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 . under the first inventor to file provisions of the AIA .
Claims 1-9 are pending and are being examined.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 04/08th/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner's Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
The Examiner respectfully notes that when reading the preamble in the context of the entire claim, any preamble recitation is not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL-The specification shall contain a written description of the
invention, and of the manner and process of making and using it, in such full, clear, concise,
and exact terms as to enable any person skilled in the art to which it pertains, or with which it
is most nearly connected, to make and use the same, and shall set forth the best mode
contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-A IA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the
manner and process of making and using it, in such full, clear, concise, and exact terms as to
enable any person skilled in the art to which it pertains, or with which it is most nearly
connected, to make and use the same, and shall set forth the best mode contemplated by the
inventor of carrying out his invention.
Claims 1-9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first
paragraph, as failing to comply with the written description requirement. The claim( s)
contains subject matter which was not described in the specification in such a way as to
reasonably convey to one skilled in the relevant art that the inventor or a joint inventor,
or for applications subject to pre-A IA 35 U.S.C. 112, the inventor(s), at the time the
application was filed, had possession of the claimed invention.
Regarding claims 1, and 6, “installing a program in a machine learning model used for training by a user” does not appear to contain support in the instant specification. The specification does not provide any details about how a person of ordinary skill in the art would install such program or use it for training.
The remaining claims are rejected with respect to their dependence on the
rejected claims.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-9 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre- A IA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, and 6, installing a program in a machine learning model used for training by a user is indefinite. The specification does not make clear how a program for training by a user is installed or used in a machine learning model. In the interest of further examination this is interpreted as using external programs (hooks) for training by a user, and further uses of installing a program in a machine learning model used for training by a user in the claim are interpreted similarly.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over HOFER (US20260105319A1), in view of CHOI (US20240202596A1), in view of GREEN (US20160063384A1), in view of AJGAONKAR (US20240037915A1), in view of SHUKLA (US20170056764A1).
Regarding claim 1, HOFER teaches Installation of a program in a machine learning model used for training by a user ([0115] In one approach, the system implements custom PyTorch hooks that execute at epoch boundaries or specified training iterations. These hooks access training metrics from the model and optimizer objects, format the metrics according to the communication protocol, transmit them to the language model component, receive optimization recommendations, and apply hyperparameter modifications to optimizer parameters, learning rate schedulers, or model configurations. The examiner notes that HOFER teaches PyTorch hooks which is a program used to access a machine learning model’s training and optimization and can be used to change the model parameters (training)).
return it to the user ([0119] return hyperparameter recommendations in structured formats that training code can parse and apply according to custom implementation requirements. The examiner notes that HOFER teaches returning hyperparameters to a user for use in creating a machine learning mode).
However, HOFER is not relied upon to explicitly teach:
A plug-and-play explainable artificial intelligence (PnP XAI) system
a model import module configured to ….. receive a model configuration file through user input via the installed program, configure a machine learning model using the received model configuration file.
a dataset import module configured to receive a dataset configuration file through user input via the installed program, configure a dataset using the received dataset configuration file.
return it to the user.
a user manager module configured to receive the returned model and dataset through user input and adjust the received model and dataset into forms applicable to an explainable artificial intelligence (XAI) algorithm.
a kernel manager module configured to receive the model and dataset adjusted into forms applicable to an XAI algorithm; and an XAI library module configured to receive a model and dataset information from the kernel manager module, calculate an XAI algorithm of a model, and return an XAI algorithm calculation result value to the kernel manager module.
On the other hand, CHOI teaches A plug-and-play explainable artificial intelligence (PnP XAI) system ([0002] The present invention relates to a method of providing an explanation of an artificial intelligence model, and more specifically, to a method of providing an explanation of an artificial intelligence model based on a plug-and-play mode, by which a module for explaining a given artificial intelligence model is automatically found in a plug-and-play mode to provide an explanation in a way that is easy for a user to understand. The examiner notes that HOFER and CHOI are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate A plug-and-play explainable artificial intelligence (PnP XAI) system as taught by CHOI [0002] to provide an explanation in a way that is easy for a user to understand [0002]).
