DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
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.
Status of the Claims
This communication is in response to communications received on 6/28/24. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification as there are no amendments. Therefore, Claims 1-20 is/are pending and have been addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 6/28/24 and 5/23/25 was/were considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application(s) filed in China on 12/30/21. Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Response to Arguments
There are no arguments.
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(s) 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1, 13, and 16 that, under its broadest reasonable interpretation, is directed to model training.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 1-20 recites a series of steps).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added):
Claim 1: transmitting, by a first network element, a federated model training request message to at least one second network element when performing a federated model training process corresponding to a model training task, wherein the at least one second network element is a network element participating in the federated model training process, and samples corresponding to training data used by different second network elements for the federated model training process are different but have same sample features;
receiving, by the first network element, first information transmitted by the at least one second network element, wherein the first information comprises at least a first training result, and the first training result corresponds to training data used by the second network element for the federated model training process; and
performing, by the first network element, model training based on a first model and the first training result reported by the at least one second network element, to obtain a target model and/or a second training result.
Claim(s) 13: same analysis as claim(s) 1.
Claim 13 additionally: performing, by the second network element, model training based on the federated model training request message to obtain a first training result;
wherein the first training result corresponds to training data used by the second network element for the federated model training process, a sample used by the second network element for the federated model training and a sample used by a fifth network element for the federated model training are different but have same sample features, and the fifth network element is a network element, other than the second network element, among a plurality of network elements participating in the federated model training process.
Claim 16: wherein the method comprises: receiving, by a fourth network element, related information of a target model that is transmitted by a first network element, wherein the related information of the target model is used to represent at least that the target model is a horizontal federated model.
Dependent claims 2-12, 14-15, and 17-20 recite the same or similar abstract idea(s) as independent claim(s) 1, 13, and 16 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
mathematical relationships, mathematical formulas or equations, or mathematical calculations because the invention is directed to the application of mathematical processes as they are associated with model training.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea a communications device, comprising a processor and a memory (claim(s) 18-20).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0010, 0325]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0016, 0325]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
Claim Rejections - 35 USC § 102
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.
(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) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xin et al. (US 2023/0083982 A1).
Regarding claim 1, 13, and 16, Xin teaches a model training method, comprising: {a model training method, wherein the method comprises: – claim 13}
{a model training method, – claim 16}
transmitting, by a first network element, a federated model training request message to at least one second network element when performing a federated model training process corresponding to a model training task, wherein the at least one second network element is a network element participating in the federated model training process, and samples corresponding to training data used by different second network elements for the federated model training process are different but have same sample features {receiving, by a second network element, a federated model training request message transmitted by a first network element, wherein the federated model training request message is used to request the second network element to participate in a federated model training process corresponding to a model training task;
performing, by the second network element, model training based on the federated model training request message to obtain a first training result – claim 13} [see at least Figs. 9A-9B and [0343-0344, 0347] for a model training method where in steps 901-902 a server (element 1) sends data to client 1 and client 2 (element 2) for the clients to perform training “a server NWDAF determines that clients NWDAFs that perform horizontal federated training are a client NWDAF 1 and a client NWDAF 3 is used”;
Fig. 9A and [0349] “Step 903: The client NWDAF 1 or the client NWDAF 3 performs a training process based on data that is obtained by the client NWDAF 1 or the client NWDAF 3 and the configuration parameter, to obtain a sub-model.”;
[0343, 0179] ([0343]) training data are different but have same sample features “clients NWDAFs that perform horizontal federated training are a client NWDAF 1 and a client NWDAF 3” ([0179]) “Horizontal federated learning (Horizontal FL, or HFL): A feature repetition rate is very high, but data samples differ from each other greatly.”];
receiving, by the first network element, first information transmitted by the at least one second network element, wherein the first information comprises at least a first training result, and the first training result corresponds to training data used by the second network element for the federated model training process {transmitting, by the second network element, first information to the first network element, wherein the first information comprises at least the first training result,
wherein the first training result corresponds to training data used by the second network element for the federated model training process, a sample used by the second network element for the federated model training and a sample used by a fifth network element for the federated model training are different but have same sample features, and the fifth network element is a network element, other than the second network element, among a plurality of network elements participating in the federated model training process – claim 13} [see at least Figs. 9A-9B and [0343-0344, 0347] for a model training method where in steps 901-902 a server (element 1) sends data to client 1 and client 2 (element 2 and element 5) for the clients to perform training “a server NWDAF determines that clients NWDAFs that perform horizontal federated training are a client NWDAF 1 and a client NWDAF 3 is used”;
Fig. 9B and [0351-0353] “Step 904: The client NWDAF 1 sends, to the server NWDAF, the sub-model obtained by the client NWDAF 1 through training …
Step 905: The client NWDAF 3 sends, to the server NWDAF, the sub-model obtained by the client NWDAF 3 through training”]; and
performing, by the first network element, model training based on a first model and the first training result reported by the at least one second network element, to obtain a target model and/or a second training result [see at least Fig. 9B and [0358-0363] “Step 906: The server NWDAF aggregates the sub-model obtained by the client NWDAF 1 through training and the sub-model obtained by the client NWDAF 3 through training, to obtain an updated model after a current round of iteration.
