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 1-29 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) 1-29 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, 28 and 29 recites limitation, “obtain an indication of a first set of machine learning (ML) based network-side models applicable at a network entity; and transmit first signaling indicating a second set of ML-based network-side models supported by the UE, wherein the first set includes the ML-based network-side models in the second set”. 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 obtain an indication of a first set of machine learning (ML) based network side models; and transmit first signaling indicating a second set of ML-based network-side models supported by the UE. The obtaining step, analyzing what should be included and then transmitting step recited in the claim is no more than an abstract idea i.e., mental step of collecting data, analyzing and then outputting certain result, i.e., "collecting information, analyzing it, and displaying certain results of the 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) (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 do 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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Patent No. US 11916754 B2
Claim 1-29 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-32 of Patent No. US 11916754 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in pending application are transparently found in Patent No. US 11916754 B2 with obvious wording variation. For example, compare Claim 1 of pending application with claim 1 and 2 of Patent No. US 11916754 B2, they both recite
An apparatus for wireless communication at a user equipment (UE), comprising (An apparatus for wireless communication at a user equipment (UE), comprising):
at least one memory (memory ) comprising computer-executable instructions (instructions stored in the memory and executable by the at least one processor to cause the apparatus to) ; and
one or more processors (at least one processor) configured to execute the computer-executable instructions and cause the UE to (instructions stored in the memory and executable by the at least one processor to cause the apparatus to):
obtain an indication of a first set of machine learning (ML) (receive a machine learning model of one or more machine learning models i.e., obtain an indication of a first set of ML) based network-side models applicable at a network entity (receive a machine learning model of one or more machine learning models, a set of parameters corresponding to the machine learning model, or a configuration corresponding to a neural network function of one or more neural network functions based at least in part on an address and the capability information, the address being for the machine learning model, the set of parameters, or the configuration, wherein: the one or more machine learning models, the one or more neural network functions, or any combination thereof are associated with a machine learning model repository that is included in or coupled with a network entity i.e., based network side models applicable at a network entity); and
transmit first signaling indicating a second set of ML-based network-side models supported by the UE, wherein the first set includes the ML-based network-side models in the second set (claim 2, transmit, to the network entity, a request message that comprises an indication of the machine learning model, the neural network function, or both, wherein receiving the machine learning model, the neural network function, or both is based at least in part on the request message i.e., transmit first signaling indicating a second set of ML-based network-side models supported by the UE, wherein the first set includes the ML-based network-side models in the second set).
Further, analyzing and comparing dependent claims 2-13 of the pending application with claims 3-13 of Patent No. US 11916754 B2 it was found that they recite the same limitation with wording changes.
Similarly, analyzing and comparing independent claims 14, 28 and 29 of the pending application including its dependent claims with claims 14, 27 and 30 including its dependent claims of Patent No. US 11916754 B2 it was found that they recite the same limitation with wording changes.
Patent No. US 12355633 B2
Claim 1-29 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-22 of Patent No. US 12355633 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in pending application are transparently found in Patent No. US 12355633 B2 with obvious wording variation. For example, compare Claim 1 of pending application with claim 1 and 2 of Patent No. US 12355633 B2, they both recite
An apparatus for wireless communication at a user equipment (UE), comprising (A user equipment (UE), comprising):
at least one memory comprising computer-executable instructions (one or more memories storing processor-executable code); and
one or more processors configured to execute the computer-executable instructions and cause the UE to (one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to):
obtain an indication of a first set of machine learning (ML) based network-side models applicable at a network entity (receive a machine learning configuration corresponding to a neural network function of the one or more neural network functions or a machine learning model of the one or more machine learning models); and
transmit first signaling indicating a second set of ML-based network-side models supported by the UE, wherein the first set includes the ML-based network-side models in the second set ( transmit, to the network entity, a request message that comprises an indication of the machine learning model, the neural network function, or both, wherein the one or more processors are individually or collectively operable to execute the code to cause the UE to receive the machine learning configuration based at least in part on the request message).
