DETAILED ACTION
1. Claims 1, 4-13, 16, 17, and 20-23 are presented for consideration.
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. Claim(s) 1, 4-9, 11-13, 16, 17, 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. [ US Patent Application No 2021/0015375 ], in view of Jung et al. [ US Patent Application No 2021/0105848 ].
3. As per claim 1, Kim discloses the invention as claimed including a method for processing partial input missing of an AI network, wherein the method comprises:
in a case that there is a missing input in a plurality of inputs of an artificial intelligence (AD network [ i.e. missing value ] [ 702, Figure 7; and paragraphs 0014, 0015, and 0079 ], replacing, by a first communication device, the missing input with a target input or a default value to obtain an output of the AI network [ i.e. replacing the missing value with a point data value before or after the missing value, and trains the artificial intelligent algorithm using point data in which the missing value has been replaced ] [ 705, 707, Figure 7; and paragraphs 0024, 0058, 0080, and 0081 ]; wherein
the target input comprises at least one of the following:
one or more inputs before the missing input; or one or more inputs after the missing input [ 704, 706, and 710, Figure 7; and paragraph 0080 ], and
the default value comprises one of the following: a constant value, a constant vector, a constant two-dimensional matrix, or a constant multidimensional matrix [ i.e. calculate an average of the point data in each section including a predetermined number of point data, generate representative point data in the each section, and replace the point data with the representative point data ] [ paragraphs 0057, and 0064 ].
Kim does not specifically disclose
wherein
one or more resource domains occupied by the plurality of inputs of the AI network comprise at least one of the following: a frequency domain, a time domain, a code domain, a spatial domain, a delay domain, a Doppler field, a Fourier Transform domain, an S domain, or a Z domain;
the one or more resource domains occupied by the plurality of inputs of the AI network comprise a first resource domain and a second resource domain; and
an input and/or output of the AI network comprises one of the following:
a reference signal, channel state information, beam information, channel prediction information, positioning information, prediction information of a higher layer service or parameter, management information of a higher layer service or parameter, or control signaling; wherein
the missing input occupies a plurality of resource domains, and the target input is obtained according to at least one of the following:
a resource corresponding to the missing input in at least one resource domain of the plurality of resource domains; one or more resources before the resource corresponding to the missing input in at least one resource domain of the plurality of resource domains; or one or more resources after the resource corresponding to the missing input in at least one resource domain of the plurality of resource domains.
Jung does not specifically disclose
wherein
one or more resource domains occupied by the plurality of inputs of the AI network comprise at least one of the following: a frequency domain, a time domain, a code domain, a spatial domain, a delay domain, a Doppler field, a Fourier Transform domain, an S domain, or a Z domain [ i.e. frequency band ] [ Figure 1; and paragraphs 0098, 0178, and 0320 ];
the one or more resource domains occupied by the plurality of inputs of the AI network comprise a first resource domain and a second resource domain [ i.e. location, operation, remaining time ] [ Figure 4C, 5C, and 6C; and paragraphs 0160, 0193, and 0194 ]; and
an input and/or output of the AI network comprises one of the following:
a reference signal, channel state information, beam information, channel prediction information, positioning information, prediction information of a higher layer service or parameter, management information of a higher layer service or parameter, or control signaling [ i.e. RSSI ] [ Figure 5C; and paragraphs 0102, and 0176 ]; wherein
the missing input occupies a plurality of resource domains [ i.e. network connection is lost ] [ paragraphs 0015, and 0025 ], and the target input is obtained according to at least one of the following:
a resource corresponding to the missing input in at least one resource domain of the plurality of resource domains; one or more resources before the resource corresponding to the missing input in at least one resource domain of the plurality of resource domains; or one or more resources after the resource corresponding to the missing input in at least one resource domain of the plurality of resource domains [ i.e. input the environment information ] [ S540, Figure 5B; and paragraphs 0008, and 0136 ].
It would have been obvious to a person skill in the art before the effective filing date of the claimed invention to combine the teaching of Kim and Jung because the teaching of Jung would enable to managing network connection based on learned information [ Jung, paragraph 0002 ].
4. As per claim 4, Jung discloses wherein the missing input comprises a j-th resource in a second resource domain of an i-th resource in a first resource domain, and the target input comprises one of the following: a (-M1)-th resource to a (j-1)-th resource in the second resource domain of the i-th resource of the first resource domain; a (j+1)-th resource to a (j+M2)-th resource in the second resource domain of the i-th resource of the first resource domain; a j-th resource in the second resource domain of an (i-N1)-th resource to an (i-1)-th resource in the first resource domain; a (j-M1)-th resource to a (j-1)-th resource in the second resource domain of an (i-N1)-th resource to an (i-1)-th resource in the first resource domain; a (j+1)-th resource to a G+M2)-th resource in the second resource domain of an (i-N1)-th resource to an (i-1)-th resource in the first resource domain; a j-th resource in the second resource domain of an (i+1)-th resource to an (i+N1)-th resource in the first resource domain; a (j-M1)-th resource to a (j-1)-th resource in the second resource domain of an (i+1)-th resource to an G+N1)-th resource in the first resource domain; or a §+1)-th resource to a j+M2)-th resource in the second resource domain of an (i+1)-th resource to an G+N1)-th resource in the first resource domain, wherein i,j, N1, and M1 are each a positive integer [ i.e. first and second point in time ] [ paragraphs 0162, and 0193 ]
5. As per claim 5, Kim discloses wherein there are a plurality of target inputs, and the replacing, by a first communication device, the missing input with a target input to obtain an output of the AI network comprises: replacing the missing input with a result obtained after averaging or performing a combinatorial operation on the plurality of target inputs to obtain an output of the AI network [ i.e. replacing values ] [ Figure 7; and paragraphs 0079-0081 ].
