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 .
DETAILED ACTION
This action is in response to application filed 04/12/2024.
Claims 1-13 are pending in this application.
Drawings
The drawings are objected to because Figure 3 contains two instances of Step 308. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni et al. (US 2021/0335365 A1) in view of PG et al. (US 2025/0118294 A1)
Regarding claim 1, Aharoni discloses a computer-implemented method for re-engaging a user after a telecommunication disconnection, the method executed by one or more servers in communication with a first user device, comprising:
a. monitoring, by the one or more servers, telecommunication interactions between the first user device and a second user device to detect a disconnection event ([0064]: During this exchange between the first user 202 and the second user 204, the call monitoring system 210 can monitor the telephone call, and can use the speech recognizer 244 to perform speech recognition on the utterances 212, 214, and 216 of both the first user 202 and the second user 204);
b. upon detecting the disconnection event, categorizing, by the one or more servers, the nature of the disconnection utilizing a signal processing and detection unit ([0047]: The interruption classifier 136 can classify the interruption as a given type of interruption from multiple disparate interruption types 138. The interruption types 138 can be mutually exclusive, and can include, for example, a non-meaningful interruption, a non-critical meaningful interruption, a critical meaningful interruption, and/or other types of interruptions. [0062]: The call monitoring system 210 can monitor the telephone call to detect that the first user 202 hangs up the phone 206 while on hold);
c. analyzing, by the one or more servers, the context of the telecommunication interaction prior to the disconnection based on data received from the first user device and stored in a customer interaction history database ([0038]-[0039]: The transcription generator 114 may then use those keywords or phrases to determine the subject matter of the utterance 110 of the representative 102. The transcription generator 114 may use the subject matter of the utterance 110 of the representative 102 to generate a transcription of a response. The transcription generator 114 uses a model trained using machine learning to determine subject matter of and/or an appropriate response to the utterance 110 of the representative 102. The call initiating system 104 may access training data that includes a log of previous conversations. The previous conversations may be specific to a type of business or organization, such as a restaurant business, an airline business, a government agency, and/or conversations specific to other businesses or organizations. Each of the utterances in the corresponding conversations may include keyword labels);
d. generating, by the one or more servers, a contextually relevant response utilizing an artificial intelligence and machine learning engine, where the response is adapted based on the categorized nature of the disconnection and the analyzed context ([0009]: classifying the received user utterance as a given type of interruption of multiple disparate types of interruptions, and determining, based on the given type of interruption, whether to continue providing, for output at the corresponding computing device of the user, the synthesized speech of the bot. [0038]-[0039]: The transcription generator 114 may then use those keywords or phrases to determine the subject matter of the utterance 110 of the representative 102. The transcription generator 114 may use the subject matter of the utterance 110 of the representative 102 to generate a transcription of a response).
However, Aharoni does not disclose e. converting, by the one or more servers, the generated response into an audio message using a voice synthesis module that replicates the voice of the agent from the second user device; and f. transmitting, by the one or more servers, the audio message to the first user device to re-engage the user.
In an analogous art, PG discloses e. converting, by the one or more servers, the generated response into an audio message using a voice synthesis module that replicates the voice of the agent from the second user device ([0031]: a text response at step 212 which is transmitted at step 214 by the chat engine to text-to-speech engine 222. At step 224, the deepfake processor 30, which may be in communication with VCA 48, substitutes the voice of the primary agent for speech from the text-to-speech engine. [0035]: If the primary agent is unavailable (step 312), the user call is transferred to a secondary agent or bot at step 314. Deepfake audio technology is used to substitute the primary agent's voice at step 316, which involves analyzing and copying resonant characteristics of the primary agent's voice, which allows for continuity in customer service); and f. transmitting, by the one or more servers, the audio message to the first user device to re-engage the user. [0040]: the secondary agent or bot accesses the content and context of the primary agent's prior sessions with the user, or of all agents' and bots' prior sessions with the user, to help with a smooth transition and continuity in the conversation with the user. [0043]: deepfake audio of the primary agent is generated by the system modelling the primary agent's voice so that the secondary agent or bot has his/her output synthesized).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni to comprise “e. converting, by the one or more servers, the generated response into an audio message using a voice synthesis module that replicates the voice of the agent from the second user device; and f. transmitting, by the one or more servers, the audio message to the first user device to re-engage the user” taught by PG.
One of ordinary skilled in the art would have been motivated because it would have enabled to analyze and copy resonant characteristics of an agent's voice to help with continuity in the conversation with the user when the agent is unavailable (PG, [0043]).
