CTNF 18/893,320 CTNF 87019 DETAILED ACTION This Office Action is in response to the filing of a Continuation (CON) of Application 17/5279501 which is now United States Patent 12,101,438. In response to the Preliminary Amendment filed 10/07/2024 which has been entered the status of the Claims is as follows. No Claims have been amended. Claims 1-19 have been cancelled. Claims 20-39 have been added. Claims 20-39 are pending in this application, with Claims 20, 29 and 36 being independent. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Objections 07-29-01 AIA Claim s 29 and 38 are objected to because of the following informalities: Claim 29 states in part… A computer-implemented a method comprising:. Claim 38 states in part …wherein the software instructions to determine the at least one key term within the personal information associated with the first user o comprise a different user actively highlighting the at least one key term within the input text data . Appropriate correction is required. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. Claims 20-39 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of U.S. Patent No. 12,101,438; hereinafter referred to as Patent (‘438). Although the claims at issue are not identical, they are not patentably distinct from each other. Claim 20 of the instant application mirrors Claim 1 of Patent (‘438) with the exception of the following: The step of receiving, by at least one processor, an input text data retrieved from a transcription associated with a previously recorded audio data file between at least one user of a plurality of user s and at least one agent associated with a call center recited in Patent (‘438) is recited as receiving, by a processor, an input text data retrieved from a transcription between a first user and a second user in Claim 20 of the instant application. The step of utilizing, by the at least one processor, a tonal rule engine algorithm of the trained machine learning model to determine at least one key term within the personal information associated with the at least one user of the plurality of users based on a perceived reaction of the at least one user when the at least one user spoke the at least one key term to the agent associated with the call center in Patent (‘438) is recited as utilizing, by the processor, a tonal rule engine algorithm of a trained machine learning model to determine at least one key term within personal information associated with the first user based on a perceived reaction of the first user from the transcript recited in the instant application. The step of automatically extracting, by the at least one processor, a plurality of tuples from the input text data by utilizing a term frequency inverse document frequency algorithm on the at least one key term within the input text data, wherein each tuple represents a relationship and an object, wherein the relationship is between the at least one user and the object recited in Patent (‘438) is recited as automatically extracting, by the processor, a plurality of tuples from the input text data by utilizing a term frequency inverse document frequency algorithm on the at least one key term within the input text data, wherein each tuple represents a relationship and an object, wherein the relationship is between the first user and the object in the instant application. The step of automatically generating, by the at least one processor, based on the plurality of tuples with the additional information , a call script for conducting, by the at least one agent associated with the call center, a subsequent call with the at least one user in Patent (‘438) is recited as automatically generating, by the processor, based on the plurality of tuples, a script for conducting a subsequent interaction with the first user in the instant application. Claim 21 of the instant application is wholly contained within Claim 2 of Patent (’438). Claim 22 of the instant application mirrors Claim 3 of Patent (’438) with the exception of the following: The step of wherein determining the at least one key term within the personal information associated with the at least one user of the plurality of user comprises a different user of the plurality of users actively highlighting the at least one key term within the input text data in Patent (‘268) is recited as wherein determining at least one key term within the personal information associated with the first user comprises a different user actively highlighting the at least one key term within the input text data in the instant application. Claim 23 of the instant application mirrors Claim 4 of Patent (’438) with the exception of the following: The step of wherein determining the at least one key term within the personal information associated the at least one user of the plurality of users comprises utilizing a facial recognition algorithm to perceive a reaction associated with the at least one user in Patent (‘438) is recited as wherein determining the at least one key term within the personal information associated the first user comprises utilizing a facial recognition algorithm to perceive a reaction associated with the at least one user in the instant application. Claim 24 of the instant application mirrors Claim 5 of Patent (’438). Claim 25 of the instant application mirrors Claim 6 of Patent (’438) with the exception of the following: The step of wherein the plurality of factors comprise pitch deviation, cultural identification, length of silence, and different languages in Patent (‘438) is recited as a plurality of factors associated with a confidence positivity score related to the sentiment analysis comprise pitch deviation, cultural identification, length of silence, and different languages in the instant application. Claim 26 of the instant application mirrors Claim 7 of Patent (’438) with the exception of the following: The step of wherein automatically extracting, based, at