Office Action 1: Non-Final Rejection (2024-07-25)
Rejection types: 101 - Patent-Eligible Subject Matter, 103 - Obviousness

CLAIMS:
Claims

WHAT IS CLAIMED IS:

1\. A privacy-enhanced federated decision-making method for an interaction between a federated data terminal and a federated coordinator, comprising:
step 1: sending, by the federated coordinator, a learning signal to the federated data terminal based on decision-making task request information, and receiving, by the federated data terminal, the learning signal and making a local decision based on a decision-making task characteristic;
step 2: calculating and updating, by the federated data terminal, information about a local decision-making model based on the local decision-making result, to obtain the updated information about the local decision-making model and report the updated information about the local decision-making model and an intermediate result of calculating a parameter of the local decision-making model to the federated coordinator;
step 3: calculating and updating, by the federated coordinator, a global decision-making model by using the updated information about the local decision-making model, and
aggregating a new federated decision-making model by using the intermediate result of calculating the parameter of the local decision-making model;
step 4: determining, by the federated coordinator, whether a training termination condition is met; when the training termination condition is met, generating, by the federated coordinator based on the trained global decision-making model, coordination information for coordinating decision making of the federated data terminal; when the training termination condition is not met, return to step 1; and
step 5: storing, by the federated coordinator, the new federated decision-making model and the coordination information for coordinating the decision-making of the federated data terminal to a global storage module, and sending the new federated decision-making model and the coordination information to the federated data terminal; and generating, by the federated data terminal, a terminal decision-making model by combining the local decision- making model and the new federated decision-making model based on the coordination information delivered by the federated coordinator and whether the new federated decision- making model is needed to guide local decision-making, wherein the terminal decision-making model is used for the local decision-making to generate an optimal local decision.
2\. The privacy-enhanced federated decision-making method according to claim 1, wherein the aggregating of the new federated decision-making model is based on whether the federated data terminal needs the federated decision-making model to guide the local decision making;
the federated coordinator aggregates the new federated decision-making model by using the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal, and the aggregating of the new federated decision-making model comprises three cases:
case 1: when the federated data terminal does not need the federated coordinator to guide the local decision-making, skipping, by the federated data terminal, reporting the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator; skipping, by the federated coordinator, receiving the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; skipping, by the federated coordinator, aggregating the new federated decision- making model; and after the training is completed, skipping, by the federated coordinator, sending the federated decision-making model to the federated data terminal;
case 2: when the federated data terminal needs the federated coordinator to completely guide the local decision-making, reporting, by the federated data terminal, all information in the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator; receiving, by the federated coordinator, the all information in the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; aggregating, by the federated coordinator, the new federated decision-making model based on the all information in the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; and
after the training is completed, sending, by the federated coordinator, the new federated decision-making model to the federated data terminal, wherein the new federated decision- making model is configured for guiding the federated data terminal in the local decision making;
and case 3: when the federated data terminal needs the federated coordinator to partially guide the local decision making, reporting, by the federated data terminal, partial information of the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator; receiving, by the federated coordinator, the partial information of the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; aggregating, by the federated coordinator, the new federated decision-making model based on the partial information of the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; and after the training is completed, sending, by the federated coordinator, the new federated decision-making model to the federated data terminal, wherein the new federated decision-making model is configured for guiding the federated data terminal in the local decision making.

3\. A privacy-enhanced federated decision-making apparatus implementing the privacy- enhanced federated decision-making method according to claim 1, comprises:
a first terminal sending and receiving module, a local decision-making module, a local calculation module, a local storage module, a second terminal sending and receiving module, a coordinator sending and receiving module, a global storage module, a federated calculation module, and a global decision-making module.

4\. The privacy-enhanced federated decision-making apparatus according to claim 3, wherein the first terminal sending and receiving module is configured to interact with the federated coordinator, send the parameter of the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator, and receive the learning signal, the coordination information, and the federated decision-making model delivered by the federated coordinator;
the local decision-making module is configured to make the local decision for each federated data terminal based on the decision-making task characteristic and data in the local storage module, make a decision based on the local decision-making model after receiving the learning signal received and sent by the second terminal sending and receiving module, and
transmit a decision-making result to the local calculation module for further calculation;
the local calculation module is configured to calculate an updated parameter of the local decision-making model after local decision making based on the decision-making result of the local decision-making module and a calculation parameter received by the first terminal sending and receiving module, store the updated parameter of the local decision-making model and a new intermediate result of calculating the updated parameter of the local decision-making model in the local storage module, and perform a calculation for aggregating the federated decision-making model and the local decision-making model into the terminal decision-making model;
the local storage module is configured to store local data, the calculation parameter, the local decision-making model, the terminal decision-making model, and the new intermediate result of calculating the updated parameter of the local decision-making model, send the updated parameter of the local decision-making model and the new intermediate result of calculating the updated parameter of the local decision-making model to the federated coordinator via the first terminal sending and receiving module, and send, when there is a local decision-making task, the local decision-making model and the local data to the local calculation module for the local decision making; and
the second terminal sending and receiving module is configured to receive dynamic changes in local resources and data, update the latest local data to the local storage module, receive a local decision-making task request, report the local decision-making task request to the federated coordinator via the federated data terminal, and send a terminal decision-making strategy to a specific executor.

5\. The privacy-enhanced federated decision-making apparatus according to claim 3, wherein the coordinator sending and receiving module is configured to interact with the federated data terminal, send the learning signal, the coordination information, and the federated decision-making model to the corresponding federated data terminal, and send the information about the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model to the federated calculation module for updating a global decision-making model and aggregate the federated decision-making model, wherein the information about the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model is sent by the federated data terminal;
the global storage module is configured to store the global decision-making model, store the parameter of the global decision-making model integrated by the federated calculation module, and send, after the global decision-making model is trained, the global decision- making model to the global decision-making module to generate the coordination information for coordinating decision making of the federated data terminal;
the federated calculation module is configured to process the information about the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model, calculate and update the global decision-making model by using the information about the local decision-making model, and aggregate the new federated decision- making model by using the intermediate result of calculating the parameter of the local decision-making model; and
after the global decision-making model is trained, the global decision-making module generates, based on the trained global decision-making model, the coordination information for coordinating the decision making of the federated data terminal and stores the coordination information in the global storage module.

6\. A privacy-enhanced federated decision-making system comprises the privacy-enhanced federated decision-making apparatus according to claim 3.

7\. The privacy-enhanced federated decision-making method according to claim 1, wherein the learning signal comprises a Boolean variable or a numerical variable. 22 23 24 25 26

OFFICE ACTION:
Notice of Pre-AIA  or AIA  Status
1.	The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
2.    NO restrictions warranted at initial time of filing for patent.
Priority
3.    Applicant claims Chinese priority under 35 USC 119e to Chinese application filed on 09/28/2021.
Information Disclosure Statement
4.    The information disclosure statement (IDS) submitted on 11/24/2022, the submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Oath/Declaration
5.    Applicant’s Oath was filed on 06/24/2022.
Drawings
6.    Applicant’s drawings filed on 06/24/2022 has been inspected and is in compliance with MPEP 608.01.
Specification
7.    Applicant’s specification filed on 06/24/2022 has been inspected and is in compliance with MPEP 608.02.
Claim Objections
8.    NO objections warranted at initial time of filing for patent.
Remarks
9.	Examiner request Applicant review relevant prior art under the conclusion of this office action.
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.
10.	Claims 1 and associated claims 2-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step one: Are the claims at issue directed to a statutory category?
Yes. The claims recites a series of steps i.e., sending, by a federated coordinator, a learning signal to the federated data terminal, calculating and updating, by the federated data terminal, information about a local decision-making model, calculating and updating, by the federated data coordinator, a global decision-making model, determining, by the federated coordinator, whether a training termination condition is met and storing, by the federated coordinator, the new federated decision-making model and the coordination information.
Step 2A – Prong 1: Is a Judicial Exception recited?
