DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-18 are presented for examination.
Claim Objections
Claims 6, 8, 15 and 17 are objected to because of the following informalities,
Claims 6 and 8 [line 1]: “The apparatus according claim 1” should be “The apparatus according to claim 1”
Claims 15 and 17 [line 1]: “The apparatus according claim 10” should be “The apparatus according to claim 10”
Appropriate corrections are required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bar-El et al (US 20200167480 A1) hereafter Bar-El, and further in view of Nasr-Azadani et al (US 20200387836 A1) hereafter Nasr-Azadani.
With respect to claim 1, Bar-El teaches an apparatus of a first network entity managing artificial intelligence or machine learning trustworthiness in a network (a Trustworthiness Management Server (or TMS) that includes a trustworthiness query sent or transmitted from a relying party to a computerized server [par. 0011-0013]), the apparatus comprising
transmitting circuitry configured to transmit a first artificial intelligence or machine learning trustworthiness related message towards a second network entity managing artificial intelligence or machine learning trustworthiness in an artificial intelligence or machine learning pipeline in said network (the TMS may send or transmit a response message indicating a level of trustworthiness level that is indicated by a set of scores or numerical values. A target device may send or transmit a device parameters message (DPM) to TMS. TMS may generate a trustworthiness report which may comprise one or more trustworthiness scores with regard to the target device. Querying device may utilize its query agent to generate a trustworthiness query message (TQM) which inquiries about the trustworthiness of target device. A trustworthiness report transmitter 124 is able to send or transmit the trustworthiness report to querying device [par. 0013, 0029, 0034]), and
receiving circuitry configured to receive a second artificial intelligence or machine learning trustworthiness related message from said second network entity (the TM server may send or transmit back to the querying device the trustworthiness report (TR) pertaining to target device responding to the received TQM. A TQM receiver configured to receive an incoming TQM and able to pass it to a TQM processing unit [par. 0029, 0034, 0038]), wherein
said first artificial intelligence or machine learning trustworthiness related message comprises a first information element including at least one first artificial intelligence or machine learning model explainability related parameter (the recipient device may operate as a querying entity and may utilize a query agent to query a trust management server with regard to the trustworthiness of the IoT device. The server may receive from the IoT device a set of values indicating various parameters of the IoT device. The data collection agent may measure, sense, determine and/or collect values of parameters of target device [par. 0012, 0013, 0029]).
However, Bar-El does not explicitly teach said first artificial intelligence or machine learning trustworthiness related message is related to artificial intelligence or machine learning model explainability as a trustworthiness factor out of trustworthiness factors including at least artificial intelligence or machine learning model fairness, artificial intelligence or machine learning model explainability, and artificial intelligence or machine learning model robustness, said second artificial intelligence or machine learning trustworthiness related message is related to artificial intelligence or machine learning model explainability as said trustworthiness factor.
In the same field of endeavor, Nasr-Azadani teaches said first artificial intelligence or machine learning trustworthiness related message is related to artificial intelligence or machine learning model explainability as a trustworthiness factor out of trustworthiness factors including at least artificial intelligence or machine learning model fairness, artificial intelligence or machine learning model explainability, and artificial intelligence or machine learning model robustness (thousands of real-time ML models may be integrated in a framework for collecting data from tens of thousands or more distributed sensors. Such framework is called machine learning model management framework (MLMM framework). The MLMM framework may provide fairness, explainability and robustness in the production environment and during the life cycle [par. 0024-0027, 0032-0035]),
said second artificial intelligence or machine learning trustworthiness related message is related to artificial intelligence or machine learning model explainability as said trustworthiness factor (the model reasoning/explainability monitoring and testing units may be configured to perform counterfactual reasoning, what-if reasoning, feature-aware reasoning (such as LIME, k-LIME, SHAPLEY). Those units may be provided as plug-ins of the automated MLMM framework to detect any issues. The model results then are determined whether they are trustworthy [par. 0024-0027, 0035-0039]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of monitoring, detecting and making revisions to machine learning models to prevent declines and maintain robustness and fairness as suggested by Nasr-Azadani into the concept of managing trustworthiness of electronic devices like IoT device as suggested by Bar-El because both of these systems addressing the process of detecting the trustworthiness of the system based on model results by analyzing the output of the model. Doing so would be desirable because the concept of Bar-El would be more efficient by monitoring machine learning models to quantify the performance with explainable reasons of any performance issues, any missing input data to provide fairness, explainability and robustness of the machine learning models (Nasr-Azadani [par. 0024-0027]).
