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 .
Introduction
The claims 52-71 are pending in this application. This is a non-final office action in response to Application Number 18/862,662 filed on 4 November 2024 with a preliminary amendment also filed on 4 November 2024 in which the specification is amended, no claims are amended, claims 1-51 are canceled, and claims 52-71 are added; the instant application is a 371 of PCT/EP2023/061900 filed on 5 May 2023 and also claims priority to provisional application 63/338,523 filed on 5 May 2022.
The applicant of record is Telefonaktiebolaget LM Ericsson (publ) in Stockholm, Sweden. The application papers have been signed by a U.S.-registered patent practitioner. The inventors of record are Pablo Soldati and Euhanna Ghadimi.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4 November 2024 was filed on the filing date of the instant application and before the mailing date of the first office action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 53 is objected to because of the following informalities:
Claim 53, eighth limitation recites “first training data;.” (two types of punctuation marks).
Appropriate correction is required.
Claim Interpretation
The claims have been considered according to the latest Patent Eligibility Guidelines and are considered eligible.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 52, 55, 59, 61-62, 66, 68, and 70-71 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Guim Bernat et al. (U.S. Patent Publication 2019/0042488), hereinafter referred to as Guim.
Regarding claim 52, Guim disclosed a method performed by a first network node for configuring a second network node (see Guim Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node” | Fig. 1, [0034]: system using an AI distributed shared memory controller architecture; examiner notes that additional details about Fig. 1 are described in [0035]-[0051]), the method comprising:
transmitting to the second network node a first message (see Guim [0039]: “In one configuration, the DSM controller 140 can receive a request from a data consumer node, such as the second computing platform 150 that includes the AI hardware platform 152, for AI training data…”) for configuring the second network node with respect to collection of at least first training data for use in training a first model (see Guim [0038]: “…the DSM controller 140 can facilitate a distribution and sharing of AI training data between the data consumer node(s) and the data provider node(s) in the storage rack or data center 100. The data consumer node can consume the received AI training data for training of an AI model that runs at the data consumer node. In one example, the DSM controller 140 can maintain a tracking table that tracks a storage of AI training data on different data provider nodes on a per AI model ID basis. Therefore, the DSM controller 140 can receive a request for AI training data from a data consumer node, identify a data provider node that possesses the requested AI training data, and then instruct the data provider node to send the AI training data to the data consumer node…” | [0039]: “…Therefore, based on the AI model ID corresponding to the requested AI training data, the DSM controller 140 can access the tracking table to determine a particular data provider node that stores the requested AI training data corresponding to the AI model ID. The DSM controller 140 can send an instruction to the data provider node that stores the requested AI training data, and the instruction can instruct the data provider node to send the AI training data to the data consumer node, such as the second computing platform 150…” | Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node”), wherein the first message comprises first data collection configuration information that comprises (examiner notes that the list below is written in a format such that the configuration information comprises A, B, and/or C. Examiner notes that this list can be interpreted such that one, two, or three items from the list are included in the configuration information):
a first process identifier identifying a first process that uses the first model (see Guim Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node”; examiner notes that to be able to send the AI training data, the data provider node must know the address and/or an identifier of the data consumer node | [0073]: memory controller receives a request to perform an operation with respect to a model ID; the memory controller the determines the applicable storage device to be used for performing the requested operation; the operation is then performed on the identified device, i.e. instruction for performing the operation would include the identifier),
a first model identifier identifying the first model (see Guim [039]: “…The AI training data indicated in the request can correspond to an AI model ID of an AI model (e.g., AI Model A 154, AI Model B 156 or AI Model C 158) that runs on the second computing platform 150…” | [0040]: “…Therefore, the AI hardware platform 152 can send a request to the DSM controller 140 for vehicle sensor data. The request can include the vehicle AI model ID to inform the DSM controller 140 of the vehicle AI model that is to consume the vehicle sensor data…”; [0042]: “…the DSM controller can identify, using a tracking table, a suitable AI data set (that corresponds to the AI model ID)…the DSM controller can send an instruction to the storage node to return the AI data set” wherein the AI data set is identified by an AI data set ID which corresponds to the AI model ID), and/or
a first model version identifier identifying a version of the first model.
