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 .
This Office Action is in response to the amendment filed November 21, 2025. Claims 1, 3, 8, 11, 13, 18, 21, 23, and 28 have been amended. Claims 2, 12, and 22 have been cancelled. Claims 1, 3-5, 8-11, 13-15, 18-21, 23-25, and 28-33 remain pending.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 3-5, 8-11, 13-15, 18-21, 23-25, and 28-33 are rejected under 35 U.S.C. 103 as being unpatentable over Cutler et al (US Patent Application No. 2018/0218727), hereinafter Cutler, in view of Thenthiruperai et al (US Patent No. 7,069,014), hereinafter Thenthiruperai and further in view of Chae et al (US Patent Application Publication No. 2020/0058290), hereinafter, Chae.
Cutler discloses a system for artificially generated speech for a communication session. Regarding claims 1 and 11, Cutler discloses [Figure 2; para 0041; 0043] a method and a non-transitory computer-readable medium having stored thereon computer-executable instructions, which, when executed by at least one processor, cause the at least one processor to provide voice communication between a speaker and a recipient over a communication network by performing operations comprising: receiving an input speech utterance from the speaker [speech captured by microphone at transmission side -- para 0024; 0037]; converting the input speech utterance to text [ASR used to convert speech to text – para 0024; 0038-0040]; transmitting at least the text over the communication network [transmission of text with audio – para 0024; 0038-0040; 0044]; converting the transmitted text into an output speech utterance that simulates a voice of the speaker [conversion of TTS based on user-specific model specific to the speech captured at transmission side -- para 0024; 0042; 0045-0048]; and providing the output speech utterance to the recipient [conversion of TTS based on user-specific model specific to the speech captured at transmission side -- para 0024; 0042; 0045-0048] and wherein the transmitted text is converted into the output speech utterance by one or more trained artificial intelligence models [Cutler’s text-to-speech converter 218; user specific model specific to the user from whom speech is captured –para 0024; 0004-0006; 0009-0010; 0035; dynamically updating/training receiver side and transmission side models - 0010; 0038; 0056; 0058-0059; Cutler’s voice models used for multiparty scenarios – para 0063].
Cutler fails to teach evaluating a bandwidth of a connection to the communication network at the side of the speaker and further processing data if the bandwidth is sufficient for transmitting voice. In a similar field of endeavor, Thenthiruperai teaches bandwidth-determined selection for wireless devices, providing for determining the currently available bandwidth with a bandwidth manager for transmissions over a wireless network; provides for rendering only appropriate data or content in a specific format that is appropriate for the current bandwidth; and allows for changing the interaction medium (audio+video; audio; or audio+text) [col. 4, lines 6-16; col. 9, lines 14-39; col. 10,line 33 to col. 11, line 33; col. 12, line 65 to col. 13, line 13; col. 13, lines15-26; col. 14, line 61 to col. 15, line 10]. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the bandwidth evaluation techniques and appropriate content transmission depending on the current bandwidth suggested by Thenthiruperai, in the system of Cutler/Chae, so as to ensure the recipients receive all the necessary transmitted data with minimal loss, errors or disruption, thereby improving system performance and the users’ experience.
Cutler fails to teach a first trained artificial intelligence model transforms the transmitted text into an intermediate speech utterance and a second trained artificial intelligence model transforms the intermediate speech utterance into the output speech utterance. In a similar field of endeavor, Chae teaches artificial intelligence apparatus for correcting synthesized speech by receiving a first text, generating a first synthesized speech signal, acquiring corrected speech features using speech correction models and generating a second synthesized speech using the corrected speech features [Fig. 9; para 0286-0295]. Chae teaches the system provides for improvement of the quality of synthesized speech [para 0012]. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing correction models speech synthesis, suggested by Chae, in the system of Cutler, for the purpose of improving the quality of the generated synthetic speech, as suggested by Chae.
Regarding claim 3, the combination of Cutler, Chae and Thenthiruperai teaches the input speech utterance is transmitted as voice and as text if the connection has sufficient bandwidth [Cutler at para 0049; Thenthiruperai’s rendering only appropriate data or content in a specific format that is appropriate for the current bandwidth and changing the interaction medium (audio+video; audio; or audio+text) col. 4, lines 6-16; col. 9, lines 14-39; col. 10,line 33 to col. 11, line 33; col. 12, line 65 to col. 13, line 13; col. 13, lines15-26; col. 14, line 61 to col. 15, line 10].
Regarding claim 4, the combination of Cutler, Chae and Thenthiruperai teaches the transmitted text is converted into the output speech utterance by a text-to-speech algorithm [Cutler’s text-to-speech converter 218; Chae at Fig 9].
Regarding claim 5, the combination of Cutler, Chae and Thenthiruperai teaches the text-to-speech algorithm uses a phoneme library suitable for simulating different speakers [Cutler’s voice models used for multiparty scenarios – para 0063].
Regarding claims 8-10, the combination of Cutler, Chae and Thenthiruperai teaches a bank of trained models [Cutler’s text-to-speech converter 218; user specific model specific to the user from whom speech is captured –para 0024; 0004-0006; 0009-0010; 0035; dynamically updating/training receiver side and transmission side models -- 0010; 0038; 0056; 0058-0059; Cutler’s voice models used for multiparty scenarios – para 0063; Chae’s speech correction models ].
