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 .
Claim Rejections - 35 USC §103
2. 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- 20 are rejected under 35 U.S.C. 103 as being unpatentable over George et al (Pub. No. US 2022/0101839 A1; hereinafter George) in view of Subramaniam et al (Pub. No. US 2020/0342032 A1; hereinafter Subramaniam)
Consider claims 1, 8, and 16, George clearly shows and discloses a system, a computer processing system and a method performed by one or more computers, the method comprising: receiving, by an audio recording text and as a result of a speech-to-text conversion, a text of a plurality of conversations between a plurality of customers and an agent (various stages or steps of bot authoring workflow 400 are shown using the intent mining process of the present invention (or simply “present intent mining process”). To initiate the workflow 400, conversations or conversation data may be imported for mining. Such conversations data may consist of previously occurring interactions between agents and customers. Such conversation data may be natural language conversations consisting of multiple back and forth messaging turns. The conversations, for example, may have occurred via a chat interface, through text, or via voice calls) (paragraphs: 0084 and fig. 8); identifying, by a model, a context of one conversation of the plurality of conversation from one or more conversational insights (the intent mining process of the present invention functions by mining intents from tens of thousands of conversations; an exemplary algorithm for implementing the present intent mining engine or process 500 will now be discussed. As will be seen, this algorithm may be approximately broken down into several steps, with will be referred to herein as: 1) identifying intent-bearing utterances; 2) generating candidate intents; 3) identifying salient intents; 4) semantic grouping of intents; 5) intent labeling; and 6) utterance-intent association. Other steps may include the masking of personally identifiable information in utterances. Another additional step may include the computation of intent analytics) (paragraphs: 0083, and 0090); identifying, by the model, an intent of the one conversation from one or more conversational insights (the intent mining process of the present invention functions by mining intents from tens of thousands of conversations and finds a robust and diverse set of utterances belonging to each one. Further, the intent mining process helps to gain insights into the conversations by providing conversational analytics. It also provides the bot author with an opportunity to analyze intents and make modifications) (paragraphs: 0083); and identifying, by the model, an area for training the agent based on the intent and the context (the analytics module 250 may have access to the data stored in the storage device 220, including the customer database 222 and agent database 223. The analytics module 250 also may have access to the interaction database 224, which stores data related to interactions and interaction content (e.g., transcripts of the interactions and events detected therein), interaction metadata; bot authoring” refers to the process of creating a conversational bot or chatbot with NLU capabilities. This process generally involves defining intents, identifying entities, formulating utterances, training NLU models, testing the bot and finally publishing it) (paragraphs: 0050, 0082, and 0090, and figs: 8-10); however, George does not disclose another way for identifying, by the model, an area for training the agent based on the intent and the context.
In the same field of endeavor, Subramaniam clearly specifically discloses another way for identifying, by the model, an area for training the agent based on the intent and the context (skill bot is configured to receive user input, parse or otherwise process the received input, and identify or select an intent that is relevant to the received user input. In order for this to happen, the skill bot has to be trained. In certain embodiments, a skill bot is trained based upon the intents configured for the skill bot and the example utterances associated with the intents) (paragraphs: 0088)
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was made to incorporate the teaching of Subramaniam into teaching of George for the purpose of providing another way for identifying an area for training the agent based on the intent and the context.
Consider claims 2, 9, and 17, George and Subramaniam clearly show the system, the method, and the computer processing system, further comprising identifying areas for training based on the plurality of conversations occurring during a time period (George: paragraphs: 0113, 0128).
Consider claims 3, and 10, George and Subramaniam clearly show the system, and the method, , further comprising identifying areas for training the agent based on a record of the agent looking up information in the knowledge management database (George: paragraphs: 0076, 0092, and 0127).
Consider claims 4, 11, and 18, George and Subramaniam clearly show the system, the method, and the computer processing system, further comprising identifying areas for training the agent based on a use of a chatbot by the agent for information at a frequency above a threshold (George: paragraphs: 0082, 0087, 0094-0095, 0106-0107).
Consider claims 5, 13, and 19, DESAI and Subramaniam clearly show the system, the method, and the computer processing system, further comprising providing, through a user interface, the identified training to the agent (George: paragraphs: 0059, 0062, 0081, and 0082).
Consider claims 6, 14, and 20, DESAI and Subramaniam clearly show the system, the method, and the computer processing system, wherein the identified training is provided in response to a new conversation about the identified training area (George: paragraphs: 0067-0068, 0092).
Consider claims 7, and 15, DESAI and Subramaniam clearly show the system, and the method, wherein the identified training is provided when the agent has not received a customer call within a predefined time period (George: paragraphs: 0098, and 0161).
Consider claim 12, DESAI and Subramaniam clearly show the method, further comprising, after identifying areas to train the agent, querying the knowledge management database for information related to the identified areas (George: paragraphs: 0037, 0038, 0063).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amal Zenati whose telephone number is 571- 270- 1947. The examiner can normally be reached on 8:00 -5:00 M-F.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached on 571- 272- 7488. The fax phone number for the organization where this application or proceeding is assigned is 571- 273-8300.
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/AMAL S ZENATI/Primary Examiner, Art Unit 2693