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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 30 December, 2025 has been entered.
Claims 1-2, 4-8, 10-11, 14-19, and 21-25 are pending.
Claims 1, 5-6, 8, 10-11, 14, 16, 19, and 21-22 are amended.
Claims 3, 9, 12-13, and 20 are canceled.
Claims 23-25 are new.
Response to Arguments
Applicant argues that the prior art of record does not teach the currently amended limitations of claim 1. Examiner finds the argument persuasive and a new ground of rejection is presented herewith.
With regard to amended claim 14, Applicant argues that the prior art of record does not teach the currently amended limitation. Examiner respectfully disagrees. Hansmann-Tykowski are in an analogous field of endeavor of determining the most relevant communication content to present to a user. For example, Hansmann [0031] provides “The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc. The messaging session may be associated with a context depending on use cases”. Based on such excerpt from Hansmann, it is reasonable to interpret that multi-modal communications are considered and context determined in the system taught by Hansmann. Based on such rationale, Examiner concludes that the prior art of record teaches the currently amended limitations of claim 14.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4-8, 10, 14-19, and 21-25 are rejected under 35 U.S.C. 103 as being unpatentable over Hansmann et al (US 2022/0321511), in view of Tysowski et al (US 2008/0270560), further in view of Yuan (US 2016/0255034).
Regarding claim 1, Hansmann teaches a method, comprising:
determining, by a server of a unified communication system, one or more intents of a user, the one or more intents being represented in natural language (Hansmann figs.1A, B and 2);
obtaining, by a transformer engine of the server, a set of messages associated with one or more addresses of the user (Hansmann figs.1A, B and 2);
determining, by the transformer engine, relevance scores for message-intent pairs based on the one or more intents and natural language information of the one or more messages the set of messages, wherein each of the relevance scores represents a likelihood that a message from the set of messages corresponds to an intent from the one or more intents (Hansmann figs.1A, B and 2).
Hansmann teaches the above but Hansmann does not explicitly teach [[and]] selecting, by the server, a subset of messages based on the relevance scores, wherein the subset of messages includes n messages from the set of messages having highest ones of the relevance scores for a selected intent from the one or more intents, and n is a positive integer; and causing, by the server, display of the subset of messages to the user in a graphical user interface associated with the unified communication system. However, in a similar field of endeavor, Tysowski teaches selecting, by the server, a subset of messages based on the relevance scores, wherein the subset of messages includes n messages from the set of messages having highest ones of the relevance scores for a selected intent from the one or more intents, and n is a positive integer; and causing, by the server, display of the subset of messages to the user in a graphical user interface associated with the unified communication system (Tysowski [0042-0043] provides “In the example of FIG. 4a, it can be seen that the prioritized messages displayed in the priority viewport 210 are arranged in reverse chronological order. The messages may be ordered in other manners, including, but not limited to, chronological order and in order of priority. In the particular example of FIG. 4a, it can be seen that there are more messages than there is space for display in the priority viewport 210, as the number of unread important messages as indicated by the indicator of unread important messages 217 is fifteen, and only six prioritized message listing entries are visible in the priority viewport 210 (along with one partially displayed entry)…The nature of a prioritized message is preferably determined not only with reference to a designated importance level that may be established by a sender of a message 15, but also with reference to its characteristics and its treatment by the user of the communication device 10. The determination of a received message as a prioritized message may be seen with reference to FIG. 6. Preferably, certain evaluation events, such as the receipt of a message, determination that a previously received and unread message has been awaiting user operation for at least a preset time interval, or a user operation on a received message, trigger the determination of the message's priority level.”).
One of ordinary skill in the art before the effective filing date of Applicant’s claimed invention would have recognized the utility of implementing the feature of prioritizing select messages as taught by Tysowski, in the Hansmann system, in order to aid users in not missing important messages.
Hansmann-Tysowski teaches the above including selecting a subset of messages based on relevance scores but Hansmann-Tysowski does not explicitly teach intents are determined based on user activity data of the user stored by the unified communication system and relevance scores for selection of messages are determined based on the unified communication system data by processing tokenized representations of the set of messages and tokenized representations of the one or more intents and wherein the relevance score for the selection of messages is for message-intent pairs.
