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 .
Response to Arguments
Applicant's arguments filed 10/03/2025 have been fully considered but they are not persuasive.
With regards to applicant’s argument that the combined teachings of Ohana, Fink et al and Ni et al fail to disclose applicant’s amendment that call for a first part uttered by the first speaker during the call and a second part subsequently uttered by the second speaker during the and the sentiment momentum representing a relative change of utterance sentiment from the first part uttered by the first speaker to the second part uttered by the second speaker as of the end of the second part of the call, and the relative change as the sentiment momentum represents improvement of sentiment at the end of the second part uttered by the second speaker during the call; the examiner maintains. The Ni et al reference teaches the concept of where the call sentiment is determined based on the determination of how the customer sentiment subsequently shifts from positive to negative or from negative to positive (second uttered part of second speaker in response to another speaker uttered statement) and what was the driver of the shift of sentiment (e.g. particular statement made by the agent (first uttered part of first/another speaker) (Ni et al, para 0043).
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.
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.
Claims 1, 3-5, 8, 10, 12, 14-17 & 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ohana, US Patent Pub. 20190341023 A1, in view of Fink et al, US Patent Pub. 20140188457 A1, in view of Ni et al, US Patent Pub. 20200089767 A1.
Re Claim 1, Ohana discloses a computer-implemented method for generating sentiment of a call (para 0045; para 0018: system includes a processor along with memory for storage capacity as emphasized in para 0021), the method comprising: receiving an utterance of the call (para 0015: system receives audio call communication, transforms said audio call into transcripts and breaks it down into sentences for further analysis; para 0045), the call including one or more utterances (paras 0015, 0045: call conversations typically include more than one utterances with numerous sentences and multiple words between parties), the utterance including one or more sentences spoken by a first speaker of a plurality of speakers (paras 0015, 0045: call conversations typically include more than one utterances with numerous sentences and multiple words between multiple parties), and a sentence including one or more words (paras 0015, 0045: call conversations typically include more than one utterances with numerous sentences and multiple words between parties); generating, for a set of sentences in the utterance, one or more sentence sentiments (para 0045: determining sentiment of each sentence), the one or more sentence sentiments representing sentiment associated with one or more individual sentences in the set of sentences (para 0045: determining sentiment of each sentence); generating, based on an aggregate of the one or more sentence sentiments of the utterance spoken by the first speaker, an utterance sentiment of the first speaker (para 0045: determining sentiment of each sentence and also determining whether each sentence is relevant to the ongoing call conversation (call sentiment) while the sentence sentiment before relevant sentence to call communication are determined is read as the general utterance); generating, based upon the utterance sentiment, a call sentiment (para 0045: determining sentiment of each sentence and also determining whether each sentence is relevant to the ongoing call conversation (call sentiment) while the sentence sentiment before relevant sentence to call communication are determined is read as the general utterance), the call sentiment representing sentiment of the call (para 0045: determining sentiment of each sentence and also determining whether each sentence is relevant to the ongoing call conversation (call sentiment) while the sentence sentiment before relevant sentence to call communication are determined is read as the general utterance); and providing the call sentiment (para 0045: determining sentiment of each sentence and also determining whether each sentence is relevant to the ongoing call conversation (call sentiment) while the sentence sentiment before relevant sentence to call communication are determined is read as the general utterance); but fails to disclose wherein the call sentiment represents an aggregate of at least the utterance sentiment of the first speaker and another utterance sentiment of a second speaker of the plurality of speakers, and wherein the second speaker is distinct from the first speaker; generating a sentiment momentum associated with the call, the sentiment momentum indicating a sentiment trend during the call, the sentiment trend indicating a fluctuation of sentiment across a first part uttered by the first speaker during the call and a second part subsequently uttered by the second speaker during the call, the sentiment momentum representing a relative change of utterance sentiment from the first part uttered by the first speaker to the second part uttered by the second speaker as of the end of the second part of the call, and the relative change as the sentiment momentum represents improvement of sentiment at the end of the second part uttered by the second speaker during the call; generating an overall call sentiment by adjusting the call sentiment based upon the sentiment momentum, wherein the overall call sentiment is based on the fluctuation of sentiment across the utterance spoken by the first speaker during a first part of the two or more parts of the call and said another utterance spoken by said second speaker of the plurality of speakers during a second part of the two or more parts of the call, the second part subsequent to the first part, and provide the overall call sentiment.
