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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/24/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 1-2, 4-11, and 13-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luo et al. Patent No.: US 11694460 B1 in view of Najumudeen et al. Patent No.: US 12174867 B2.
Regarding Claim 1 Luo teaches A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: receive, via the communication interface, from a computing device, configuration parameters; (Luo, page 12-13, column 4 paragraph 4 – column 5 paragraph 1, teaches the receiving of parameters that are used to configuring the natural language processing system (i.e. configuration parameters) from a user input into a GUI) receive, from a source data store, historical response data indicating a plurality of previous responses to requests; (Luo, page 14, column 7 paragraph 2 – column 8 paragraph 1, teaches an extractor that receives from a machine readable document database (i.e. source data store) previous documents that were processed in response to previous requests) based on the configuration parameters and the historical response data, extract, using a machine learning algorithm, intelligence information associated with the requests; (Luo, page 14-16, column 8 paragraph 2 – column 11 paragraph 1, teaches a word model which is a machine learning model that takes in the parameters and the historical responses from the extractor and the GUI and produces word embeddings such as feature vectors that can reconstruct linguistic context of words (i.e. extract intelligence information)) build an intelligence model using the extracted intelligence information; (Luo, page 16-17, column 11 paragraph 2 – column 13 paragraph 1, teaches the building of a document model configured to process and score machine readable documents, that is built on the output of the word model (i.e. intelligence information))
Luo does not teach …1receive, via the communication interface, from the computing device, a subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 10, column 4 paragraph 3, teaches an end-user submitting a request through a communication device that is then received by the communication interface.)
Further, Luo does not teach automatically derive, using the intelligence model, one or more actions in response to the subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the Natural language processing unit (NPL) (i.e. intelligence model) determining the best action to take based on the request that was submitted by the user. The NPL processes the user request and finds an action to take based on previous actions and data stored in its databases.)
Further, Luo does not teach process the subsequent request by executing the one or more actions in response to the subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the natural language processing unit generating an action plan based off the users request and executing the action plan in order to solve the users’ request.)
Further, Luo does not teach determine an accuracy of the intelligence model based on the configuration parameters; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 16, column 16 paragraph 4, teaches the use of a feedback unit that is used to determine the accuracy of the outputs of the models that are used in the system based on the provided parameters.)
Further, Luo does not teach and responsive to the accuracy of the intelligence model being below a threshold, execute a self-learning algorithm based on the processed subsequent requests to improve the accuracy of the intelligence model. However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 16, column 15 paragraph 5 column 16 paragraph 1-4, teaches the use of a feedback system that is able to evaluate the accuracy of the machine learning model that is used in the system. Further it teaches the means for the neural network to be able to be updated to improve the accuracy and effectiveness of the model. Najumudeen, page 13, column 9 paragraph 3 – column 10 paragraph 1, teaches the ability to use thresholds to determine when an action should automatically be taken by the system. The system is capable of using a threshold to determine if the system needs to be optimized or improved based on a threshold an execute the optimization process (i.e. self-learning.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Najumudeen’s teaching of an intelligence model that automatically takes actions based on requests with Luo’s teaching of a machine learning model that takes in data and creates an intelligence model. The motivation to do so would be to enable the intelligence model of the system to automatically take the action that it deemed best to solve the request rather than waiting to receive an input to take an action.
Regarding claim 2 the combination of Luo and Najumudeen teaches The computing platform of claim 1, wherein extracting the intelligence information associated with the requests includes determining intent of the requests. (Luo, page 14, column 8 paragraph 3, teaches the ability of the word model being able to produce linguistic contexts (i.e. the intents of the requests) when it is producing its outputs to be used in the creation of the intelligence model.)
Regarding claim 4 the combination of Luo and Najumudeen teaches The computing platform of claim 1, wherein receiving the configuration parameters includes receiving different configurations for different lines of business. (Luo, page 14-15, column 8 paragraph 5 – column 9 paragraph 1, teaches the ability of the GUI to specify the use of different attributes (i.e. configuration parameters) including specifying a line of business and using different attributes based on that line of business.)
Regarding claim 5 the combination of Luo and Najumudeen teaches The computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: store, in the source data store, the one or more actions executed in response to the subsequent request. (Najumudeen, page 15-16, column 14 paragraph 6 – column 15 paragraph 1, teaches the storing of the results of the system into the data stores that are utilized by the system (i.e. store, in the source data store, the one or more actions executed in response to the subsequent request)
Regarding claim 6 the combination of Luo and Najumudeen teaches The computing platform of claim 1, wherein processing the subsequent request includes executing an automation process. (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the ability of the system upon receiving a request to automatically perform an action in response to a received request.)