However, in a similar field of endeavor, Yuan teaches intents are determined based on user activity data of the user stored by the unified communication system and relevance scores for selection of messages are determined based on the unified communication system data wherein the relevance score for the selection of messages is for message-intent pairs (Yuan figs. 1-2; [0018-0019] provides “…an intelligent messaging system 200 includes a score generation module 202, a display module 204, and a database 206. The modules of the intelligent messaging system 200 may be implemented on or executed by a single device such as an intelligent messaging device, or on separate devices interconnected via a network. The aforementioned intelligent messaging device may be, for example, one or more client machines or application servers. The score generation module 202 is configured to calculate professional importance scores associated with messages, and the display module 204 is configured to adjust the display of these messages based on their calculated professional importance scores…According to various example embodiments, the system 200 is configured to calculate a professional importance score of an electronic message, such as an electronic message (e.g., e-mail, text message, etc.) received by a user (e.g., member of an online social networking service such as LinkedIn®). The professional importance score may indicate the inferred importance of the message in advancing the recipient's professional or a career-related interests”),
by processing tokenized representations of the set of messages and tokenized representations of the one or more intents (Yuan [0122] provides for tokenizing to compare text).
One of ordinary skill in the art before the effective filing date of Applicant’s claimed invention would have recognized the utility of implementing the feature of tokenizing and comparing text as taught by Yuan, in the Hansmann-Tysowski system, in order to prioritize messages on the basis of the subject matter of the text.
Regarding claim 2, the method of claim 1, wherein the set of messages comprises at least one of voicemail messages, videomail messages, email messages, short messaging service messages, instant messages, social media messages, video messages, or push notifications (Hansmann [0030] provides “Messaging may be a written communication sent over a variety of digital channels such as email, SMS and in-app chat.”; [0031] provides “The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format.”).
Regarding claim 4, the method of claim 1, wherein determining the one or more intents comprises: receiving, by the server, a user input representing the one or more intents, the user input comprising at least one of natural language text or natural language speech (Hansmann [0057] provides “…a language model may receive as input the real customer complaint transcripts 600 so that it can be trained in step 613 to determine language patterns for different categories of users involved in the real customer complaint transcripts 600”).
Regarding claim 5, the method of claim 1, wherein the user activity data comprises user activity with respect to the set of messages and determining the one or more intents comprises determining one or more intents based on the user activity with respect to the set of messages (Yuan [0030] provides for prior interaction and correspondence between the user and the sender). Motivation provided with reference to claim 1.
Regarding claim 6, Hansmann-Tysowski teaches the method of claim 1, wherein the user activity data comprises data of synchronous communications and asynchronous communications processed by the unified communication system, and determining the one or more intents comprises determining the one or more intents based on the data of synchronous communications and asynchronous communications processed by the unified communication system (Hansmann [0031] provides “The intercepted electronic message may, for example, be provided during a messaging session. The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc. The messaging session may be associated with a context depending on use cases”).
Regarding claim 7, the method of claim 1, wherein the transformer engine comprises at least one of a large language model or a generative pretrained transformer (Hansmann [0047]).
Regarding claim 8, the method of claim 1, wherein determining the one or more intents comprises: determining, by the transformer engine, a first set of intents based on synchronous communications of the user via the unified communication system (Hansmann Abstract provides “Message intents of the received electronic message and one or more related intents may be determined” ; [0031] provides “The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc.”); and determining, by the transformer engine, a second set of intents based on asynchronous communications of the user via the unified communication system (Hansmann Abstract provides “Message intents of the received electronic message and one or more related intents may be determined” ; [0031] provides “The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc.”).
Hansmann-Tysowski teaches a transformer engine, but Hansmann-Tysowski does not explicitly teach combining the first set of intents and the second set of intents for use together in determining the relevance scores for the message-intent pairs. However, in a similar field of endeavor, Yuan teaches combining the first set of intents and the second set of intents for use together in determining the relevance scores for the message-intent pairs Yuan figs. 1-2; [0018-0019] provides “…an intelligent messaging system 200 includes a score generation module 202, a display module 204, and a database 206. The modules of the intelligent messaging system 200 may be implemented on or executed by a single device such as an intelligent messaging device, or on separate devices interconnected via a network. The aforementioned intelligent messaging device may be, for example, one or more client machines or application servers. The score generation module 202 is configured to calculate professional importance scores associated with messages, and the display module 204 is configured to adjust the display of these messages based on their calculated professional importance scores…According to various example embodiments, the system 200 is configured to calculate a professional importance score of an electronic message, such as an electronic message (e.g., e-mail, text message, etc.) received by a user (e.g., member of an online social networking service such as LinkedIn®). The professional importance score may indicate the inferred importance of the message in advancing the recipient's professional or a career-related interests”; [0122] provides for tokenizing to compare text, wherein the same concept can be applied to subset of messages). Motivation to combine provided with reference to claim 1.