Regarding claim 2, Aharoni-PG discloses the method of claim 1, further comprising: initiating contact with the first user device utilizing the one or more servers immediately following the disconnection event to ensure prompt re-engagement (Aharoni, [0034]: The bot can identify the interruption, classify the interruption into one of multiple disparate interruption types, and continue the telephone conversation on behalf of the user based on the type of interruption).
Regarding claim 3, Aharoni-PG discloses the method of claim 1, wherein the voice synthesis module integrates speech-to-text and text-to-speech technologies to facilitate the conversion of the generated response into the audio message (PG, [0031]: the ASR engine 32 identifies the user's speech and the first database 38 is accessed to match the user with his/her primary agent. In this example, the secondary agent or bot 44 at step 210 receive the user's speech as text (e.g. speech to text) generated at step 208 by a chat engine. The secondary agent or bot 44 then enters, such as by typing or automatically generating (if a bot), a text response at step 212 which is transmitted at step 214 by the chat engine to text-to-speech engine 222). The same rationale applies as in claim 1.
Regarding claim 6, Aharoni-PG discloses the method of claim 1wherein the signal processing and detection unit is further configured to distinguish between different types of disconnection events, including accidental disconnections and strategic disconnections initiated by the user (Aharoni, [0047]: The interruption classifier 136 can classify the interruption as a given type of interruption from multiple disparate interruption types 138. The interruption types 138 can be mutually exclusive, and can include, for example, a non-meaningful interruption, a non-critical meaningful interruption, a critical meaningful interruption, and/or other types of interruptions. [0066]: the first user 202 attempts to hang up the phone 206 while the phone 206 is in the first state 226 because the first user 202 does not want to wait on hold).
Regarding claim 7, Aharoni-PG discloses the method of claim 1, wherein the customer interaction history database stores interaction data including previous communications between the first user device and the second user device, which is utilized in analyzing the context of the telecommunication interaction (PG, [0027]: The user history in first database 38 is accessible by the agent devices 22, 24, 26, 28 and by the bot 44 in order to obtain information of prior interactions with a user). The same rationale applies as in claim 1.
Claims 4 and 12 is rejected under 35 U.S.C. 103 as being unpatentable Aharoni in view of PG, as applies to claim 1, in further view of Pandey et al. (US 2024/0422264 A1).
Regarding claim 4, Aharoni-PG discloses the method of claim 1.
However, Aharoni-PG does not disclose further comprising: triggering, by the one or more servers, an alternative communication method based on the user's response to the audio message or the categorized nature of the disconnection, wherein the alternative communication method includes sending a text message to the first user device.
In an analogous art, Pandey discloses further comprising: triggering, by the one or more servers, an alternative communication method based on the user's response to the audio message or the categorized nature of the disconnection, wherein the alternative communication method includes sending a text message to the first user device ([0044]: This module will ensure redirection of disconnected customer interactions to appropriate digital channels. In an example, this module may send an SMS text message to the customer, notifying the customer about the disconnection and suggesting connection over an alternate, digital channel).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “triggering, by the one or more servers, an alternative communication method based on the user's response to the audio message or the categorized nature of the disconnection, wherein the alternative communication method includes sending a text message to the first user device” taught by Pandey.
One of ordinary skilled in the art would have been motivated because it would have enabled for detecting, prioritizing, and reconnecting disconnected customer interactions at a contact center (Pandey, [0002]).
Regarding claim 12, Aharoni-PG discloses the method of claim 1.
However, Aharoni-PG does not disclose further comprising: integrating the method into a customer service platform that supports multiple communication channels, including voice calls and SMS, enabling the system to select the most appropriate channel for re-engagement based on the user's previous communication preferences and the nature of the disconnection.
In an analogous art, Pandey discloses further comprising: integrating the method into a customer service platform that supports multiple communication channels, including voice calls and SMS, enabling the system to select the most appropriate channel for re-engagement based on the user's previous communication preferences and the nature of the disconnection ([0010]: the selected channel is a short message service (SMS) text channel, a real-time chat (RTC) channel, or a social media channel. [0012]: the selected channel is a voice channel or a video channel. [0090]: since wait times of as little as 15 seconds have a measurable effect on call abandonment and customer satisfaction, it is desirable for the prioritized reconnection system to select an appropriate agent, select an appropriate contact channel, and re-connect the disconnected customer).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “integrating the method into a customer service platform that supports multiple communication channels, including voice calls and SMS, enabling the system to select the most appropriate channel for re-engagement based on the user's previous communication preferences and the nature of the disconnection” taught by Pandey.
One of ordinary skilled in the art would have been motivated because it would have enabled for detecting, prioritizing, and reconnecting disconnected customer interactions at a contact center (Pandey, [0002]).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Aharoni in view of PG, as applies to claim 1, in further view of Fields et al. (US 2024/0303745 A1).
