DETAILED ACTION
Response to Amendment
This Office Action is responsive to applicant’s remarks and amendments filed on March 03, 2026 after the non-final rejection of the application.
The Amendment filed 3/3/26 has been entered. Claims 1-20 are pending, of which claims 1, 4, 16, 19, and 20 were amended.
The rejections of amended claims 1 and 20 have been considered but is moot in view of the new ground of rejection necessitated by the addition of limitations.
Response to Arguments
Applicant’s arguments, with respect to the rejections of [amended claims 1 and 20] have been considered but are not persuasive.
To be specific, the applicant argued that Chai and Hu do not disclose or teach at least the following elements: inputting the n pieces of first target subband information into a first Artificial Intelligence (AI) subnetwork of a plurality of AI subnetworks comprised in a first AI network model, wherein the plurality of AI subnetworks are arranged in a nested manner and correspond to different maximum input amounts in amended claims 1 and 20.
Amended independent claims 1 and 20 (and their corresponding dependent claims) are now rejected using the combination of Chai, Hu, and Moore.
Consequently, the rejections of claims 1-20 is still to be maintained.
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 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.
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.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 claim at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1-3, 5-9, 12-13, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Chai et al. (US 20190258917 A1), hereinafter referred to as Chai, in view of Hu et al. (US 20210204300 A1), hereinafter referred to as Hu, and further in view of Moore et al. (US 10,402,726 B1), ), hereinafter referred to as Moore.
Regarding claim 1:
Chai discloses a communication information sending method, comprising: dividing, by a first communications device, first communication information into n pieces of first target subband information (decomposing input data into first subband and second subband [0054]),
inputting the n pieces of first target subband information into a first Artificial Intelligence (AI) subnetwork, a maximum input amount of the first of a first Al network model (feeding input data having 32 bits and being decomposed into first subband, e.g., n pieces of target subband information -- into first neural networks [0026, lines 11-14 and 0054]),
wherein a maximum input amount of the first Al subnetwork is N pieces of subband information (first subband fed in first neural network is allocated 16 bits [0026 and 0064]), and a maximum input amount of the first Al network model is M pieces of subband information, wherein n, M, and N are all integers greater than 0, and n<N<M (neural network has 32 bits [0026]); and
generating the second communication information by the first Al subnetwork (generating unfused output, e.g., second communication information [0027]).
Chai does not further disclose the generated second communication is then sent; a plurality of AI subnetworks in a first AI network model are arranged in a nested manner and correspond to different maximum input amounts.
Hu, from the same field of endeavor, discloses the generated second communication is then sent (predictions of allocations of machine learning model are sent [0094-0095]).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to send the second communication information output {by the first Al subnetwork} – in response to receiving subband information; thus significantly improving bandwidth allocations but using a machine learning model or AI – Hu [0034].
Chai in view of Hu does not further specifying a plurality of AI subnetworks in a first AI network model are arranged in a nested manner and correspond to different maximum input amounts.
Moore, from the same field of endeavor, teaches a plurality of AI subnetworks in a first AI network model are arranged in a nested manner (multiple variational autoencoders {VAEs} are included in multiple neural networks [col.16, lines 44-56]) and correspond to different maximum input amounts. (if size of input data set is less than or equal to a threshold, input data is provided to multiple neural networks [col.16, lines 1-15])
Also, Moore adds that the threshold may be set at a size for which the multiple neural networks are able to generate the simulations [col.6, lines 1-5].
Thus, Moore does indirectly suggest a plurality of AI subnetworks in a first AI network model correspond to different maximum input amounts.
It woud be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to determine the maximum input amount of AI subnetworks; thus being able to generate improved output in fashion relative to input - [Moore - col.9, lines 3-24] ,wherein outputs are generated for large amounts of data through the training of AI networks– Moore [col.14, lines 16-21].
Regarding claim 2:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai further discloses the dividing, by a first communications device, first communication information into n pieces of first target subband information comprises:
dividing the first communication information into one or more pieces of first target subband information based on a target resource of the first communication information, wherein the target resource comprises at least one of the following: a frequency domain resource (decomposing input data based on frequency [0025]), a time domain resource, a spatial domain resource, a code domain resource, a time delay domain resource, a Doppler domain resource, a Fourier transform domain resource, an S domain resource, or a Z domain resource.
