherein the processor comprises a multiplexer configured to perform a bit-selection operation that involves selecting one of a first scale value and a second scale value based on a selection bit (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claim 10, Simpson and Afzal teach/suggest all the claimed features of claim 9 above, where Simpson and Afzal further teach/suggest the neural network operation apparatus comprising: wherein the processor is configured to determine the selection bit based on a type of a neural network (Simpson Fig. 4; [0007]; [0022]-[0023]; [0042]-[0045]; and Afzal, col. 2, ll. 6-31; col. 5, ll. 20-24; col. 8, ll. 12-41), wherein it would have been obvious that the resulting combination of the references would further teach/suggest the above claimed features.
As per claims 11-20, claims 11-20 are rejected in accordance to the same rational and reasoning as the above rejection of claims 1-10, as claims 11-20 is the method accomplished by the neural network operation apparatus of claims 1-10.
II. CLOSING COMMENTS
CONCLUSION
STATUS OF CLAIMS IN THE APPLICATION
The following is a summary of the treatment and status of all claims in the application as recommended by M.P.E.P. 707.07(i):
CLAIMS REJECTED IN THE APPLICATION
Per the instant office action, claims 1-20 have received a first action on the merits and are subject of a first action non-final.
DIRECTION OF FUTURE CORRESPONDENCES
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUN KUAN LEE whose telephone number is (571)272-0671. The examiner can normally be reached Monday-Friday.
IMPORTANT NOTE
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Idriss Alrobaye can be reached on (571) 270-1023. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHUN KUAN LEE/Primary Examiner
Art Unit 2181 October 13, 2026