Yes. The claim recites the limitation of sending, by a federated coordinator, a learning signal to the federated data terminal, calculating and updating, by the federated data terminal, information about a local decision-making model, calculating and updating, by the federated data coordinator, a global decision-making model. This limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “data terminal,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by the federated terminal” language, the claim encompasses a user being asked to make a decision about a local issue drawing a decision flow chart/model, calculating and updating the user’s answer in the mind using global and intermediate parameters using a paper and pen to make an optimal decision in his/her mind. The mere nominal recitation of a generic terminal/coordinator does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitation of determining, by the federated coordinator, whether a training termination condition is met and storing, by the federated coordinator, the new federated decision-making model and the coordination information. As drafted, the limitations are a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “by a terminal,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a federated terminal” language, the determining and storing step in the context of this claim encompasses the a mental process using a paper and pen to make an optimal decision. In fact, the claim could even use mathematical relationships to arrive and deliver an optimal score. Thus, the claim recites a mental process.
Step 2A – Prong 2: Are the claims integrated into a practical application recited?
No. The claim recites five elements: sending, by a federated coordinator, a learning signal to the federated data terminal, calculating and updating, by the federated data terminal, information about a local decision-making model, calculating and updating, by the federated data coordinator, a global decision-making model, determining, by the federated coordinator, whether a training termination condition is met and storing, by the federated coordinator, the new federated decision-making model and the coordination information.. The sending, calculating and updating, calculating and updating, determining and storing steps are recited at a high level of generality (i.e., as a general means of using local and global parameters to update a decision tree to determine an optimal decision), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The processor that performs the sending, calculating and updating, calculating and updating, determining and storing steps are also recited at a high level of generality, and merely automates the obtaining, determining, augmenting, and providing steps step. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component (processor).
The combination of these additional elements is no more than mere instructions to apply the exception using a generic computer component (processor). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
Step 2b: Does the claims provide an inventive concept?
No. As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the sending, calculating and updating, calculating and updating, determining and storing steps were considered to be extra-solution activity in Step 2A, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the example does not provide any indication that the processor is anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer Option 2.
For these reasons, there is no inventive concept in the claim, and thus it is ineligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
11.	Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over U.S.  Publication No. 20210117395 hereinafter Wang in view of U.S. Publication No. 20220158890 hereinafter Karapantelakis.
As per claim 1, Wang discloses:
A privacy-enhanced federated decision-making method for an interaction (para 0007 “The purpose of this invention is to provide a whole-lifecycle encrypted big data analysis method and system for the data from the different sources, based on the whole-lifecycle encryption secure sharing, trade secret protection and federated learning of big data research analysis, to solve the current problem of the privacy protection in big data multi-party collaborative analysis.” Para 0010 “Processing the data from the multiple data sources to obtain the data for each data source involved in the calculation, which is required by the analysis, wherein the raw data of each data source would never cross the border of the local area, and encryption is required for all the uploaded data.”), comprising:
step 1: sending a learning signal based on decision-making task request information, and receiving, by the federated data terminal, the learning signal and making a local decision based on a decision-making task characteristic (para 0064 “Step S100: processing the data from the multiple data sources to obtain the data for each data source involved in the calculation, which is required by the analysis, wherein the raw data of each data source would never cross the border of the local area, and encryption is required for all the uploaded data.” para 0084 “ Relying on the system architecture and data processing procedures provided by the invention, authorized users can develop a wide range of whole-lifecycle encrypted big data analysis methods in compliance with various business secret protection regulations, including but not limited to: (1) ID Matching: Fast index matching of labeled data across the multiple data sources; (2) Data Query: Double-blind data query, i.e., the data queryer cannot know the content of the database, while the data provider cannot know the specific queried information of the data queryer. (3) Data Analysis: Including various big data analysis statistical methods (for example, analysis of variance, Kolmokolov-Smilov test, and genome-wide association analysis, etc.).”);
step 2: calculating and updating information about a local decision-making model based on the local decision-making result, to obtain the updated information about the local decision-making model and report the updated information about the local decision-making model and an intermediate result of calculating a parameter of the local decision-making model to a federated coordinator (para 0066 “ Step S110, Process the data from the different data sources to obtain the data with a unified format. Specifically, define the expression, essence, structure and/or coding strategy of related data according to data analysis requirements (e.g., for a specific algorithm, a unified structure of the data, parameters, mapping files, and preprocessed files for all data sources). The purpose of this step is to unify the data structure and parameter coding for all data sources, which can achieve data standardization to facilitate collaborative calculation.” Para 0068 “ Step S130, process the transmitted data in a verified Trusted Execution Environment (TEE), such as data collision check, to form a database of all the processed data, which is a global database: all data from the different data sources involved in the calculation. For example, decrypt the reordered data in the trusted execution environment, and perform a data collision check algorithm based on the indexing data to remove redundant data. The remaining data is the data union of different data sources that form the global database. Also, the global database could be the data intersection or the difference, or the combination. Because the memory of TEE is relatively small, in order to improve the calculation efficiency of data collision check for big data, the present invention can adopt at least one of the following algorithm: binary search tree, sequential search, binary search, chunking algorithm, red-black tree, balanced search tree, hash table, trie, suffix tree, Bloom Filter, brute-force, Rabin-Karp algorithm, KMP algorithm, Boyer-Moore algorithm, Sunday algorithm, Horspool algorithm and other algorithms to perform chunk processing under trusted computing environment. In the meantime, the number of data collisions is recorded in the system (i.e., the amount of repeating data is recorded using block chain, database or file system).”);
step 3: calculating and updating a global decision-making model by using the updated information about the local decision-making model, and aggregating a new federated decision-making model by using the intermediate result of calculating the parameter of the local decision-making model (para 0067 “ Step S120, Encrypt the data with the same format according to analysis requirements and transmit them to a secure computing server. Specifically, perform security inspections on each data source and the secure computing server, generate corresponding security reports and security keys, and perform data encryption (either part of the data or all the data after data modeling) in each data source based on the analysis requirements is using the corresponding security key; encrypted data (or part of the encrypted data depends on the application requirements) is then transmitted to the secure computing server. The encrypted data includes the indexing data, reordering data and/or other data, other data may include identity information fields. For example, before uploading local modeling parameters, reorder the data according to the indexing data, and generate a mapping file at the same time to ensure the correspondence between the reordered data and the original data, then encrypt the reordered data and transmit it to the secure computing server. Encryption processing includes: remote security inspections of data sources and secure computing server (e.g., Intel's remote attestation technology), each data source initiates remote attestation process to the secure computing server to obtain an attestation report (verified certification report) and security key (symmetric key). Each data source uses the corresponding symmetric key to encrypt local modeling data and upload the encrypted data to the secure computing server based on specific analysis requirements to ensure the reliability of the secure computing server. This process achieves the verification of the secure computing environment and the exchange of the data encryption key.” Para 0068 “Step S130, process the transmitted data in a verified Trusted Execution Environment (TEE), such as data collision check, to form a database of all the processed data, which is a global database: all data from the different data sources involved in the calculation. For example, decrypt the reordered data in the trusted execution environment, and perform a data collision check algorithm based on the indexing data to remove redundant data. The remaining data is the data union of different data sources that form the global database. Also, the global database could be the data intersection or the difference, or the combination. Because the memory of TEE is relatively small, in order to improve the calculation efficiency of data collision check for big data, the present invention can adopt at least one of the following algorithm: binary search tree, sequential search, binary search, chunking algorithm, red-black tree, balanced search tree, hash table, trie, suffix tree, Bloom Filter, brute-force, Rabin-Karp algorithm, KMP algorithm, Boyer-Moore algorithm, Sunday algorithm, Horspool algorithm and other algorithms to perform chunk processing under trusted computing environment. In the meantime, the number of data collisions is recorded in the system (i.e., the amount of repeating data is recorded using block chain, database or file system).”);