Furthermore, GREEN teaches a model import module configured to ….. receive a model configuration file through user input via the installed program, configure a machine learning model using the received model configuration file ([0019] The model builder application constructs the one or more inference models according to the information contained in the model configuration data received from the configuration file and/or compiled configuration database, with this information having been customized to the first asset and the desired one or more inference models. The examiner notes that HOFER and GREEN are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate a model import module configured to ….. receive a model configuration file through user input via the installed program, configure a machine learning model using the received model configuration file as taught by GREEN [0019] to infer a status of one or more of the first assets from operational data received from the one or more first assets during its operation [0019]).
Furthermore, AJGAONKAR teaches a dataset import module configured to receive a dataset configuration file through user input via the installed program, configure a dataset using the received dataset configuration file ([0072] Machine learning model 356 can include test configuration file 314, which includes test preprocessing parameters that are applied to raw video data 20 to create test video data 321, and database 358, within which information (e.g., datasets) for identifying test preprocessing parameters is stored and accessed. The examiner notes that AJGAONKAR teaches using a configuration file to create a test dataset. The examiner further notes that HOFER and AJGAONKAR are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate a dataset import module configured to receive a dataset configuration file through user input via the installed program, configure a dataset using the received dataset configuration file as taught by AJGAONKAR [0072] to produce improved test outputs 350 [0072]).
Furthermore, SHUKLA teaches return it to the user ([0033] The map operation returns a distributed dataset by applying a user specified function to each element of a distributed dataset. The examiner notes that SHUKLA teaches returning a configured dataset to a user. The examiner notes that HOFER and SHUKLA are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate return it to the user as taught by SHUKLA [0033] to allow the processing of large volumes of data in parallel [0033]).
Furthermore, AJGAONKAR teaches a user manager module configured to receive the returned model and dataset through user input and adjust the received model and dataset into forms applicable to an explainable artificial intelligence (XAI) algorithm ([0064] With enough training datasets in database 358, implementation module 362 can identify the test preprocessing parameters that are most predictive of a change in test output 350 and that would produce a test output 350 that satisfies the baseline criterion and is a greater improvement upon previous test outputs 350 (and main output 50). The examiner further notes that HOFER and AJGAONKAR are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate a user manager module configured to receive the returned model and dataset through user input and adjust the received model and dataset into forms applicable to an explainable artificial intelligence (XAI) algorithm as taught by AJGAONKAR [0064] to produce improved test outputs 350 [0064]).
Furthermore, AJGAONKAR teaches a kernel manager module configured to receive the model and dataset adjusted into forms applicable to an XAI algorithm; and an XAI library module configured to receive a model and dataset information from the kernel manager module, calculate an XAI algorithm of a model, and return an XAI algorithm calculation result value to the kernel manager module ([0072] Machine learning model 356 can be configured to determine and store any algorithms, neural networks, or other predictive systems/methods that determine preprocessing parameters predictive to produce improved test outputs 350. The examiner notes that AJGAONKAR teaches determining a machine learning algorithm to be used in a machine learning model to generate improved results. The examiner further notes that HOFER and AJGAONKAR are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate a kernel manager module configured to receive the model and dataset adjusted into forms applicable to an XAI algorithm; and an XAI library module configured to receive a model and dataset information from the kernel manager module, calculate an XAI algorithm of a model, and return an XAI algorithm calculation result value to the kernel manager module as taught by AJGAONKAR [0072] to produce improved test outputs 350 [0072]).
