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 .
Claims 1, 3-4, and 6-9, and 11-22 are pending in this application. Claims 2, 5, and 10 are canceled, claims 9 and 19-21 are amended, and claims 22-23 are new by preliminary amendment filed 2 April 2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 11-19 and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 11 recites “An indication information sending method, comprising: sending neural network indication information configured to instruct a communication node to generate, according to N original neural networks, a target neural network, wherein N is a positive integer.” An information sending method is a mental process which merely communicates information, such as between two people. This judicial exception is not integrated into a practical application because the information is merely “configured to instruct,” but does not actually cause a communication node to generate a neural network. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional element is the step of “sending neural network indication information . . .” Sending information is mere data gathering, which is insignificant extra-solution activity and is therefore not significantly more than an abstract idea.
Claim 12 recites “sending the N original neural networks.” This does not recite a practical application because the neural networks do not do anything practical; they are merely data that are sent from one entity to another. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because sending data is mere data gathering, which is insignificant extra-solution activity and is therefore not significantly more than an abstract idea.
Claim 13 recites additional details about the neural network indication information. Information is merely data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 14 recites additional details about the neural network indication information. Information is merely data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 15 recites additional details about the K original neural networks. Since the neural networks are not used to perform any operations, they are merely data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 16 recites additional details about the second-level information. Information is merely data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 17 recites additional details about the second-level information. Information is merely data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 18 recites additional details about the first-level information. Information is merely data, which is an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 19 recites additional steps of sending and receiving information. Sending and receiving information is mere data gathering and therefore not significantly more than an abstract idea. The claim does not recite a practical application and does not include any additional elements.
Claim 22 recites the method of claim 11, so it recites the abstract idea of claim 11. This judicial exception is not integrated into a practical application because the claim only “implements the indication sending method of claim 11,” which sends data, but does not actually cause the communication node to generate a neural network. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements are “A communication node, comprising a processor configured to, when executing a computer program.” These are generic computing components recited at a high degree of generality, so they do not render the claim significantly more than an abstract idea.
Claim 23 recites the method of claim 11, so it recites the abstract idea of claim 11. This judicial exception is not integrated into a practical application because the claim only “implements the indication sending method of claim 11,” which sends data, but does not actually cause the communication node to generate a neural network. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements are “A non-transitory computer-readable storage medium storing a computer program which, when executed by a processor . . .” These are generic computing components recited at a high degree of generality, so they do not render the claim significantly more than an abstract idea.
Claim Rejections - 35 USC § 102
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, 3-4, 6-7, 9, 11-17, and 19-22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al (U.S. 2021/0342687, hereinafter “Wang”).
Regarding Claim 1, Wang teaches a neural network generation method (fig. 7; ¶ [0101] – [0103]—the method comprises a base station signaling user equipment to generate a neural network), comprising:
receiving neural network indication information (fig. 7, step 710; ¶ [0106]—the user equipment receives indication information as a message from the base station); and
generating a target neural network according to N original neural networks and the neural network indication information, wherein N is a positive integer (fig. 7, step 715; ¶ [0108]—the user equipment UE generates a target neural network according to index values to a neural network table as indicated in the message. The neural network table is described in fig. 12 and ¶ [0044] and [0156]; the architecture configurations can be considered N original neural networks).
Regarding Claim 11, Wang teaches an indication information sending method (fig. 7; ¶ [0101] – [0103]—the method comprises a base station signaling user equipment, i.e. sending indication information), comprising:
sending neural network indication information configured to instruct a communication node to generate, according to N original neural networks, a target neural network, wherein N is a positive integer (fig. 7, step 710; ¶ [0106]—the base station sends neural network indication information as a message to the user equipment {communication node}. Fig. 7, step 715; ¶ [0108]—the user equipment UE generates a target neural network according to index values to a neural network table as indicated in the message. The neural network table is described in fig. 12 and ¶ [0044] and [0156]; the architecture configurations can be considered N original neural networks).
Regarding Claims 3 and 13, Wang teaches wherein the neural network indication information comprises at least first-level information configured to indicate K original neural networks in the N original neural networks, wherein K is a positive integer less than or equal to N (¶ [0162] – [0166]—the indication information may specify a combination of neural network architecture elements from one or multiple neural network tables. The index values that reference architecture elements may be K original neural networks. Alternatively, there may be multiple neural network tables, and each specified table may be K original neural networks. See also fig. 16-1 and ¶ [0185] – [0188]—neural network table 1212 contains N original neural networks, such as element sets 1602, 1604, and 1606 {and obviously others in addition}. The indication comprises K of these networks, such as 1602 and 1604).
