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 .
This office action is in response to Applicant’s preliminary amendment filed on 06/05/2024.
Claims 1-15, 18-20, 41, and 42 are currently pending.
Claim Objections
Claims 9 and 11 are objected to because of the following informalities:
For claim 9, the claim recites “the base station” and there is no proper antecedent basis for this claim element.
For claim 11, the claim recites “the base station” and there is no proper antecedent basis for this claim element.
Appropriate correction is required.
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.
Claims 1-15, 18-20, 41, and 42 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ye et al. (US 2024/0235767 A1; hereafter YE).
With respect to claim 1, YE discloses an information processing method (Abstract; Title), performed by a user equipment (UE) (102, 104 of FIG. 1; 202 of FIG. 2), the information processing method comprising:
according to a quantity of one or more artificial intelligence (AI) models corresponding to a demodulation reference signal (DMRS) pattern (paragraphs [0050], [0053, and [0054]), using an AI model of the one or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]) to perform channel (paragraph [0057]) estimation (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8).
With respect to claim 2, YE further discloses the information processing method according to claim 1, wherein according to the quantity of the one or more the AI models corresponding to the DMRS pattern, using the AI model corresponding to the DMRS pattern to perform the channel estimation comprises one of:
in response to the one or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]) being one AI model, using the one AI model to perform the channel estimation (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8);
in response to the one or more AI models corresponding to the DMRS pattern being a plurality of AI models, based on AI model indication information,
determining one AI model from the plurality of AI models to perform the channel estimation; in response to the one or more AI models corresponding to the DMRS pattern being a plurality of AI models, determining any one of the plurality of AI models to perform the channel estimation; or
in response to the one or more AI models corresponding to the DMRS pattern being a plurality of AI models, selecting one AI model matching one of a moving speed of the UE, channel quality, computational capability of the UE, and storage capability of the UE from the plurality of AI models to perform the channel estimation.
With respect to claim 3, YE further discloses the information processing method according to claim 1, further comprising:
determining whether to use (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) the AI model (paragraphs [0050], [0053, and [0054]) to perform the channel estimation (paragraph [0057]).
With respect to claim 4, YE further discloses the information processing method according to claim 3, further comprising:
receiving configuration information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) that indicates the DMRS pattern (paragraphs [0050], [0053, and [0054]); or
determining the DMRS pattern based on at least one of mobility information, channel quality information, computational capability information, or storage capability information of the UE (102, 104 of FIG. 1; 202 of FIG. 2).
With respect to claim 5, YE further discloses the information processing method according to claim 4, wherein the configuration information further comprises:
AI indication information for indicating (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) whether to enable the AI model to perform the channel estimation (paragraph [0057]).
With respect to claim 6, YE further discloses the information processing method according to claim 4, wherein the configuration information further comprises:
AI model indication information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) for instructing the UE to determine the AI model for the channel estimation from the one or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]).
With respect to claim 7, YE further discloses the information processing method according to claim 1, further comprising:
receiving model information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) of at least one of the one or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]); or
determining model information of at least one of the one or more AI models corresponding to the DMRS pattern according to a protocol agreement.
With respect to claim 8, YE further discloses the information processing method according to claim 7, wherein receiving the model information of the at least one of the one or more AI models corresponding to the DMRS pattern comprises:
in response to determining that there is no AI model corresponding to the DMRS pattern in the UE (102, 104 of FIG. 1; 202 of FIG. 2), receiving the model information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) of the at least one of the one or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]).
With respect to claim 9, YE further discloses the information processing method according to claim 7, further comprising:
reporting first recommended information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8), wherein the first recommended information indicates the DMRS pattern used by the UE (102, 104 of FIG. 1; 202 of FIG. 2), or the first recommended information indicates the DMRS pattern (paragraphs [0050], [0053, and [0054]) used by the UE and the AI model required by the UE;
wherein the first recommended information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) is for the base station (112, 114 in FIG. 1; 218 in FIG. 2) to determine the model information.
With respect to claim 10, YE further discloses the information processing method according to claim 3, wherein determining whether to use the AI model to perform the channel estimation comprises one of:
in response to determining that there is the AI model (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) corresponding to the DMRS pattern in the UE (102, 104 of FIG. 1; 202 of FIG. 2), determining to use the AI model corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]) to perform the channel estimation (paragraph [0057]); or
in response to determining that there is no AI model corresponding to the DMRS pattern in the UE, determining not to use the AI model corresponding to the DMRS pattern to perform the channel estimation.
With respect to claim 11, YE further discloses the information processing method according to claim 4, further comprising:
reporting second recommended information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8), wherein the second recommended information indicates a DMRS pattern (paragraphs [0050], [0053, and [0054]) recommended for use by the UE (102, 104 of FIG. 1; 202 of FIG. 2) and is for the base station (112, 114 in FIG. 1; 218 in FIG. 2) to determine the configuration information.
With respect to claim 12, YE further discloses the information processing method according to claim 11, comprising:
determining the DMRS pattern (paragraphs [0050], [0053, and [0054]) recommended for use (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) by the UE (102, 104 of FIG. 1; 202 of FIG. 2) based on at least one of the mobility information, the channel quality information, the computational capability information, or the storage capability information of the UE (102, 104 of FIG. 1; 202 of FIG. 2).