Regarding claim 31, the combination of Cutler, Chae and Thenthiruperai teaches a third artificial intelligence model synthesizes an emotion of the speaker based on tags that are received along with the transmitted text, wherein the tags specify a characteristic of the input speech utterance, including at least one characteristic of a group of characteristics, the group of characteristics including: an intonation, a speed of speech, one or more detected emotions, and a respective duration of each individual word of the input speech utterance [Chae para 0007; 0218; 0240; 0251; 0277-0278; 0301-0302; 0309-0310 – where providing a third model to provide Chae’s emotion synthesis is an obvious requiring only routine skill in the art].
Regarding claim 21, Cutler discloses [Figure 2; para 0041; 0043] a non-transitory computer-readable medium having stored thereon computer-executable instructions, which, when executed by at least one processor, cause the at least one processor to provide voice communication between a speaker and a recipient over a communication network by performing operations comprising: receiving an input speech utterance from the speaker [speech captured by microphone at transmission side -- para 0024; 0037]; converting the input speech utterance to text [ASR used to convert speech to text – para 0024; 0038-0040]; transmitting at least the text over the communication network [transmission of text with audio – para 0024; 0038-0040; 0044]; converting the transmitted text into an output speech utterance that simulates a voice of the speaker [conversion of TTS based on user-specific model specific to the speech captured at transmission side -- para 0024; 0042; 0045-0048]; and providing the output speech utterance to the recipient [conversion of TTS based on user-specific model specific to the speech captured at transmission side -- para 0024; 0042; 0045-0048] and wherein the transmitted text is converted into the output speech utterance by one or more trained artificial intelligence models [Cutler’s text-to-speech converter 218; user specific model specific to the user from whom speech is captured –para 0024; 0004-0006; 0009-0010; 0035; dynamically updating/training receiver side and transmission side models - 0010; 0038; 0056; 0058-0059; Cutler’s voice models used for multiparty scenarios – para 0063].
Cutler fails to teach evaluating a bandwidth of a connection to the communication network at the side of the speaker and further processing data if the bandwidth is sufficient for transmitting voice. In a similar field of endeavor, Thenthiruperai teaches bandwidth-determined selection for wireless devices, providing for determining the currently available bandwidth with a bandwidth manager for transmissions over a wireless network; provides for rendering only appropriate data or content in a specific format that is appropriate for the current bandwidth; and allows for changing the interaction medium (audio+video; audio; or audio+text) [col. 4, lines 6-16; col. 9, lines 14-39; col. 10,line 33 to col. 11, line 33; col. 12, line 65 to col. 13, line 13; col. 13, lines15-26; col. 14, line 61 to col. 15, line 10]. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the bandwidth evaluation techniques and appropriate content transmission depending on the current bandwidth suggested by Thenthiruperai, in the system of Cutler/Chae, so as to ensure the recipients receive all the necessary transmitted data with minimal loss, errors or disruption, thereby improving system performance and the users’ experience.
Cutler fails to teach the system is implemented in a vehicle. In a similar field of endeavor, Chae teaches artificial intelligence apparatus for correcting synthesized speech by receiving a first text, generating a first synthesized speech signal, acquiring corrected speech features using speech correction models and generating a second synthesized speech using the corrected speech features [Fig. 9; para 0286-0295] and suggests the system can be implemented in a vehicle [para 0056]. Chae teaches the system provides for improvement of the quality of synthesized speech [para 0012]. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the system of Cutler in a vehicle, so as to provide enhanced communication sessions for drivers while in a vehicle, with minimal distractions or disruptions for the vehicle driver, thereby improving safety during communications while driving.
Claims 13-15, 18-20, 23-25, 28-30 and 32-33 are rejected under similar rationale as claims 3-5, 8-10 and 31.
Response to Arguments
Applicant's arguments filed November 21, 2025 have been fully considered but they are not persuasive.
Applicant argues the references cited by the examiner, even if properly combinable, do not disclose, teach, or suggest the features for "evaluating a bandwidth of a connection to the communication network at the side of the speaker" and "converting the input speech utterance to text if the bandwidth is sufficient for transporting voice signals.” The Examiner respectfully disagrees. As indicated in the rejection above, Thenthiruperai teaches bandwidth-determined selection for wireless devices, providing for determining the currently available bandwidth with a bandwidth manager for transmissions over a wireless network; provides for rendering only appropriate data or content in a specific format that is appropriate for the current bandwidth; and allows for changing the interaction medium (audio+video; audio; or audio+text) [col. 4, lines 6-16; col. 9, lines 14-39; col. 10,line 33 to col. 11, line 33; col. 12, line 65 to col. 13, line 13; col. 13, lines15-26; col. 14, line 61 to col. 15, line 10]. Given the teachings of Cutler and Thenthiruperai, one having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the bandwidth evaluation techniques and appropriate content transmission depending on the current bandwidth suggested by Thenthiruperai, in the system of Cutler/Chae, so as to ensure the recipients receive all the necessary transmitted data with minimal loss, errors or disruption, thereby improving system performance and the users’ experience. The teachings of Cutler in combination with the teachings of Thenthiruperai provide adequate support for the limitation as claimed.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659