DETAILED CORRESPONDENCE
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This final office action on merits is in response to the communication received on 21 January 2026. Amendments to claims 1 and 10 are acknowledged and have been carefully considered. Claims 2, 3, 7, 11, 12, 16, 19 and 20 are cancelled. Claims 1, 4-6, 8-10, 13-15, 17 and 18 are pending and considered below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 4-6, 8-10, 13-15, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over McCormack et al. (20160198047) and in further view of Terry (20190220774) and Yablokov et al. (20130268470).
Claims 1 and 10: McCormack discloses a system for generating leads, the system comprising: a memory for storing machine readable code; and a processor operatively coupled to the memory, the processor configured to utilize an instance deployment module ([34, 37]), wherein the instance deployment module is configured to:
build a personality profile on a first instance, said personality profile modeled after one or more attributes of a system user ([60 “characteristics of the customer may include a personality type, a conversation style, interests, demographics, and so forth”]), said attributes comprising personality traits and mannerisms ([66 “complement to one personality type and a response template ‘C’ and a response template ‘D’ may complement another personality type,”]),
send a provisional message from said first instance to an email management suite of a second instance ([56 “monitoring module 232 may monitor parameters associated with a communication session, according to an embodiment of the present invention. The parameters may include, but not restricted to, a type of communication session, customer information, query, and so forth. Examples of the type of communication session may include, but not limited to, a voice call, a video call, an email communication,” 69 “type of communication session may include, but not restricted to, a voice call, a video call, an email communication, Short Messaging Service (SMS) communication, instant messaging (IM), web chat conversation, blog on a website of an organization or an enterprise, group chat on a social networking website, a post on a social networking page or a social network group, managing parameters and communications sessions between consumers and resources specific to email communications where in sessions reads on instances]);
McCormack does not explicitly disclose, however Terry discloses:
receive an incoming message and employ a neural network to analyze said incoming message and to generate an incoming message confidence score ([71 “AI interface 510 allows the AI platform (or multiple AI models) to process the response for context, intents, sentiments and associated confidence scores. The classification engine 550 includes a suite of tools that enable better classification of the messages using machine learned models,” 123 “algorithms that may be employed to categorize a given document, including Hardrule, Naïve Bayes, Sentiment, neural nets including convolutional neural networks and recurrent neural networks and variations, k-nearest neighbor, other vector based algorithms,” 124 “classification has been generated, the system renders intents from the message. Intents, in this context, are categories used to answer some underlying question related to the document. The classifications may map to a given intent based upon the context of the conversation message. A confidence score, and accuracy score, are then generated for the intent,” 132]) wherein said confidence score is produced at least in part by a natural language processing model, said model trained on prior email correspondence from said system user ([66 “natural language processing by the AI is required, and the AI (or multiple AI models) must be correctly trained to make the appropriate inferences and classifications of the response message. The user may leverage the AI manager 330 to review documents the AI has processed and has made classifications,” 73 “natural language processing by the AI is required, and the AI (or multiple AI models) must be correctly trained to make the appropriate inferences and classifications of the response message. The user may leverage the AI manager 330 to review documents the AI has processed,” 75 “disclosed systems and methods for machine learned classification of natural language are state of the art, and represent some of the most sophisticated language AI analytics currently available,” 95, 96, 98 “natural language account manager assistant 570 may additionally have access to all supported channels by which instructions are sent, including email, SMS, mobile user interfaces, web based accounts and the like,” 124 “classifications may map to a given intent based upon the context of the conversation message. A confidence score, and accuracy score, are then generated for the intent. Intents are used by the model to generate actions,” 129 “conversation messages being classified, as noted earlier, and a confidence measure being determined for the classification. This confidence is then compared to a threshold, and messages with too low of confidence are slated for review by the training desk (at 1410). In many situations, this confidence threshold is set at 95%. At this level, the vast amount of classifications not requiring human review are accurately classified, and the workload required by the human operators is manageable,” 130-132]) Examiner Note: Examiner under a broadest reasonable interpretation interprets the disclosures of Terry as cited to above to disclose the generation of confidence scores as related to the analysis of emails and correspondence determination with respect to submitted information.