Further, analyzing and comparing dependent claims 2-13 of the pending application with claims 3-11 of Patent No. US 12355633 B2 it was found that they recite the same limitation with wording changes.
Similarly, analyzing and comparing independent claims 14, 28 of the pending application including its dependent claims with claims 12, 21 including its dependent claims of Patent No. US 12355633 B2 it was found that they recite the same limitation with wording changes.
Note the issued claims of Patent No. US 11916754 B2 and Patent No. US 12355633 B2 are narrower in scope such that the claimed limitations as recited in pending application are encompassed by Patent No. US 11916754 B2 and Patent No. US 12355633 B2 respectively.
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) 1-29 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hao Dandan WO 2024033078 A1 (Referred to as Hao).
Regarding Claim 1, Hao teaches an apparatus for wireless communication at a user equipment (UE) (Page 19 Line 32-35, Fig. 10 schematically illustrates a processor-based implementation of a wireless device 1000 for operation in a wireless communication network), comprising:
at least one memory (Fig. 10 Unit 1060) comprising computer-executable instructions (Page 20 L 10-15, The memory 1060 may include suitably configured program code to be executed by the processor(s) 1050); and
one or more processors (Fig. 10 Unit 1050) configured to execute the computer-executable instructions (Page 20 L 10-15, The memory 1060 may include suitably configured program code to be executed by the processor(s) 1050) and cause the UE to:
obtain an indication (Page 14 L 25-28 and Fig. 6 Step 601, the base station 100 broadcasts system information 601 i.e., obtain an indication) of a first set of machine learning (ML) based network-side models applicable at a network entity (Page 14 L 25-30, the UE 10 decided to camp on a cell served by the base station 100, the UE 10 receives the system information 601 and reads the included model ID list(s) i.e., first set. Based on this information, the UE 10 can check whether or not it can support one or more of the indicated ML models, i.e., select corresponding ML models supported by the UE 10, as indicated by block 602 i.e., first set of machine learning (ML) based network-side models applicable at a network entity); and
transmit first signaling (Page 14 L 32-34, the UE 10 then sends a report 604 of the selected ML models to the base station 100 i.e., transmitting first signaling) indicating a second set of ML-based network-side models supported by the UE (Page 14 L 32-36, the report 604 may identify the ML models that are supported by the UE 10 (including the ML models optionally downloaded at block 603) i.e., second set in terms of one or more model ID lists i.e., indicating a second set of ML-based network-side models supported by the UE),
wherein the first set includes the ML-based network-side models in the second set (Page 14 L 28-35, Based on this information, the UE 10 can check whether or not it can support one or more of the indicated ML models, i.e., select corresponding ML models supported by the UE 10, as indicated by block 602. This selection may be accomplished per type of uplink control signaling. In some cases, the UE 10 may also select an ML model which is not yet supported by the UE 10 and trigger download of such ML model from a server, as indicated by block 603. When initiating connection setup with the base station 100, the UE 10 then sends a report 604 of the selected ML models to the base station 100. For example, the report 604 may identify the ML models that are supported by the UE 10 (including the ML models optionally downloaded at block 603) in terms of one or more model ID lists i.e., wherein the first set includes the ML-based network-side models in the second set).
Regarding Claim 2, Hao teaches wherein the first signaling comprises radio resource control (RRC) signaling comprising a capability report for the UE (Page 15 L 8-12, and Page 14 L 10-14).
Regarding Claim 3, Hao teaches wherein the one or more processors are further configured to cause the UE to: receive second signaling indicating at least one of the second set of ML-based network-side models, wherein the at least one of the second set of ML-based network-side models is activated at the network entity; and transmit, to the network entity, output of an ML-based UE-side model that is compatible with the at least one of the second set of ML-based network-side models (Page 14 L32 – Page 15 L 10).
Regarding Claim 4, Hao teaches wherein: the ML-based UE-side model comprises an ML-based channel state information (CSI) UE-side model configured to generate compressed CSI; and the at least one of the second set of ML-based network-side models comprises at least one ML-based CSI network-side model configured to reconstruct CSI from the compressed CSI(Page 14 L 15-25, Page 14 L 35- Page 15 L 5 and Page 3 L5-15).