least in part, on the sentiment analysis of the at least one key term within the input text data, comprises utilizing an entity recognition algorithm in Patent (‘268) is recited as wherein automatically extracting, based, at least in part, on a sentiment analysis of the key term within the input text data, comprises utilizing an entity recognition algorithm in the instant application. Claim 27 of the instant application mirrors Claim 8 of Patent (’438) with the exception of the following: The step of comprising automatically initiating , by the processor , an interaction session with the at least one user based on an automatically generated call script associated with the plurality of tuple s in Patent (‘438) is recited as automatically initiating an interaction session with the first user based on an automatically generated call script associated with the plurality of tuples in the instant application. Claim 28 of the instant application mirrors Claim 9 of Patent (’438) with the exception of the following: The step of instructing , by the processor, a computing device associated with the at least one agent of the call center to display the plurality of tuples associated with the at least one user in Patent (‘438) is recited as instructing a computing device associated with the second user to display the plurality of tuples associated with the first user in the instant application. Claim 29 of the instant application mirrors Claim 10 of Patent (‘438) with the exception of the following: The step of receiving, by at least one processor, an input text data retrieved from a transcription associated with a previously recorded audio data file between at least one user of a plurality of users and at least one agent associated with a call center in Patent (‘438) is recited as receiving, by a processor, an input text data retrieved from a transcription between a first user and a second user in the instant application. The step of identifying, by the at least one processor, utilizing a natural language processing algorithm, the personal information associated with the at least one user of the plurality of users from the input text data by inputting the input text data into the trained machine learning model comprising the natural language processing algorithm in Patent (‘438) is recited as identifying, by the processor, personal information associated with the first user from the input text data by inputting the input text data into a trained machine learning model in the instant application. The step of utilizing, by the at least one processor, a tonal rule engine algorithm of the trained machine learning model to determine at least one key term within the personal information associated with the at least one user of the plurality of users based on a perceived reaction of the at least one user when the at least one user spoke the at least one key term to the agent associated with the call center in Patent (‘438) is recited as determining, by the processor, at least one key term within the personal information associated with the first user based on a perceived reaction of the first user in the instant application. The steps of automatically extracting, by the at least one processor, a plurality of tuples from the input text data by utilizing a term frequency inverse document frequency algorithm on the at least one key term within the input text data , wherein each tuple represents a relationship and an object , wherein the relationship is between the at least one user and the object ; automatically generating, by the at least one processor, based on the plurality of tuples and the additional information , a call script for conducting , by the at least one agent associated with the call center , a subsequent call with the at least one user; and instructing, by the processor, a computing device associated with the at least one agent of the call center to display a generated call script and the plurality of tuples associated with the at least one user in Patent (‘438) is recited as automatically extracting, by the processor, a plurality of tuples from the input text data, wherein each tuple represents a relationship and an object; automatically generating, by the processor, based on the plurality of tuples, a call script for conducting a subsequent interaction with the first user; and instructing, by the processor, a computing device associated with the second user to display a generated call script and the plurality of tuples associated with the first user in the instant application. Claim 30 of the instant application mirrors Claim 11 of Patent (’438) with the exception of the following: The step of wherein identifying , utilizing the natural language processing algorithm, personal information associated with the at least one user of the plurality of users from the input text data by inputting the input text data into a statistical based model comprising the natural language processing algorithm in Patent (‘438) is recited as wherein identifying personal information associated with the first user from the input text data by inputting the input text data into a statistical based model comprising a natural language processing algorithm in the instant application. Claim 31 of the instant application mirrors Claim 12 of Patent (’438) with the exception of the following: The step of wherein determining the at least one key term within the personal information associated with the at least one user of the plurality of user comprises a different user of the plurality of users actively highlighting the at least one key term within the input text data in Patent (‘438) is recited as wherein determining the at least one key term within the personal information associated with the first user comprises a different user actively highlighting the at least one key term within the input text data in the instant application. Claim 32 of the instant application mirrors Claim 13 