step 4: determining whether a training termination condition is met; when the training termination condition is met, generating, by the federated coordinator based on the trained global decision-making model, coordination information for coordinating decision making of the federated data terminal; when the training termination condition is not met, return to step 1 (para 0068 “Step S130, process the transmitted data in a verified Trusted Execution Environment (TEE), such as data collision check, to form a database of all the processed data, which is a global database: all data from the different data sources involved in the calculation. For example, decrypt the reordered data in the trusted execution environment, and perform a data collision check algorithm based on the indexing data to remove redundant data. The remaining data is the data union of different data sources that form the global database. Also, the global database could be the data intersection or the difference, or the combination. Because the memory of TEE is relatively small, in order to improve the calculation efficiency of data collision check for big data, the present invention can adopt at least one of the following algorithm: binary search tree, sequential search, binary search, chunking algorithm, red-black tree, balanced search tree, hash table, trie, suffix tree, Bloom Filter, brute-force, Rabin-Karp algorithm, KMP algorithm, Boyer-Moore algorithm, Sunday algorithm, Horspool algorithm and other algorithms to perform chunk processing under trusted computing environment. In the meantime, the number of data collisions is recorded in the system (i.e., the amount of repeating data is recorded using block chain, database or file system).” para 0074 “Step S200, in accordance with the predetermined requirements, i.e., specific analysis methods, according to the federated collaborative computing specifications, with the data corresponding to each data source in the global database, federated model training of specific analysis method is performed in the trusted computing environment. The analysis methods include logistic regression, decision trees, support vector machines, various neural network algorithms, and data statistical analysis methods. Specifically, the local calculation of each data source in the federated learning process may or may not be performed in the local trusted execution environment. During the federated learning process, the encrypted intermediate data is transmitted from the local data source to the trusted execution environment in the secure computing server, and it is processed in the trusted execution environment of the secure computing server, the model training result is finally obtained after a number of iterations.”); and
step 5: storing the new federated decision-making model and the coordination information for coordinating the decision-making of the federated data terminal to a global storage module, and sending the new federated decision-making model and the coordination information to the federated data terminal; and generating, by the federated data terminal, a terminal decision-making model by combining the local decision- making model and the new federated decision-making model based on the coordination information delivered by the federated coordinator and whether the new federated decision- making model is needed to guide local decision-making, wherein the terminal decision-making model is used for the local decision-making to generate an optimal local decision (para 0069 “Step S140, the data corresponding to each data source in the global database is transmitted back to the corresponding data source, that is, the data of each corresponding data source involving in the calculation, to form a local feature database of the data source; The returned data includes the indexing data or the feature data determined according to specific requirements. Further, the returned data is encrypted before transmission. For example, perform remote security inspection on the data source, obtain a security report (verified certification report) and a security key (symmetric key), then use the security key to encrypt and transmit the returned data.” Para 0071 “ Further, after obtaining data with a unified data format, the data of each data source is divided into multiple small data sets to reduce the amount of data in each data set, and an encrypted Oblivious RAM Tree is established for each data set. Divide the data set into small data sets (down from the TB level to the GB level), and build Oblivious RAM Tree respectively. This method will reveal the access frequency of GB-level data. The invention uses Oblivious RAM Tree to ensure that it is difficult for an attacker (having the right to observe the storage area) to obtain information during the data access phase. However, on the other hand, when an Oblivious RAM Tree is too large, the access efficiency of the trusted execution environment decreases significantly. Therefore, the invention adopts the above-mentioned method of dividing the data set of the total data into multiple small data sets, and establishing an encrypted Oblivious RAM Tree for each small data set to improve its efficiency.” Para 0073 “Specifically, the data is fetched in random order within the trusted execution environment; then it is re-encrypted and stored in order. For example, an encrypted Oblivious RAM Tree is established for the entire data set, and the data is fetched in random order (i.e., traversing the entire data set) within a trusted execution environment (TEE, such as Intel's SGX). Then the data is re-encrypted and stored to the hard disk in order. The time complexity of data access is O (1) and no additional storage overhead. Although this process introduces the leakage of access frequency, it is difficult to map the leaked frequency with specific data. This process can also improve the algorithm efficiency. Further, one-time preprocessing based on an encrypted Oblivious RAM Tree can be used to improve the efficiency of data extraction.”).
Wang does not disclose:
decision-making method for an interaction between a federated data terminal and a federated coordinator
sending, by the federated coordinator, a learning signal to the federated data terminal
calculating and updating, by the federated data terminal, information about a local decision-making model
calculating and updating, by the federated data coordinator, a global decision-making model
determining, by the federated coordinator, whether a training termination condition is met
storing, by the federated coordinator, the new federated decision-making model
Karapantelakis discloses:
decision-making method for an interaction between a federated data terminal and a federated coordinator (para 0006 “According to one aspect, a method performed by a network node for enabling root cause analysis in across a plurality of network systems is described. The method includes receiving update information of a first local root cause analysis mechanism of a first network operator from a plurality of network operators; generating, based on the update information, a new node to be added to a global root cause decision tree, wherein the global root cause decision tree is to be shared by at least two of the plurality of network operators; and requesting storage of the new node in a distributed ledger that is shared by the plurality of network operators. The method further includes participating in a verification operation of the new node; and responsive to determining that the verification operation is successful, adding an entry including the new node to the distributed ledger as part of the global root cause decision tree.”)
sending, by the federated coordinator, a learning signal to the federated data terminal (para 0070 “At operation 301a, the RCA controller 103A transmits a request to subscribe to events of local root cause analysis mechanisms. The request is received at the NEU 102A, which transmits, at operation 301b, the request to the storage 101A. Upon receipt of the request, the storage 101A subscribes the RCA controller 103A for receiving events related to one or more root cause mechanisms stored in the storage 101A. In some embodiments, upon receiving the request, the storage 101 may subscribe the RCA controller 103A to receive all updates for any local root cause analysis mechanism that is stored in the storage. Alternatively, the request may include an identifier of the local root cause analysis mechanism and the storage may subscribe the RCA controller 103A to receive updates for the local root cause analysis mechanism that the identifier identifies. Upon subscribing the RCA controller 103A to receive updates, the first storage 101A transmits, at operation 302a, a response confirming the subscription. Upon receipt of the response from the storage 101A, the NEU 102A transmits the response, at operation 302b, to the RCA controller 103A.”)
calculating and updating, by the federated data terminal, information about a local decision-making model (para 0071 “At operation 303, an update of creation of a local root cause analysis mechanism occurs in the storage 101A. In some embodiments, a new local root cause analysis mechanism is added to the storage 101A for the network 106A. In other embodiments, an update to a local root cause analysis mechanism already stored in storage 101A occurs. Upon the update or creation of the local root cause analysis mechanism, an update information is transmitted to the NEU 102A at operation 304a. At operation 304a, the NEU 102A transmits the update information to the RCA controller 103A.)
calculating and updating, by the federated data coordinator, a global decision-making model (para 0073 and 0074 “At operation 305, the RCA controller 103A generates, based on the update information, a new node to be added to a global root cause decision tree. The global root cause decision tree is to be shared by at least two of the multiple network operators, e.g., network operator 106A and network operator 106B.” Para 0074 “A decision tree is a flowchart-like structure in which each internal node represents a test/decision on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). A path from a root to a leaf of the decision tree represents one or more classification rules. Thus the local root cause analysis mechanisms are translated into a global decision tree that is to be stored in the distributed ledger. A node of the global decision tree is an entry in the DL and is generated based on the update information received.”)
determining, by the federated coordinator, whether a training termination condition is met (para 0077 “One or more decision nodes (which are also referred to as intermediate nodes herein). A decision node is an intermediate node between a root node and another intermediate node, between an intermediate node and a root cause node, or between a root node and a root cause node, which indicates a decision to be made. The decision can be as simple as if-then rules, or can also be more complex, e.g., a trained prediction model (e.g. using regression, classification, clustering etc.). Based on a given input, a decision node produces an output and may branch out to more than one outcomes. For example, given as input a temperature value, one branch could indicate “critical temperature” and another “normal temperature.”)
storing, by the federated coordinator, the new federated decision-making model (para 0081 “Once the new node is generated, the RCA Controller 103A records the new node in the distributed ledger, at operation 312. The recordation of the new node in the distributed ledger includes the operations 307, 308, and 310. At operation 307, the RCA controller 103A requests storage of the new node in the distributed ledger that is shared by the network operators. At operation 308, the RCA controller 103A participates in a verification operation of the new node. Responsive to determining that the verification operation is successful, the RCA controller 103A adds, at operation 310, an entry including the new node to the distributed ledger as part of the global root cause decision tree.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the whole-lifecycle encrypted big data analysis method and system for the data from the different sources of Wang to include the decision-making method for an interaction between a federated data terminal and a federated coordinator, as taught by Karapantelakis.
The motivation would have been to share root cause analysis information between devices while also maintaining a needed degree of confidentiality.
