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 communication is response to claims filed on 02/05/24.
Claims 1-30 are presented for examination.
Information Disclosure Statement’s
4. The information disclosure statement(s) submitted on 07/02/25 & 02/05/24 have being considered by the examiner and made of record in the application file.
Drawing
5. The drawings filed on 02/05/24 are accepted by the examiner.
Claim Rejections - 35 USC § 103
6. 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.
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 of this title, 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 claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
7. Claims 1-7, 12-18 & 22-26 are rejected under 35 U.S.C. 103 as being unpatentable over Takeda et al. (hereinafter referred as Takeda) US Patent Application Publication No. 2020/0374844 A1, in view of Shen et al. (hereinafter referred as Shen) US Patent Application Publication No. 2022/0342713 A1.
Regarding claims 1 & 22: Takeda discloses an apparatus (See FIG. 17 & Para. 0197; a user terminal 20 (i.e., UE))/a method for wireless communication by a user equipment (UE), comprising:
a memory (See FIG. 15 & Para. 0197; a user terminal (i.e., UE) 20 includes a memory 1002); and
at least one processor (See FIG. 15 & Para. 0197; a user terminal (i.e., UE) 20 includes a processor 1001)) coupled to the memory and configured:
to receive signaling configuring monitoring occasions for an indication to switch between the plurality of machine learning model groups (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling. the UE monitors the slot format-reporting DCI (SFI monitoring) and controls the switching of BWPs. The BWP can be acquired through machine learning mode);
to monitor the occasions in accordance with the signaling (See Para. 0059; the UE monitors the slot format-reporting DCI (SFI monitoring) and controls the switching of BWPs); and
to switch to one of the plurality of machine learning model groups (corresponding to BWPs) in response to receiving the indication detected during the monitored occasions (See Para. 0053; FIG. 11 shows a case in which DCI to include BWP-indicating information to command activation of BWP #1 is transmitted in slot #1, and DCI to include BWP-indicating information to command activation of BWP #2 is transmitted in slot #2. when part of a number of BWPs (for example, only one BWP) configured for the UE is activated, the UE switches the BWP to control the transmission and receipt of data and the like).
Takeda does not explicitly discloses to receive a message configuring a plurality of machine learning model groups.
However, Shen from the same field of endeavor discloses receive a message configuring a plurality of machine learning model groups (corresponding to AI/ML models) (See Para. 0070; a terminal 102 communicates with a network device 104 via a network such as a 5G network, a 6G network, etc. The network device 104 allocate corresponding AI/ML task configuration information to the terminal. The AI/ML model distributed by the network to the terminal includes one or more AI/ML models).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include receive a message configuring a plurality of machine learning model groups as taught by Shen in the system of Takeda to ensure the reliability, timeliness and efficiency of AI/ML operations based on a terminal (See abstract; line 7).
Regarding claims 2 & 23 : The combination of Takeda and Shen disclose an apparatus.
Furthermore, Shen discloses the apparatus, in which the signaling configuring the monitoring occasions deactivates or actives monitoring for at least one of the plurality of machine learning model groups (See Para. 0053; FIG. 11 shows a case in which DCI to include BWP-indicating information to command activation of BWP #1 is transmitted in slot #1, and DCI to include BWP-indicating information to command activation of BWP #2 is transmitted in slot #2. when part of a number of BWPs (for example, only one BWP) configured for the UE is activated, the UE switches the BWP to control the transmission and receipt of data and the like).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in which the signaling configuring the monitoring occasions deactivates or actives monitoring for at least one of the plurality of machine learning model groups as taught by Shen in the system of Takeda to ensure the reliability, timeliness and efficiency of AI/ML operations based on a terminal (See abstract; line 7).
Regarding claims 3 & 24 : The combination of Takeda and Shen disclose an apparatus.
Furthermore, Takeda discloses the apparatus, in which the signaling configuring the monitoring occasions is received via radio resource control (RRC) signaling, a downlink control information (DCI) message (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling).
Regarding claim 4: The combination of Takeda and Shen disclose an apparatus.
Furthermore, Takeda discloses the apparatus, in which the signaling is part of a handover procedure (corresponding to switching to a new BWP) (See Para. 0053; FIG. 11 shows a case in which DCI to include BWP-indicating information to command activation of BWP #1 is transmitted in slot #1, and DCI to include BWP-indicating information to command activation of BWP #2 is transmitted in slot #2. when part of a number of BWPs (for example, only one BWP) configured for the UE is activated, the UE switches the BWP to control the transmission and receipt of data and the like).
Regarding claims 5 & 26: The combination of Takeda and Shen disclose an apparatus.
Furthermore, Takeda discloses the apparatus, in which the signaling is based on a mode of the UE or a type of the UE (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling. the UE monitors the slot format-reporting DCI (SFI monitoring) and controls the switching of BWPs).
Regarding claims 6 & 25: The combination of Takeda and Shen disclose an apparatus.
Furthermore, Shen discloses the apparatus, in which the signaling is received in a general machine learning configuration, not associated with any of the plurality of machine learning model groups (See Para. 0070; a terminal 102 communicates with a network device 104 via a network such as a 5G network, a 6G network, etc. The network device 104 allocate corresponding AI/ML task configuration information to the terminal. The AI/ML model distributed by the network to the terminal includes one or more AI/ML models).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in which the signaling is received in a general machine learning configuration, not associated with any of the plurality of machine learning model groups as taught by Shen in the system of Takeda to ensure the reliability, timeliness and efficiency of AI/ML operations based on a terminal (See abstract; line 7).