of Patent (’438) with the exception of the following: The step of wherein determining the at least one key term within the personal information associated the at least one user of the plurality of users comprises utilizing a facial recognition algorithm to perceive a reaction associated with the at least one user in Patent (‘438) is recited as wherein determining the at least one key term within the personal information associated the first user comprises utilizing a facial recognition algorithm to perceive a reaction associated with the first user in the instant application. Claim 33 of the instant application mirrors Claim 14 of Patent (’438) with the exception of the following: The step of wherein a sentiment analysis based on a utilization of the facial recognition algorithm to perceive the reaction associated with the at least one user in Patent (‘438) is recited as wherein a sentiment analysis based on a utilization of the facial recognition algorithm to perceive the reaction associated with the first user in the instant application. Claim 34 of the instant application mirrors Claim 15 of Patent (’438) with the exception of the following: The step of wherein the plurality of factors comprise pitch deviation, cultural identification, length of silence, and different languages in Patent (‘438) is recited as wherein further comprising a plurality of factors associated with a confidence positivity score related to the sentiment analysis comprise pitch deviation, cultural identification, length of silence, and different languages in the instant application. Claim 35 of the instant application mirrors Claim 16 of Patent (’438). Claim 36 of the instant application and Claim 17 of Patent (‘428) mirror one another. Claim 36 is a system to execute the method of Claim 20 and the system includes a non-transient computer memory storing software to perform the method of Claim 20. The double patenting mapping applied to Claim 20 of the instant application to Claim 1 of Patent (‘438) equally applies here. Claim 37 of the instant application mirrors Claim 18 of Patent (’438) with the exception of the following: The step of wherein the software instructions to identify , utilizing the natural language processing algorithm, personal information associated with the at least one user of the plurality of users from the input text data by inputting the input text data into a statistical based model comprise the natural language processing algorithm in Patent (‘438) is recited as wherein the software instructions to identify personal information associated with the first user from the input text data by inputting the input text data into a statistical based model comprise the natural language processing algorithm in the instant application. Claim 38 of the instant application mirrors Claim 19 of Patent (’438) with the exception of the following: The step of wherein the software instructions to determine the at least one key term within the personal information associated with the at least one user of the plurality of user comprise a different user of the plurality of users actively highlighting the at least one key term within the input text data in Patent (‘438) is recited as wherein the software instructions to determine the at least one key term within the personal information associated with the first user comprise a different user actively highlighting the at least one key term within the input text data in the instant application. Claim 39 of the instant application mirrors Claim 20 of Patent (’438) with the exception of the following: The step of wherein determining the at least one key term within the personal information associated the at least one user of the plurality of users comprises utilizing a facial recognition algorithm to perceive a reaction associated with the at least one user in Patent (‘438) is recited as wherein determining the at least one key term within the personal information associated the first user comprises utilizing a facial recognition algorithm to perceive a reaction associated with the first user in the instant application. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 20, 21, 23-30, 32-37 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Raanani et al (2018/0096271 A1) in view of Nielsen et al (2014/0074589 A1) . As per Claim 20, Raanani teaches a computer-implemented a method comprising: receiving, by a processor, an input text data retrieved from a transcription between a first user and a second user (Figure 1 – Reference 105 ; Figure 4 – References 405 and 410 ; Figure 10 – Reference 1005 ; Page 1, Paragraph [0014]; Page 4, Paragraph [0034]; Page 7, Paragraph [0055]; Page 12, Paragraph [0097]). (Note: In paragraph [0014], Raanani describes a call modeling system that includes an offline analysis component and a real-time analysis component. The offline analysis component receives transcripts of audio calls between call center agents and customers and performs analysis to extract features from the transcripts) Raanani also teaches utilizing, by the processor, a tonal rule engine algorithm of a trained machine learning model to determine at least one key term within personal information associated with the first user based on a perceived reaction of the first user from the transcript (Page 1, Paragraph [0014]; Page 3, Paragraphs [0028] and [0029]; Page 4, Paragraph [0034]). (Note: In paragraph [0014], Raanani indicates that extracted features may include but are not limited to speech rate, speech volume, emotions tone, timber, etc. In paragraph [0028] and [0029], Raanani describes utilizing facial expression or body language during video communication to judge/interpret the customer reaction to the conversation occurring between the agent and the caller [e.g. to be honest with you I don’t know – with the associated body language/voice characteristics]) Raanani further teaches automatically extracting, by the processor, a