As per claim 2, Wang in view of Karapantelakis discloses:
The privacy-enhanced federated decision-making method according to claim 1, wherein the aggregating of the new federated decision-making model is based on whether the federated data terminal needs the federated decision-making model to guide the local decision making; the federated coordinator aggregates the new federated decision-making model by using the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal, and the aggregating of the new federated decision-making model comprises three cases: case 1: when the federated data terminal does not need the federated coordinator to guide the local decision-making, skipping, by the federated data terminal, reporting the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator; skipping, by the federated coordinator, receiving the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; skipping, by the federated coordinator, aggregating the new federated decision- making model; and after the training is completed, skipping, by the federated coordinator, sending the federated decision-making model to the federated data terminal; case 2: when the federated data terminal needs the federated coordinator to completely guide the local decision-making, reporting, by the federated data terminal, all information in the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator; receiving, by the federated coordinator, the all information in the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; aggregating, by the federated coordinator, the new federated decision-making model based on the all information in the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; and after the training is completed, sending, by the federated coordinator, the new federated decision-making model to the federated data terminal, wherein the new federated decision- making model is configured for guiding the federated data terminal in the local decision making; and case 3: when the federated data terminal needs the federated coordinator to partially guide the local decision making, reporting, by the federated data terminal, partial information of the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator; receiving, by the federated coordinator, the partial information of the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; aggregating, by the federated coordinator, the new federated decision-making model based on the partial information of the intermediate result of calculating the parameter of the local decision-making model reported by the federated data terminal; and after the training is completed, sending, by the federated coordinator, the new federated decision-making model to the federated data terminal, wherein the new federated decision-making model is configured for guiding the federated data terminal in the local decision making (Wang para 0036, 0080, and 0121 ) and (Karapantelakis para 0077, 0091, 0133, and 0144, The motivation would have been to share root cause analysis information between devices while also maintaining a needed degree of confidentiality.)
As per claim 3, Wang in view of Karapantelakis discloses:
A privacy-enhanced federated decision-making apparatus implementing the privacy- enhanced federated decision-making method according to claim 1, comprises: a first terminal sending and receiving module, a local decision-making module, a local calculation module, a local storage module, a second terminal sending and receiving module, a coordinator sending and receiving module, a global storage module, a federated calculation module, and a global decision-making module (Wang para 0042, 0044, 0047, 0053, 0055, and 0089) (Karapantelakis para 0154 “While one embodiment does not implement virtualization, alternative embodiments may use different forms of virtualization—represented by a virtualization layer 1320. In these embodiments, the instance 1340 and the hardware that executes it form a virtual server which is a software instance of the modules stored on the computer readable storage medium 13712.” Also see Figs. 3a-b, 4, 6, 8a-8c para 0039, 0088, 0090, The motivation would have been to share root cause analysis information between devices while also maintaining a needed degree of confidentiality.).
As per claim 4, Wang in view of Karapantelakis discloses:
The privacy-enhanced federated decision-making apparatus according to claim 3, wherein the first terminal sending and receiving module is configured to interact with the federated coordinator, send the parameter of the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model to the federated coordinator, and receive the learning signal, the coordination information, and the federated decision-making model delivered by the federated coordinator; the local decision-making module is configured to make the local decision for each federated data terminal based on the decision-making task characteristic and data in the local storage module, make a decision based on the local decision-making model after receiving the learning signal received and sent by the second terminal sending and receiving module, and transmit a decision-making result to the local calculation module for further calculation; the local calculation module is configured to calculate an updated parameter of the local decision-making model after local decision making based on the decision-making result of the local decision-making module and a calculation parameter received by the first terminal sending and receiving module, store the updated parameter of the local decision-making model and a new intermediate result of calculating the updated parameter of the local decision-making model in the local storage module, and perform a calculation for aggregating the federated decision-making model and the local decision-making model into the terminal decision-making model; the local storage module is configured to store local data, the calculation parameter, the local decision-making model, the terminal decision-making model, and the new intermediate result of calculating the updated parameter of the local decision-making model, send the updated parameter of the local decision-making model and the new intermediate result of calculating the updated parameter of the local decision-making model to the federated coordinator via the first terminal sending and receiving module, and send, when there is a local decision-making task, the local decision-making model and the local data to the local calculation module for the local decision making; and the second terminal sending and receiving module is configured to receive dynamic changes in local resources and data, update the latest local data to the local storage module, receive a local decision-making task request, report the local decision-making task request to the federated coordinator via the federated data terminal, and send a terminal decision-making strategy to a specific executor (Wang Fig. 1 and 2) and Karapantelakis Figs. 3a-b, 4, 6, 8a-8c, The motivation would have been to share root cause analysis information between devices while also maintaining a needed degree of confidentiality.).
As per claim 5, Wang in view of Karapantelakis discloses:
The privacy-enhanced federated decision-making apparatus according to claim 3, wherein the coordinator sending and receiving module is configured to interact with the federated data terminal, send the learning signal, the coordination information, and the federated decision-making model to the corresponding federated data terminal, and send the information about the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model to the federated calculation module for updating a global decision-making model and aggregate the federated decision-making model, wherein the information about the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model is sent by the federated data terminal; the global storage module is configured to store the global decision-making model, store the parameter of the global decision-making model integrated by the federated calculation module, and send, after the global decision-making model is trained, the global decision- making model to the global decision-making module to generate the coordination information for coordinating decision making of the federated data terminal; the federated calculation module is configured to process the information about the local decision-making model and the intermediate result of calculating the parameter of the local decision-making model, calculate and update the global decision-making model by using the information about the local decision-making model, and aggregate the new federated decision- making model by using the intermediate result of calculating the parameter of the local decision-making model; and after the global decision-making model is trained, the global decision-making module generates, based on the trained global decision-making model, the coordination information for coordinating the decision making of the federated data terminal and stores the coordination information in the global storage module (Wang Fig. 1 and 2) and Karapantelakis Figs. 3a-b, 4, 6, 8a-8c, The motivation would have been to share root cause analysis information between devices while also maintaining a needed degree of confidentiality.).
As per claim 6, the implementation of the method of claim 1 and the apparatus of claim 3 will execute the system of claim 6. The claim is analyzed with respect to claim 1 and 3.
As per claim 7, Wang in view of Karapantelakis discloses:
The privacy-enhanced federated decision-making method according to claim 1, wherein the learning signal comprises a Boolean variable or a numerical variable (Karapantelakis para 0079, The motivation would have been to share root cause analysis information between devices while also maintaining a needed degree of confidentiality.).
Conclusion
12.	The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publication No. 20200110982 discloses on paragraph 0110 “Non-limiting embodiments of the disclosed subject matter are directed to systems, methods, and computer program products for local approximation of a first (e.g., teacher, global, large, and/or the like) predictive model, including, but not limited to, generating generated data items using a generative model (e.g., a deep generative model, a variational auto-encoder (VAE), and/or the like) based on a data item of interest (e.g., selected and/or the like), and using the generated data items to train a second (e.g., student, local, small, and/or the like) predictive model to approximate the first predictive model. Such embodiments provide techniques and systems that allow the second predictive model, as trained based on the generated data items, to mimic the behavior of (e.g., accurately approximate the output of, be locally faithful to, and/or the like) the first predictive model around the data item of interest, and, thus, the behavior of the first predictive model may be more easily interpreted by a human based on the second predictive model. Additionally or alternatively, such embodiments provide techniques and systems that employ knowledge distillation (e.g., transfer knowledge from the (large, cumbersome) first predictive model to (small, easily understandable) second predictive model based on training the second predictive model using the output of the generative model) that may be more accurate and/or effective since the generated data items may be ensured to have semantic meaning and may be more efficient since the generated data items may be generated in a (reduced-) dimensional space of the generative model (e.g., rather than the high-dimensional space of the input). Additionally or alternatively, such embodiments provide techniques and systems that enable interpretation of the predictive models (e.g., interpretation of the first predictive model based on interpretation of the second predictive model) that is agnostic to the types of models (e.g., may be applied to any type of first predictive model and/or any type of second predictive model). Additionally or alternatively, non-limiting embodiments of the disclosed subject matter provide techniques and systems that allow visual interpretation of the predictive models (e.g., interpretation of the first predictive model based on visual interpretation of the second predictive model), e.g., by displaying multiple views (e.g., simultaneously, successively, and/or the like) with multiple different graphics, visualizations, statistics, visual analytics, and/or the like that depict, summarize, and/or the like multiple different portions of information that enable a human to understand the predictive models and/or behaviors thereof. Additionally or alternatively, non-limiting embodiments of the disclosed subject matter provide techniques and systems that include configurable graphical elements, e.g., to adjust settings, hyperparameters, functioning, inputs, outputs, and/or the like of the generative model, the second predictive model, and/or the like, and such adjustments may affect the training and/or outputs of the second predictive model (e.g., after training and/or retraining), thereby providing additional insights into the first predictive model.