Regarding claim 7 the combination of Luo and Najumudeen teaches The computing platform of claim 1, wherein processing the subsequent request includes providing assistance to an administrative computing device. (Najumudeen, page 10-11, column 4 paragraph 3-6 – column 5 paragraph 1, teaches the requests coming from an enterprise organization which would include requests coming from administrative computing devices that are active within the enterprise organization computing environment.)
Regarding claim 8 the combination of Luo and Najumudeen teaches The computing platform of claim 1, wherein extracting the intelligence information associated with the requests includes vectorizing words in the requests and assigning weights to the words. (Luo, page 15, column 9 paragraph 3-4 – column 10 paragraph 1-3, teaches the word model that teaches the creation of feature vectors for each word in the request (i.e. vectorizing words) and getting frequency information to determine how often a word appears and give it an importance based on that information (i.e. assigning weights to the words)
Regarding claim 9 the combination of Luo and Najumudeen teaches The computing platform of claim 1, further including instructions that, when executed, cause the computing platform to: prompt a user of the computing device to set the configuration parameters. (Luo, page 15, column 4 paragraph 4 – column 5 paragraph 1, teaches the use of a GUI that is able to prompt the used to input or select parameters that they want to be associated with the system or requests.)
Regarding Claim 10 Luo teaches A method, comprising: at a computing platform comprising at least one processor, a communication interface, and memory: receiving, by the at least one processor, via the communication interface, from a computing device, configuration parameters; (Luo, page 12-13, column 4 paragraph 4 – column 5 paragraph 1, teaches the receiving of parameters that are used to configuring the natural language processing system (i.e. configuration parameters) from a user input into a GUI) receiving, by the at least one processor, from a source data store, historical response data indicating a plurality of previous responses to requests; (Luo, page 14, column 7 paragraph 2 – column 8 paragraph 1, teaches an extractor that receives from a machine readable document database (i.e. source data store) previous documents that were processed in response to previous requests) based on the configuration parameters and the historical response data, extract, using a machine learning algorithm, intelligence information associated with the requests; (Luo, page 14-16, column 8 paragraph 2 – column 11 paragraph 1, teaches a word model which is a machine learning model that takes in the parameters and the historical responses from the extractor and the GUI and produces word embeddings such as feature vectors that can reconstruct linguistic context of words (i.e. extract intelligence information)) building, by the at least one processor, an intelligence model using the extracted intelligence information; (Luo, page 16-17, column 11 paragraph 2 – column 13 paragraph 1, teaches the building of a document model configured to process and score machine readable documents, that is built on the output of the word model (i.e. intelligence information))
Luo does not teach …1 receiving, by the at least one processor, via the communication interface, from the computing device, a subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 10, column 4 paragraph 3, teaches an end-user submitting a request through a communication device that is then received by the communication interface.)
Further, Luo does not teach automatically deriving, by the at least one processor, using the intelligence model, one or more actions in response to the subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the Natural language processing unit (NPL) (i.e. intelligence model) determining the best action to take based on the request that was submitted by the user. The NPL processes the user request and finds an action to take based on previous actions and data stored in its databases.)
Further, Luo does not teach processing, by the at least one processor, the subsequent request by executing the one or more actions in response to the subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the natural language processing unit generating an action plan based off the users request and executing the action plan in order to solve the users’ request.)
Further, Luo does not teach determining, by the at least one processor, an accuracy of the intelligence model based on the configuration parameters; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 16, column 16 paragraph 4, teaches the use of a feedback unit that is used to determine the accuracy of the outputs of the models that are used in the system based on the provided parameters.)
Further, Luo does not teach and responsive to the accuracy of the intelligence model being below a threshold, executing, by the at least one processor, a self-learning algorithm based on the processed subsequent requests to improve the accuracy of the intelligence model. However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 16, column 15 paragraph 5 column 16 paragraph 1-4, teaches the use of a feedback system that is able to evaluate the accuracy of the machine learning model that is used in the system. Further it teaches the means for the neural network to be able to be updated to improve the accuracy and effectiveness of the model. Najumudeen, page 13, column 9 paragraph 3 – column 10 paragraph 1, teaches the ability to use thresholds to determine when an action should automatically be taken by the system. The system is capable of using a threshold to determine if the system needs to be optimized or improved based on a threshold an execute the optimization process (i.e. self-learning.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Najumudeen’s teaching of an intelligence model that automatically takes actions based on requests with Luo’s teaching of a machine learning model that takes in data and creates an intelligence model. The motivation to do so would be to enable the intelligence model of the system to automatically take the action that it deemed best to solve the request rather than waiting to receive an input to take an action.
Regarding claim 11 the combination of Luo and Najumudeen teaches The method of claim 10, wherein extracting the intelligence information associated with the requests includes determining intent of the requests. (Luo, page 14, column 8 paragraph 3, teaches the ability of the word model being able to produce linguistic contexts (i.e. the intents of the requests) when it is producing its outputs to be used in the creation of the intelligence model.)
