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 § 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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without “significantly more”. Claim(s) 1-20 is/are directed to Abstract Idea such as an idea standing alone such as an instantiated concept, pan or scheme, as well as a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper for example using measurement received from a mobile device, transmitting from the source relay node to a donor access node.
The apparatus and the method claim 1, 14 and 20 recites limitation, “receiving, by user equipment (UE), a measurement reporting configuration sent by a network-side device, wherein the measurement reporting configuration comprises at least one of the following: artificial intelligence (AI) model related information, AI model input information, AI model output information, an AI model inference trigger condition, a cell handover event related configuration, reporting content expected by the network-side device, total duration of an AI model prediction output, the number or interval of time points of the AI model prediction output, or first indication information, wherein the first indication information is used to indicate whether the UE triggers measurement reporting based on a prediction result of an AI model; and performing, by the UE, AI model inference on at least one handover candidate cell based on the measurement reporting configuration to obtain an inference result, and performing a cell handover based on the inference result”. Since the claim is directed to a process and a machine, which is one of the statutory categories of the invention (Step 1: YES).
The claim is then analyzed to determine whether it is directed to any judicial exception. The claim recites
receiving, by user equipment (UE), a measurement reporting configuration sent by a network-side device; performing, by the UE, AI model inference on at least one handover candidate cell based on the measurement reporting configuration to obtain an inference result, and performing a cell handover based on the inference result. The receiving step and then performing AI model inference on at least one handover candidate cell based on the measurement reporting configuration to obtain an inference result i.e., analyzing step and then performing a cell handover i.e., outputting certain results of collection and analysis where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) recited in the claim is no more than an abstract idea i.e., mental process of collecting information, , analyzing it, and displaying certain results of the collection and analysis etc. (Step 2A: Prong One Abstract Idea=Yes).
The claim is then analyzed if it requires an additional elements or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception – i.e., limitation that are indicative of integration into a practical application: improving to the functioning of a computer or to any other technology or technical field. In the current claims, there is no additional elements that would integrate the abstract idea into a practical application (Step 2A: Prong Two Abstract Idea=Yes).
Next the claim as a whole is analyzed to determine if there are additional limitation recited in the claim such that the claim amount to significantly more than an abstract idea. The claim requires the additional limitation of a computer with the central processing unit, memory, a printer, an input and output terminal and a program. These generic computer components are claimed to perform the basic functions of storing, retrieving and processing data through the program that enables. In the current scenario, there are no additional elements that would amount to significantly more than the abstract idea. Therefore, the claim does not amount to significantly more than the abstract idea itself (Step 2B: No). Accordingly, the claim is not patent eligible.
Further, dependent claims does not add any positive limitation or step that recite within the scope of the claim and does not carry patentable weight they are also rejected for the same reasons as independent claims.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1, 14 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Applicant is claiming “reporting configuration comprises at least one of the following: artificial intelligence (AI) model related information, AI model input information, AI model output information, an AI model inference trigger condition, a cell handover event related configuration, reporting content expected by the network-side device, total duration of an AI model prediction output, the number or interval of time points of the AI model prediction output, or first indication information” but then claiming “wherein the first indication information is used to ….”. It is not clear that if one of the reporting configurations is not first indication information then how UE will trigger measurement reporting based on a prediction result of an AI model. The claim is vague and indefinite and difficult for one having ordinary skill in the art to understand the invention.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2-8, 15-18, rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 1 and 14 from which claim 2-8, 15-18 depends recites “reporting configuration comprises at least one of the following: artificial intelligence (AI) model related information, AI model input information, AI model output information, an AI model inference trigger condition, a cell handover event related configuration, reporting content expected by the network-side device, total duration of an AI model prediction output, the number or interval of time points of the AI model prediction output, or first indication information”. Claim 2-8 and 15-18 cannot completely depend on claim 1 and 14 because in claim 1 applicant is claiming at least one of the following: …. so, if reporting configuration comprises AI model related information, then claim 2-8 and 15-18 fail to further limit the subject matter of the claim upon which it depends or for failing to include all the limitations of the claim upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Je et al. Pub. No. US 20230014613 A1