APPLICANT REBUTTAL: Remarks/Arguments
1. Claim Status
Claims 1-7 are currently pending for examination in the present application.
Claims 1 and 4 are currently amended for clarification purposes.
Support for the amendments to claim 1 can be found in the Specification1 at, for example, paras. 0095, 0097, 0108, 0066, and 0105.
2. Claim Rejections Under 35 U.S.C. §101
The Office Action rejected claims 21-40 under 35 U.S.C. §101 as being directed to non-statutory subject matter.    The Applicant respectfully traverses the rejection.
For
completeness purposes, the Applicant addresses each prong of the subject matter eligibility test under 35 U.S.C. §101 as set forth generally at MPEP §2106 (III).
However, the Applicant
respectfully requests that the Examiner give special attention to the analysis under the
first and second prongs of ste
p
2A
as it relates to improvements in the relevant technology.
Claim 1
Step 1:
ication are made with respect to the publication of the present application, U S
(hereinafter "Specification")
Claim 1 is directed to a method, which corresponds to a process, which is one of the statutory categories set forth in 35 U.S.C. §101.  Therefore, the claim warrants further assessment under step 2A of the eligibility analysis.
Step 2A, First Prong:
The first prong of step 2A of the subject matter eligibility test involves determining whether the claims recite an abstract idea, law of nature or natural phenomenon.  See MIPEP §2106.04(II)(A).  In particular, the MPEP provides that "Examiners should accordingly be careful to distinguish claims that
recite
an exception (which require further eligibility analysis) and claims that
merel
y
involve an exception (which are eli
g
ible and do not require further eli
g
ibility anal
y
sis)."
(II)(A) (emphasis added). While the Applicant acknowledges that a claim cannot preempt an abstract idea (see, e.g. Mayo Collaborative Servs. v. Prometheus Labs.Inc., 566 U.S. 66, 79-80, 86-87 (2012)), the claim does not constitute a preemption that monopolizes all applications of applying a concept.
In particular, the Applicant respectfully submits that claim 1 of the present application is similar to Example 39 of the Subject Matter Eligibility Examples directed to a "Method for Training a Neural Network for Facial Recognition." Specifically, that example was directed to training a neural network to improve robustness of detection. The improvement to the training data set and the training of a neural network was not an abstract idea because:
[I]t does not recite any mathematical relationships, formulas, or calculations.
While some of the limitations may be based on mathematical concepts, the mathematical concepts are not recited in the claims. Further, the claim does not
recite a mental process because the steps are not practically performed in the human mind. Finally, the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people. Thus, the claim is eligible because it does not recite a judicial
exception.
Example 39 of the Subject Matter Eligibility Examples, Analysis under Step 2A - Prong 1. Similarly, claim 1 recites
u
p
datin
g
at least one of
a number of la
v
ers of a neural network
imp
lementin
g
the local decision-makin
g
model, a connection between neurons of the neural network, or a we
ig
ht or
g
radient value of the neural network
from which a new federated decision-making model is aggregated, and
combinin
g
the local decision-makin
g
model and the new federated decision-makin
g
model
to
g
enerate a terminal decision-makin
g
model.
Here,
the claimed updating  of neural network   parameters, aggregating a new   federated decision-making model and generating a terminal decision-making model is not composed entirely of mathematical concepts, although it is based on mathematical concepts. Further, the claim does not recite a mental process because the steps cannot practically performed in the human mind due to the complexity of the generation of the models/neural network. In addition, the update of the parameters of a neural network and the generation of the claimed decision-making models is not a method of organizing human activity such as a fundamental economic concept or managing interactions between people.  Thus, similar to Example 39 of the Subject Matter Eligibility Examples, claim 1 is directed to patent-eligible subject matter under 35 U.S.C. §101. As such, withdrawal of the rejection is respectfully requested.
Step 2A, Second Prong:
Even if step 1 and step 2A, first prong were met, the Applicant respectfully submits that claim 21 is patent-eligible under the second prong of step 2A. The second prong of step 2A of the subject matter eligibility test involves determining whether the claims recite additional elements that integrate the judicial exception into a practical application.  See MPEP §2106.04(II)(A). As specifically set forth in the MPEP, "[a] claim reciting a judicial exception is
not directed to the judicial exception
if it also recites additional elements demonstrating that the claim as a whole integrates the exception into a practical application.  One way to demonstrate such integration
is when the claimed invention improves
the functioning of a computer or
improves another technolo
zy
or technical field."
MPEP §2106.04(d)(1) (emphasis added).
As discussed in the Specification, artificial intelligence (AI) technologies are heavily dependent on a large amount of raw data to ensure accuracy of the models.   See, e.g., Specification, para. 0003. However, a problem that arises in employing a large amount of raw data is that it is difficult to maintain privacy and personal information of individuals that may be present in the data. See e.g., Specification paras. 0003-0004. Thus, when data is shared from multiple locations and companies for purposes of improving AI models, conventional methods encrypt the data before sending it.  See, e.g., Specification, paras. 0005-0006.  While encryption can prevent malicious attackers from accessing the data during transmission, encryption does not prevent the data collector from misusing the data. See, e.g., Specification,
para. 0007. To address the issue, embodiments of the present application employ an improved method of federated learning, in which different parties participate in model training but keep data locally without privacy leakage. See, e.g., Specification, paras. 0008-0009. In particular, embodiments of the present application utilize a data terminal to update neural network parameters locally and determine whether to transmit the updated parameters to a federated coordinator based on a protection degree of the federated data terminal for local privacy. See, e.g., Specification, 0095, 0097, 0108, 0066, and 0105.  This provides more flexibility in training of the models and enables a federated coordinator to update a federated decision-making model, which the data terminal can combine with its local decision-making model to obtain a terminal decision-making model that is employed to generate an optimal local decision. See, e.g., Specification, FIG. 1, steps 104-106 and accompanying text.  Thus, in this way, for example, conditioning the reporting of an updated neural network parameter from a federated data terminal to a federated coordinator on a protection degree of the federated data terminal for local privacy, as claimed in claim 1, can improve the generation and accuracy of an AI model in a federated scheme while at the same time ensuring privacy of the raw data.  See, e.g., Specification, para. 0066.  Accordingly, the Applicant respectfully submits that claim 1 is directed to a practical application, and is patent-eligible under 35 U.S.C. §101, as it provides an improvement in the technical field.
Step 2B:
While the Applicant respectfully asserts that claim 1 is patent-eligible for at least the reasons discussed above, the analysis under Step 2B also illustrates that claim 1 is directed to statutory subject matter under 35 U.S.C. §101.  Step 2B of the subject matter eligibility test involves determining whether the claims recite additional elements that amount to significantly more than the judicial exception, i.e., provide an inventive concept. See MPEP §2106.04(II)(A). In particular, the MPEP notes that, to determine whether a claim is patent-eligible under step 2B, "examiners should consider whether the claim 'purport(s) to improve' . . . . "any other technology or technical field."'  MPEP §2106.05(a).  As discussed in detail above, claim 1 provides a significant improvement the technical field, and the claim is patentable eligible under Step 2B for the same reasons discussed above with respect to step 2A.  The Applicant also respectfully submits that the claimed features are not routine or conventional.  Accordingly, claim 1 is directed to statutory subject matter under 35 U.S.C. §101. As such, withdrawal of the rejection and allowance of the claim is respectfully requested.
Claims 2-7
Claims 2-7 are dependent from claim 1. The Applicant respectfully submits that claims 2-7 are also directed to statutory subject matter due at least to their dependencies from claim 1. As such, withdrawal of the rejection and allowance of the claims is respectfully requested.