Regarding claim 13 the combination of Luo and Najumudeen teaches The method of claim 10, wherein receiving the configuration parameters includes receiving different configurations for different lines of business. (Luo, page 14-15, column 8 paragraph 5 – column 9 paragraph 1, teaches the ability of the GUI to specify the use of different attributes (i.e. configuration parameters) including specifying a line of business and using different attributes based on that line of business.)
Regarding claim 14 the combination of Luo and Najumudeen teaches The method of claim 10, further comprising: store, by the at least one processor, in the source data store, the one or more actions executed in response to the subsequent request. (Najumudeen, page 15-16, column 14 paragraph 6 – column 15 paragraph 1, teaches the storing of the results of the system into the data stores that are utilized by the system (i.e. store, in the source data store, the one or more actions executed in response to the subsequent request)
Regarding claim 15 the combination of Luo and Najumudeen teaches The method of claim 10, wherein processing the subsequent request includes executing an automation process. (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the ability of the system upon receiving a request to automatically perform an action in response to a received request.)
Regarding claim 16 the combination of Luo and Najumudeen teaches The method of claim 10, wherein processing the subsequent request includes providing assistance to an administrative computing device. (Najumudeen, page 10-11, column 4 paragraph 3-6 – column 5 paragraph 1, teaches the requests coming from an enterprise organization which would include requests coming from administrative computing devices that are active within the enterprise organization computing environment.)
Regarding claim 17 the combination of Luo and Najumudeen teaches The method of claim 10, wherein extracting the intelligence information associated with the requests includes vectorizing words in the requests and assigning weights to the words. (Luo, page 15, column 9 paragraph 3-4 – column 10 paragraph 1-3, teaches the word model that teaches the creation of feature vectors for each word in the request (i.e. vectorizing words) and getting frequency information to determine how often a word appears and give it an importance based on that information (i.e. assigning weights to the words)
Regarding claim 18 the combination of Luo and Najumudeen teaches The method of claim 1, further including instructions that, when executed, cause the computing platform to: prompt a user of the computing device to set the configuration parameters. (Luo, page 15, column 4 paragraph 4 – column 5 paragraph 1, teaches the use of a GUI that is able to prompt the used to input or select parameters that they want to be associated with the system or requests.)
Regarding Claim 19 Luo teaches One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, memory, and a communication interface, cause the computing platform to: receive, via the communication interface, from a computing device, configuration parameters; (Luo, page 12-13, column 4 paragraph 4 – column 5 paragraph 1, teaches the receiving of parameters that are used to configuring the natural language processing system (i.e. configuration parameters) from a user input into a GUI) receive, from a source data store, historical response data indicating a plurality of previous responses to requests; (Luo, page 14, column 7 paragraph 2 – column 8 paragraph 1, teaches an extractor that receives from a machine readable document database (i.e. source data store) previous documents that were processed in response to previous requests) based on the configuration parameters and the historical response data, extract, using a machine learning algorithm, intelligence information associated with the requests; (Luo, page 14-16, column 8 paragraph 2 – column 11 paragraph 1, teaches a word model which is a machine learning model that takes in the parameters and the historical responses from the extractor and the GUI and produces word embeddings such as feature vectors that can reconstruct linguistic context of words (i.e. extract intelligence information)) build an intelligence model using the extracted intelligence information; (Luo, page 16-17, column 11 paragraph 2 – column 13 paragraph 1, teaches the building of a document model configured to process and score machine readable documents, that is built on the output of the word model (i.e. intelligence information))
Luo does not teach …1receive, via the communication interface, from the computing device, a subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 10, column 4 paragraph 3, teaches an end-user submitting a request through a communication device that is then received by the communication interface.)
Further, Luo does not teach automatically derive, using the intelligence model, one or more actions in response to the subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the Natural language processing unit (NPL) (i.e. intelligence model) determining the best action to take based on the request that was submitted by the user. The NPL processes the user request and finds an action to take based on previous actions and data stored in its databases.)
Further, Luo does not teach process the subsequent request by executing the one or more actions in response to the subsequent request; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 12-13, column 8 paragraph 7 – column 9 paragraph 1, teaches the natural language processing unit generating an action plan based off the users request and executing the action plan in order to solve the users’ request.)
Further, Luo does not teach determine an accuracy of the intelligence model based on the configuration parameters; However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 16, column 16 paragraph 4, teaches the use of a feedback unit that is used to determine the accuracy of the outputs of the models that are used in the system based on the provided parameters.)