With respect to claim 2, the combination of Bar-El and Nasr-Azadani teaches further comprising
translating circuitry configured to translate an acquired artificial intelligence or machine learning quality of trustworthiness into requirements related to artificial intelligence or machine learning model explainability as said trustworthiness factor (Nasr-Azadani, the automated MLMM framework may contain technical components for identifying and quantifying relationship in the dataset to provide explainability to the monitored performance issues of the machine learning model [par. 0024-0027]), and
identifying circuitry configured to identify said second network entity based on said acquired artificial intelligence or machine learning quality of trustworthiness (Bar-El, the TM server may send or transmit back to the querying device the trustworthiness report (TR) pertaining to target device responding to the received TQM. A TQM receiver configured to receive an incoming TQM and able to pass it to a TQM processing unit [par. 0029, 0034, 0038]), wherein
said first artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability capability information request (Bar-El, a TQM generator able to generate a TQM pertaining to a particular target device. A freshness requester unit able to request from caching agent to obtain a fresh or non-cached trustworthiness report, and a freshness determination unit able to determine a level of freshness of a received trustworthiness report [par. 0091-0094]), and
said second artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability capability information response (Bar-El, the TM server may need to pre-approve or pre-enroll the query agent in order to receive responses to queries. The query agent of querying device may generate and transmit a TQM to the TM server [par. 0091-0094]), and
said second artificial intelligence or machine learning trustworthiness related message comprises a second information element including at least one second artificial intelligence or machine learning model explainability related parameter (Bar-El, a recipient device may operate as a querying entity and may utilize a query agent to query a trust management server with regard to the trustworthiness of the IoT device. The server may receive from the IoT device a set of values indicating various parameters of the IoT device. The data collection agent may measure, sense, determine and/or collect values of parameters of target device [par. 0012, 0013, 0029]).
With respect to claim 3, the combination of Bar-El and Nasr-Azadani teaches wherein
said at least one first artificial intelligence or machine learning model explainability related parameter includes a list indicative of a cognitive network function scope (Bar-El, the data collection agent may comprise multiple measurement components or measurement units or modules, and may output a list or set of discrete values that correspond to various device parameters [par. 0054]), and
said at least one second artificial intelligence or machine learning model explainability related parameter includes at least one of a list indicative of supported artificial intelligence or machine learning model explanation methods, a list indicative of supported artificial intelligence or machine learning model explainability metrics, and a list indicative of supported artificial intelligence or machine learning model explanation aggregation period lengths (Nasr-Azadani, the model performance monitoring and testing units may perform accuracy metrics monitoring and testing such as classification precision, classification recalls, regression, statistical metrics monitoring and testing such as coverage, confidence interval, p-value, model consistency monitoring and testing [par. 0035]).
With respect to claim 4, the combination of Bar-El and Nasr-Azadani teaches further comprising
determining circuitry configured to determine, based on acquired capability information with respect to artificial intelligence or machine learning model explainability as said trustworthiness factor, whether requirements related to artificial intelligence or machine learning model explainability as said trustworthiness factor can be satisfied (Nasr-Azadani, the MLMM framework may quantify sensitivities and feature importance in a machine learning model via performance monitoring. The MLMM may further ascertain bias in the predictions of the ML model for facilitating processing of training dataset and model assumptions [par. 0024-0027]), wherein
said first artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability configuration request (Bar-El, a TQM generator able to generate a TQM pertaining to a particular target device. A freshness requester unit able to request from caching agent to obtain a fresh or non-cached trustworthiness report, and a freshness determination unit able to determine a level of freshness of a received trustworthiness report [par. 0091-0094]), and
said second artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability configuration response (Bar-El, the TM server may need to pre-approve or pre-enroll the query agent in order to receive responses to queries. The query agent of querying device may generate and transmit a TQM to the TM server [par. 0091-0094]).
With respect to claim 5, the combination of Bar-El and Nasr-Azadani teaches wherein
said at least one first artificial intelligence or machine learning model explainability related parameter includes at least one of a list indicative of cognitive network function instances within a cognitive network function scope of an artificial intelligence or machine learning model explanation collection job, state information indicative of activation or inactivation of said artificial intelligence or machine learning model explanation collection job, start time information indicative of when said artificial intelligence or machine learning model explanation collection job is started, stop time information indicative of when said artificial intelligence or machine learning model explanation collection job is stopped, aggregation period information indicative of an artificial intelligence or machine learning model explanation aggregation period length of said artificial intelligence or machine learning model explanation collection job, keeping time information indicative of for how long artificial intelligence or machine learning model explanations resulting from said artificial intelligence or machine learning model explanation collection job are to be stored, method information indicative of an artificial intelligence or machine learning model explanation method to be used for said artificial intelligence or machine learning model explanation collection job, and filter information indicative of at least one type of artificial intelligence or machine learning model explanations to be collected by said artificial intelligence or machine learning model explanation collection job (Nasr-Azadani, a list of cognitive network function instances is basically a list of specific deployed AI/ML network functions (instances). The input data processing component may include data transformation functions and data loading functions. For example, access to the components of the MLMM framework may be provided via an API functions. These API functions may be integrated to provide applications and/or user interfaces for the various types of users of the MLMM [par. 0044, 0045, 0049]).