Regarding Claim 1, Je teaches a cell handover method (Fig. 4 and Para 155, a configuration of the AI-based handover), comprising:
receiving, by user equipment (UE) (Fig. 16a Step 1620 and Para 391, the UE may receive feedback configuration information from the BS i.e., UE is receiving) , a measurement reporting configuration sent by a network-side device ((Fig. 16a Step 1620 and Para 391, operation 1620, the UE may receive feedback configuration information from the BS i.e., a measurement reporting configuration sent by a network-side device),
wherein the measurement reporting configuration comprises at least one of the following: artificial intelligence (AI) model related information, AI model input information, AI model output information, an AI model inference trigger condition, a cell handover event related configuration, reporting content expected by the network-side device, total duration of an AI model prediction output, the number or interval of time points of the AI model prediction output, or first indication information (Para 391-399, the UE may receive the following information in order to record the learning data on the basis of the feedback configuration information. NN input info: input value information of the HO NN, for example, wireless signal intensity (RSRP, RSRQ, SINR, or the like), throughput, an SRS, and a wireless signal which can be measured by the UE such as cqi and the like NN output info: output value format of the HO NN (for example, the existence of nonexistence of the HO (one-hot encoding) or a cell ID) and is generated by the UE according to an HO performance reference value which is a performance value. Ex1, {HO, not HO}, Ex2, {Serving cell, Neighbor cell1(Cell ID), Neighbor cell2, . . . } Performance: performance reference of the HO, for example, maximization of a radio signal for a predetermined period, minimization of pingpong, and the like, Training data period: NN learning data collection period, Training data start time: NN learning data collection start time point, Training data end time: NN learning data collection end time point refer to as measurement reporting configuration comprises at least one of the following: artificial intelligence (AI) model related information, AI model input information, AI model output information, an AI model inference trigger condition, a cell handover event related configuration, reporting content expected by the network-side device, total duration of an AI model prediction output, the number or interval of time points of the AI model prediction output, or first indication information)
wherein the first indication information is used to indicate whether the UE triggers measurement reporting based on a prediction result of an AI model (P.S. Part of alternative language and should not be given weight Para 406-407 and Fig. 16A Step 1630 and 1640, 1630, the UE may collect training data on the basis of feedback configuration information. The UE records input info of the NN and out info of the NN in every training data period from a training data start time to a training data end time, and the output info of the NN configures the right answer based on performance. In operation 1640, the UE may detect expiration of the learning section. That is, the UE may detect a training data end time. When the training data end time arrives, the UE may record the number of pieces of learning data recorded in every training data period in # of data i.e., the first indication information is used to indicate whether the UE triggers measurement reporting based on a prediction result of an AI model); and
performing, by the UE, AI model inference on at least one handover candidate cell based on the measurement reporting configuration to obtain an inference result (Para 408 and Fig. 16A Step 1650, the UE may feedback training data for the AI-based handover to the serving BS according to a format configured by the feedback configuration information. The UE may store a set of the NN input list and the NN output list and transmit the stored result to the BS of the serving cell i.e., performing, by the UE, AI model inference on at least one handover candidate cell based on the measurement reporting configuration to obtain an inference result, and
performing a cell handover based on the inference result (fig. 16A Step 1660 and Para 409, the BS may learn a neural network model for the AI-based handover on the basis of the learning data acquired from the UE. The BS may learn and update the structure information (NN) and the weight information (W) of the neural network model for the AI-based handover. The BS may acquire the updated structure information and weight information according to the learning result i.e., performing a cell handover based on the inference result).
Regarding Claim 9, Je teaches wherein the inference result comprises at least one of the following: predicted signal quality of the at least one handover candidate cell within the total duration of the AI model prediction output or real signal quality of the at least one handover candidate cell (289-291).