6. As per claim 6, Jung discloses wherein the missing input comprises a plurality of adjacent second resources in the second resource domain of a first resource in the first resource domain; and the target input is a plurality of the second resources in the second resource domain of a resource near the first resource in the first resource domain [ i.e. lost connection ] [ Figures 4C and 5C; and paragraphs 0160, 0193, and 0194 ].
7. As per claim 7, Kim discloses wherein the missing input includes a plurality of adjacent inputs, and the target input is the first input before or after the missing input; or the missing input includes a plurality of adjacent inputs, and the target input is a target input of the first or the last input in the plurality of adjacent inputs [ 704, 706, and 710, Figure 7; and paragraph 0080 ].
8. As per claim 8, Kim discloses wherein the method further comprises: if the target input is missing or part of the target input is missing, replacing the target input with an input near the target input or a next-level target input of the target input to obtain an output of the AI network [ 704, 706, and 710, Figure 7; and paragraph 0080 ].
9. As per claim 9, Jung discloses wherein the first communication device does not expect or the AI network does not allow occurrence of at least one of the following: consecutive missing of adjacent inputs; more than K1 consecutive missing inputs; or more than K2 missing inputs in all, wherein K1 and K2 are each an integer greater than or equal to 2 [ Figure 7; and paragraphs 0079-0081 ].
10. As per claim 11, Jung discloses wherein the AI network allows the missing input to satisfy a specific pattern or rule [ Abstract; and paragraphs 0133, and 0134 ]
11. As per claim 12, Jung discloses wherein the method further comprises: sending at least one of the following to a second communication device: an identifier of the missing input; a quantity of the missing input; a pattern or a rule of the missing input; the output obtained by using an AI algorithm or a non-Al algorithm other than the AI network; or a performance loss caused by the missing input; or the method further comprises: sending the output of the AI network to a second communication device, wherein the first communication device is a terminal, and the second communication device is a network side device; or the first communication device is a network side device, and the second communication device is a terminal; or the first communication device is a terminal, and the second communication device is a terminal; or the first communication device is a network side device, and the second communication device is a network side device [ i.e. relay device ] [ paragraphs 0103-0107, and 0114 ].
12. As per claim 13, it is rejected for similar reasons as stated above in claim 1.
13. As per claim 16, it is rejected for similar reasons as stated above in claim 6.
14. As per claim 17, it is rejected for similar reasons as stated above in claim 1.
15. As per claim 20, it is rejected for similar reasons as stated above in claim 6.
16. As per claim 21, it is rejected for similar reasons as stated above in claim 9.
17. As per claim 22, it is rejected for similar reasons as stated above in claim 11.
18. Claim(s) 10, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. [ US Patent Application No 2021/0015375 ], in view of Jung et al. [ US Patent Application No 2021/0105848 ], and further in view of Cheim [ US Patent Application No 2021/0117449 ].
19. As per claim 10, Kim in view of Jung does not specifically disclose wherein the method further comprises: no longer using the AI network if at least one of the following occurs: consecutive missing of adjacent inputs; more than K1 consecutive missing inputs; or more than K2 missing inputs in all, wherein K1 and K2 are each an integer greater than or equal to 2; or the method further comprises: no longer using the AI network if a missing input occurs in the AI network, wherein the first communication device does not expect or the AI network does not allow occurrence of a missing input. Cheim discloses wherein the method further comprises: no longer using the AI network if at least one of the following occurs: consecutive missing of adjacent inputs; more than K1 consecutive missing inputs; or more than K2 missing inputs in all, wherein K1 and K2 are each an integer greater than or equal to 2; or the method further comprises: no longer using the AI network if a missing input occurs in the AI network, wherein the first communication device does not expect or the AI network does not allow occurrence of a missing input [ paragraphs 0074, 0075, 0226, and 0227 ]. It would have been obvious to a person skill in the art before the effective filing date of the claimed invention to combine the teaching of Kim, Jung and Cheim because the teaching of Cheim would enable to provide improved methods and devices that are capable of reliably performing a condition classification even if not all input parameter values required by an automatic classification procedure are available [ Cheim, paragraph 0011 ].
20. As per claim 23, it is rejected for similar reasons as stated above in claim 10.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 4-13, 16, 17, and 20-23 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kaya et al. [ US Patent Application No 2022/0190883 ] discloses beam prediction for wireless networks
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/DUSTIN NGUYEN/Primary Examiner, Art Unit 2446
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