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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-6, 8, and 16-22 have been considered but are moot in view of the new grounds of rejections.
Regarding claims 9-14, Applicant argues: “The Office Action (p. 5) alleges that Kaniganti discloses this subject matter. However, a review of the Kaniganti reference for "paralanguage" or similar terms shows that these terms do not appear in Kaniganti. Therefore, Kaniganti specifically, as well as the combination of references generally, does not appear to show or suggest these amended features of claim 9” (Remarks, page 13-14).
In response, Examiner respectfully disagrees. Applicant’s specification in para 0043 of the pgpub states: “a telephone call could provide insights into the user's emotion, tone, pitch, speed of talking, and other similar paralanguage markers”. And Kaniganti discloses features to “detecting the customer sentiment based on the customer intent” – such as being upset and providing a recommendation (see para 0031).
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 1-3, 5-6, 9-12, 14, 16-17, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Kaniganti et al. (US Pub 2024/0089372) and in further view of Noorizadeh, Emad (US Pub 2022/0044676) and in further view of Koneru et al. (US Pub 2023/0199118).
Regarding claim 1, Kaniganti discloses a system, comprising:
a computing device comprising a processor and a memory; and
machine-readable instructions stored in the memory that, when executed by the processor, cause the computing device to at least:
receive a conversation request from a user of a client device (para 0009);
transcribe, in real-time, a conversation to a transcript, the conversation being representative of a media recording that occurs between at least an agent and the user (para 0009, 0016, 0023, 0069);
determine, using a natural language processor (NLP), an intent of the user based at least in part on the transcript (para 0009, 0021, 0070);
generate one or more recommendations based at least in part on the intent of the user (para 0009, 0024, 0045, 0071).
Kaniganti does not disclose wherein determining the intent of the user includes the natural language processor extrapolating, using historical data of the user, the meaning of a word or phrase communicated by the user; receive an input from the agent, the input comprising at least one of a second intent of the user or additional information; modify the one or more recommendations based at least in part on the input; and return the one or more modified recommendations to the client device for presentation to the user.
Noorizadeh discloses wherein determining the intent of the user includes the natural language processor extrapolating, using historical data of the user, the meaning of a word or phrase communicated by the user (para 0023, para 0058 and fig. 2, element 220 – “comparing phrase with historical data from this user”).
Therefore, it would have been obvious to a person of ordinary skilled in the art before the effective filing date of the claimed invention to modify Kaniganti with the teachings of Noorizadeh in order to provide a more personalized and accurate way for resolving the meaning of a word or phrase.
Kaniganti in view of Noorizadeh does not disclose receive an input from the agent, the input comprising at least one of a second intent of the user or additional information; modify the one or more recommendations based at least in part on the input; and return the one or more modified recommendations to the client device for presentation to the user.
Koneru discloses receive an input from the agent, the input comprising at least one of a second intent of the user or additional information (para 0092-0093, 0105 – override or add new intent by the agent); modify the one or more recommendations based at least in part on the input; and return the one or more modified recommendations to the client device for presentation to the user (0140, 0142 and figs. 5A-5D, 7A 7B – for example, agent can modify the intent from “book train” to “book flight” and in fig. 8 and 0151 – discloses after agent modified information, the second response 818 (“recommendation”) sent to the customer device – which can be the updated dialog flow such as from “book train” to “book flight”).
Therefore, it would have been obvious to a person of ordinary skilled in the art before the effective filing date of the claimed invention to modify Kaniganti in view of Noorizadeh with the teachings of Noorizadeh in order to improve customer service by overriding the identified intent by the agent so customer can be directed to the correct dialog flow.
Regarding claim 2, Kaniganti discloses wherein the machine-readable instructions that transcribe the conversation further cause the computing device to at least:
process the conversation using a speech-to-text engine as conversation text;
identify a first speaker in the conversation as the user of the client device;
identify a second speaker in the conversation as the agent; and
correlate at least a portion of the conversation text with the first speaker or the second speaker to generate the transcript (para 0016-0017, 0023, 0069).
Regarding claim 3, Kaniganti discloses wherein the one or more recommendations are further based at least in part on a user profile and historical user data (Kaniganti, para 009, 0013, 0030).
Regarding claim 5, Kaniganti discloses wherein the one or more recommendations are configured to change dynamically based at least in part on the conversation (para 0045-0048).
Regarding claim 6, Kaniganti discloses wherein the machine-readable instructions further cause the computing device, when executed by the processor, to analyze the conversation in real-time to measure an effectiveness of the one or more recommendations (para 0030).