Regarding claim 10, the method of claim 1, The method of claim 1, wherein determining the one or more intents comprises: determining, by the transformer engine, a first set of intents based on communications in a first asynchronous communication modality of the user via the unified communication system (Hansmann [0031] provides “The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc. The messaging session may be associated with a context depending on use cases”);
[[and]] determining, by the transformer engine, a second set of intents based on communications in a second asynchronous communication modality of the user via the unified communication system (Hansmann [0031] provides “The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc. The messaging session may be associated with a context depending on use cases”);
Hansmann-Tysowski teaches a transformer engine, but Hansmann-Tysowski does not explicitly teach combining the first set of intents and the second set of intents for use together in determining the relevance scores for the message-intent pairs. However, in a similar field of endeavor, Yuan teaches combining the first set of intents and the second set of intents for use together in determining the relevance scores for the message-intent pairs Yuan figs. 1-2; [0018-0019] provides “…an intelligent messaging system 200 includes a score generation module 202, a display module 204, and a database 206. The modules of the intelligent messaging system 200 may be implemented on or executed by a single device such as an intelligent messaging device, or on separate devices interconnected via a network. The aforementioned intelligent messaging device may be, for example, one or more client machines or application servers. The score generation module 202 is configured to calculate professional importance scores associated with messages, and the display module 204 is configured to adjust the display of these messages based on their calculated professional importance scores…According to various example embodiments, the system 200 is configured to calculate a professional importance score of an electronic message, such as an electronic message (e.g., e-mail, text message, etc.) received by a user (e.g., member of an online social networking service such as LinkedIn®). The professional importance score may indicate the inferred importance of the message in advancing the recipient's professional or a career-related interests”; [0122] provides for tokenizing to compare text, wherein the same concept can be applied to subset of messages). Motivation to combine provided with reference to claim 1.
Regarding claim 14, the method of claim 1, wherein determining the one or more intents comprises: determining, by the transformer engine, the one or more intents based on a relationship between a first communication and a second communication, wherein the first receipt of a communication is received over a first modality while the user is participating in [[a]]the second communication over a second modality Hansmann [0031] provides “The messaging session may involve the exchange of electronic messages including text, multimedia, and/or voice in a real-time format or a non-real-time format. The real time format may involve instant messaging or chat and the non-real time format may involve email, posting to a dynamic forum or feed, etc. The messaging session may be associated with a context depending on use cases”).
Regarding claim 15, the method of claim 1, wherein the server of the unified communication system comprises a Contact Center as a Service server, wherein the set of messages comprises recordings or transcripts of contact center engagements, wherein the one or more intents are associated with at least one of quality assurance or training (Hansmann [0038] provides “…the chat application may be used for testing or training a user by asking questions to the user. The user may provide answers to the questions. The electronic message may, for example, comprise the text of a question”).
Regarding claim 16, this claim contains limitations found within those of claim 1, and the same rationale of rejection applies, where applicable.
Regarding claim 17, this claim contains limitations found within those of claim 3, and the same rationale of rejection applies, where applicable.
Regarding claim 18, this claim contains limitations found within those of claim 4, and the same rationale of rejection applies, where applicable.
Regarding claim 19, this claim contains limitations found within those of claim 1, and the same rationale of rejection applies, where applicable.
Regarding claim 21, the method of claim 1, wherein determining the relevance scores for a specific one of the message-intent pairs comprises identifying, by the transformer engine, one or more tokens of a respective message from the specific one of the message-intent pairs that are most relevant to one or more tokens of a respective intent from the specific one of the message-intent pairs, and using the identified tokens in determining the relevance score for the specific one of the message-intent pairs (Yuan figs.2-3 and corresponding description). Motivation provided with reference to claim 1.
Regarding claim 22, the method of claim 21, wherein the relevance scores for the specific one of the message-intent pairs comprises using, by the transformer engine, an embedding vector for tokens of a respective message from the specific one of the message-intent pairs and an embedding vector for tokens of a respective intent from the specific one of the message-intent pairs in determining the relevance score for the specific one of the message-intent pairs (Yuan [0122] provides “the cosine similarity is calculated as the vector cosine of the word vectors” providing for embedding in a vector format for comparison purposes). Motivation provided with reference to claim 1.
Regarding claim 23, the method of claim 1, wherein determining the relevance scores for a specific one of the message-intent pairs comprises applying, by the transformer engine, a self-attention mechanism to tokens of a respective message from the specific one of the message-intent pairs and tokens of a respective intent from the specific one of the message-intent pairs to determine tokens that are most relevant (Hansmann [0079]).