With respect to claim 13, YE discloses an information processing method (Abstract; Title), performed by a base station (112, 114 in FIG. 1; 218 in FIG. 2), the information processing method comprising:
sending configuration information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) for indicating a quantity of one or more artificial intelligence (AI) models corresponding to a demodulation reference signal (DMRS) pattern (paragraphs [0050], [0053, and [0054]),
wherein the quantity of the one or more AI models corresponding to the DMRS pattern is for instructing a user equipment (UE) (102, 104 of FIG. 1; 202 of FIG. 2) to determine an AI model of the one or more AI models to perform channel estimation according to the quantity of the one (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]).
With respect to claim 14, YE further discloses the information processing method according to claim 13, wherein the quantity of the one or more AI models corresponding to the DMRS pattern in the configuration information being for instructing the UE to determine the AI model to perform the channel estimation according to the quantity of one or more AI models corresponding to the DMRS pattern, comprises one of:
in response to the one or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]) being one AI model, the configuration information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) being for instructing the UE (102, 104 of FIG. 1; 202 of FIG. 2) to use the one AI model to perform the channel estimation (paragraph [0057]);
in response to the one or more AI models corresponding to the DMRS pattern being a plurality of AI models, the configuration information being for instructing the UE to determine, based on AI model indication information, one AI model from the plurality of AI models to perform the channel estimation;
in response to the one or more AI models corresponding to the DMRS pattern being a plurality of AI models, the configuration information being for instructing the UE to determine any one of the plurality of AI models to perform the channel estimation; or in response to the one or more AI models corresponding to the DMRS pattern being a plurality of AI models, the configuration information being for instructing the UE to select one AI model matching one of a moving speed of the UE, channel quality, computational capability of the UE, and storage capability of the UE from the plurality of AI models to perform the channel estimation.
With respect to claim 15, YE further discloses the information processing method according to claim 13, wherein the configuration information further comprises at least one of:
the DMRS pattern, and the configuration information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) is for instructing the UE (102, 104 of FIG. 1; 202 of FIG. 2) whether to use the AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]) to perform channel estimation (paragraph [0057]);
AI indication information for indicating whether to enable the AI model to perform channel estimation, and in response to the AI indication information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) indicating to enable the AI model to perform channel estimation, the AI indication information is for instructing the UE (102, 104 of FIG. 1; 202 of FIG. 2) to use the AI model corresponding to the DMRS pattern to perform channel estimation;
AI model indication information for instructing the UE to select the AI model to perform channel estimation from the AI models corresponding to the DMRS pattern.
With respect to claim 18, YE further discloses the information processing method according to claim 15, further comprising:
in response to determining that there is no AI model corresponding (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) to the DMRS pattern in the UE (102, 104 of FIG. 1; 202 of FIG. 2), sending model information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) of at least one of the one or more AI models corresponding to the DMRS pattern (paragraphs [0050], [0053, and [0054]).
With respect to claim 19, YE further discloses the information processing method according to any one of claim 15, comprising:
receiving second recommended information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8); and determining the configuration information based on the DMRS pattern (paragraphs [0050], [0053, and [0054]) recommended for use by the UE (102, 104 of FIG. 1; 202 of FIG. 2) and indicated by the second recommended information.
With respect to claim 20, YE discloses an information processing method (Abstract; Title), performed by a base station (112, 114 in FIG. 1; 218 in FIG. 2), the information processing method comprising:
receiving first recommended information(300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8); wherein the first recommended information indicates a demodulation reference signal (DMRS) pattern (paragraphs [0050], [0053, and [0054]) used by a user equipment (UE) (102, 104 of FIG. 1; 202 of FIG. 2), or
the first recommended information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8) indicates the DMRS pattern used by the UE and an artificial intelligence (AI) model (paragraphs [0050], [0053, and [0054]) required by the UE; wherein the AI model is for the UE (102, 104 of FIG. 1; 202 of FIG. 2) to perform channel estimation (paragraph [0057]);
determining, based on the first recommended information, model information of the AI model corresponding to the DMRS pattern required by the UE (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8); and sending the model information (300, 302, 304, 306 in FIG. 3; 702, 704 in FIG. 7; 802, 804, 806 in FIG. 8).
With respect to claim 41, YE discloses a communication device (102, 104 of FIG. 1; 202 of FIG. 2), comprising:
a processor (204, 206, 208 in FIG. 2); and a memory (204, 206, 208 in FIG. 2) for storing executable instructions (204, 206, 208 in FIG. 2) for the processor; wherein the processor is configured to execute the executable instructions (204, 206, 208 in FIG. 2) to implement the information processing method according to claim 1.
With respect to claim 42, YE discloses a non-transitory computer storage medium (204, 206, 208 in FIG. 2) having a computer executable program stored thereon, wherein the executable program is executed by a processor (204, 206, 208 in FIG. 2) to implement the information processing method according to claim 1.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Brian T O'Connor whose telephone number is (571)270-1081. The examiner can normally be reached Mon-Fri Flex 10am-6:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gary Mui can be reached at 571-270-1420. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRIAN T O CONNOR/Primary Examiner, Art Unit 2465 June 18, 2026
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