Regarding claim 55, Guim disclosed the method of claim 52, wherein the first data collection configuration information further comprises (examiner notes that the following list is written in “or” form):
information indicating a number of training data samples to be collected (see Guim [0053]: storing the number of instances of training data for a given AI model ID; [0045]: each AI model ID is associated with a defined amount of bandwidth for reading or storing AI training data; each AI model ID is defined with an amount of data that can be provided per AI model ID, i.e. amount of training data to be collected);
information indicating a minimum number of training data samples to be collected;
information indicating a maximum number of training data samples to be collected;
information indicating a number of training data episodes, wherein each training data episode consists of multiple training data samples;
information indicating a minimum number of training data episodes to be collected; and/or
information indicating a maximum number of training data episodes to be collected.
Regarding claim 59, Guim disclosed the method of claim 52, wherein
the method further comprises receiving a first training data report transmitted by the second network node (see Guim [0038]: “…the DSM controller 140 can facilitate a distribution and sharing of AI training data between the data consumer node(s) and the data provider node(s) in the storage rack or data center 100…Therefore, the DSM controller 140 can receive a request for AI training data from a data consumer node, identify a data provider node that possesses the requested AI training data, and then instruct the data provider node to send the AI training data to the data consumer node…” | [0039]: “…Based on the instruction received from the DSM controller 140, the data provider node can send the AI training data to the second computing platform 150…”), and
the first training data report is associated with the first model and comprises the first training data (see Guim [0038]: “…the DSM controller 140 can facilitate a distribution and sharing of AI training data between the data consumer node(s) and the data provider node(s) in the storage rack or data center 100. The data consumer node can consume the received AI training data for training of an AI model that runs at the data consumer node. In one example, the DSM controller 140 can maintain a tracking table that tracks a storage of AI training data on different data provider nodes on a per AI model ID basis. Therefore, the DSM controller 140 can receive a request for AI training data from a data consumer node, identify a data provider node that possesses the requested AI training data, and then instruct the data provider node to send the AI training data to the data consumer node…” | [0039]: “…Therefore, based on the AI model ID corresponding to the requested AI training data, the DSM controller 140 can access the tracking table to determine a particular data provider node that stores the requested AI training data corresponding to the AI model ID. The DSM controller 140 can send an instruction to the data provider node that stores the requested AI training data, and the instruction can instruct the data provider node to send the AI training data to the data consumer node, such as the second computing platform 150…Based on the instruction received from the DSM controller 140, the data provider node can send the AI training data to the second computing platform 150…”).
Regarding claim 61, Guim disclosed the method of claim 52, wherein
the first message is for further configuring the second network node with respect to the collection of training data for use in producing a third model (see Guim [0038]: “…the DSM controller 140 can facilitate a distribution and sharing of AI training data between the data consumer node(s) and the data provider node(s) in the storage rack or data center 100. The data consumer node can consume the received AI training data for training of an AI model that runs at the data consumer node. In one example, the DSM controller 140 can maintain a tracking table that tracks a storage of AI training data on different data provider nodes on a per AI model ID basis. Therefore, the DSM controller 140 can receive a request for AI training data from a data consumer node, identify a data provider node that possesses the requested AI training data, and then instruct the data provider node to send the AI training data to the data consumer node…” | [0039]: “…Therefore, based on the AI model ID corresponding to the requested AI training data, the DSM controller 140 can access the tracking table to determine a particular data provider node that stores the requested AI training data corresponding to the AI model ID. The DSM controller 140 can send an instruction to the data provider node that stores the requested AI training data, and the instruction can instruct the data provider node to send the AI training data to the data consumer node, such as the second computing platform 150…” | Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node”), and
the first message further comprises third data collection configuration information that comprises (examiner notes that the following list is written in “or” form):
a third process identifier identifying a third process that uses the third model (see Guim Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node”; examiner notes that to be able to send the AI training data, the data provider node must know the address and/or an identifier of the data consumer node | [0037]: running a plurality of AI models | [0073]: memory controller receives a request to perform an operation with respect to a model ID; the memory controller the determines the applicable storage device to be used for performing the requested operation; the operation is then performed on the identified device, i.e. instruction for performing the operation would include the identifier),
a third model identifier identifying the second model (see Guim [0037]: multiple models | [0039]: “…The AI training data indicated in the request can correspond to an AI model ID of an AI model (e.g., AI Model A 154, AI Model B 156 or AI Model C 158) that runs on the second computing platform 150…” | [0040]: “…Therefore, the AI hardware platform 152 can send a request to the DSM controller 140 for vehicle sensor data. The request can include the vehicle AI model ID to inform the DSM controller 140 of the vehicle AI model that is to consume the vehicle sensor data…”; [0042]: “…the DSM controller can identify, using a tracking table, a suitable AI data set (that corresponds to the AI model ID)…the DSM controller can send an instruction to the storage node to return the AI data set” wherein the AI data set is identified by an AI data set ID which corresponds to the AI model ID), and/or
a third model version identifier identifying a version of the second model.