Regarding claim 3:
Chai in view of Hu and Moore discloses all limitations of claim 2, and –
Chai further discloses dividing, in a unit of a frequency domain unit resource, the first communication information into the one or more pieces of first target subband information on the frequency domain resource, wherein the frequency domain unit resource comprises at least one of the following: a carrier, a Resource Block (RB), a Physical Resource Block (PRB), a subband (decomposing input data into subbands [0003]), a Precoding Resource block Group (PRG), and a Bandwidth Part (BWP) (decomposed into different frequency bands, e.g., bandwidth part [0046, lines 13-14]); dividing, in a unit of a time domain unit resource, the first communication information into the one or more pieces of first target subband information on the time domain resource, wherein the time domain unit resource comprises at least one of the following: a symbol, a slot, a half-slot, a frame, a subframe, a radio frame, a millisecond, or a second; dividing, in a unit of a spatial domain unit resource, the first communication information into the one or more pieces of first target subband information on the spatial domain resource, wherein the spatial domain unit resource comprises at least one of the following: an antenna, an antenna element, an antenna panel, a transmit/receive unit, a beam, a layer, a rank, or an antenna angle; or dividing, in a unit of a code domain unit resource, the first communication information into the one or more pieces of first target subband information on the code domain resource, wherein the code domain unit resource comprises at least one of the following: an orthogonal code, a quasi- orthogonal code, or a semi-orthogonal code.
Regarding claim 5:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai does not further disclose, while Hu further discloses a size of the second communication information is a fixed value (output is zero slots [0075]), or a size of the second communication information is determined by an amount of the input first target subband information (outputs from machine learning model indicating an amount of bandwidth to allocate to the terminal – as result from receiving information on data to be transferred, e.g., input amount [0111-0112]).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to determine the size of communication information; thus being able to efficiently handle data transfers for terminals – Hu [0007].
Regarding claim 6:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai does not disclose, while Hu further discloses the size of the second communication information is directly proportional to the amount of the input first target subband information (providing output in response to receiving inputs including priority weights [0083, 0085]); or
an increment of the size of the second communication information is directly proportional to an increment of the amount of the input first target subband information (set of model outputs are generated for different terminals [0085], each of them has an amount of data transfer backlog [0033, lines 5-8]).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to determine the size of communication information; thus being able to efficiently handle data transfers for terminals – Hu [0007].
Regarding claim 7:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai further discloses the communication information sending method according to claim 1, wherein before the dividing, by a first communications device, first communication information into n pieces of first target subband information, the method further comprises:
training the first AI network model by using a plurality of training samples (training neural network with subbands with different weights [0007]), wherein one of the training samples comprises: m pieces of subband information (wherein subband has different information contents [0007, lines 1-4]) and a size of output information corresponding to the m pieces of subband information (and outputs correspond to first and second trained subbands [0027]), wherein m < M ({trained} subband fed in first neural network is allocated with m=16 bits) [0026 and 0064]) and neural network has M=32 bits [0026]).
Regarding claim 8:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai does not, while Hu further teaches the second communication information comprises one of the following:
a reference signal;
a signal carried on a channel;
channel state information; beam information;
channel prediction information;
interference information;
positioning information;
trajectory information;
prediction information of a high-layer service and parameter (allocation prediction and number of allocated slots [0094]);
management information of a high-layer service and parameter; or
control signaling.
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to generate the allocation prediction; thus allowing resources assignment for communications – Hu [0095].
Regarding claim 9:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai further discloses:
dividing, by the first communications device, third communication information into n+p pieces of second target subband information, wherein p is an integer greater than or equal to 0 (decomposing input data, e.g., third communication information, into first and second subbands including different features of pattern, e.g., n and p pieces of subband information, respectively [0054]) ;
inputting n pieces of second target subband information into the first AI subnetwork (feeding first subband into first neural network [0054]), and inputting other p pieces of second target subband information in the n+p pieces of second target subband information into a second AI subnetwork of the first AI network model (feeding second subband into second neural network [0054]), wherein a maximum input amount of the second AI subnetwork is P pieces of subband information wherein P is an integer greater than 0 and p <P<M (second subband fed in first neural network is allocated 8 bits [0026, lines 11-17 and 0054]), ; and
generating the fourth communication information by the first Al subnetwork (generating unfused output, e.g., second communication information [0027]).