3. Claim Rejections Under 35 U.S.C. §103
The Office Action rejected claims 1-7 under 35 U.S.C. §103 as being unpatentable over Wang (U.S. Patent Application Publication No. 20210117395) in view of Karapantelakis (U.S. Patent Application Publication No. 20220158890).  The Applicant respectfully traverses the rejection.
Claim 1
The Cited Art Fails to Teach or Render Obvious All Claim Limitations
To support an obviousness rejection, MPEP §2143.03 requires the "consideration" of every claimed feature in an obviousness determination and MPEP § 2141.02 requires consideration of the "[claimed] invention and prior art as a whole." Further, the Board of Patent Appeal and Interferences confirmed that a proper obviousness determination still requires the Office to make "a searching comparison of the claimed invention - including all its limitations - with the teaching of the prior art." In re Wada and Murphy, Appeal 2007-3733 (January 14, 2008), citing in re Ochiai, 71 F.3d 1565, 1572 (Fed. Cir. 1995) and CFMT v. Yieldup Intern. Corp., 349 F.3d 1333, 1342 (Fed. Cir. 2003) (emphasis in original). Moreover, the necessary presence of all claim features is axiomatic, since the Supreme Court has long held that obviousness is a question of law based on underlying factual inquiries, including ... ascertaining the differences between the claimed invention and the prior art. Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966) (emphasis added).   Indeed, Applicant submit that this is why Section 904 of the MPEP instructs Examiners to conduct an art search that covers "the
invention as described and claimed." (emphasis added). In sum, it remains well-settled law
that an obviousness rejection requires at least a consideration of all of the claim elements.
Claim 1 of the present application recites:
A privacy-enhanced federated decision-making method for an interaction
between a federated data terminal and a federated coordinator, comprising:
step 1: sending, by the federated coordinator, a learning signal to the
federated data terminal based on decision-making task request information, and receiving, by the federated data terminal, the learning signal and making a local
decision based on a decision-making task characteristic;
step 2: calculating and updating, by the federated data terminal, information
about a local decision-making model based on the local decision-making result, to
obtain the updated information about the local decision-making model and
implement reporting of the updated information about the local decision-making
model and an intermediate result of calculating a parameter of the local
decision-making model to the federated
coordinator, wherein the updated
information includes an update of the parameter and the updated parameter
comprises at least one of a number of la
v
ers of a neural network implementin
g
the local decision-makin
g
model, a connection between neurons of the neural network, or a wei
g
ht or
g
radient value of the neural network, and wherein the reporti
ng
comprises determini
ng
whether to report the updated information to
the federated coordinator based on a protection de
g
ree of the federated data
terminal for local privac
y;
step 3: calculating and updating, by the federated coordinator, a global
decision-making model by using the updated information about the local
decision-making model, and aggregating a new federated decision-making model
by using the intermediate result of calculating the parameter of the local
decision-making model;
step 4: determining, by the federated coordinator, whether a training
termination condition is met; when the training termination condition is met,
generating, by the federated coordinator based on the trained global
decision-making model, coordination information for coordinating decision
making of the federated data terminal; when the training termination condition is
not met, return to step 1; and
step 5: storing, by the federated coordinator, the new  federated
decision-making model and the coordination information for coordinating the decision-making of the federated data terminal to a global storage module, and sending
the new federated decision-makin
g
model and the coordination information to the federated data terminal;
and generating, by the federated data
terminal, a terminal decision-making  model by
combini
ng
the local decision-maki
ng
model and the new federated decision-maki
ng
model
based on the coordination information delivered by the federated coordinator and whether the new   federated  decision-making  model is needed  to  guide local decision-making, wherein the terminal decision-making model is used for the local decision-making to
g
enerate an optimal local decision based on the combined local decision-maki
ng
model and new federated decision-makin
g
model.
In support of the rejection, the Office Action cites paragraphs 0066 and 0068 of Wang as teaching the claimed reporting of an updated parameter. See Office Action, p. 9, para. 2 to p. 10, para. 1. However, Wang discloses using encryption to transmit the cited data, similar to the prior methods discussed above with respect to the rejection under 35 U.S.C. §101. See Wang, para. 0068. Wang does not seem to disclose or render obvious conditioning the reporting of an updated neural network parameter from a federated data terminal to a federated coordinator on a protection degree of the federated data terminal for local privacy, as claimed in claim 1. Furthermore, Karapantelakis fails to cure the deficiencies of Wang. Karapantelakis was cited as teaching various aspects of a federated data terminal and a federated coordinator (see Office Action, p. 16, para. 2 to p. 20, para. 3). The reference does not seem to disclose or in any way indicate that the reporting of an updated neural network parameter from a federated data terminal to a federated coordinator can or should be conditional on a protection degree of the federated data terminal for local privacy in the manner recited in claim 1. In addition, modifying the cited references to obtain these features would not be implemented based on common sense in view of the teachings of the references.
Accordingly, claim 1 is patentable over the cited references, as the references do not disclose or render obvious all elements of the claim.
Modifyin
g
the Cited References So That Re
p
ortin
g
of an U
n
dated Neural Network Parameter from a Federated Data Terminal to a Federated Coordinator is Conditioned on
a Protection De
g
ree of the Federated Data Terminal for Local Privacy
,
Is Not Obvious Because the Modification Would be Based on Im
p
ermissible Hindsi
g
ht of the Present
App
lication
"To reach a proper determination under 35 U.S.C. 103, the Examiner must step backward in time and into the shoes worn by the hypothetical 'person of ordinary skill in the art' when the invention was unknown and just before it was made.  In view of all factual information, the examiner must then make a determination whether the claimed invention 'as a whole' would have been obvious at that time to that person. Knowledge of applicant's disclosure must be put aside in reaching this determination, yet kept in mind in order to determine the 'differences,' conduct the search and evaluate the 'subject matter as a whole' of the invention. The tendency to resort to 'hindsight' based upon applicant's disclosure is often difficult to avoid due to the very nature of the examination process. However,
im
p
ermissible hindsi
g
ht must be avoided and the legal conclusion must be reached on the basis of the facts gleaned from the prior art."
MPEP §2142 (emphasis added).
As discussed above, a problem with utilizing encryption to maintain privacy of raw data used in multiple locations for purposes of training an AI model is that encryption does not
prevent the data collector from misusing the data.  See, e.g., Specification, para. 0007.  To address the problem, embodiments of the present application employ an improved method of federated learning in which different parties participate in model training but condition the reporting of updated neural network parameters to a federated coordinator by a federated data terminal on a protection degree of the federated data terminal for local privacy.  See, e.g., Specification, paras. 0008-0009, 0095, 0097, 0108, 0066, and 0105. As discussed above, these features can improve the generation and accuracy of an AI model in a federated scheme while at the same time ensuring privacy of the raw data. See, e.g., Specification, para. 0066. The cited references nowhere recognize this benefit or provide any other reason for conditioning reporting of an updated neural network parameter in the manner claimed.  Thus, any assertion that the claimed features are obvious would clearly be based on impermissible hindsight of the present application. "Hindsight is impermissible when an examiner rejects an application in reliance upon teachings not drawn from any prior art disclosure, but from the applicant's own disclosure." In Re Nilssen, No. 91-1123, 1991 U.S. App. LEXIS 9796, at *3-4 (Fed. Cir. May 8, 1991) (citing In re Deminski, 796 F.2d 436, 443 (Fed. Cir. 1986) and W.L. Gore & Assocs., Inc. v. Garlock, Inc., 721 F.2d 1540, 1553 (Fed. Cir. 1983)) (emphasis added).
Accordingly, claim 1 is not obvious over the cited references, as modifying the cited art so that reporting of an updated neural network parameter from a federated data terminal to a federated coordinator is conditioned on a protection degree of the federated data terminal for
local privacy, as claimed in claim 1, is not obvious because the modification would be based on impermissible hindsight of the present application.