Further, Luo does not teach and responsive to the accuracy of the intelligence model being below a threshold, execute a self-learning algorithm based on the processed subsequent requests to improve the accuracy of the intelligence model. However, Najumudeen in analogous art teaches this limitation (Najumudeen, page 16, column 15 paragraph 5 column 16 paragraph 1-4, teaches the use of a feedback system that is able to evaluate the accuracy of the machine learning model that is used in the system. Further it teaches the means for the neural network to be able to be updated to improve the accuracy and effectiveness of the model. Najumudeen, page 13, column 9 paragraph 3 – column 10 paragraph 1, teaches the ability to use thresholds to determine when an action should automatically be taken by the system. The system is capable of using a threshold to determine if the system needs to be optimized or improved based on a threshold an execute the optimization process (i.e. self-learning.)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Najumudeen’s teaching of an intelligence model that automatically takes actions based on requests with Luo’s teaching of a machine learning model that takes in data and creates an intelligence model. The motivation to do so would be to enable the intelligence model of the system to automatically take the action that it deemed best to solve the request rather than waiting to receive an input to take an action.
Claims 3, 12, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Luo et al. Patent No.: US 11694460 B1 in view of Najumudeen et al. Patent No.: US 12174867 B2 in further view of Roy Patent No.: US 9426036 B1.
Regarding claim 3 the combination of Luo and Najumudeen teaches The computing platform of claim 1, wherein receiving the configuration parameters includes, …1 word inclusion and exclusion criteria, …2 (Luo, page 12-13, column 4 paragraph 4 – column 5 paragraph 1, teaches the use of an audit word parameter that is set by a GUI and is used by the system. (i.e. configuration parameter) This parameter specifies words to be used and does not include words that are not meant to be used (i.e. word inclusion and exclusion criteria.)
The combination of Luo and Najumudeen does not teach …3 receiving bias application information …2 and a threshold calibration setting However, Roy in analogous art teaches this limitation (Roy, page 9, column 6 paragraph 1, teaches the use of configuration parameters for a machine learning model, these configuration parameters include the use of threshold values (i.e. a threshold calibration setting) and weights (i.e. bias application information).)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Roy’s teaching of machine learning system parameters with the combination of Luo and Najumudeen’s teaching of a machine learning model that takes in data and creates an intelligence model and processes requests. The motivation to do so would be to enable the users to specify the parameters of the system in order to enable mora accurate results to be produced based on the users’ specific needs.
Regarding claim 12 the combination of Luo and Najumudeen teaches The method of claim 10, wherein receiving the configuration parameters includes, …1 word inclusion and exclusion criteria, …2 (Luo, page 12-13, column 4 paragraph 4 – column 5 paragraph 1, teaches the use of a audit word parameter that is set by a GUI and is used by the system. (i.e. configuration parameter) This parameter specifies words to be used and does not include words that are not meant to be used (i.e. word inclusion and exclusion criteria.)
The combination of Luo and Najumudeen does not teach …3 receiving bias application information …2 and a threshold calibration setting However, Roy in analogous art teaches this limitation (Roy, page 9, column 6 paragraph 1, teaches the use of configuration parameters for a machine learning model, these configuration parameters include the use of threshold values (i.e. a threshold calibration setting) and weights (i.e. bias application information).)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Roy’s teaching of machine learning system parameters with the combination of Luo and Najumudeen’s teaching of a machine learning model that takes in data and creates an intelligence model and processes requests. The motivation to do so would be to enable the users to specify the parameters of the system in order to enable mora accurate results to be produced based on the users’ specific needs.
Regarding claim 20 the combination of Luo and Najumudeen teaches The one or more non-transitory computer-readable media of claim 19, …1 word inclusion and exclusion criteria, …2 (Luo, page 12-13, column 4 paragraph 4 – column 5 paragraph 1, teaches the use of a audit word parameter that is set by a GUI and is used by the system. (i.e. configuration parameter) This parameter specifies words to be used and does not include words that are not meant to be used (i.e. word inclusion and exclusion criteria.)
The combination of Luo and Najumudeen does not teach …3 receiving bias application information …2 and a threshold calibration setting However, Roy in analogous art teaches this limitation (Roy, page 9, column 6 paragraph 1, teaches the use of configuration parameters for a machine learning model, these configuration parameters include the use of threshold values (i.e. a threshold calibration setting) and weights (i.e. bias application information).)
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine Roy’s teaching of machine learning system parameters with the combination of Luo and Najumudeen’s teaching of a machine learning model that takes in data and creates an intelligence model and processes requests. The motivation to do so would be to enable the users to specify the parameters of the system in order to enable mora accurate results to be produced based on the users’ specific needs.
Conclusion
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/THOMAS BERNARD LANE/ Examiner, Art Unit 2142
/HAIMEI JIANG/ Primary Examiner, Art Unit 2142