Regarding claim 9, Kaniganti discloses a method comprising: capturing a conversation, the conversation being representative of an interaction occurring between at least an agent and a user of a client device (para 0009, 0016, 0023, 0069); transcribing, in real-time, the conversation to a transcript; determining, using a natural language processor (NLP), an intent of the user based at least in part on the transcript (para 0009, 0021, 0070),
generating one or more recommendations based at least in part on the intent of the user (para 0009, 0024, 0045, 0071);
analyzing the conversation for paralanguage using a conversation analysis module; and modifying the one or more recommendations based at least in part on the paralanguage (para 018-0019; 0023; 0031 – discloses “detecting the customer sentiment based on the customer intent” – such as being upset and providing a recommendation).
Kaniganti does not discloses wherein determining the intent of the user includes the natural language processor extrapolating, using historical data of the user, the meaning of a word or phrase communicated by the user;
Noorizadeh discloses wherein determining the intent of the user includes the natural language processor extrapolating, using historical data of the user, the meaning of a word or phrase communicated by the user (para 0023, para 0058 and fig. 2, element 220 – “comparing phrase with historical data from this user”).
Therefore, it would have been obvious to a person of ordinary skilled in the art before the effective filing date of the claimed invention to modify Kaniganti with the teachings of Noorizadeh in order to provide a more personalized and accurate way for resolving the meaning of a word or phrase.
Regarding claim 10, see rejection of claim 2.
Regarding claim 11 and 20, see rejection of claim 5.
Regarding claims 12 and 17, see rejection of claim 3.
Regarding claim 14, Kaniganti discloses further comprising storing, at least one of a plurality of actions taken by the agent during the conversation or a log of resources accessed by the agent (para 0009, 0024, 0045, 0071).
Regarding claim 16, see rejection of claim 1.
Regarding claim 21, Kaniganti wherein the machine-readable instructions that cause the computing device to modify the one or more recommendations based at least in part on the input further cause the computing device to at least: analyze the transcript for paralanguage using a conversation analysis module; and modify the one or more recommendations based at least in part on the paralanguage (para 018-0019; 0023; 0031 – discloses “detecting the customer sentiment based on the customer intent” – such as being upset and providing a recommendation).
Regarding claim 22, see rejection of claim 21.
Claims 4, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kaniganti et al. (US Pub 20240089372) in view of Noorizadeh, Emad (US Pub 2022/0044676) and in further view of Koneru et al. (US Pub 2023/0199118) and in further view of Raviv, Ariel (US Patent 11,205,196).
Regarding claim 1, Kaniganti in view of Noorizadeh and Koneru discloses the system of claim 1.
Kaniganti in view of Noorizadeh and Koneru does not disclose identify the intent of the user as a travel request;
generate a travel recommendation based at least in part on the travel request and previous travel history; and
store the travel recommendation.
Raviv discloses identify the intent of the user as a travel request;
generate a travel recommendation based at least in part on the travel request and previous travel history; and
store the travel recommendation in the global CRM (col. 10, lines 33 – col. 11, line 19 – “The travel recommendation system 110 may generate a user profile corresponding to a user based on historical travel-related information corresponding to the user (Step 406). For example the travel recommendation system 110 may generate and maintain a user profile based on historical information (i.e., previous travel, prior purchases, and travel corresponding to friends and/or social media relationships) corresponding to the user”).
Therefore, it would have been obvious to a person of ordinary skilled in the art before the effective filing date of the claimed invention to modify Kaniganti in view of Noorizadeh and Koneru with the teachings of Raviv in order to generate a travel recommendation to a destination that the user did not already attend.
Regarding claims 13 and 19, see rejection of claim 4.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kaniganti et al. (US Pub 20240089372) in view of Noorizadeh, Emad (US Pub 2022/0044676) and in further view of Koneru et al. (US Pub 2023/0199118) and in further view of Byrd et al. (US Pub 2010/0104087).
Regarding claim 1, Kaniganti in view of Noorizadeh and Koneru discloses the system of claim 1.
Kaniganti in view of Noorizadeh and Koneru does not disclose wherein the machine-readable instructions further cause the computing device, when executed by the processor, to receive an action from the agent, wherein the action can comprise at least one of flagging, highlighting, or correcting a portion of the transcript.
Byrd discloses wherein the machine-readable instructions further cause the computing device, when executed by the processor, to receive an action from the agent, wherein the action can comprise at least one of flagging, highlighting, or correcting a portion of the transcript (para 0024 – “component 300 displays the present call transcript at the agent's computer. In a preferred embodiment, displaying the transcript in real-time allows the agent to edit the transcript, for instance, correcting incorrectly-recognized words”).
Therefore, it would have been obvious to a person of ordinary skilled in the art before the effective filing date of the claimed invention to modify Kaniganti in view of Noorizadeh and Koneru with the teachings of Byrd in order to allow the agent to determine the quality of the transcript and to use manual logging if the quality of the transcript is too low (Byrd, para 0024).
Regarding claim 18, see rejection of claim 8.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NAFIZ E HOQUE whose telephone number is (571)270-1811. The examiner can normally be reached M-F 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached at (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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/NAFIZ E HOQUE/Primary Examiner, Art Unit 2693
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