Inventor Identified a Problem Others Did Not
The case of In re Omeprazole Patent Litigation, 536 F.3d 1361, 87 USPQ2d 1865 (Fed. Cir. 2008), is one in which the claims in question were found to be nonobvious in the context of an argument to combine prior art elements. The Omeprazole case can also be analyzed in view of the discovery of a previously unknown problem by the patentee. The Federal Circuit stated that even if all the limitations of the claim was disclosed by prior art, the invention nevertheless was patentable because the inventor identified a problem that was not known in the field and had a reason to add additional coating and therefore increasing the cost of the product whereas a person having ordinary skill in the art had no reason to add another coating and therefore increasing the product cost. In re Omeprazole Patent Litigation, 536 F.3d 1361 (Fed. Cir. 2008). Accordingly, In re Omeprazole provides that even assuming all of the elements are disclosed in prior art references, unobviousness can still be established by satisfying a two-prong test that 1) that the inventor identified a problem others did not, and 2) to solve the added an element or a process step that increased the cost, and a person having ordinary skill would not have increased the cost without knowing the problem.
Here, as discussed above, the inventors have identified a problem in that existing methods utilizing encryption to maintain privacy of data used in multiple locations for purposes
of training an AI model is do not prevent the data collector from misusing the data. In order to solve this problem, the present inventors have conceived of a design in which reporting of an updated neural network parameter from a federated data terminal to a federated coordinator is conditioned on a protection degree of the federated data terminal for local privacy. A person having ordinary skill would not have increased the cost of implementing these features without recognizing the problem identified by the present inventors or recognizing any other reason for reporting an updated neural network parameter in the manner claimed.
Accordingly, the Applicant respectfully submits that claim 1 is patentable over the cited reference for at least the reasons discussed above.
Claims 2-7
Claims 2-7 are dependent from claim 1. Thus, claims 2-7 are patentable over the cited references due at least to their dependencies from claim 1.  As such, withdrawal of the rejections and allowance of all pending claims 1-7 are respectfully requested.
Conclusion

NEXT OFFICE ACTION: Notice of Pre-AIA  or AIA  Status
1.	The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Reasons for Allowance
2.	Claims 1-7 including all of the limitations of the base claim and any intervening claims are allowed.
Closest Prior Art:
U.S. Publication No. 20220229904 discloses on paragraph 0017 “The privacy-protection-based data processing model acquisition method according to the embodiments of the present invention has at least the following beneficial effects. In the embodiments of the present invention, firstly, sensor data is acquired, an initial model is trained using the sensor data, and corresponding intermediate data models are obtained and integrated to form an integrated data model; secondly, new data is processed through the integrated data model and random noise to acquire a label category corresponding to the new data; finally, the integrated data model is trained by using the new data and the corresponding label category, to acquire a data model, so that a plurality of participants may construct a machine learning model without sharing data. In the present invention, a plurality of models is constructed on a plurality of pieces of sensor data, all the models are integrated, and noise perturbations are added, to finally obtain a comprehensive result, so as to achieve better performance than a single sensor model. Noise is introduced into the model, which leads to uncertainty to some extent, so as to fully cover contributions of any individual user and any Internet of Things individual and protect privacy of participants, thereby solving the technical problem of poor data privacy protection of Internet of Things.”
U.S. Publication No. 20230076559 discloses on paragraph 0147 “Further, the present system is applicable in interpreting the model decisions using the local and global importance weightage of each feature. The local importance weightage is directly inferred from the importance weightage associated with input neural layers. For inferring global importance, the local importance weightage of each sample is normalized with respect to the model decision of that sample. The normalized local importance weightage from all samples are then averaged to provide global importance. The averaging can be further graded based on the various decisions and binding of the model decision. Further, the present system is applicable in network analysis based on the importance weightage attributed to each neural layer in the neural network. The two modes together provide huge information for each neural layer, such as: bias to input ratio, activation saturation, and positive and negative importance weightage (unit-wise and complete neural layer). Using this information, the neural layers may be modified to increase or decrease variability and reduce network bias. Major changes to the neural network architecture via complete shutdown of neural nodes or pathways are also possible based on the total contribution of that component.”
U.S. Publication No 20210103838 discloses on paragraph 0061 “Also, the explainability system 106 performs a sensitivity analysis of the decision data at sensitivity analysis module 118. The sensitivity analysis module 118 performs analysis by using one or more machine learning algorithms. In an embodiment of the present disclosure, the one or more machine learning algorithms include the tree-based models, the feed-forward neural network, and the linear model. The explainability system 106 calculates the sensitivity of the model used for making the decision by the decision-making system 102. In general, the sensitivity of the model is defined as how sensitive the model is to the change of each of the features. The sensitivity is calculated by making small changes in the feature values across the range of the features and measuring the corresponding change in model performance.”  Paragraph 0063 “ In an embodiment of the present disclosure, the explainability system 106 performs
global explanation and local explanation of the model used by the decision-making system 102. The global explanation and local explanation are done by the auxiliary model 120 of the explainability system 106. The auxiliary model 120 performs an auxiliary analysis on the data fed to the auxiliary model 120. The explainability system 106 mimics the model used by the decision-making system 102 using the decision data. In an embodiment of the present disclosure, the explainability system 106 selects the optimal model from between the mimic model and the original model of the explainability system 106. In an embodiment of the present disclosure, the optimal model for the decision-making system 102 is selected by using hyperparameter optimization. In general, the hyperparameter is a parameter from a prior distribution; it captures the prior belief before data is observed. The hyperparameter optimization helps optimize the mimic model and the model used by the decision-making system 102 in order to identify the optimal model.”
The following is an Examiner’s Statement of Reasons for Allowance:
Claims 1-7 are allowable over prior art references taken individually or in combination fails to particularly disclose, fairly suggests or render obvious are argued by the applicant which examiner considers persuasive as set forth above
Although the prior art discloses sending, by the federated coordinator, a learning signal to the federated data terminal based on decision-making task request information, and receiving, by the federated data terminal, the learning signal and making a local decision based on a decision-making task characteristic and calculating and updating, by the federated data terminal, information about a local decision-making model based on the local decision-making result, to obtain the updated information about the local decision-making model, no one or two references anticipates or obviously suggest implement reporting of the updated information about the local decision-making model and an intermediate result of calculating a parameter of the local decision-making model to the federated coordinator, wherein the updated information includes an update of the parameter and the updated parameter comprises at least one of a number of layers of a neural network implementing the local decision-making model, a connection between neurons of the neural network, or a weight or gradient value of the neural network, and wherein the reporting comprises determining whether to report the updated information to the federated coordinator based on a protection degree of the federated data terminal for local privacy.
Furthermore, storing, by the federated coordinator, the new federated decision-making model and the coordination information for coordinating the decision-making of the federated data terminal to a global storage module, and sending the new federated decision-making model and the coordination information to the federated data terminal.
Lastly, generating, by the federated data terminal, a terminal decision-making model by combining the local decision-making model and the new federated decision-making model based on the coordination information delivered by the federated coordinator and whether the new federated decision-making model is needed to guide local decision-making, wherein the terminal decision-making model is used for the local decision-making to generate an optimal local decision based on the combined local decision-making model and new federated decision-making model.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee.  Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion

CITED REFERENCE SUMMARIES: Ref US20220158890A1: Claims:
A method performed by a network node for enabling root cause analysis in across a plurality of network systems, the method comprising: receiving update information of a first local root cause analysis mechanism of a first network operator from a plurality of network operators; generating, based on the update information, a new node to be added to a global root cause decision tree, wherein the global root cause decision tree is to be shared by at least two of the plurality of network operators; requesting storage of the new node in a distributed ledger that is shared by the plurality of network operators; participating in a verification operation of the new node; and responsive to determining that the verification operation is successful, adding an entry including the new node to the distributed ledger as part of the global root cause decision tree.
receiving update information of a first local root cause analysis mechanism of a first network operator from a plurality of network operators;
generating, based on the update information, a new node to be added to a global root cause decision tree, wherein the global root cause decision tree is to be shared by at least two of the plurality of network operators;
requesting storage of the new node in a distributed ledger that is shared by the plurality of network operators;
participating in a verification operation of the new node; and
responsive to determining that the verification operation is successful, adding an entry including the new node to the distributed ledger as part of the global root cause decision tree.
The method of claim 1 , wherein the update information includes input data format, output data format, decision module information, and an identifier of the first network operator, and wherein the new node includes a type of node, an identifier of the new node, an identifier of a parent node in the global root cause decision tree, input data format, output data format, decision module information, and an identifier of the first network operator.
The method of claim 1 , wherein the update information includes input data format and output data format, and wherein the participating in the verification operation of the new node includes determining whether the new node is an update to an existing node that is part of the global root cause decision tree in the distributed ledger.