With respect to claim 6, the combination of Bar-El and Nasr-Azadani teaches further comprising
determining circuitry configured to determine said second network entity based on an acquired trustworthiness information demand with respect to artificial intelligence or machine learning model explainability as said trustworthiness factor (Nasr-Azadani, the MLMM framework may quantify sensitivities and feature importance in a machine learning model via performance monitoring. The MLMM may further ascertain bias in the predictions of the ML model for facilitating processing of training dataset and model assumptions [par. 0024-0027]), wherein
said first artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability query request (Bar-El, a TQM generator able to generate a TQM pertaining to a particular target device. A freshness requester unit able to request from caching agent to obtain a fresh or non-cached trustworthiness report, and a freshness determination unit able to determine a level of freshness of a received trustworthiness report [par. 0091-0094]), and
said second artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability query response (Bar-El, the TM server may need to pre-approve or pre-enroll the query agent in order to receive responses to queries. The query agent of querying device may generate and transmit a TQM to the TM server [par. 0091-0094]), and
said second artificial intelligence or machine learning trustworthiness related message comprises a second information element including at least one second artificial intelligence or machine learning model explainability related parameter (Bar-El, a recipient device may operate as a querying entity and may utilize a query agent to query a trust management server with regard to the trustworthiness of the IoT device. The server may receive from the IoT device a set of values indicating various parameters of the IoT device. The data collection agent may measure, sense, determine and/or collect values of parameters of target device [par. 0012, 0013, 0029]).
With respect to claim 7, the combination of Bar-El and Nasr-Azadani teaches wherein
said at least one first artificial intelligence or machine learning model explainability related parameter includes at least one of a list indicative of cognitive network function instances within a cognitive network function scope of an artificial intelligence or machine learning model explanation query, start time information indicative of a begin of a timeframe for which artificial intelligence or machine learning model explanations are queried with said artificial intelligence or machine learning model explanation query, and stop time information indicative of an end of said timeframe for which artificial intelligence or machine learning model explanations are queried with said artificial intelligence or machine learning model explanation query (Nasr-Azadani, a list of cognitive network function instances is basically a list of specific deployed AI/ML network functions (instances). The input data processing component may include data transformation functions and data loading functions. For example, access to the components of the MLMM framework may be provided via an API functions. These API functions may be integrated to provide applications and/or user interfaces for the various types of users of the MLMM [par. 0044, 0045, 0049]), and
said at least one second artificial intelligence or machine learning model explainability related parameter includes at least one of time information indicative of when key performance indicators considered for an artificial intelligence or machine learning model explanation were reported, cognitive network function information indicative of at least one cognitive network function from which said key performance indicators considered for said artificial intelligence or machine learning model explanation were reported, and a list indicative of a plurality of decision classifications and a number of decisions per decision classification (Nasr-Azadani, MLMM framework may provide explainable reasoning behind the predictions of a ML model, such explainability provides the logic behind predictive decisions made by the ML model and allows for human interactions, collaboration and model sensitivity analysis through some algorithms [par. 0030]).