Regarding claim 62, the claim contains the limitations, substantially as claimed, as described in claim 52 above. Examiner notes that claims 52 and 62 both describe methods, however from opposing perspectives. Guim disclosed, as recited in claim 62: A method performed by a second network node (see Guim Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node” | Fig. 1, [0034]: system using an AI distributed shared memory controller architecture; examiner notes that additional details about Fig. 1 are described in [0035]-[0051]), the method comprising:
receiving from a first network node a first message (see Guim [0039]: “In one configuration, the DSM controller 140 can receive a request from a data consumer node, such as the second computing platform 150 that includes the AI hardware platform 152, for AI training data…”) for configuring the second network node with respect to the collection of at least first training data for use in training a first model (see Guim [0038]: “…the DSM controller 140 can facilitate a distribution and sharing of AI training data between the data consumer node(s) and the data provider node(s) in the storage rack or data center 100. The data consumer node can consume the received AI training data for training of an AI model that runs at the data consumer node. In one example, the DSM controller 140 can maintain a tracking table that tracks a storage of AI training data on different data provider nodes on a per AI model ID basis. Therefore, the DSM controller 140 can receive a request for AI training data from a data consumer node, identify a data provider node that possesses the requested AI training data, and then instruct the data provider node to send the AI training data to the data consumer node…” | [0039]: “…Therefore, based on the AI model ID corresponding to the requested AI training data, the DSM controller 140 can access the tracking table to determine a particular data provider node that stores the requested AI training data corresponding to the AI model ID. The DSM controller 140 can send an instruction to the data provider node that stores the requested AI training data, and the instruction can instruct the data provider node to send the AI training data to the data consumer node, such as the second computing platform 150…” | Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node”), wherein the first message comprises first data collection configuration information that comprises (examiner notes that the list below is written in a format such that the configuration information comprises A, B, and/or C. Examiner notes that this list can be interpreted such that one, two, or three items from the list are included in the configuration information):
a first process identifier identifying a first process that uses the first model (see Guim Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node”; examiner notes that to be able to send the AI training data, the data provider node must know the address and/or an identifier of the data consumer node | [0073]: memory controller receives a request to perform an operation with respect to a model ID; the memory controller the determines the applicable storage device to be used for performing the requested operation; the operation is then performed on the identified device, i.e. instruction for performing the operation would include the identifier),
a first model identifier identifying the first model (see Guim [039]: “…The AI training data indicated in the request can correspond to an AI model ID of an AI model (e.g., AI Model A 154, AI Model B 156 or AI Model C 158) that runs on the second computing platform 150…” | [0040]: “…Therefore, the AI hardware platform 152 can send a request to the DSM controller 140 for vehicle sensor data. The request can include the vehicle AI model ID to inform the DSM controller 140 of the vehicle AI model that is to consume the vehicle sensor data…”; [0042]: “…the DSM controller can identify, using a tracking table, a suitable AI data set (that corresponds to the AI model ID)…the DSM controller can send an instruction to the storage node to return the AI data set” wherein the AI data set is identified by an AI data set ID which corresponds to the AI model ID), and/or
a first model version identifier identifying a version of the first model.