However, Fink et al discloses a sentiment analysis system that teaches the concept of determining the total sentiment score of a communication between at least two parties (Fink et al, para 0046: cumulative sentiment; para 0031: sentiment total score), where the communication could be a chat conversation, a phone call conversation etc (Fink et al, para 0046: phone call and chat communications); such that the sentiment is determined for each party on the communication (Fink et al, para 0046: sentiment determined for the customer(one party) and the company representative(other party); paras 0020-0021). It would have been obvious to modify the Ohana system such that sentiment is determined for each individual on the communication and then use the cumulative sentiment for subsequent processing as taught in Fink et al for the purpose of being able to ascertain respective sentiments of all parties thus optimizing feedback based on the respective parties sentiments.
Furthermore, Ni et al discloses a system that teaches the concept of a machine learning algorithm training a prediction model that is able to label sentences within a transcript based on whether they are a positive, negative or neutral sentiment (Ni et al, paras 0042-0043: sentiment score generated during the conversation(either via audio phone, chat or email) is displayed on a GUI, para 0052: being able to determine the transition from positive to negative sentences is read as momentum indicating fluctuation of sentiment across two parts of a call…positive and negative parts; whereby a score numerical value can be used to represent the sentiment classification); where a score is obtained for a communication session (Ni et al, para 0053: communication session could be a call conversation), whereby a final score classification (i.e. final sentiment classification) of the communication session can be determined based on the sentiment trend (Ni et al, fig. 3: illustrates the sentiment trend of each communication session; para 0043) throughout the communication session (Ni et al, fig. 3; paras 0056-0059). Ni et al also teaches the concept of where the call sentiment is determined based on the determination of how the customer sentiment subsequently shifts from positive to negative or from negative to positive (second uttered part of second distinct speaker in response to another speaker uttered statement) and what was the driver of the shift of sentiment (e.g. particular statement made by the agent (first uttered part of first/another speaker) (Ni et al, para 0043). It would have been obvious to modify the combined teachings of Ohana and Fink et al such that it incorporates a machine learning algorithm that is able to train a prediction model to label the sentences based on whether they are a positive and negative sentiment thus determining momentum/trend leading to fluctuation between positive, negative and neutral parts of the sentence then scores the communication session according to the trend such that sentiment analysis is accounted for communication between the sentiment driver statement/uttered part by the first speaker and the subsequent sentiment shift responded by the second uttered part of the second/distinct speaker as taught in Ni et al for the purpose of being able to ascertain positive and negative sentiments and their drivers/triggers within the communication sessions.
Re Claim 3, the combined teachings of Ohana, Fink et al and Ni et al disclose the computer-implemented method according to claim 1, wherein the method further comprises: generating, based on utterance sentiment associated with utterances made by a participant to the call, speaker sentiment for the participant (Ohana, para 0045: determining sentiment of each sentence and also determining whether each sentence is relevant to the ongoing call conversation (call sentiment) while the sentence sentiment before relevant sentence to call communication are determined is read as the general utterance).
Claims 4-5 have been analyzed and rejected according to claim 1.