create an optimal response ([83 “message suggestor 564 may generate this proposed response based upon the suspected classification, and present this to the human reviewer. If the classification was indeed correct, the user may then quickly approve the suggested response, rather than drafting a response from scratch,” 84 “suggestions presented to the user may fall into eleven discrete categories. These may include continue messaging, skip to follow-up, stop messaging, do not email, no contacted, received contacted, action required, alert, send resources, out of office and check back later. Each of these response suggestions will be described in greater detail below. It should be noted that these eleven response suggestions are not limiting, and more or fewer actions may be available to the training desk user,” 92 “Send resources is an action where information is returned in the response to the target. This response may include linked information to external information sources, embedded information, or attached information (when communication is via an email channel),” 121 “extractions have been combined in various ways, which results in an exponential increase in combinations as more features are desired. In response, the present method performs each feature extraction in discrete steps (on an atomic level) and the extractions can be “chained” as desired to extract a specific feature set,”]), based at least partly on said incoming message personality profile and confidence score, including the natural language understanding model ([58, 62 “user interface 210, a message generator 220, and a message response system 230. The user interface 210 may be utilized to access the message generator 220 and the message response system 230 to set up messaging conversations, and manage those conversations throughout their life cycle,” 66 “natural language processing by the AI is required, and the AI (or multiple AI models) must be correctly trained to make the appropriate inferences and classifications of the response message. The user may leverage the AI manager 330 to review documents the AI has processed and has made classifications,” 71 “AI interface 510 allows the AI platform (or multiple AI models) to process the response for context, intents, sentiments and associated confidence scores. The classification engine 550 includes a suite of tools that enable better classification of the messages using machine learned models,” 73 “natural language processing by the AI is required, and the AI (or multiple AI models) must be correctly trained to make the appropriate inferences and classifications of the response message. The user may leverage the AI manager 330 to review documents the AI has processed,” 75 “disclosed systems and methods for machine learned classification of natural language are state of the art, and represent some of the most sophisticated language AI analytics currently available,” 80 “client profile (client value and/or patience level) and lastly low confidence classifications,” 95, 96, 98 “natural language account manager assistant 570 may additionally have access to all supported channels by which instructions are sent, including email, SMS, mobile user interfaces, web based accounts and the like,” 142 “traits like persistence, politeness, promptness, playfulness, perceived education level, and confidence may be selected. The responses generated for a given personality profile, while maintaining the same content, may vary significantly based upon these selections,”]). Examiner Note: Examiner under a broadest reasonable interpretation interprets the disclosures of Terry to determine an optimal response by implementing a wide variety of data management methods and as well integrating a personality profile and confidence score as derived from natural language model analysis and implementing the provision of an optimal response to system users. As well, Terry discloses the procedures implemented by AI management modules as well as the creation of optimal responses as detailed above with respect to cited to reference Terry.
Therefore it would be obvious for McCormack to receive an incoming message and employ a neural network to analyze said incoming message and to generate an incoming message confidence score, wherein said confidence score is produced at least in part by a natural language processing model, said model trained on prior email correspondence from said system user, and create an optimal response, based at least partly on said incoming message personality profile and confidence score, including the natural language understanding model as per the steps of Terry in order to employ a neural network or artificial intelligence to analyze system messages and other content and use the collected data with respect to confidence scores to generate more precise responses to the message related data and result in better system performance and optimized message exchanges between system users.
McCormack does not explicitly disclose, however Yablokov discloses:
said email management suite comprising a spam filter configured to place messages in a spam folder based upon the content of the messages ([38 “Messages 100 (e.g., emails, SMS or MMS messages) are received into a SPAM filter 130 of a SPAM filtering system 120, which is installed on a computer system 110 of the user….SPAM filter 130 determines parameters of received messages 100 and checks these parameters against parameters stored in a filtering rules database 140. If the parameters of the message 100 match the parameters from the database 140, a certain action is taken against the message 100 (for example, placing the message into quarantine, or deletion of the message),” 47 “rule broadcast module 340 sends updated filtering rules from the common filtering rules database 330 to all user computer systems 110. Thus, the updated filtering rules from the common rules filtering database 330 are migrated to the filtering rules databases 140 on user computer systems,”]),
correct and resend said provisional message, if said provisional message was placed in said spam folder by said spam filter, thereby enhancing deliverability of said provisional message to a target contact ([47 “rule broadcast module 340 sends updated filtering rules from the common filtering rules database 330 to all user computer systems 110. Thus, the updated filtering rules from the common rules filtering database 330 are migrated to the filtering rules databases 140 on user computer systems,” 51 “parameter, based on which the message is identified as containing SPAM, can be a unique identifier of the sender. As an example of such unique identifier for email is the email address of the sender. For SMS and MMS messages, such a parameter could be the mobile telephone number of the sender. Note also that on the Internet there are openly accessible resources that are routinely updated and contain information listing unique identifiers of senders of SPAM messages,” 52 “Messages 100 are received into the SPAM filter 130 of the SPAM filtering system 120 installed on the computer system 110 of the user 160. The SPAM filter 130 determines parameters of received messages 100 and checks these parameters against parameters stored in a filtering rules database 140. If the parameters of the message 100 match the parameters from the database 140, a certain action (e.g., message deletion or quarantine) is taken against the message,” 56, 62 “SPAM filtering system is a part of user AV application that includes other modules, such as, for example, a file anti-virus, a web anti-virus, a firewall, an anti-phishing module, an update module, etc. Thus, user knowledge and competence can be determined by user actions that involve the entire set of modules of the AV application,”]), Examiner Note: Examiner notes that the claimed element terms “correct and resend said provisional message,” are not to be found in the written description, that is, the term correct, resend, or provisional does not occur in the written description and therefore Examiner has interpreted the claimed elements with respect to a person of skill in the art. That is, given the paucity of available data in the written description with respect to the above noted elements, the rejection is maintained.