Step 907: The server NWDAF sends the updated model to the client NWDAF 1 and the client NWDAF 3. …
Step 908: After determining that federated training is terminated, the server NWDAF determines a target model based on the updated model.
Step 909: The server NWDAF may allocate a version identifier (Version ID) and/or an analytics result type identifier (analytics ID) corresponding to the target model (which is referred to as Trained Model, Global Model, or Optimal Model).”]
{wherein the method comprises: receiving, by a fourth network element, related information of a target model that is transmitted by a first network element, wherein the related information of the target model is used to represent at least that the target model is a horizontal federated model – claim 16 } [see at least [0368] “Step 911: The client NWDAF 1 and the client NWDAF 3 send, to an NEF network element, the target model and at least one of the model identifier Model ID, the version identifier Version ID, and the analytics result type identifier analytics ID that correspond to the target model.”;
[0371-0372] “Step 912: The server NWDAF registers the supported analytics ID and the corresponding valid range with the NRF network element.
In this embodiment of this application, the valid range corresponding to the analytics ID in step 912 includes a valid range of the analytics ID on the client NWDAF.”].
Regarding claim 2, 14, and 17, Xin teaches the method according to claim 1, wherein the federated model training request message comprises at least one of the following:
model instance identification information, wherein the model instance identification information corresponds to the target model and is allocated by the first network element;
type information of the model training task;
identification information of the model training task;
first indication information, used to indicate that the federated model training process is a horizontal federated learning process;
related information of a first filter, used to define at least one of a target object, target time, or a target area that correspond to the model training task;
related information of the first model, wherein the related information of the first model is used by each second network element to perform local model training;
model training configuration information;
reporting information of the first training result; or
related information of each network element participating in the federated model training process [see at least [0284] “an initial model”;
[0285] “an algorithm type”;
[0285] “The training set selection criterion is a limitation for each feature.”;
[0284] “a training termination condition, maximum training time, or maximum waiting time”;
[0285] “an initial model”;
[0284] “an initial model”;
[0350, 0285] ([0350]) “reporting process” ([0285]) “Training termination condition: For example, a maximum quantity of iterations.”].
Regarding claim 3 and 15, Xin teaches the method according to claim 2, wherein the model instance identification information corresponds to at least one of the following:
related information of the first network element;
first time, used to indicate that the model training task is performed based on training data generated within the first time;
second time, used to indicate completion time of the federated model training process; or
related information of the second network element; and/or
the reporting information of the first training result comprises at least one of the following:
a reporting format of the first training result; or
a reporting condition of the first training result [see at least [0284] “an initial model”;
[0284] “a training termination condition, maximum training time”;
[0284] “an initial model”;
[0350, 0285] ([0350]) “reporting process” ([0285]) “Training termination condition: For example, a maximum quantity of iterations.”].
Regarding claim 4, Xin teaches the method according to claim 2, wherein the model training configuration information comprises at least one of the following:
model structure information;
model hyperparameter information;
type information of training data in the federated model training process;
model training condition information, used to indicate, to the second network element, a condition under which the first network element starts model training based on the first training result reported by the second network element;
model training count information, used to indicate the number of times of local model training that the second network element needs to perform before transmitting the first information to the first network element; or
model training duration information, used to indicate duration of local model training that the second network element needs to perform before transmitting the first information to the first network element [see at least [0285] “model parameter”;
[0284] “a training termination condition, maximum training time, or maximum waiting time”;
[0284] “a training set selection criterion”;
[0284] “maximum waiting time”;
[0285] “Training termination condition: For example, a maximum quantity of iterations”;
[0285] “maximum training time”].