Regarding claim 5, Aharoni-PG discloses the method of claim 1.
However, Aharoni-PG does not disclose wherein the artificial intelligence and machine learning engine employs Python with TensorFlow or PyTorch for analyzing the context of the telecommunication interaction and generating the contextually relevant response.
In an analogous art, Fields discloses wherein the artificial intelligence and machine learning engine employs Python with TensorFlow or PyTorch for analyzing the context of the telecommunication interaction and generating the contextually relevant response ([0033]: the implemented ML methods and algorithms are directed toward at least one of a plurality of categorizations of ML, such as supervised learning, unsupervised learning, and reinforcement learning. In one aspect, the ML based algorithms may be included as a library or package executed on server(s) 105. For example, libraries may include the TensorFlow based library, the PyTorch library, and/or the scikit-learn Python library. [0056]: The ML chatbot may use the long-term memory to store information about the user (e.g., preferences, chat history, etc.) which may improve an overall user experience by enabling the ML chatbot to personalize and/or provide more informed responses).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “wherein the artificial intelligence and machine learning engine employs Python with TensorFlow or PyTorch for analyzing the context of the telecommunication interaction and generating the contextually relevant response” taught by Fields.
One of ordinary skilled in the art would have been motivated because it would have enabled a chatbot to personalize and/or provide more informed responses (Fields, [0056]).
Claims 8 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni in view of PG, as applies to claim 1, in view of DeCharms et al. (US 2024/0273793 A1).
Regarding claim 8, Aharoni-PG discloses the method of claim 1.
However, Aharoni-PG does not disclose further comprising: employing the voice synthesis module to implement voice mimicking using either Google Cloud Speech API or Amazon Polly for the conversion of the generated response into the audio message.
In an analogous art, DeCharms discloses further comprising: employing the voice synthesis module to implement voice mimicking using either Google Cloud Speech API or Amazon Polly for the conversion of the generated response into the audio message ([0235]: Text content may be converted into spoken audio content 500. Software may provide for audio styling or preprocessing of audio to indicate the presence of text element parameters or attributes including features or scores. For example, written text may be processed by a text to speech model such as AWS Polly or Google Speech or Murf. [0439]: Text to speech software may also include voice cloning to create synthetic speech similar to a particular human or synthetic voice).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “further comprising: employing the voice synthesis module to implement voice mimicking using either Google Cloud Speech API or Amazon Polly for the conversion of the generated response into the audio message” taught by DeCharms.
One of ordinary skilled in the art would have been motivated because it would have enabled to perform text to speech including voice cloning to create synthetic speech similar to a particular human (DeCharms, [0439]).
Claim 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni in view of PG, as applies to claim 1, in view of Sirakanyan et al. (US 2018/0060870 A1).
Regarding claim 9, Aharoni-PG discloses the method of claim 1, wherein the method is further configured for application in scenarios where the telecommunication interaction is related to offering products or services (Aharoni, [0036]: The information may include the requested date and time of the reservation (e.g., tomorrow at 7:00 pm), the requested business (e.g., Burger Palace), and number of people in the party (e.g., two). For requests other than restaurant reservations, the information may include the name of a requested service provider (e.g., an airline company, a utilities provider, and/or any other service provider), a description of the request for the service provider (e.g., making/modifying/discontinuing a service or reservation), and/or any other information that may be solicited by the representative 102 in performing the task on behalf of the user).
However, Aharoni-PG does not disclose the re-engagement is tailored to offer alternative products or services based on the analyzed context and the user's needs.
In an analogous art, Sirakanyan discloses the re-engagement is tailored to offer alternative products or services based on the analyzed context and the user's needs ([0057]: the contact does hang up within 10 seconds, then in step 198, the server determines that the contact is not interested in any offers (e.g. context and needs) and the PBX calls the next contact that was selected by the distributor 182. The task scheduling module 126 may automatically create a new task by adding this contact to a new list to be called at a later time with a different recording offering a different product).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “the re-engagement is tailored to offer alternative products or services based on the analyzed context and the user's needs” taught by Sirakanyan.
One of ordinary skilled in the art would have been motivated because it would have enabled to provide alternative product offers that better matches contact’s interest (Sirakanyan, [0057]).
Regarding claim 10, Aharoni-PG discloses the method of claim 1.
However, Aharoni-PG does not disclose further comprising: adapting the generated response to include an offer for an alternative service or product when the initial interaction prior to disconnection was related to a specific offer, wherein the adaptation is based on the likelihood of matching the user's preferences and potential for revenue generation identified through the context analysis.