Regarding Claim 10, Je teaches wherein the process of performing a cell handover based on the inference result comprises: triggering, by the UE, measurement reporting based on the inference result (Para 409).
Regarding Claim 11, Je teaches wherein the triggering measurement reporting based on the inference result comprises: in a case that predicted signal quality of a target handover candidate cell always meets a preset handover event entry condition within the total duration of the AI model prediction output, triggering, by the UE, reporting of a first measurement report; in a case that predicted signal quality of a target handover candidate cell always meets a preset handover event leave condition within the total duration of the AI model prediction output, triggering, by the UE, reporting of a second measurement report; or in a case that real signal quality of a target handover candidate cell meets a preset handover event entry condition within measurement reporting trigger duration configured by the network-side device, triggering, by the UE, reporting of a third measurement report, wherein the target handover candidate cell is one or more of the at least one handover candidate cell (Para 204 and 409).
Regarding Claim 12, Je teaches wherein the total duration of the AI model prediction output and the measurement reporting trigger duration are duration separately configured by the network-side device; or both the total duration of the AI model prediction output and the measurement reporting trigger duration are duration configured by the network-side device by using the measurement reporting configuration (Para 303).
Regarding Claim 13, Je teaches wherein the performing a cell handover based on the inference result comprises: receiving, by the UE, a handover command sent by the network-side device, wherein the handover command is used to instruct to hand over the UE to a target cell that is selected from the at least one handover candidate cell based on the inference result; and initiating, by the UE, a random access request to the target cell according to the handover command, wherein the random access request is used for requesting access to the target cell (Para 227).
Regarding Claim 14, it has been rejected for the same reasons as claim 1 and further teaches User equipment (UE) (Fig. 18 and Para 437), comprising a processor (Fig. 18 Unit 1805 and Para 439) and a memory (Fig. 18 Unit 1803 and Para 443), wherein the memory stores a program or an instruction capable of running on the processor, and the program or the instruction, when executed by the processor (Para 443).
Regarding Claim 19, it has been rejected for the same reasons as claim 9.
Regarding Claim 20, it has been rejected for the same reasons as claim 1 and further a non-transitory readable storage medium (Fig. 18 Unit 1803 and Para 443), wherein the non-transitory readable storage medium stores a program or an instruction, and the program or the instruction, when executed by a processor of user equipment (UE) (Para 443)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. Pub. No. US 20250184768 A1 - MONITORING METHOD AND APPARATUS OF AI/ML MODEL
Geng et al. Pub. No. US 20230422124 A1 - CELL HANDOVER METHOD AND APPARATUS
Zeng et al. Pub. No. US 20230209390 A1 - Intelligent Radio Access Network
Huangfu et al. Pub. No. US 20230179490 A1 - ARTIFICIAL INTELLIGENCE-BASED COMMUNICATION METHOD AND COMMUNICATION APPARATUS
Kovacs et al. Pub. No. US 20220335337 A1 - PROVIDING PRODUCER NODE MACHINE LEARNING BASED ASSISTANCE
WO 2025071986 A1 - METHODS, ARCHITECTURES, APPARATUSES AND SYSTEMS FOR HANDLING AI/ML OPERATIONS DURING A HANDOVER OF A WIRELESS TRANSMIT-RECEIVE UNIT TO A TARGET NETWORK NODE
CA 3035020 C - SYSTEM AND METHOD FOR CHANNEL MEASUREMENT AND INTERFERENCE MEASUREMENT IN WIRELESS NETWORK
WO 2022119496 A1 - CLI REPORTING FOR HANDOVER
WO 2021064275 A1 - RADIO ACCESS INFORMATION REPORTING IN WIRELESS NETWORK
3GPP TSG RAN #93e RP-211662 – Moderator’s summary for discussion Dated Sept 13 -17, 2021
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NIZAR N. SIVJI
Primary Examiner
Art Unit 2647
/NIZAR N SIVJI/Primary Examiner, Art Unit 2647
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