Regarding Claims 4 and 14, Wang teaches wherein the neural network indication information further comprises second-level information configured to indicate a sub-neural network of an original neural network of L original neural networks in the K original neural networks, wherein L is a positive integer less than or equal to K (fig. 16-1 and ¶ [0185] – [0188]—each of the K networks 1602, 1604, and 1606 comprise sub-networks in the form of multiple index values, such as 1608, 1610, and 1612 within network 1602. These are specified in second-level information in the indication).
Regarding Claims 6 and 16, Wang teaches wherein the second-level information is configured to indicate at least one of following information: a number of repetitions of the sub-neural network; parameter sharing enable of the sub-neural network; a parallel or series relationship of the sub-neural network; or a sequence of the sub-neural network (fig. 16-1, ¶ [0189]—the second-level information indicates a sequence of the sub-neural network, such as index values 1608, 1610, and 1612 in the bottom portion of fig. 16-1).
Regarding Claims 7 and 17, Wang teaches wherein the second-level information comprises L indication sets, and each indication set of the L indication sets is configured to indicate a sub-neural network of an original neural network (fig. 16-1 and ¶ [0185] – [0188]—each index value can be considered an indication set that indicates a sub-neural network).
Regarding Claim 9 and 19, Wang teaches wherein after generating the target neural network, the method further comprises:
acquiring a network training reference signal; and training a neural network parameter of the target neural network according to the network training reference signal (¶ [0050], [0067], and [0079]—parameters of the neural networks are trained, obviously in response to a training signal. ¶ [0162] and [0173] describe training the networks stored in the neural network tables);
wherein acquiring the network training reference signal comprises: sending reference signal request information; and receiving reference signal response information, wherein the reference signal response information comprises the network training reference signal; or, receiving the network training reference signal; or, sending reference signal request information; and after receiving reference signal response information, receiving the network training reference signal (¶ [0154] – [0155]—the inputs, outputs, and parameters that are compared to thresholds comprise the reference signals, which are recited in vague terms by the present claim).
Regarding Claim 12, Wang teaches sending the N original neural networks (¶ [0035]—the N original neural networks are stored in neural network table that is sent to the user equipment).
Regarding Claim 15, Wang teaches wherein the K original neural networks satisfy at least one of following conditions: the K original neural networks comprise at least one scenario conversion neural network; the K original neural networks comprise at least one interference and/or noise cancellation neural network (¶ [0164]—the K original networks comprise neural networks configured to process signal-to-interference-plus-noise ratio (SINR) measurements and encoding schemes {scenario conversion}).
Regarding Claim 20, Wang teaches a communication node, comprising a processor (fig. 2, user equipment 110 with processor 210; ¶ [0042] – [0043]) configured to, when executing a computer program, implement the neural network generation method of claim 1 (see claim 1, above).
Regarding Claim 21, Wang teaches a non-transitory computer-readable storage medium storing a computer program (fig. 2, user equipment 110 with computer readable media 212; ¶ [0042] – [0043]) which, when executed by a processor, implements the neural network generation method of claim 1 (see claim 1, above).
Regarding Claim 22, Wang teaches a communication node, comprising a processor (fig. 2, user equipment 110 with processor 210; ¶ [0042] – [0043]) configured to, when executing a computer program, implement the indication information sending method of claim 11 (see claim 11, above).
Regarding Claim 23, Wang teaches a non-transitory computer-readable storage medium storing a computer program (fig. 2, user equipment 110 with computer readable media 212; ¶ [0042] – [0043]) which, when executed by a processor, implements the indication information sending method of claim 11 (see claim 11, above).
Allowable Subject Matter
Claims 8 and 18 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. None of the prior art of record teaches “wherein the first-level information is configured to indicate a sequence of the K original neural networks” in the context of the present claims. In Wang, the K original neural networks (such as networks 1602, 1604, and 1606 in fig. 16-1 and ¶ [0188]) are used individually as separate neural networks. ¶ [0190] – [0191] explains that one of the neural networks is selected to process a communication based on tests and metrics for each of the neural networks. Indicating a sequence of these networks in the first-level information would change the principle of operation of Wang, so it would not be obvious under 35 U.S.C. 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This art includes:
Wan et al. (U.S. 2024/0232575) teaches sending an indication that specifies generating a neural network from predefined modules and cells, selecting K neural network elements out of N elements; it does not specify a topology or ordering of the K elements, so it does not teach the limitations of claims 8 or 18
Zhong, Zhao, et al. (“Blockqnn: Efficient block-wise neural network architecture generation,” IEEE transactions on pattern analysis and machine intelligence 43.7 (2020): 2314-2328) teaches using a Q-learning (reinforcement learning) system to determine which of multiple neural network blocks to stack together to generate a neural network
Andreas, Jacob, et al. (“Neural module networks,” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016) teaches parsing a natural language question to generate an instruction, which when executed, dynamically generates a neural network from modules to answer the question
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/HAL SCHNEE/Primary Examiner, Art Unit 2129
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