plurality of tuples from the input text data by utilizing a term frequency inverse document frequency algorithm on the at least one key term within the input text data, wherein each tuple represents a relationship and an object, wherein the relationship is between the first user and the object (Page 6, Paragraph [0043]). Raanani does not teach automatically generating, by the processor, based on the plurality of tuples, a script for conducting a subsequent interaction with the first user. However, Nielsen teaches automatically generating, by the processor, based on the plurality of tuples, a script for conducting a subsequent interaction with the first user (Page 6, Paragraph [0090] – Page 7, Paragraph [0091]; Page 9, Paragraphs [0127] and [0128]). (Note: In paragraph [0092], Nielsen describes the acquisition of at least one identifying feature [i.e. generated unique identifier] from a remote server. In paragraph [0134], Nielsen describes captured information [i.e. plurality of tuples] being stored on a web server [i.e. remote server]. In paragraph [0120], Nielsen describes a customer care provider [CCP] customer relationship management [CRM] application accessing data from a CCP database [i.e. external database]) (Note: In paragraph [0121], Nielsen describes using a key [e.g. generated unique identifier – product/warranty serial number] to access a customer record in the CCP database. In paragraphs [0122] and [0123], Nielsen describes utilizing a unique identifier to obtain relevant information [i.e. plurality of tuples] regarding a customer. In paragraphs [0127] and [0128], Nielsen describes generating a call script for marketing products and/or services to customers) It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method taught by Raanani with the method taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claims 21, 30 and 37, the combination of Raanani and Nielsen teaches identifying utilizing a natural language processing algorithm, personal information associated with the first user from the input text data by inputting the input text data into a statistical based model as described in Claims 1, 10 and 17. It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method and system taught by Raanani with the method and system taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claims 23, 32 and 39, the combination of Raanani and Nielsen teaches wherein determining the at least one key term within the personal information associated the first user comprises utilizing a facial recognition algorithm to perceive a reaction associated with the at least one user (Raanani: Page 2, Paragraph [0016]; Page 3, Paragraph [0029]). It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method and system taught by Raanani with the method and system taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claims 24 and 33, the combination of Raanani and Nielsen teaches wherein a sentiment analysis based on a utilization of the facial recognition algorithm to perceive the reaction associated with the at least one user (Raanani: Figure 2 – Reference 215 ; Page 1, Paragraph [0014]; Page 6, Paragraph [0045]). It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method and system taught by Raanani with the method and system taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claims 25 and 34, the combination of Raanani and Nielsen teaches a plurality of factors associated with a confidence positivity score related to the sentiment analysis comprise pitch deviation, cultural identification, length of silence, and different languages (Page 1, Paragraph [0014]; Page 2, Paragraphs [0019] and [0021]; Page 6, Paragraph [0045] and [0046]). (Note: In paragraph [0014], Raanani describes voice signal associated features [e.g. tone/timber - pitch deviation]) (Note: In paragraph [0014], Raanani also describes personal attributes [i.e. accent – i.e. different languages; gender – i.e. cultural identification]. In paragraph [0045], Raanani describes extracting low-level features and statistical data over the extracted features. In paragraph [0046], Raanani describes measuring a conversation flow [i.e. silence times and silence duration]) It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method and system taught by Raanani with the method and system taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claims 26 and 35, the combination of Raanani and Nielsen teaches wherein automatically extracting, based, at least in part, on a sentiment analysis of the key term within the input text data, comprises utilizing an entity recognition algorithm as described in Claim 20. It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method and system taught by Raanani with the method and system taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claim 27, the combination of Raanani and Nielsen teaches automatically initiating an interaction session with the first user based on an automatically generated call script associated with the plurality of tuples as described in Claim 20. It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method taught by Raanani with the method taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claim 28, the combination of Raanani and Nielsen teaches instructing a computing device associated with the second user to display the plurality of tuples associated with the first user (Page 3, Paragraph [0025]). (Note: In paragraph [0025], Raanani describes presenting guidance to a call center agent via an agent display/graphical user interface or some other interface) It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method taught by Raanani with the method taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claim 29, the combination of Raanani and Nielsen teaches a computer-implemented method described in Claim 20. Raanani also teaches receiving, by a processor, an input text data retrieved from a transcription between a first user and a second