convert said provisional message to an outgoing message, if said provisional message was not placed in said spam folder by said spam filter ([38 “simple SPAM filtering system, in which the filtering rules are set by a user 160. Messages 100 (e.g., emails, SMS or MMS messages) are received into a SPAM filter 130 of a SPAM filtering system 120, which is installed on a computer system 110 of the user 160. According to the exemplary embodiment, a computer system 110 can be a PC or a mobile device (e.g., a notebook, a tablet or a mobile phone). The SPAM filter 130 determines parameters of received messages 100 and checks these parameters against parameters stored in a filtering rules database,” 39 “match is not found, the message 100 is sent to the user 160. However, if the message 100 containing SPAM is not detected by the SPAM filtering system 120 and the message is sent to the user 160, the user 160 can change filtering rules in the database,” 43 “filtering rules modification system in accordance to the exemplary embodiment. The filtering rules are perfected and modified based on user reports. Each user of the computer systems 110, which act as clients of the cloud service 200, can send a SPAM detection report to a filtering rules modification system,” 53-55]), Examiner Note: Examiner notes that the term convert data occurs at paragraph [32] of the written description however the recited term convert provisional message does not occur in the written description and as well the conversion of data simply converts data into percentages and does not relate to provisional or any other data type. That is, given the paucity of available data in the written description with respect to the above noted elements, the rejection is maintained.
deploy the outgoing message ([40 “users are given an opportunity to send SPAM reports to a central processing system. The central processing system analyzes the reports and changes the filtering rules of the SPAM filtering system 120 on all user computers,” 41-45]).
Therefore it would be obvious for McCormack to said email management suite comprising a spam filter configured to place messages in a spam folder based upon the content of the messages, correct and resend said provisional message, if said provisional message was placed in said spam folder by said spam filter, thereby enhancing deliverability of said provisional message to a target contact, convert said provisional message to an outgoing message, if said provisional message was not placed in said spam folder by said spam filter, convert said provisional message to an outgoing message, if said provisional message was not placed in said spam folder by said spam filter and deploy the outgoing message as per the steps of Yablokov in order to employ a neural network or artificial intelligence to analyze system messages and other content and use the collected data with respect to confidence scores to generate more precise responses to the message related data and result in better system performance and optimized message exchanges between system users.
Claims 4 and 13: McCormick in view of Terry and Yablokov discloses the system of claims 1 and 10, and McCormick does not explicitly disclose, however Terry discloses:
assign a response confidence score to each of a plurality of response templates, wherein the optimal response is determined by the response template with the highest response confidence score ([69-71 “scores from ai, determining intent and messaging and message template and confidence scores,”]).
Therefore it would be obvious for McCormack to assign a response confidence score to each of a plurality of response templates, wherein the optimal response is determined by the response template with the highest response confidence score as per the steps of Terry in order to employ a neural network or artificial intelligence to analyze system messages and other content and use the collected data with respect to confidence scores to generate more precise responses to the message related data and result in better system performance and optimized message exchanges between system users.
Claims 5 and 14: McCormick in view of Terry and Yablokov discloses the system of claims 4 and 13, and McCormick does not explicitly disclose, however Terry discloses:
further configured to compare each response confidence score against a confidence score threshold ([82 “compare to score threshold,”]).
Therefore it would be obvious for McCormack wherein the response is further configured to compare each response confidence score against a confidence score threshold as per the steps of Terry in order to employ a neural network or artificial intelligence to analyze system messages and other content and use the collected data with respect to confidence scores to generate more precise responses to the message related data and result in better system performance and optimized message exchanges between system users.
Claims 6 and 15: McCormick in view of Terry and Yablokov discloses the system of claims 1 and 10, and McCormick does not explicitly disclose, however Terry discloses:
determine that an actionable opportunity is present in the incoming message based on said incoming message confidence score; and notify said system user that an actionable opportunity is present, said notification containing the outgoing message and the incoming message ([124 “model generates actions,” 83 “action response based on a model]).