The method of claim 3 , wherein the participating in a verification operation of the new node includes: responsive to determining that the new node is not an update to an existing node, performing the following: supplying test data; performing a root cause analysis based on the test data and the global root cause decision tree when including the new node; determining, based on a result of the root cause analysis and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein determining that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
responsive to determining that the new node is not an update to an existing node, performing the following: supplying test data; performing a root cause analysis based on the test data and the global root cause decision tree when including the new node; determining, based on a result of the root cause analysis and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein determining that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
supplying test data;
performing a root cause analysis based on the test data and the global root cause decision tree when including the new node;
determining, based on a result of the root cause analysis and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and
wherein determining that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
The method of claim 3 , wherein the participating in a verification operation of the new node includes: responsive to determining that the new node is not an update to an existing node, performing the following: supplying test data; determining, based on the test data and decision module information of the new node and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein determining that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
responsive to determining that the new node is not an update to an existing node, performing the following: supplying test data; determining, based on the test data and decision module information of the new node and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein determining that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
determining, based on the test data and decision module information of the new node and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and
The method of claim 3 , wherein the participating in a verification operation of the new node includes: responsive to determining that the new node is an update to an existing node, performing the following: determining ( 1026 ) whether the input data format and the output data format of the new node are respectively identical to input data format and output data format of the existing node; and responsive to determining that the input data format and the output data format of the new node are respectively identical to the input data format and output data format of the existing node, updating the new node to include a bias, wherein the bias can be used to determine which one of the existing node and the new node is to be used when performing a root cause analysis.
responsive to determining that the new node is an update to an existing node, performing the following: determining ( 1026 ) whether the input data format and the output data format of the new node are respectively identical to input data format and output data format of the existing node; and responsive to determining that the input data format and the output data format of the new node are respectively identical to the input data format and output data format of the existing node, updating the new node to include a bias, wherein the bias can be used to determine which one of the existing node and the new node is to be used when performing a root cause analysis.
determining ( 1026 ) whether the input data format and the output data format of the new node are respectively identical to input data format and output data format of the existing node; and
responsive to determining that the input data format and the output data format of the new node are respectively identical to the input data format and output data format of the existing node, updating the new node to include a bias, wherein the bias can be used to determine which one of the existing node and the new node is to be used when performing a root cause analysis.
The method of claim 6 , wherein responsive to determining that the input data format and the output data format of the new node are not respectively identical to the input data format and output data format of the existing node, updating the new node to include a bias and a conversion parameter, wherein the bias is to be used when performing a root cause analysis based on at least one of the existing node and the new node, and wherein the conversion parameter allows to convert data from the existing node into a format that is compatible with a format of data from the new node.
- 15 . (canceled)
A network node for enabling root cause analysis in across a plurality of network systems, the network node comprising: processing circuitry; and a computer memory storing a set of computer readable instructions that when executed by the processing circuitry cause the network node to: receive update information of a first local root cause analysis mechanism of a first network operator from a plurality of network operators; generate, based on the update information, a new node to be added to a global root cause decision tree, wherein the global root cause decision tree is to be shared by at least two of the plurality of network operators; request storage of the new node in a distributed ledger that is shared by the plurality of network operators; participate in a verification operation of the new node; and responsive to determining that the verification operation is successful, add an entry including the new node to the distributed ledger as part of the global root cause decision tree.
processing circuitry; and
a computer memory storing a set of computer readable instructions that when executed by the processing circuitry cause the network node to: receive update information of a first local root cause analysis mechanism of a first network operator from a plurality of network operators; generate, based on the update information, a new node to be added to a global root cause decision tree, wherein the global root cause decision tree is to be shared by at least two of the plurality of network operators; request storage of the new node in a distributed ledger that is shared by the plurality of network operators; participate in a verification operation of the new node; and responsive to determining that the verification operation is successful, add an entry including the new node to the distributed ledger as part of the global root cause decision tree.
receive update information of a first local root cause analysis mechanism of a first network operator from a plurality of network operators;
generate, based on the update information, a new node to be added to a global root cause decision tree, wherein the global root cause decision tree is to be shared by at least two of the plurality of network operators;
request storage of the new node in a distributed ledger that is shared by the plurality of network operators;
participate in a verification operation of the new node; and
responsive to determining that the verification operation is successful, add an entry including the new node to the distributed ledger as part of the global root cause decision tree.
The network node of claim 16 , wherein the update information includes input data format, output data format, decision module information, and an identifier of the first network operator, and wherein the new node includes a type of node, an identifier of the new node, an identifier of a parent node in the global root cause decision tree, input data format, output data format, decision module information, and an identifier of the first network operator.
The network node of claim 16 , wherein the update information includes input data format and output data format, and wherein to participate in the verification operation of the new node includes to determine whether the new node is an update to an existing node that is part of the global root cause decision tree in the distributed ledger.
The network node of claim 18 , wherein to participate in a verification operation of the new node includes to: responsive to determining that the new node is not an update to an existing node, perform the following: supply test data; perform a root cause analysis based on the test data and the global root cause decision tree when including the new node; determine, based on a result of the root cause analysis and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein to determine that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
responsive to determining that the new node is not an update to an existing node, perform the following: supply test data; perform a root cause analysis based on the test data and the global root cause decision tree when including the new node; determine, based on a result of the root cause analysis and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein to determine that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
supply test data;
perform a root cause analysis based on the test data and the global root cause decision tree when including the new node;
determine, based on a result of the root cause analysis and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and
wherein to determine that the verification operation is successful includes determining that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
The network node of claim 18 , wherein to participate in a verification operation of the new node includes to: responsive to determining that the new node is not an update to an existing node, perform the following: supply test data; determine, based on the test data and decision module information of the new node and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein to determine that the verification operation is successful includes to determine that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
responsive to determining that the new node is not an update to an existing node, perform the following: supply test data; determine, based on the test data and decision module information of the new node and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein to determine that the verification operation is successful includes to determine that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
determine, based on the test data and decision module information of the new node and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the new node is a legitimate node that can be added as an entry in the global root cause decision tree; and
wherein to determine that the verification operation is successful includes to determine that the new node is a legitimate node that can be added as an entry in the global root cause decision tree.
The network node of claim 18 , wherein to participate in a verification operation of the new node includes to: responsive to determining that the new node is an update to an existing node, perform the following: determine whether the input data format and the output data format of the new node are respectively identical to input data format and output data format of the existing node; and responsive to determining that the input data format and the output data format of the new node are respectively identical to the input data format and output data format of the existing node, update the new node to include a bias, wherein the bias can be used to determine which one of the existing node and the new node is to be used when performing a root cause analysis.
responsive to determining that the new node is an update to an existing node, perform the following: determine whether the input data format and the output data format of the new node are respectively identical to input data format and output data format of the existing node; and responsive to determining that the input data format and the output data format of the new node are respectively identical to the input data format and output data format of the existing node, update the new node to include a bias, wherein the bias can be used to determine which one of the existing node and the new node is to be used when performing a root cause analysis.
determine whether the input data format and the output data format of the new node are respectively identical to input data format and output data format of the existing node; and
responsive to determining that the input data format and the output data format of the new node are respectively identical to the input data format and output data format of the existing node, update the new node to include a bias, wherein the bias can be used to determine which one of the existing node and the new node is to be used when performing a root cause analysis.
The network node of claim 21 , wherein responsive to determining that the input data format and the output data format of the new node are not respectively identical to the input data format and output data format of the existing node, update new node to include a bias and a conversion parameter, wherein the bias is to be used when performing a root cause analysis based on at least one of the existing node and the new node, and wherein the conversion parameter allows to convert data from the existing node into a format that is compatible with a format of data from the new node.
The network node of claim 21 , wherein the network node is further to: supply test data; perform a root cause analysis based on the test data and the global root cause decision tree when including the updated new node; determine, based on a result of the root cause analysis and by one or more additional results of root cause analysis performed by one or more other network nodes in the distributed ledger, whether the updated new node is a legitimate node that can be added as an entry in the global root cause decision tree; wherein to determine that the verification operation is successful includes to determine that the updated new node is a legitimate node that can be added as an entry in the global root cause decision tree; and wherein to add the entry in the distributed ledger includes to add the entry 
[Truncated to input limit]