user (Figure 1 – Reference 105 ; Figure 4 – References 405 and 410 ; Figure 10 – Reference 1005 ; Page 1, Paragraph [0014]; Page 4, Paragraph [0034]; Page 7, Paragraph [0055]; Page 12, Paragraph [0097]); identifying, by the processor, personal information associated with the first user from the input text data by inputting the input text data into a trained machine learning model (Raanani – Budget/Finances: Page 1, Paragraph [0014]; Page 2, Paragraph [0019]; Page 11, Paragraph [0085]). Raanani further teaches determining, by the processor, at least one key term within the personal information associated with the first user based on a perceived reaction of the first user (Page 1, Paragraph [0014]; Page 3, Paragraphs [0028] and [0029]; Page 4, Paragraph [0034]); automatically extracting, by the processor, a plurality of tuples from the input text data, wherein each tuple represents a relationship and an object (Page 6, Paragraph [0043]). Raanani additionally teaches instructing, by the processor, a computing device associated with the second user to display a generated call script and the plurality of tuples associated with the first user (Page 3, Paragraph [0025]); but does not teach automatically generating, by the processor, based on the plurality of tuples, a call script for conducting a subsequent interaction with the first user. However, Nielsen teaches automatically generating, by the processor, based on the plurality of tuples, a call script for conducting a subsequent interaction with the first user (Page 6, Paragraph [0090] – Page 7, Paragraph [0091]; Page 9, Paragraphs [0127] and [0128]). It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method taught by Raanani with the method taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty. As per Claim 36, the combination of Raanani and Nielsen teaches a computer-implemented method described in Claims 20 and 29. Raanani also teaches a system comprising: a non-transient computer memory, storing software instructions (Figure 10 – Reference 1010 and 1020 ; Page 12, Paragraphs [0098] and [0099]); at least one processor of a first computing device associated with a user (Figure 10 – Reference 1005 ; Page 12, Paragraphs [0098] and [0099]). It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method and system taught by Raanani with the method and system taught by Nielsen to build upon the momentum associated with the successful resolution of a customer request by delivering customized consumer offers [i.e. upselling/cross-selling] in an attempt to increase enterprise revenue and promote brand loyalty . 07-22-aia AIA Claim (s) 22, 31 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Raanani et al (2018/0096271 A1) in view of Nielsen et al (2014/0074589 A1) as applied to Claim s 20, 29 and 36 above, and further in view of Chaves (6,510,414 B1) . As per Claim 22, 31 and 38, the combination of Raanani and Nielsen teaches a computer-implemented method and system described in Claims 20, 29 and 36; but does not teach wherein determining at least one key term within the personal information associated with the first user comprises a different user actively highlighting the at least one key term within the input text data. However, Chaves teaches wherein determining at least one key term within the personal information associated with the first user comprises a different user actively highlighting the at least one key term within the input text data (Column 6, Lines 50-60). It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify the method and system taught by Raanani and Nielsen with the method and system as taught by Chaves to allow a contact center agent to make changes in customer personal information when necessary to ensure that the information the enterprise [i.e. change in address] has is up to date and current so the enterprise can better assist customers . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Vo et al (2021/0334593 A1), Raanani et al (2017/0187880 A1), Blair et al (10,878,505 B1), Jin et al (2022/0399006 A1), Lipton et al (2022/0375605 A1), Thomson et al (2019/0013038 A1), Gramacho et al (2021/0006656 A1), Chesler (2012/0116899 A1) and Villaizan (11,011,160 B1). Each of these describes systems and methods of implementing speech recognition within a contact center . Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHARYE POPE whose telephone number is (571)270-5587. The examiner can normally be reached Monday - Friday 8AM - 4PM. 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, 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. 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. KHARYE POPE Primary Examiner Art Unit 2693 /KHARYE POPE/Primary Examiner, Art Unit 2693 Application/Control Number: 18/893,320 Page 2 Art Unit: 2693 Application/Control Number: 18/893,320 Page 3 Art Unit: 2693 Application/Control Number: 18/893,320 Page 4 Art Unit: 2693 Application/Control Number: 18/893,320 Page 5 Art Unit: 2693 Application/Control Number: 18/893,320 Page 6 Art Unit: 2693 Application/Control Number: 18/893,320 Page 7 Art Unit: 2693 Application/Control Number: 18/893,320 Page 8 Art Unit: 2693 Application/Control Number: 18/893,320 Page 9 Art Unit: 2693 Application/Control Number: 18/893,320 Page 10 Art Unit: 2693 Application/Control Number: 18/893,320 Page 11 Art Unit: 2693 Application/Control Number: 18/893,320 Page 12 Art Unit: 2693 Application/Control Number: 18/893,320 Page 13 Art Unit: 2693 Application/Control Number: 18/893,320 Page 14 Art Unit: 2693 Application/Control Number: 18/893,320 Page 15 Art Unit: 2693 Application/Control Number: 18/893,320 Page 16 Art Unit: 2693 Application/Control Number: 18/893,320 Page 17 Art Unit: 2693 Application/Control Number: 18/893,320 Page 18 Art Unit: 2693 Application/Control Number: 18/893,320 Page 19 Art Unit: 2693 Application/Control Number: 18/893,320 Page 20 Art Unit: 2693
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