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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/18/2024 and 08/27/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 1, 4, 10, and 18 objected to because of the following informalities:
Each of claims 1, 4, 10, and 18 contain the phrase “and/or” in the claimed limitations. For the purpose of examination, the examiner is interpreting this phrase as “or” for each of the claims where the phrase “and/or” is recited.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
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 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, 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.
Claim(s) 1-5, 7-10, and 12-18 are rejected under 35 U.S.C. 103 as being unpatentable over Intel Corporation “Functional Framework of AI/ML enabled NG-RAN Network”, 3GPP TSG RAN WG3 R3-212299, hereinafter 3GPP, in view of Ottersten et al. (US 20210345134 A1), hereinafter Ottersten.
For claim 1,
3GPP teaches a communication apparatus configured to communicate with another communication apparatus different from the communication apparatus in a mobile communication system using a machine learning technology, the communication apparatus comprising (at least [Section 2], [Annex A], [Section 3.1], and [FIG. 4.2-1] a host communicates with a model training host or model inference host in an AI/ML enabled NG-RAN network):
a controller configured to perform machine learning processing of learning processing to derive a learned model by using learning data and/or inference processing to infer inference result data from inference data by using the learned model (at least [Section 2], [Annex A], [Section 3.1], and [FIG. 4.2-1] a machine learning process is used to process training data to derive a ML model. Inference data is processed and output using the learning model. It is well known in the art that a controller may be used for this processing.);
3GPP does not explicitly teach, however Ottersten teaches a transmitter configured to transmit, to the other communication apparatus, a message comprising an information element related to a processing capacity and/or a storage capacity usable by the communication apparatus for the machine learning processing (at least [0206] and [0231] an information message comprising ML capabilities, including processing and storage capacities, is transmitted).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for having a communication apparatus communicating with an other communication apparatus using machine learning technology with the method of Ottersten for transmitting an information message comprising ML capabilities, including processing and storage capabilities, to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its available capacity.
For claim 2, 3GPP and Ottersten teach claim 1.
3GPP does not explicitly teach, however Ottersten further teaches wherein the information element is an information element indicating execution capability of the machine learning processing in the communication apparatus (at least [0216] information message indicating a device’s ML capabilities, which would reasonably include its execution capabilities.).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the information message including the execution capability of the machine learning processing of the device to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its execution capability.
For claim 3, 3GPP and Ottersten teach claim 2.
3GPP does not explicitly teach, however Ottersten further teaches a receiver configured to receive, from the other communication apparatus, a transmission request by which the message comprising the information element is requested to be transmitted (at least [0216] and [FIG. 6] a base station queries a UE/device about its ML capabilities with a ML capability query message),
wherein the transmitter is configured to transmit the message comprising the information element to the other communication apparatus in response to receiving the transmission request (at least [0216] and [FIG. 6] a UE/device responds to the ML capability query with an ML capability response).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the communication apparatus receiving a transmission request for the device’s ML capabilities to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its capability.
For claim 4, 3GPP and Ottersten teach claim 2.
3GPP does not explicitly teach, however Ottersten further teaches the controller comprises a processor and/or a memory by which the machine learning processing is performed (at least [0178-0179] memory and processor for carrying out machine learning processing), and
the information element comprises information indicating capability of the processor and/or capability of the memory (at least [0205 and ][0218] processing and storage capabilities may be included in the ML capability response).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the controller having a processor and memory and the information message including processor and memory capabilities to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its capability.
For claim 5, 3GPP and Ottersten teach claim 2.
3GPP further teaches wherein the information element comprises information indicating execution capability of the inference processing (at least [Section 2], [Annex A], [Section 3.1], and [FIG. 4.2-1] inference host assistance information from the model inference host includes capability of the model inference host. It would have been obvious to one of ordinary skill in the art that the capability information of the model inference host may include the execution capability of the inference processing).
For claim 7, 3GPP and Ottersten teach claim 1.
3GPP does not explicitly teach, however Ottersten further teaches wherein the information element is an information element indicating a load status related to the machine learning processing in the communication apparatus (at least [0231] information for executing machine learning processing includes processing power, storage capacity, and load capacity).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the information message including the load status related to the machine learning processing of the device to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its load capability.
For claim 8, 3GPP and Ottersten teach claim 7.
3GPP does not explicitly teach, however Ottersten further teaches a receiver configured to receive, from the other communication apparatus, information by which transmission of the message comprising the information element is requested or configured (at least [0216] and [FIG. 6] a base station queries a UE/device about its ML capabilities with a ML capability query message),
wherein the transmitter is configured to transmit the message comprising the information element to the other communication apparatus in response to reception of the information by the receiver (at least [0216] and [FIG. 6] a UE/device responds to the ML capability query with an ML capability response).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the communication apparatus receiving a transmission request for the device’s ML capabilities to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its capability.
For claim 9, 3GPP and Ottersten teach claim 7.
3GPP does not explicitly teach, however Ottersten further teaches wherein the transmitter is configured to transmit the message comprising the information element to the other communication apparatus in response to a value indicating the load status satisfying a threshold condition or in a periodic manner (at least [0231] information for executing machine learning processing includes processing power, storage capacity, and load capacity. It would have been obvious to one of ordinary skill in the art that a device wouldn’t be used for ML processing if its load status did not satisfy a threshold condition.).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the information message being sent in response to the load status satisfying a threshold to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its load capability.
For claim 10, 3GPP and Ottersten teach claim 7.