Regarding claim 24, the method of claim 1, wherein determining the one or more intents comprises expanding, by the transformer engine, natural language text entered by the user in order to clarify a subject matter of the intent (Hansmann [0078-0080]).
The method of claim 25, wherein expanding the natural language text entered by the user comprises using the set of messages and the user activity data in expanding the natural language text (Hansmann [0078-0080]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Hansmann et al (US 2022/0321511), in view of Tysowski et al (US 2008/0270560), in view of Yuan (US 2016/0255034), further in view of Stefanski et al (US 2021/0074288).
Regarding claim 11, Hansmann Tysowski teaches the method of claim 1, but Hansmann Tysowski does not explicitly teach wherein determining the one or more intents comprises: determining, by the transformer engine, a first set of intents based on communications in a private communication modality of the user via the unified communication system; and combining the first set of intents and the second set of intents for use together in determining the relevance scores for the message-intent pairs.
However, in a similar field of endeavor, Yuan teaches combining the first set of intents and the second set of intents for use together in determining the relevance scores for the message-intent pairs Yuan figs. 1-2; [0018-0019] provides “…an intelligent messaging system 200 includes a score generation module 202, a display module 204, and a database 206. The modules of the intelligent messaging system 200 may be implemented on or executed by a single device such as an intelligent messaging device, or on separate devices interconnected via a network. The aforementioned intelligent messaging device may be, for example, one or more client machines or application servers. The score generation module 202 is configured to calculate professional importance scores associated with messages, and the display module 204 is configured to adjust the display of these messages based on their calculated professional importance scores…According to various example embodiments, the system 200 is configured to calculate a professional importance score of an electronic message, such as an electronic message (e.g., e-mail, text message, etc.) received by a user (e.g., member of an online social networking service such as LinkedIn®). The professional importance score may indicate the inferred importance of the message in advancing the recipient's professional or a career-related interests”; [0122] provides for tokenizing to compare text, wherein the same concept can be applied to subset of messages). Motivation to combine provided with reference to claim 1.
Hansmann Tysowski-Yuan teaches the above but Hansmann Tysowski-Yuan does not explicitly teach wherein determining the one or more intents comprises: determining, by the transformer engine, a first set of intents based on communications in a private communication modality of the user via the unified communication system. However, in a similar field of endeavor, Stefanski teaches wherein determining the one or more intents comprises: determining, by the transformer engine, a first set of intents based on communications in a private communication modality of the user via the unified communication system (Stefanski [0084] provides “The electronic computing device receives signals representative of the audio inquiry (directly from the microphone 220 or through monitoring audio communications on the talk group channel or private channel) and analyzes the signals to determine the intent and/or content of the audio inquiry. For example, the electronic computing device may include a natural language processing (NLP) engine configured to determine the intent and/or content of the audio inquiry. The electronic computing device may also be configured to determine a response to the audio inquiry (for example, in accordance with a process 400 illustrated in FIG. 4) and provide the response to an output device of the communication device 200 (for example, one or more of the speaker 222 via a generated audio response and the screen 205 via a generated text, graphic, and/or video-based response)”); and determining, by the transformer engine, a second set of intents based on communications in a group communication modality of the user via the unified communication system. However, in a similar field of endeavor, Stefanski teaches (Stefanski [0084] provides “The electronic computing device receives signals representative of the audio inquiry (directly from the microphone 220 or through monitoring audio communications on the talk group channel or private channel) and analyzes the signals to determine the intent and/or content of the audio inquiry. For example, the electronic computing device may include a natural language processing (NLP) engine configured to determine the intent and/or content of the audio inquiry. The electronic computing device may also be configured to determine a response to the audio inquiry (for example, in accordance with a process 400 illustrated in FIG. 4) and provide the response to an output device of the communication device 200 (for example, one or more of the speaker 222 via a generated audio response and the screen 205 via a generated text, graphic, and/or video-based response)”).
One of ordinary skill in the art before the effective filing date of Applicant’s claimed invention would have recognized the utility of implementing the feature of analyzing both group and private communications of a user at taught by Stefanski, in the Hansmann Tysowski Yuan system, in order to create a comprehensive analysis of the user’s communications.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Glass et al US 2005/0060643.
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/I.R/Examiner, Art Unit 2459 /SCHQUITA D GOODWIN/Primary Examiner, Art Unit 2459