Regarding claim 7: The combination of Takeda and Shen disclose an apparatus.
Furthermore, Takeda discloses the apparatus, in which the signaling is based on a mode of the UE or a type of the UE (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling. the UE monitors the slot format-reporting DCI (SFI monitoring) and controls the switching of BWPs).
Regarding claim 12: Takeda discloses a method of wireless communication by a base station (See FIG. 17 & Para. 0197; a base station 10), comprising:
transmitting, to the UE, signaling configuring the monitoring occasions for an indication to switch between the plurality of machine learning model groups (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling. the UE monitors the slot format-reporting DCI (SFI monitoring) and controls the switching of BWPs).
Takeda does not explicitly discloses transmitting, to a user equipment (UE), a message configuring a plurality of machine learning model groups.
However, Shen from the same field of endeavor discloses transmitting, to a user equipment (UE), a message configuring a plurality of machine learning model groups (corresponding to AI/ML models) (See Para. 0070; a terminal 102 communicates with a network device 104 via a network such as a 5G network, a 6G network, etc. The network device 104 allocate corresponding AI/ML task configuration information to the terminal. The AI/ML model distributed by the network to the terminal includes one or more AI/ML models).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include transmitting, to a user equipment (UE), a message configuring a plurality of machine learning model groups as taught by Shen in the system of Takeda to ensure the reliability, timeliness and efficiency of AI/ML operations based on a terminal (See abstract; line 7).
Regarding claim 13: The combination of Takeda and Shen disclose a method.
Furthermore, Shen discloses the method, in which the signaling configuring the monitoring occasions deactivates or actives monitoring for at least one of the plurality of machine learning model groups (See Para. 0053; FIG. 11 shows a case in which DCI to include BWP-indicating information to command activation of BWP #1 is transmitted in slot #1, and DCI to include BWP-indicating information to command activation of BWP #2 is transmitted in slot #2. when part of a number of BWPs (for example, only one BWP) configured for the UE is activated, the UE switches the BWP to control the transmission and receipt of data and the like).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in which the signaling configuring the monitoring occasions deactivates or actives monitoring for at least one of the plurality of machine learning model groups as taught by Shen in the system of Takeda to ensure the reliability, timeliness and efficiency of AI/ML operations based on a terminal (See abstract; line 7).
Regarding claim 14: The combination of Takeda and Shen disclose a method.
Furthermore, Takeda discloses the method, in which the signaling configuring the monitoring occasions is transmitted via radio resource control (RRC) signaling, downlink control information (DCI) message (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling).
Regarding claim 15: The combination of Takeda and Shen disclose a method.
Furthermore, Takeda discloses the method, in which the signaling configuring the monitoring occasions is part of a handover procedure (corresponding to switching to a new BWP) (See Para. 0053; FIG. 11 shows a case in which DCI to include BWP-indicating information to command activation of BWP #1 is transmitted in slot #1, and DCI to include BWP-indicating information to command activation of BWP #2 is transmitted in slot #2. when part of a number of BWPs (for example, only one BWP) configured for the UE is activated, the UE switches the BWP to control the transmission and receipt of data and the like).
Regarding claim 16: The combination of Takeda and Shen disclose a method.
Furthermore, Takeda discloses the method, in which the signaling configuring the monitoring occasions is based on a mode of the UE or a type of the UE (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling. the UE monitors the slot format-reporting DCI (SFI monitoring) and controls the switching of BWPs).
Regarding claim 17: The combination of Takeda and Shen disclose a method.
Furthermore, Shen discloses the method, in which the signaling configuring the monitoring occasions is transmitted as part of a general machine learning configuration, not associated with any of the plurality of machine learning model groups (See Para. 0070; a terminal 102 communicates with a network device 104 via a network such as a 5G network, a 6G network, etc. The network device 104 allocate corresponding AI/ML task configuration information to the terminal. The AI/ML model distributed by the network to the terminal includes one or more AI/ML models).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include in which the signaling is received in a general machine learning configuration, not associated with any of the plurality of machine learning model groups as taught by Shen in the system of Takeda to ensure the reliability, timeliness and efficiency of AI/ML operations based on a terminal (See abstract; line 7).
Regarding claim 18: The combination of Takeda and Shen disclose a method.
Furthermore, Takeda discloses the method, in which the signaling configuring the monitoring occasions is transmitted based on a mode of the UE or a type of the UE (See Para. 0033 & 0059; The UE may monitor the slot format-reporting DCI in slots per with a given periodicity. The periodicity for monitoring the slot format-reporting DCI may be reported in advance from a base station to the UE through higher layer signaling. the UE monitors the slot format-reporting DCI (SFI monitoring) and controls the switching of BWPs).
Allowable Subject Matter
8. Claims 8-11, 19-21 & 27-30 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.
Conclusion
9. The prior art of record and not relied upon is considered pertinent to applicant’s disclosure.
A. Marzban et al. 2025/0380157 A1 (Title: Machine learning model monitoring..) (See Abstract, Para. 0012 & 0037-0038).
B. Hirzallah et al. 2025/0358769 A1 (Title: AI/ML positioning training and interference..) (See abstract, Para. 0006 & 00813-0016).
C. Pratik et al. 2025/0350501 A1 (Title: Recurrent equivariant interference machine for refining 5G…) (See FIG. 1, Para. 0046, 0050 & 0160).
10 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEWALE A AMBAYE whose telephone number is (571)270-1076. The examiner can normally be reached on M.F 6a.m.-2p.m..
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ian Moore can be reached on (571)272-3085. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MEWALE A AMBAYE/Primary Examiner, Art Unit 2469