Regarding claim 5, HOFFER teaches a visualization module configured to receive the XAI algorithm calculation result value from the kernel manager module and visualize it ([0090] Displaying a loss curve as an image and then using the rest of this algorithm as is instead of as the results of a linear regression or directly outputting the values of said curve should not be viewed as being sufficiently different from this algorithm, as adding that as an additional analysis would be trivial in our current implementation, and we don't do so simply because it would be time consuming both to implement and on a per-usage basis while likely providing negligible additional performance on improving the training session. The examiner notes that HOFER teaches visualizing a result of a machine learning model).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over HOFER (US20260105319A1), in view of CHOI (US20240202596A1), in view of GREEN (US20160063384A1), in view of AJGAONKAR (US20240037915A1), in view of SHUKLA (US20170056764A1), in view of GUPTE (US20220391176A1).
Regarding claim 2, HOFER teaches the system of claim 1, however, HOFER is not relied upon to explicitly teach the model configuration file includes location information of a file, model and dataset call information, XAI algorithm selection information, and parameter information necessary for calling a model.
On the other hand, GUPTE teaches the model configuration file includes location information of a file, model and dataset call information, XAI algorithm selection information, and parameter information necessary for calling a model ([0047] Model configuration files may include one or more parameters that may be used in initializing and/or operating the model 105. For example, a model may use multiple parameters that may be used with the model files to initialize the model prior to performing an operation or task. Pre-processing parameters and post-processing parameters may include any operations or tasks that may be performed before or after the tasks performed by the model, respectively, which may include preparing data into a format expected by the model or by a subsequent component that receives output from the model. For example, a pre-processing parameter may be used to convert an image from a raw image file to a first image type, which may be expected as an input to the model. In another example, a post-processing parameter may be used to convert an output generated by the model into a different output type—e.g., to determine clusters of pixels corresponding to a particular object from a semantic segmentation output of the model 105. The examiner notes that GUPTE teaches a configuration file that contains data about model parameters and model data and how to retrieve such data. The examiner further notes that HOFER and GUPTE are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate the model configuration file includes location information of a file, model and dataset call information, XAI algorithm selection information, and parameter information necessary for calling a model as taught by GUPTE [0047] to provide a framework of elements to be included as part of the model 105 [0047]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over HOFER (US20260105319A1), in view of CHOI (US20240202596A1), in view of GREEN (US20160063384A1), in view of AJGAONKAR (US20240037915A1), in view of SHUKLA (US20170056764A1), in view of ST-AMANT (US 20250342394 A1).
Regarding claim 3, HOFER teaches the system of claim 1, however, HOFER is not relied upon to explicitly teach the XAI library module comprises an XAI algorithm sub-module and the XAI algorithm sub-module comprises at least one of layer-wise relevance propagation (LRP) sub-module, a gradient-class activation map (GradCAM) sub-module, or an integrated gradient (IG) sub-module.
On the other hand, ST-AMANT teaches the XAI library module comprises an XAI algorithm sub-module and the XAI algorithm sub-module comprises at least one of layer-wise relevance propagation (LRP) sub-module, a gradient-class activation map (GradCAM) sub-module, or an integrated gradient (IG) sub-module ([0005] For example, there are “explainable AI” libraries such as GradCam, which allow data scientists to gain some insight for specific model or solution. The examiner notes that HOFER and ST-AMANT are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate the XAI library module comprises an XAI algorithm sub-module and the XAI algorithm sub-module comprises at least one of layer-wise relevance propagation (LRP) sub-module, a gradient-class activation map (GradCAM) sub-module, or an integrated gradient (IG) sub-module as taught by ST-AMANT [0005] to allow data scientists to gain some insight for specific model or solution [0005]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over HOFER (US20260105319A1), in view of CHOI (US20240202596A1), in view of GREEN (US20160063384A1), in view of AJGAONKAR (US20240037915A1), in view of SHUKLA (US20170056764A1), in view of ST-AMANT (US 20250342394 A1), in view of KO (US20240303505A1).
Regarding claim 4, HOFER teaches the system of claim 3, however, HOFER is not relied upon to explicitly teach In the calculating of the XAI algorithm, the model is analyzed to identify a computational structure of the model and the XAI algorithm is calculated using algorithm calculation classes corresponding to the identified computational structure.