Chai does not further disclose the generated second communication is then sent; which is known in the art and commonly applied in communications field for data communications, as suggested in Hu’s disclosure as below.
Hu, from the same field of endeavor, discloses the generated second communication is then sent (predictions of allocations of machine learning model are sent [0094-0095]).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to send the fourth communication information output {by the first Al subnetwork} – in response to receiving subband information; thus significantly improving bandwidth allocations but using a machine learning model or AI – Hu [0034].
Regarding claim 12:
Chai in view of Hu and Moore discloses all limitations of claim 9, and –
Chai does not disclose, while Hu further teaches in a case that p is equal to 0 (if no data to be transferred, output of model will be zero slots allocated to address backlog or input [0075]).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to derive a size of the fourth communication information is greater than or equal to a size of the second communication information, when there is no input data from a second AI network being fed into a first AI network, and input data to a first AI network is large; thus determining allocations for transmissions within the timing and performance requirements of the system - Hu [0005].
Regarding claim 13:
Chai in view of Hu and Moore discloses all limitations of claim 9, and –
Chai does not, while Hu further teaches in a case that p is equal to 0 (if no data to be transferred, output of model will be zero slots allocated to address backlog or input [0075]).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to derive a size of the fourth communication information is less than or equal to a size of the second communication information, when there is no input data from a second AI network being fed into a first AI network, and input data to a first AI network is not large; thus determining allocations for transmissions within the timing and performance requirements of the system - Hu [0005].
Regarding claim 16:
Claim 16 is rejected for substantially same reason as applied to claim above, except that claim 16 is in a device claim format.
Regarding claims 17-18:
Claims 17-18 are rejected for substantially same reason as applied to claims 2-3 above, except that claims 17-18 are in a device claim format.
Regarding claim 20:
Claim 20 is rejected for substantially same reason as applied to claim 1 above, except that claim 20 is in a non-transitory computer readable storage medium.
Claims 4 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chai in view of Hu and Moore, as applied to claims 1 and 16 above, respectively, and further in view of Shi et al. (US 20230281881 A1), hereinafter referred to as Shi.
Regarding claim 4:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai in view of Hu and Moore does not further disclose the inputting the n pieces of first target subband information into a first Al subnetwork of a first Al network model comprises: arranging the n pieces of first target subband information in a predetermined order, and inputting the n pieces of first target subband information into the first Al subnetwork, wherein the predetermined order comprises: a descending order or an ascending order of identifiers of the n pieces of first target subband information; or if an input amount of the first Al subnetwork is fixedly N pieces of subband information, inputting the n pieces of first target subband information and N-n pieces of invalid subband information into the first Al subnetwork, wherein the n pieces of first target subband information are located in a specified position.
Shi, from the same field of endeavor, discloses:
arranging the n pieces of first target subband information in a predetermined order (videos frames are arranged in sequence [0259]), and inputting the n pieces of first target subband information into the first Al subnetwork (inputting third video frame into first neural network, Fig.9), wherein the predetermined order comprises: a descending order or an ascending order of identifiers of the n pieces of first target subband information (in ascending order, e.g., first frame, second frame, …Fig.4); or
if an input amount of the first AI subnetwork is fixedly N pieces of subband information, inputting the n pieces of first target subband information and N-n pieces of invalid subband information into the first AI subnetwork, wherein the n pieces of first target subband information are located in a specified position (current video frame is an original video frame included in the current video sequence, e.g., input amount, and reference frame of current video frame may be an original video frame in the current video sequence, or may be located before or after the current video frame, and wherein reference frame of current video frame is used for a compression process of current video frame, [0116-0117]).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to arrange n pieces of target subband information in a predetermined order and determine pieces of subband information based on their locations; thus improving the performance of compression information corresponding to entire current video sequence - Shi [0220].
Regarding claim 19:
Claim 19 is rejected for substantially same reason as applied to claim 4 above, except that claim 19 is in a device claim format.
Claims 10-11 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Chai in view of Hu and Moore, as applied to claim 1 above, and further in view of Steinmetz et al. (US 20230352058 A1), hereinafter referred to as Steinmetz.