Regarding claim 66, the claim contains the limitations, substantially as claimed, as described in claim 55 above. Examiner notes that claim 66 includes one more configuration information as an option within the list in claim 55; the additional option within claim 66 is also described in claim 57. Guim disclosed, as recited in claim 66: The method of claim 62, wherein the first data collection configuration information further comprises (examiner notes that the following list is written in “or” form):
information indicating a number of training data samples to be collected (see Guim [0053]: storing the number of instances of training data for a given AI model ID; [0045]: each AI model ID is associated with a defined amount of bandwidth for reading or storing AI training data; each AI model ID is defined with an amount of data that can be provided per AI model ID, i.e. amount of training data to be collected); information indicating a minimum number of training data samples to be collected;
information indicating a maximum number of training data samples to be collected;
information indicating a number of training data episodes, wherein each training data episode consists of multiple training data samples;
information indicating a minimum number of training data episodes to be collected;
information indicating a maximum number of training data episodes to be collected; or
reporting configuration information indicating a configuration for reporting collected first training data.
Regarding claim 68, the claim contains the limitations, substantially as claimed, as described in claim 59 above and is rejected under Guim according to the rationale provided above.
Regarding claim 70, the claim contains the limitations, substantially as claimed, as described in claim 52 above and is rejected under Guim according to the rationale provided above. Guim further disclosed a first network node comprising:
a transmitter (see Guim Fig. 7 #710 network interface; examiner notes that the network interface is used for transmitting and receiving communications to/from a network); and
processing circuitry (see Guim Fig. 7 #702 processor) configured to perform the method of claim 52.
Regarding claim 71, the claim contains the limitations, substantially as claimed, as described in claim 62 above and is rejected under Guim according to the rationale provided above. Guim further disclosed a second network node comprising:
processing circuitry (see Guim Fig. 7 #702 processor); and
a receiver (see Guim Fig. 7 #710 networking interface; examiner notes that the network interface is used for transmitting and receiving communications to/from a network) for performing the method of claim 62 above.
Claim Rejections - 35 USC § 103
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.
Claims 53-54, 56-58, 60, 63-65, 67, and 69 are rejected under 35 U.S.C. 103 as being unpatentable over Guim as applied to claims 52 and 62 above, and further in view of Wahaj Arshad et al. (U.S. Patent Publication 2022/0104055), hereinafter referred to as Wahaj.
Regarding claim 53, Guim disclosed the invention, substantially as claimed, as described in the method of claim 52, but did not explicitly disclose the following limitation(s), which are taught in a related art, Wahaj, wherein the first data collection configuration information further comprises (examiner notes that the following list is written in “or” form such that one or more of the items may be included in the configuration information):
a first cell identifier identifying a first cell of a radio access network, RAN, to which the first message should be applied (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, as well as starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
a second network node identities indicating the second node to which the configuration is addressed;
an inference function identity to which the configuration is associated to or for which training data collection is configured or required;
an actor identity indicating an actor to which the configuration is associated or for which training data collection is configured or required;
a rollout worker identity indicating a rollout worker to which the configuration is associated to or for which training data collection is configured or required;
the model;
an indication of an exploration strategy to be used for the collection of the first training data;
a configuration parameter associated to an exploration strategy to be used for the collection of the first training data;.