3GPP does not explicitly teach, however Ottersten further teaches wherein the controller comprises a processor and/or a memory by which the machine learning processing is performed (at least [0178-0179] memory and processor for carrying out machine learning processing), and
the information element comprises information indicating a load status of the processor and/or a load status of the memory (at least [0231] information for executing machine learning processing includes processing power, storage capacity, and load capacity).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the information message including the load status related to the machine learning processing of the device to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its load capability.
For claim 12, 3GPP and Ottersten teach claim 1.
3GPP does not explicitly teach, however Ottersten further teaches a receiver configured to receive, from the other communication apparatus, a model used for the machine learning processing after the message is transmitted (at least [0216] and [0219] BS transmits a ML model suitable for the device based on its ML capabilities).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for a device receiving a model used for machine learning processing based on its ML capabilities to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its capability.
For claim 13, 3GPP and Ottersten teach claim 1.
3GPP does not explicitly teach, however Ottersten further teaches wherein the other communication apparatus is a base station or a core network apparatus (at least [0219] base station transmits an ML model to the UE), and
the communication apparatus is a user equipment (at least [0219] UE receives ML model from the base station).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the device transmitting being a base station and the device receiving being a user equipment as an obvious implementation that is well known in the art.
For claim 14, 3GPP and Ottersten teach claim 13.
3GPP does not explicitly teach, however Ottersten further teaches wherein the other communication apparatus is the base station (at least [0219] base station transmits messages to the UE), and
the message is an RRC message (it is well known in the art that messages between a base station and UE may be RRC messages, especially in a mobile communication system).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the other communication apparatus being a base station and the message being an RRC message as an obvious implementation that is well known in the art.
For claim 15, 3GPP and Ottersten teach claim 13.
3GPP does not explicitly teach, however Ottersten further teaches wherein the other communication apparatus is the core network apparatus (at least [0075-0076], [0090-0096], and [FIGs. 1 and 2A] core network apparatus), and
the message is a NAS message (it is well known in the art that messages between a core network apparatus and UE may be NAS messages, especially in a mobile communication system).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for transmitting an information message comprising ML capabilities with the method of Ottersten for the other communication apparatus being a core network and the message being a NAS message as an obvious alternative implementation that is well known in the art.
For claim 16, 3GPP and Ottersten teach claim 1.
3GPP does not explicitly teach, however Ottersten further teaches wherein the other communication apparatus is a core network apparatus (at least [0075-0076], [0090-0096], and [FIGs. 1 and 2A] core network apparatus), and
the communication apparatus is a base station (at least [0075-0076], [0090-0096], and [FIGs. 1 and 2A] core network apparatus and base station).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for having a communication apparatus communicating with another communication apparatus using machine learning technology with the method of Ottersten for the other communication apparatus being a core network and the communication apparatus being a base station as an obvious alternative implementation that is well known in the art.
For claim 17, 3GPP and Ottersten teach claim 1.
3GPP does not explicitly teach, however Ottersten further teaches wherein the other communication apparatus is a first base station (at least [0075-0076], [0090-0096], and [FIGs. 1, 2A and 2B] a first network node, such as a first base station), and
the communication apparatus is a second base station (at least [0075-0076], [0090-0096], and [FIGs. 1, 2A and 2B] a second network node, such as a second base station).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for having a communication apparatus communicating with another communication apparatus using machine learning technology with the method of Ottersten for the other communication apparatus being a second base station and the communication apparatus being a first base station as an obvious alternative implementation that is well known in the art.
For claim 18, it is rejected on the same basis as claim 1.
Claim(s) 6 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over 3GPP and Ottersten, in view of Soldati et al. (WO 2022013095 A1), hereinafter Soldati.
For claim 6, 3GPP and Ottersten teach claim 2.
3GPP and Ottersten do not explicitly teach, however Soldati teaches wherein the information element comprises information indicating execution capability of the learning processing (at least [Page 22, line 17 – Page 23, line 15] Information received by the RAN node may include an indication of whether the wireless device has training capability (execution capability of learning processing)).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP and Ottersten for transmitting an information message comprising ML capabilities with the method of Soldati for the information message including execution capability of the learning processing to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its capability.
For claim 11, 3GPP and Ottersten teach claim 1.
3GPP does not explicitly teach, however Ottersten further teaches wherein the transmitter is configured to transmit, to the other communication apparatus, the message comprising the information element… (at least [0206] and [0231] an information message comprising ML capabilities, including processing and storage capacities, is transmitted)
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP for having a communication apparatus communicating with an other communication apparatus using machine learning technology with the method of Ottersten for transmitting an information message comprising ML capabilities to ensure the optimal ML model is selected for use and to prevent a device being assigned a ML model and task that exceeds its available capacity.
3GPP and Ottersten do not explicitly teach, however Soldati teaches … and a model identifier associated with the information element, and the model identifier is an identifier by which a model in machine learning is identified (at least [Page 10, Lines 7-13] information about an ML model that a device is to use or has been configured to execute may include a model identifier).
Therefore, it would have bene obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of 3GPP and Ottersten for transmitting an information message with the method of Soldati for the information message including a model identifier to ensure data and model synchronization between the two devices and reduce transmission overhead in the system.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Benjamin T. Ranew whose telephone number is (571)272-2746. The examiner can normally be reached Monday - Friday 9:00 AM - 5:00 PM EST. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ayman A Abaza can be reached at (571) 270-0422. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BENJAMIN T. RANEW/Examiner, Art Unit 2429
/AYMAN A ABAZA/SPE, Art Unit 2429
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