Regarding claim 5, Xin teaches the method according to claim 4, wherein the model training condition information comprises at least one of the following:
third time, used to indicate that model training is to start in a case that waiting time of the first network element waiting for the second network element to feed back the first training result reaches the third time; or
a first threshold, used to indicate that model training is to start in a case that the number of first training results received by the first network element reaches the first threshold [see at least [0285] “The maximum waiting time is used to indicate maximum time at which the first data analytics network element waits for the third data analytics network element to feed back the sub-model during each round of iterative training.”;
[0285] “a maximum quantity of iterations”].
Regarding claim 6, Xin teaches the method according to claim 1, wherein the first training result comprises model information of a second model and/or first gradient information corresponding to the second model obtained by the second network element through training based on a local training model; and/or
the first information further comprises at least one of the following:
model instance identification information, used by the first network element to perform model association; and/or
the second network element is a network element that is obtained by the first network element from a network repository function NRF based on the model training task and that is able to support the federated model training process [see at least [0189] “Each client reports a quantity of samples and a local gradient value:”;
[0284] “an initial model”;
Fig. 8 step 807 and [0162] “the service discovery network element 300 may be a network repository function (NRF) network element”].
Regarding claim 7, Xin teaches the method according to claim 1, wherein the method further comprises:
determining, by the first network element, that a first condition is met, wherein the first condition comprises at least one of the following:
that all or some of training data corresponding to the model training task is not stored in the first network element or is not able to be obtained;
that the at least one second network element is able to provide all or some of the training data corresponding to the model training task; or
that training data corresponding to the model training task and used by different second network elements has different samples with same sample features; and/or
the method further comprises:
transmitting, by the first network element, second information to the at least one second network element in a case that a calculation result of a loss function of the target model does not meet a predetermined requirement,
wherein the second information comprises at least the second training result, and the second training result is used by the second network element to perform local model training again and re-obtain a first training result [see at least [0177] “when original data is not transmitted out of a local domain”;
Fig. 6 steps 601-602;
[0179] “Horizontal federated learning (Horizontal FL, or HFL):”;
[0195, 0361] “In the foregoing training process, the server node may control, based on a quantity of iterations, the training to end, for example, terminate the training when the training is performed for 10000 times, or control, by setting a threshold of the loss function, the training to end, for example, control the training to end when LI≤0.0001.
Step 903 to step 907 may be cyclically performed until a training termination condition set when the client NWDAF 1 and the client NWDAF 3 perform sub-model training is met.”].
Regarding claim 8, Xin teaches the method according to claim 7, wherein the transmitting second information to the at least one second network element comprises any one of the following:
for each second network element, transmitting specified information to the second network element, wherein the specified information belongs to the second information and is different from information comprised in the federated model training request message; or
for each second network element, transmitting all information in the second information to the second network element [see at least Fig 9B step 907 [0359-0361] “Step 907: The server NWDAF sends the updated model to the client NWDAF 1 and the client NWDAF 3.
It may be understood that each client NWDAF performs a plurality of rounds of iterative training, and each client NWDAF in each round of iterative training obtains, through training, a sub-model corresponding to a current round of iterative training. After the sub-model is obtained through each round of iteration training, each client NWDAF reports, to the server NWDAF, the sub-model corresponding to the current round of iteration training.
Step 903 to step 907 may be cyclically performed until a training termination condition set when the client NWDAF 1 and the client NWDAF 3 perform sub-model training is met.”].
Regarding claim 9, Xin teaches the method according to claim 1, wherein the method further comprises:
transmitting, by the first network element, second information to at least one third network element in a case that a calculation result of a loss function of the target model does not meet a predetermined requirement,
wherein the second information comprises at least the second training result, the second training result is used by the third network element to perform local model training to obtain a third training result, and the third network element is a network element that is re-determined by the first network element and that participates in the federated model training process [see at least Fig. 9B step 907 and [0195, 0361] “In the foregoing training process, the server node may control, based on a quantity of iterations, the training to end, for example, terminate the training when the training is performed for 10000 times, or control, by setting a threshold of the loss function, the training to end, for example, control the training to end when LI≤0.0001.