a starting time indicator indicating a time at which the collection of the first training data should begin (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, as well as starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
an ending time indicator indicating a time at which the collection of the first training data should end (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
a time duration indicator indicating a period of time during which the collection of the first training data should occur (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
a repetition pattern indicator indicating a repetition pattern for the collection of the first training data (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
a periodicity indicator for indicating a periodicity for the collection of the first training data (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
at least one triggering condition indicator indicating a triggering condition to be fulfilled for initiating the collection of the first training data (see Wahaj [0050]: data collection configuration includes a condition for starting or terminating data collection; [0147]: preconfiguring when data collection is triggered); and/or
at least one triggering condition indicator indicating a triggering condition to be fulfilled for terminating the collection of the first training data (see Wahaj [0050]: data collection configuration includes a condition for starting or terminating data collection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 54, Guim disclosed the invention, substantially as claimed, as described in the method of claim 52, but did not explicitly disclose the following limitation(s), which are taught in a related art, Wahaj, wherein
the first data collection configuration information further comprises a first triggering condition indicator indicating a first triggering condition to be fulfilled for initiating the collection of the first training data (see Wahaj [0147]: preconfiguring when data collection is triggered), and
the first triggering condition is at least one of:
detection of a new network deployment;
detection of a change in a key performance indicator, where the magnitude of the change exceeds a threshold (see Wahaj [0113]: condition for starting or terminating data collection, e.g., [0114]: signal quality measurement exceeds or falls below a threshold); or
detection of a learning metric satisfying a condition.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 56, Guim disclosed the invention, substantially as claimed, as described in the method of claim 52, but did not explicitly disclose the following limitation(s), which are taught in a related art, Wahaj, wherein the first data collection configuration information further comprises (examiner notes that the following list is written in “or” form):
first time interval information indicating a first interval of time during which the second network node is requested to collect at least training data associated to the use of the first process (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include starting time, duration, periodicity, and/or repetition pattern of data collection | [0065]: data collection configuration includes time window for measurements);
second time interval information indicating a second interval of time during which the second network node is requested to not to collect any training data associated to the use of the first process; and/or
time interval information indicating an interval of time and an indication that enables training data collection associated to the first process or the first model in the time interval; or
time interval information indicating an interval of time and an indication that disables training data collection associated to the first process or the first model in the time interval (see Wahaj [0050]: data collection configuration includes a condition for starting or terminating data collection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 57, Guim disclosed the invention, substantially as claimed, as described in the method of claim 52, but did not explicitly disclose the following limitation(s), which are taught in a related art, Wahaj, wherein the first data collection configuration information further comprises reporting configuration information indicating a configuration for reporting collected first training data (see Wahaj [0108]: performing data collection and reporting measurements based on the data collection configuration message).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 58, Guim-Wahaj disclosed the method of claim 57, wherein the reporting configuration information comprises (examiner notes that the following list is written in “or” form):
a reporting type identifier indicating a type of reporting (see Wahaj [0148]: configuration includes indicating the type of measurement to be reported as well as the starting time, duration of measurements, and periodicity of performing measurements; [0121]: reporting all collected measurements);
start time information indicating a starting time to initiate reporting of the first training data;
information indicating a maximum number of training data samples to be reported for each reporting instance;
information indicating a minimum number of training data samples to be reported in each reporting instance; and/or
at least one reporting triggering condition indicator indicating a triggering condition to be fulfilled for initiating the reporting of the first training data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 60, Guim disclosed the invention, substantially as claimed, as described in the method of claim 52, but did not explicitly disclose the following limitation(s), which are taught in a related art, Wahaj, wherein
the first data collection configuration information indicates a first interval of time during which the second network node is to collect first training data for use in training the first model (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc. | examiner notes that Guim is relied upon above to teach training of a model and collecting training data for training a model),