On the other hand, KO teaches In the calculating of the XAI algorithm, the model is analyzed to identify a computational structure of the model and the XAI algorithm is calculated using algorithm calculation classes corresponding to the identified computational structure ([0043] The artificial neural network model 300, which is an example of a machine learning model, is a statistical learning algorithm that is implemented based on the structure of a biological neural network or a structure that executes the algorithm, in machine learning technology and cognitive science. The examiner notes that KO teaches a machine learning algorithm that is implemented based on the structure of a biological neural network. The examiner further notes that HOFER and KO are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate In the calculating of the XAI algorithm, the model is analyzed to identify a computational structure of the model and the XAI algorithm is calculated using algorithm calculation classes corresponding to the identified computational structure as taught by KO [0043] to enable the execution of an algorithm in machine learning technology and cognitive science [0043]).
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over HOFER (US20260105319A1), in view of CHOI (US20240202596A1), in view of GREEN (US20160063384A1), in view of AJGAONKAR (US20240037915A1).
Regarding claim 6, HOFER teaches Installation of a program in a machine learning model used for training by a user ([0115] In one approach, the system implements custom PyTorch hooks that execute at epoch boundaries or specified training iterations. These hooks access training metrics from the model and optimizer objects, format the metrics according to the communication protocol, transmit them to the language model component, receive optimization recommendations, and apply hyperparameter modifications to optimizer parameters, learning rate schedulers, or model configurations. The examiner notes that HOFER teaches PyTorch hooks which is a program used to access a machine learning model’s training and optimization and can be used to change the model parameters (training)).
visualizing a result including a value calculated with the XAI algorithm ([0090] Displaying a loss curve as an image and then using the rest of this algorithm as is instead of as the results of a linear regression or directly outputting the values of said curve should not be viewed as being sufficiently different from this algorithm, as adding that as an additional analysis would be trivial in our current implementation, and we don't do so simply because it would be time consuming both to implement and on a per-usage basis while likely providing negligible additional performance on improving the training session. The examiner notes that HOFER teaches visualizing a result of a machine learning model.
However, HOFER is not relied upon to explicitly teach:
A plug-and-play explainable artificial intelligence (PnP XAI) design method.
receiving a model configuration file from the user through the installed program; configuring a machine learning model using the received model configuration file.
calculating an XAI algorithm of the configured machine learning model.
On the other hand, CHOI teaches A plug-and-play explainable artificial intelligence (PnP XAI) system ([0002] The present invention relates to a method of providing an explanation of an artificial intelligence model, and more specifically, to a method of providing an explanation of an artificial intelligence model based on a plug-and-play mode, by which a module for explaining a given artificial intelligence model is automatically found in a plug-and-play mode to provide an explanation in a way that is easy for a user to understand. The examiner notes that HOFER and CHOI are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate A plug-and-play explainable artificial intelligence (PnP XAI) system as taught by CHOI [0002] to provide an explanation in a way that is easy for a user to understand [0002]).
Furthermore, GREEN teaches receiving a model configuration file from the user through the installed program; configuring a machine learning model using the received model configuration file ([0019] The model builder application constructs the one or more inference models according to the information contained in the model configuration data received from the configuration file and/or compiled configuration database, with this information having been customized to the first asset and the desired one or more inference models. The examiner notes that HOFER and GREEN are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate receiving a model configuration file from the user through the installed program; configuring a machine learning model using the received model configuration file as taught by GREEN [0019] to infer a status of one or more of the first assets from operational data received from the one or more first assets during its operation [0019]).