Re Claim 8, the combined teachings of Ohana, Fink et al and Ni et al disclose the computer-implemented method according to claim 1, wherein the method further comprises: receiving call data (Ohana, para 0015: system receives audio call communication, transforms said audio call into transcripts and breaks it down into sentences for further analysis; para 0045), wherein the call data comprises a transcript of the call (Ohana, para 0015: system receives audio call communication, transforms said audio call into transcripts and breaks it down into sentences for further analysis; para 0045); separating the call data into the one or more sentences (Ohana, paras 0015, 0045: call conversations typically include more than one utterances with numerous sentences and multiple words between parties); storing individual sentence sentiments for the one or more sentences (Ohana, para 0033: stored sentences); grouping the one or more sentences into the one or more utterances (Ohana, para 0033: stored sentences); storing individual utterance sentiments for the one or more utterances (Ohana, para 0021: storing callees communication with the caller; para 0033: stored sentences); and storing the call sentiment (Ohana, para 0033: stored sentences includes storing call sentiments).
Claim 10 has been analyzed and rejected according to claim 1.
Claim 12 has been analyzed and rejected according to claim 1.
Claim 14 has been analyzed and rejected according to claim 3.
Claim 15 has been analyzed and rejected according to claim 1.
Claim 16 has been analyzed and rejected according to claim 1.
Claim 17 has been analyzed and rejected according to claims 1 & 8.
Claim 19 has been analyzed and rejected according to claims 1 & 8.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ohana, US Patent Pub. 20190341023 A1, Fink et al, US Patent Pub. 20140188457 A1 and Ni et al, US Patent Pub. 20200089767 A1, as applied to claim 1 above, in view of Williams et al, US Patent 11463587 B1.
Re Claim 6, the combined teachings of Ohana, Fink et al and Ni et al disclose the computer-implemented method according to claim 1, but fail to explicitly disclose wherein the method further comprises: aggregating, based on a predefined set of rules, utterance sentiment associated with the one or more utterances; and the call sentiment is generated based upon the aggregated utterance sentiment. However, Williams et al discloses a system that teaches the concept of utilizing predetermined rules that suggest determining a sentiment of a sentence of call conversations by giving more or less weight depending on a timing of the sentence in within the conversation (e.g. more weight for sentences at the end of the conversation) (Williams et al, col. 3, lines 16-28: the whole of the overall conversation which includes a plurality of sentences together make-up the aggregate). It would have been obvious to modify the Ohana system to include predefined rules that include weighting sentences at the end of a conversation more and creating aggregated sentiment analysis as taught in Williams et al for the purpose of appropriately categorizing different parts of the conversation with proper contextual wight and priorities.
Claim 7 has been analyzed and rejected according to claim 6.
Claims 9, 11, 18 & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ohana, US Patent Pub. 20190341023 A1, Fink et al, US Patent Pub. 20140188457 A1 and Ni et al, US Patent Pub. 20200089767 A1, as applied to claim 8 above, in view of Neervannan et al, US Patent Pub. 11023675 B1.
Re Claim 9, the combined teachings of Ohana, Fink et al and Ni et al disclose the computer-implemented method according to claim 8, but fail to explicitly disclose where the method further comprises: obtaining a selection of part of the call data in response to a query; and providing a sentiment for a part of the call data, wherein the part of the call data is identified based upon the query. However, Neervannan et al discloses a system that teaches the concept of classifying sentence sentiments within a text, audio call conversation, presentation etc as either positive, negative or whatever else, and wherein a user can later search the text, audio call conversation, presentation etc based on the stored sentence sentiment and results presented to a user (Neervannan et al, col. 2, lines 24-51). It would have been obvious to modify the Ohana system such the sentence sentiments can be later searched based on the type of sentiment as taught in Neervannan et al for the purpose of being able to search the desired sentence sentiment when desired.
Claim 11 has been analyzed and rejected according to claim 9.
Claims 18 & 20 have been analyzed and rejected according to claims 1, 8-9.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GEORGE C MONIKANG whose telephone number is (571)270-1190. The examiner can normally be reached Mon. - Fri., 9AM-5PM, ALT. Fridays off.
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/GEORGE C MONIKANG/Primary Examiner, Art Unit 2692 3/26/2026
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