Step 903 to step 907 may be cyclically performed until a training termination condition set when the client NWDAF 1 and the client NWDAF 3 perform sub-model training is met.”].
Regarding claim 10, Xin teaches the method according to claim 9, wherein the second training result comprises at least second gradient information of the loss function with respect to a parameter of the target model or information of the target model; and/or
the second information further comprises at least one of the following:
model instance identification information, used by the second network element to perform model association; reporting information of the first training result;
related information of each network element participating in the federated model training process; or
model training configuration information corresponding to the target model [see at least Fig. 9B steps 904-905 and [0189, 0351-0354, 0361] ].
Regarding claim 11, Xin teaches the method according to claim 1, wherein the method further comprises:
receiving, by the first network element, a model request message transmitted by a fourth network element, wherein the model request message comprises at least one of the following:
the type information of the model training task;
the identification information of the model training task; related information of a second filter, used to define at least one of a target object, target time, or a target area that correspond to the model training task; or
model feedback-related information, wherein the model feedback-related information comprises at least one of a model feedback format or a feedback condition [see at least Fig. 1 and [0145] for two way communication between elements;
[0083, 0147, 0254] “In a possible implementation, the type of the first data analytics network element includes one or more of the following information: a server, a coordinator, a centralized trainer, and a global trainer.”;
[0071] “In another example, this embodiment of this application provides a communication apparatus. The communication apparatus may be the first data analytics network element, or may be the apparatus (for example, a chip) used in the first data analytics network element. … When the communication apparatus is the first data analytics network element, the processing unit may be a processor. The communication unit may be a communication interface. The storage unit may be a memory. When the communication apparatus is the chip in the first data analytics network element, the processing unit may be the processor, and the communication unit may be collectively referred to as the communication interface. For example, the communication interface may be an input/output interface, a pin, or a circuit. The processing unit executes the computer program code stored in the storage unit, to enable the first data analytics network element to implement the method according to any one of the first aspect or the possible implementations of the first aspect. The storage unit may be a storage unit (for example, a register or a cache) in the chip, or may be a storage unit (for example, a read-only memory or a random access memory) that is in the first data analytics network element and that is outside the chip.”;
[0285] “The training set selection criterion is a limitation for each feature.”;
[0284] “a training termination condition, maximum training time, or maximum waiting time”].
Regarding claim 12, Xin teaches the method according to claim 8, wherein the method further comprises:
transmitting, by the first network element, related information of the target model to the fourth network element in a case that the calculation result of the loss function of the target model meets the predetermined requirement,
wherein the related information of the target model comprises at least one of the following:
the model instance identification information;
information of the target model;
second indication information, used to indicate that the target model is a horizontal federated learning model; or
related information of the second network element and/or the third network element [see at least Fig. 9B steps 909-910 and [0363-0364] ].
Regarding claim 18, 19, and 20, Xin teaches a communications device, comprising a processor and a memory, wherein the memory stores a program or instructions executable on the processor, and when the program or instructions are executed by the processor, the steps of the model training method according to claim 1 are implemented [see at least [0083, 0147, 0254] “In a possible implementation, the type of the first data analytics network element includes one or more of the following information: a server, a coordinator, a centralized trainer, and a global trainer.”;
[0071] “In another example, this embodiment of this application provides a communication apparatus. The communication apparatus may be the first data analytics network element, or may be the apparatus (for example, a chip) used in the first data analytics network element. … When the communication apparatus is the first data analytics network element, the processing unit may be a processor. The communication unit may be a communication interface. The storage unit may be a memory. When the communication apparatus is the chip in the first data analytics network element, the processing unit may be the processor, and the communication unit may be collectively referred to as the communication interface. For example, the communication interface may be an input/output interface, a pin, or a circuit. The processing unit executes the computer program code stored in the storage unit, to enable the first data analytics network element to implement the method according to any one of the first aspect or the possible implementations of the first aspect. The storage unit may be a storage unit (for example, a register or a cache) in the chip, or may be a storage unit (for example, a read-only memory or a random access memory) that is in the first data analytics network element and that is outside the chip.”].
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xin et al. – WO 2021/218274 A1 (relevant because it teaches same as US 2023/0083982 A1) as noted in IDS dated 5/23/25
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WEBB whose telephone number is (313)446-6615. The examiner can normally be reached on M-F 10-3.
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, Jerry O’Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES WEBB/Examiner, Art Unit 3624
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