In an analogous art, Sirakanyan discloses further comprising: adapting the generated response to include an offer for an alternative service or product when the initial interaction prior to disconnection was related to a specific offer, wherein the adaptation is based on the likelihood of matching the user's preferences and potential for revenue generation identified through the context analysis ([0057]: the contact does hang up within 10 seconds, then in step 198, the server determines that the contact is not interested in any offers (e.g. context and needs) and the PBX calls the next contact that was selected by the distributor 182. The task scheduling module 126 may automatically create a new task by adding this contact to a new list to be called at a later time with a different recording offering a different product).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “further comprising: adapting the generated response to include an offer for an alternative service or product when the initial interaction prior to disconnection was related to a specific offer, wherein the adaptation is based on the likelihood of matching the user's preferences and potential for revenue generation identified through the context analysis” taught by Sirakanyan.
One of ordinary skilled in the art would have been motivated because it would have enabled to provide alternative product offers that better matches contact’s interest (Sirakanyan, [0057]).
Claims 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Aharoni in view of PG, as applies to claim 1, in view of Sharifi et al. (US 2023/0169963 A1).
Regarding claim 11, Aharoni-PG discloses the method of claim 1.
However, Aharoni-PG does not disclose wherein the one or more servers are further configured to: analyze the effectiveness of the re-engagement strategy by monitoring the user's interaction with the transmitted audio message and adjusting future responses based on this analysis.
In an analogous art, Sharifi discloses wherein the one or more servers are further configured to: analyze the effectiveness of the re-engagement strategy by monitoring the user's interaction with the transmitted audio message and adjusting future responses based on this analysis ([0028]: the primary automated assistant can cause a numerical value to be displayed via an interface that reflects trust in the origin of the response. Thus, the user can indicate whether a response is appropriate for the provided query. Further, in instances where the user does not follow up with a response or does not further interact with the responding automated assistant, the primary automated assistant can adjust the trust metric of the responding automated assistant to reflect that the user may not have interest in future responses from that automated assistant).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “wherein the one or more servers are further configured to: analyze the effectiveness of the re-engagement strategy by monitoring the user's interaction with the transmitted audio message and adjusting future responses based on this analysis” taught by Sharifi.
One of ordinary skilled in the art would have been motivated because it would have enabled improved more robust response and/or to generate the response more efficiently (Sharifi, [0006]).
Regarding claim 13, Aharoni-PG discloses the method of claim 1.
However, Aharoni-PG disclose wherein the artificial intelligence and machine learning engine is further configured to learn from each re-engagement instance to improve the accuracy of context analysis and response generation over time, based on feedback received through the customer interaction history database and the effectiveness of previous re-engagement attempts.
In an analogous art, Sharifi discloses wherein the artificial intelligence and machine learning engine is further configured to learn from each re-engagement instance to improve the accuracy of context analysis and response generation over time, based on feedback received through the customer interaction history database and the effectiveness of previous re-engagement attempts ([0011]: historical interactions of the user with one or more of the secondary automated assistants, past responses provided by the secondary automated assistants, and/or other information that indicates which, of a plurality of secondary automated assistants, is most likely and/or most appropriate to generate a response to a given query. [0028]: the primary automated assistant can cause a numerical value to be displayed via an interface that reflects trust in the origin of the response. Thus, the user can indicate whether a response is appropriate for the provided query. Further, in instances where the user does not follow up with a response or does not further interact with the responding automated assistant, the primary automated assistant can adjust the trust metric of the responding automated assistant to reflect that the user may not have interest in future responses from that automated assistant).
Therefore, it would have been obvious before the effective filed date of the claimed invention to a person having ordinary skill in the art to modify Aharoni-PG to comprise “wherein the artificial intelligence and machine learning engine is further configured to learn from each re-engagement instance to improve the accuracy of context analysis and response generation over time, based on feedback received through the customer interaction history database and the effectiveness of previous re-engagement attempts” taught by Sharifi.
One of ordinary skilled in the art would have been motivated because it would have enabled improved more robust response and/or to generate the response more efficiently (Sharifi, [0006]).
Additional References
The prior art made of record and not relied upon is considered pertinent to applicants disclosure.
Kannan et al., US 12,238,247 A1: System and Method for Reconnecting an Inbound Originated Call.
Dhodapkar, US 2023/0047509 A1: Monitoring Data for Determining Condition Satisfaction.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN C TURRIATE GASTULO whose telephone number is (571)272-6707. The examiner can normally be reached Monday - Friday 8 am-4 pm.
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, Brian J Gillis can be reached at 571-272-7952. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/J.C.T/Examiner, Art Unit 2446
/MICHAEL A KELLER/Primary Patent Examiner, Art Unit 2446