the first message further comprises second data collection configuration information that comprises: i) a second process identifier identifying a second process (see Guim Fig. 6 #630, [0074]: “sending, from the memory controller [i.e. “first network node”], an instruction to the data provider node [i.e. “second network node”] that instructs the data provider node to send the training data to the data consumer node to enable training of the model that runs on the data consumer node”; examiner notes that to be able to send the AI training data, the data provider node must know the address and/or an identifier of the data consumer node | [0037]: running a plurality of AI models | [0073]: memory controller receives a request to perform an operation with respect to a model ID; the memory controller the determines the applicable storage device to be used for performing the requested operation; the operation is then performed on the identified device, i.e. instruction for performing the operation would include the identifier), ii) a second model identifier identifying a second model (see Guim [0037]: multiple models | [0039]: “…The AI training data indicated in the request can correspond to an AI model ID of an AI model (e.g., AI Model A 154, AI Model B 156 or AI Model C 158) that runs on the second computing platform 150…” | [0040]: “…Therefore, the AI hardware platform 152 can send a request to the DSM controller 140 for vehicle sensor data. The request can include the vehicle AI model ID to inform the DSM controller 140 of the vehicle AI model that is to consume the vehicle sensor data…”; [0042]: “…the DSM controller can identify, using a tracking table, a suitable AI data set (that corresponds to the AI model ID)…the DSM controller can send an instruction to the storage node to return the AI data set” wherein the AI data set is identified by an AI data set ID which corresponds to the AI model ID), and/or iii) a second model version identifier identifying a version of the second model, and
the second data collection information indicates a second interval of time during which the second process should be activated and further indicates that the collection of first training data for use in training the first model should be disabled during the second interval of time (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc. | [0050]: data collection configuration includes a condition for starting or terminating data collection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 63, the claim contains the limitations, substantially as claimed, as described in claim 53. Examiner notes that the configuration information in claim 63 is included as the first option of the list of claim 53, however the list in claim 53 includes additional options. Regarding claim 63, Guim disclosed the invention, substantially as claimed, as described in the method of claim 62, but did not explicitly disclose the following limitation(s), which are taught in a related art, Wahaj, wherein the first data collection configuration information further comprises a first cell identifier identifying a first cell of a radio access network (RAN) to which the first message should be applied (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, as well as starting time, duration, periodicity, and/or repetition pattern of data collection, etc.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 64, the claim contains the limitations, substantially as claimed, as described in claim 53. Examiner notes that the list of configuration information in claim 64 is included in the latter half of claim 53, however the list in claim 53 includes five additional options. Regarding claim 64, Guim disclosed the method of claim 62, wherein the first data collection configuration information further comprises (examiner notes that the following list is written in “or” form):
the model;
an indication of an exploration strategy to be used for the collection of the first training data;
a configuration parameter associated to an exploration strategy to be used for the collection of the first training data;
a starting time indicator indicating a time at which the collection of the first training data should begin (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, as well as starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
an ending time indicator indicating a time at which the collection of the first training data should end (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
a time duration indicator indicating a period of time during which the collection of the first training data should occur (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
a repetition pattern indicator indicating a repetition pattern for the collection of the first training data (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
a periodicity indicator for indicating a periodicity for the collection of the first training data (see Wahaj [0046]: a variety of data collection preferences/configuration parameters that include identifying a particular UE, group of UEs, or type of UE for which the RAN node requests to configure data collection, geographic area for the RAN node, starting time, duration, periodicity, and/or repetition pattern of data collection, etc.);
at least one triggering condition indicator indicating a triggering condition to be fulfilled for initiating the collection of the first training data (see Wahaj [0050]: data collection configuration includes a condition for starting or terminating data collection; [0147]: preconfiguring when data collection is triggered); and/or
at least one triggering condition indicator indicating a triggering condition to be fulfilled for terminating the collection of the first training data (see Wahaj [0050]: data collection configuration includes a condition for starting or terminating data collection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Guim and Wahaj to further describe types of configuration details for collecting data. Including Wahaj’s teachings regarding a variety of configuration details would provide the advantage of adding flexibility to prior art solutions for data collection procedures (see Wahaj [0069]).
Regarding claim 65, the claim contains the limitations, substantially as claimed, as described in claim 54 above and is rejected under Guim-Wahaj according to the rationale provided above.
Regarding claim 67, the claim contains the limitations, substantially as claimed, as described in claim 58 above and is rejected under Guim-Wahaj according to the rationale provided above.
Regarding claim 69, the claim contains the limitations, substantially as claimed, as described in claim 56 above and is rejected under Guim-Wahaj according to the rationale provided above.
Conclusion
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/ANGELA WIDHALM DE RODRIGUEZ/Examiner, Art Unit 2443
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