Furthermore, AJGAONKAR teaches calculating an XAI algorithm of the configured machine learning model ([0072] Machine learning model 356 can be configured to determine and store any algorithms, neural networks, or other predictive systems/methods that determine preprocessing parameters predictive to produce improved test outputs 350. The examiner notes that AJGAONKAR teaches determining a machine learning algorithm to be used in a machine learning model. The examiner further notes that HOFER and AJGAONKAR are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate calculating an XAI algorithm of the configured machine learning model as taught by AJGAONKAR [0072] to produce improved test outputs 350 [0072]).
Regarding claim 7, HOFER teaches the method of claim 6, however, HOFER is not relied upon to explicitly teach receiving a dataset configuration file from the user and configuring a dataset using the received dataset configuration file. On the other hand, AJGAONKAR teaches receiving a dataset configuration file from the user and configuring a dataset using the received dataset configuration file ([0072] Machine learning model 356 can include test configuration file 314, which includes test preprocessing parameters that are applied to raw video data 20 to create test video data 321, and database 358, within which information (e.g., datasets) for identifying test preprocessing parameters is stored and accessed. The examiner notes that AJGAONKAR teaches using a configuration file to create a test dataset. The examiner further notes that HOFER and AJGAONKAR are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate receiving a dataset configuration file from the user and configuring a dataset using the received dataset configuration file as taught by AJGAONKAR [0072] to produce improved test outputs 350 [0072]).
Regarding claim 8, HOFER teaches the method of claim 7, however, HOFER is not relied upon to explicitly teach adjusting the configured model and dataset into forms applicable to an XAI algorithm.
On the other hand, AJGAONKAR teaches adjusting the configured model and dataset into forms applicable to an XAI algorithm ([0064] With enough training datasets in database 358, implementation module 362 can identify the test preprocessing parameters that are most predictive of a change in test output 350 and that would produce a test output 350 that satisfies the baseline criterion and is a greater improvement upon previous test outputs 350 (and main output 50). The examiner further notes that HOFER and AJGAONKAR are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate adjusting the configured model and dataset into forms applicable to an XAI algorithm as taught by AJGAONKAR [0064] to produce improved test outputs 350 [0064]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over HOFER (US20260105319A1), in view of CHOI (US20240202596A1), in view of GREEN (US20160063384A1), in view of AJGAONKAR (US20240037915A1), in view of KO (US20240303505A1).
Regarding claim 9, HOFER teaches the method of claim 6, however, HOFER is not relied upon to explicitly teach In the calculating of the XAI algorithm, the model is analyzed to identify a computational structure of the model and the XAI algorithm is calculated using algorithm calculation classes corresponding to the identified computational structure.
On the other hand, KO teaches In the calculating of the XAI algorithm, the model is analyzed to identify a computational structure of the model and the XAI algorithm is calculated using algorithm calculation classes corresponding to the identified computational structure ([0043] The artificial neural network model 300, which is an example of a machine learning model, is a statistical learning algorithm that is implemented based on the structure of a biological neural network or a structure that executes the algorithm, in machine learning technology and cognitive science. The examiner notes that KO teaches a machine learning algorithm that is implemented based on the structure of a biological neural network. The examiner further notes that HOFER and KO are both directed to machine learning and both are reasonably analogous to the claimed invention. Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified HOFER’s machine learning method to incorporate In the calculating of the XAI algorithm, the model is analyzed to identify a computational structure of the model and the XAI algorithm is calculated using algorithm calculation classes corresponding to the identified computational structure as taught by KO [0043] to enable the execution of an algorithm in machine learning technology and cognitive science [0043]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
ARENAS (A Symbolic Language for Interpreting Decision Trees)
“ARENAS teaches EXPLAINDT, a symbolic language for interpreting decision trees”
HADA (ReXPlug: Explainable Recommendation using Plug and Play Language Model)
“HADA teaches ReXPlug, an end-to-end framework with a plug and play way of explaining recommendations”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAMCY ALGHAZZY whose telephone number is (571)272-8824. The examiner can normally be reached on M-F 7:30am-5:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, OMAR FERNANDEZ RIVAS can be reached on (571) 272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAMCY ALGHAZZY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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