Therefore it would be obvious for McCormack to determine that an actionable opportunity is present in the incoming message based on said incoming message confidence score; and notify said system user that an actionable opportunity is present, said notification containing the outgoing message and the incoming message as per the steps of Terry in order to employ a neural network or artificial intelligence to analyze system messages and other content and use the collected data with respect to confidence scores to generate more precise responses to the message related data and result in better system performance and optimized message exchanges between system users
Claims 8 and 17: McCormick in view of Terry and Yablokov discloses the system of claims 1 and 10, and McCormick further discloses wherein said personality profile is built by ingesting and analyzing one or more correspondence ([60 “characteristics,”]) associated with the system user ([57 “user profile,”]) utilizing a natural language understanding model ([60 “language model,”]).
Claims 9 and 18: McCormick in view of Terry and Yablokov discloses the system of claims 8 and 17, and McCormick further discloses wherein said personality profile is configured for manual adjustment of one or more attribute ([72 “modify response by personality type,”]).
Response to Arguments
Applicants arguments and amendments, see Remarks/Amendments submitted 21 January 2026 with respect to the rejection of claims 1, 4-6, 8-10, 13-15, 17 and 18 have been carefully considered and are addressed below.
Claim Rejections - 35 USC § 101
Applicants amended the independent claims to more precisely detail the determination of confidence scores by the operation of a natural language understanding model, and as well the claims include specific details with respect to the placement of provisional messages with respect to spam filters and the processing of provisional messages.
As evaluated under the requirements of the 2019 PEG Revised Step 2A Prong One and MPEP 2106, Examiner continues to maintain the instant invention is determined to include a judicial exception similar to abstract ideas related to judicial exception is similar to abstract ideas related to certain methods of organizing human activity such as commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations and as well managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
As evaluated under the requirements of the 2019 PEG Revised Step 2A Prong Two and MPEP 2106 Examiner has determined that the instant invention is directed to a practical application or system functioning improvement as related to the processing of collected and detailed information. Examiner’s conclusion is guided by the revised and amended independent claims as well as written description paragraphs [28]-[44] which specifically detail the collection and processing of user related data as well as the implementation of a wide variety of processing techniques.
Therefore the Examiner removes the rejection of all pending claims under 35 USC 101.
Claim Rejections - 35 USC § 103
Applicants arguments with respect to the rejection of all pending claims under the combination of cited to prior art has been evaluated and the Examiner continues to maintain the rejection of all pending claims under the combination of McCormack in view of Terry and further in view of Yablokov.
Applicants present arguments that the cited to prior art reference Terry does not disclose the currently amended limitations which specifically detail the processing of data with respect to the implementation of natural language processing models as trained by the analysis of email data and the subsequent creation of an optimized response based on the incoming message and a user’s personality profile.
Examiner respectfully disagrees and replies that as cited to above, Terry discloses a wide range of natural language processing model implementations and the implementation of the models for the interpretation of emails and as well the creation of an optimal response based upon a user’s personality profile. Terry clearly implements a wide range of AI related data processing implemented with NL models and the use the processed data to create optimal responses to system users and the implementation of confidence scores as well as other factors.
Thus the rejection of all pending claims under the combination of McCormack in view of Terry and further in view of Yablokov under 35 USC 103 is hereby maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant' s disclosure. See attached References Cited form 892.
See Rogynskyy et al. (20190362288) for disclosures related to the comparing of nodes as related to members and groups and the processing of the data to generate performance scores and profiles of the tracked members. See at least paras. [42]-[86].
See Terry et al (20190121856) for disclosures related to the implementation of a response-action engine which determines characteristics of conversations and the analysis of the conversation and the generation of a response. See at least paras. [75]-[100].
See Caballero et al. (20180129960) for disclosures related to the accessing and processing information as related to communications endpoints and the related calculations of confidence scores as related to pre-determined threshold scores. See at least paras. [14]-[40].
See Rangasamy et al. (20170186032) for disclosures related to systems and methods for determining spam related detection as related to publications and the comparison of the detected spam level and the associated determination of a confidence score. See at least paras. [25]-[59].
See Nelson et al. (20060004896) for disclosures related to the implementation of multiple tests as related to the processing of emails for the detection of spam as related to sent emails. See at least paras. [56]-[84].
Any inquiry concerning this communication or earlier communications from the examiner should be directed to David Stoltenberg whose telephone number is (571) 270-3472.
The examiner can normally be reached on Monday-Friday 8:30AM to 5:00PM EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner' s supervisor, Kambiz Abdi, can be reached on (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is (571)-273-8300, or the examiner' s direct fax phone number is (571) 270 4472.
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/DAVID J STOLTENBERG/Primary Examiner, Art Unit 3685