Regarding Claim 5, Hao teaches wherein the second set of ML-based network-side models supported by the UE are determined based on ML-based UE-side models, supported by the UE, that are compatible with one or more of the second set of ML-based network-side models (Page 14 L 15-37).
Regarding Claim 6, Hao teaches wherein: the ML-based UE-side models comprise at least one ML-based channel state information (CSI) UE-side model configured to generate compressed CSI; and the second set of ML-based network-side models comprise ML-based CSI network-side models configured to reconstruct CSI from the compressed CSI (Page 14 L 15-25, Page 14 L 35- Page 15 L 5 and Page 3 L5-15).
Regarding Claim 7, Hao teaches wherein the one or more processors are configured to obtain the indication of the first set of ML-based network-side models from a server (Fig. 6 Step 601 and Page 14 L 15-18).
Regarding Claim 8, Hao teaches wherein the indication of the first set of ML-based network-side models is obtained via at least one of: system information (SI); or radio resource control (RRC) signaling (Page 14 L 15).
Regarding Claim 9, Hao teaches wherein the indication of the first set of ML-based network-side models comprises: a list of identifiers (IDs) of ML-based network-side models in the first set (Page 14 L 15-16).
Regarding Claim 10, Hao teaches wherein the first signaling comprises IDs of ML-based network-side models in the second set (Page 14 L 15-23).
Regarding Claim 11, Hao teaches wherein the second signaling comprises at least one of: system information (SI); or radio resource control (RRC) signaling (Page 14 L 32- Page 15 L 15).
Regarding Claim 12, Hao teaches wherein the ML-based network-side models in the first set are associated with at least one of: a cell identifier (ID), a tracking area, or a radio access network (RAN) area code (Page 14 L 19-24).
Regarding Claim 13, Hao teaches wherein the one or more processors are further configured to cause the UE to: transmit a request for the network entity to activate another ML-based network-side model in the second set, based on a change in one or more conditions detected at the UE (Page 14 L 25-32).
Regarding Claim 14, it has been rejected for the same reasons as claim 1 and further teaches an apparatus for wireless communications at a network entity (Fig. 11, network node), comprising: at least one memory (Fig. 11 Unit 1160) comprising computer-executable instructions (Page 21 L 5-10, the memory 1160 may include software 1170 and/or firmware 1180. The memory 1160 may include suitably configured program code to be executed by the processor(s) 1150); and one or more processors (Fig. 11 Unit 1150) configured to execute the computer-executable instructions (Page 21 L 5-10, the memory 1160 may include software 1170 and/or firmware 1180. The memory 1160 may include suitably configured program code to be executed by the processor(s) 1150).
Regarding Claim 15, it has been rejected for the same reasons as claim 2.
Regarding Claim 16, it has been rejected for the same reasons as claim 3.
Regarding Claim 17, it has been rejected for the same reasons as claim 4.
Regarding Claim 18, it has been rejected for the same reasons as claim 5.
Regarding Claim 19, it has been rejected for the same reasons as claim 6.
Regarding Claim 20, it has been rejected for the same reasons as claim 7.
Regarding Claim 21, it has been rejected for the same reasons as claim 8.
Regarding Claim 22, it has been rejected for the same reasons as claim 9.
Regarding Claim 23, it has been rejected for the same reasons as claim 10.
Regarding Claim 24, Hao teaches wherein the one or more processors are further configured to cause the network entity to: select the at least one of the second set of ML-based network-side models based on the UE and at least one other UE served by the network entity (Page 14 L 15-37).
Regarding Claim 25, it has been rejected for the same reasons as claim 11.
Regarding Claim 26, it has been rejected for the same reasons as claim 12.
Regarding Claim 27, it has been rejected for the same reasons as claim 13.
Regarding Claim 28, it has been rejected for the same reasons as claim 1.
Regarding Claim 29, it has been rejected for the same reasons as claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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NIZAR N. SIVJI
Primary Examiner
Art Unit 2647
/NIZAR N SIVJI/Primary Examiner, Art Unit 2647
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