With respect to claim 8, the combination of Bar-El and Nasr-Azadani teaches further comprising
determining circuitry configured to determine said second network entity based on an acquired trustworthiness information demand with respect to artificial intelligence or machine learning model explainability as said trustworthiness factor (Nasr-Azadani, the MLMM framework may quantify sensitivities and feature importance in a machine learning model via performance monitoring. The MLMM may further ascertain bias in the predictions of the ML model for facilitating processing of training dataset and model assumptions [par. 0024-0027]), wherein
said first artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability subscription (Nasr-Azadani, access to the components of the MLMM framework may be provided via an API functions. These API functions may be integrated to provide applications and/or user interfaces for the various types of users of the MLMM [par. 0048, 0049]), and
said second artificial intelligence or machine learning trustworthiness related message is a trustworthiness explainability notification (Nasr-Azadani, the underlying of a trustworthiness explainability subscription is quite similar to a trustworthiness explainability notification. It implies a push mechanism, or system send updates. The correction circuitries may include a correction engine and data store, wherein the correction engine constitutes the main functionalities of the correction circuitries. The data store may handle various input, model output data, detection result and inspection result. These data are utilized by the correction engine for making on-demand corrections and updates to the ML models in the model surety pipeline [par. 0041]), and
said second artificial intelligence or machine learning trustworthiness related message comprises a second information element including at least one second artificial intelligence or machine learning model explainability related parameter (Bar-El, a recipient device may operate as a querying entity and may utilize a query agent to query a trust management server with regard to the trustworthiness of the IoT device. The server may receive from the IoT device a set of values indicating various parameters of the IoT device. The data collection agent may measure, sense, determine and/or collect values of parameters of target device [par. 0012, 0013, 0029]).
With respect to claim 9, the combination of Bar-El and Nasr-Azadani teaches wherein
said at least one first artificial intelligence or machine learning model explainability related parameter includes at least one of a list indicative of cognitive network function instances within a cognitive network function scope of an artificial intelligence or machine learning model explanation query, and filter information indicative of filter criteria for a subscription with respect to said artificial intelligence or machine learning model explanation query, and said at least one second artificial intelligence or machine learning model explainability related parameter includes at least one of time information indicative of when key performance indicators considered for an artificial intelligence or machine learning model explanation were reported, cognitive network function information indicative of at least one cognitive network function from which said key performance indicators considered for said artificial intelligence or machine learning model explanation were reported, and a list indicative of a plurality of decision classifications and a number of decisions per decision classification (Nasr-Azadani, a list of cognitive network function instances is basically a list of specific deployed AI/ML network functions (instances). The input data processing component may include data transformation functions and data loading functions. For example, access to the components of the MLMM framework may be provided via an API functions. These API functions may be integrated to provide applications and/or user interfaces for the various types of users of the MLMM [par. 0044, 0045, 0049]).
With respect to claim 10, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
With respect to claim 11, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 2. Therefore, it is rejected for the same reason as claimed in claim 2 above.
With respect to claim 12, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 3. Therefore, it is rejected for the same reason as claimed in claim 3 above.
With respect to claim 13, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 4. Therefore, it is rejected for the same reason as claimed in claim 4 above.
With respect to claim 14, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 5. Therefore, it is rejected for the same reason as claimed in claim 5 above.
With respect to claim 15, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 6. Therefore, it is rejected for the same reason as claimed in claim 6 above.
With respect to claim 16, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 7. Therefore, it is rejected for the same reason as claimed in claim 7 above.
With respect to claim 17, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 8. Therefore, it is rejected for the same reason as claimed in claim 8 above.
With respect to claim 18, it is an apparatus of a second network entity that is corresponding to the apparatus of a first network entity of claim 9. Therefore, it is rejected for the same reason as claimed in claim 9 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
D’Alessandro et al (US 20210243173 A1) disclosed a method of protecting signaling messages in a hop-by-hop network communication link between a source node and a destination node, a source node public digital signature verification key and a respective source node private digital signature key associated with said public digital signature verification key are provided to the source node. The source node public digital signature verification key associated with the source node private digital signature key is also provided to the destination node.
Das et al (US 12547926 B2) disclosed Bias metrics may be captured at different stages for training a machine learning model. A training job may specify bias metrics to capture at multiple different stages of a machine learning pipeline for a feature of a training data set used to train a machine learning model. The training job may be executed and the bias metrics determined at the stages as specified in the training job. The bias metrics for the different stages may be stored.
Vandikas et al (US 20240256973 A1) disclosed a method for use in a distributed machine learning process for training a machine learning model, wherein the training is distributed across a plurality of computing nodes and updates to the machine learning model, as determined by the plurality of computing nodes, are aggregated using secure multi party computation. The method includes: i) obtaining an aggregated characteristic of updates to the machine learning model provided by a first subset of the plurality of computing nodes; ii) comparing the aggregated characteristic to an equivalent reference; and iii) identifying whether the first subset of nodes are contributing updates that are corrupting the machine learning model, based on the comparison.
Das et al (US 20220172101 A1) disclosed Feature attribution may be captured as part of a machine learning pipeline. A training job may include a request to determine feature attribution as part of a machine learning pipeline that trains a machine learning model from a training data set. A reference data set for determining the feature attribution of the machine learning model may be identified. The feature attribution may be determined based on the reference data set. The feature attribution of the trained machine learning model may be stored.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143