Regarding claim 10:
Chai in view of Hu and Moore discloses all limitations of claim 9, and –
Chai does not further disclose input information of the first AI subnetwork further comprises at least one of the following: the other p pieces of second target subband information, intermediate information of the second AI subnetwork, or output information of the second AI subnetwork; and input information of the second AI subnetwork further comprises at least one of the following: the n pieces of target subband information, intermediate information of the first AI subnetwork, or output information of the first AI subnetwork.
Steinmetz, from the same field of endeavor, teaches input information of the first AI subnetwork further comprises the other p pieces of second target subband information, intermediate information of the second AI subnetwork, or output information of the second AI subnetwork (input 4101 and 4251 fed into neural network/ AI subnetwork 4311, Fig.4A);
and input information of the second AI subnetwork further comprises at least one of the following: the n pieces of target subband information, intermediate information of the first AI subnetwork, or output information of the first AI subnetwork (input 4101 and 4251 fed into neural network/ AI subnetwork 4301, Fig.4A);
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to feed two inputs from two respective AI subnetworks into one AI subnetwork; thus efficiently performing predictions based further on combined inputs [0089].
Regarding claim 11:
Chai in view of Hu and Moore discloses all limitations of claim 9, and –
Chai in view of Hu and Moore does not further disclose first output information, second output information, or a result obtained after calculation is performed on the first output information and the second output information by a preset algorithm, wherein the first output information is determined by an output of the first AI subnetwork or an output of the second AI subnetwork; or the second output information is determined by the output of the first AI subnetwork or the output of the second AI subnetwork.
Steinmetz, from the same field of endeavor, teaches first output information, second output information, or a result obtained after calculation is performed on the first output information and the second output information by a preset algorithm (first output 4411 and second output 4421, Fig.4A, by emulating signaling processing algorithm [0060, lines 1-4]), wherein the first output information is determined by an output of the first AI subnetwork or an output of the second AI subnetwork (output at TCN2 is determined by its output and TCN1 output, Fig.8A);
the second output information is determined by the output of the first AI subnetwork or the output of the second AI subnetwork (Fig.8A);
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to identify first and second outputs; thus determining the quality mix of two inputs - Steinmetz [0009].
Regarding claim 14:
Chai in view of Hu and Moore discloses all limitations of claim 1, and –
Chai in view of Hu and Moore does not further disclose dividing, by the first communications device, third communication information into n+p pieces of second target subband information, wherein p is an integer greater than or equal to 0; inputting n pieces of second target subband information into the first AI subnetwork, and inputting other p pieces of second target subband information in the n+p pieces of second target subband information into the first AI subnetwork, wherein p < N; and sending fourth communication information output by the first AI subnetwork.
Steinmetz, from the same field of endeavor, discloses: dividing, by the first communications device, third communication information into n+p pieces of second target subband information, wherein p is an integer greater than or equal to 0 (dividing input into 4101 and 4251, Fig.4A));
inputting n pieces of second target subband information into the first AI subnetwork (inputting 4101 into 4311, Fig.4a), and inputting other p pieces of second target subband information in the n+p pieces of second target subband information into the first AI subnetwork, wherein p < N (inputting 4251 into 4311, Fig.7); and
sending fourth communication information output by the first AI subnetwork (sending output 4411, e.g., fourth communication information output, Fig.4A).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to send the output derived from two inputs from two respective AI subnetworks; thus determining the quality mix of two inputs - Steinmetz [0009].
Regarding claim 15:
Chai in view of Hu, Moore, and Steinmetz discloses all limitations of claim 14, and –
Chai in view of Hu, Moore does not, while Steinmetz further discloses the fourth communication information comprises a first output information or a second output information, wherein a size of the first output information is the same as a size of the second communication information (first output 4411, second output 4421 in Fig.4A in Steinmetz).
Therefore, it would be obvious to one of ordinary skill in the art at the time before the effective filing date of the claimed invention to determine the output size according to input size; thus getting a best solution to a problem by using a neural decision support system - Steinmetz [0069].
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAMQUYEN THAI whose telephone number is (571)270-7245. The examiner can normally be reached on 9:00am-5:00pm. Examiner interviews are available via telephone, in-person, and videoconferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at: http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ayman A. Abaza can be reached on 571-270-0422. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.Q.T./
/AYMAN A ABAZA/ Primary Examiner, Art Unit 2465
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