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, including the fee set forth in 37 CFR1.17(e), was filed in this application after final rejection. Since this application is eligiblefor 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 withdrawnpursuant to 37 CFR 1.114. Applicant's submission filed on 04/24/2026 has been entered.
Status of the Claims
Claims 1-3, 6-12, 14, 16-20, and 22 are pending.
Response to Applicant’s Arguments
In response to “Applicant respectfully submits that, in contrast to the express intent of Dawson, to improve a music listening experience of a group of people in a location currently amended claim 1 requires that "one or more first sentiment metrics" be determined for a single individual, based on "one or more speaker features" extracted from the audio signal for that single individual, for use in determining and outputting a speaker representation of that single individual, "the caller"”.
According to the Supreme Court, if a technique has been used to improve one device, and a person of ordinary skill in the art would recognize that it would improve similar device in the same way, using the technique is obvious unless its actual application is beyond his/her skill. KSR Int’l. Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007).
The combination does not seek to adopt a music delivery system to delivery music to a group of participants.
Rather, Dawson taught a well known sentiment identifier that captures paralanguage sentiments from audio such as a speaker’s voice quality, rate, pitch, volume, mood, speaking style, rhythm, intonation, and stress (¶51). According to Dawson, the technique to capture these paralanguage sentiments has been applied to obtain data representative of a sentiment or emotional state of participants (¶51).
One ordinarily skilled in the art before the effective filing date of the invention would look to Dawson to adopt its sentiment identifier to predictable determine a sentiment or emotional state of participants in a voice call because the sentiment identifier can capture verbal sentiments interpreted from a transcript of participant’s spoken words as well as from paralanguage sentiments (¶51).
In response to “Applicant further respectfully submits that the skilled person would not seek to combine Dawson with Chen as proposed in the Advisory Action and Office Action, by modifying Dawson to extract a "speaker feature" and determine a "sentiment metric" for "the caller", as required by currently amended claim 1, because Dawson teaches away from such modification (see MPEP 2145.X.D), by identifying as deficient those prior art configurations”.
An inference of nonobviousness is especially strong where the prior art’s teachings undermine the very reason being proffered as to why a person of ordinary skill would have combined the known elements. Depuy Spine, Inc. v. Medtronic Sofamor Danek, Inc., 567 F.3d 1314, 1326, 90 USPQ2d 1865, 1873 (Fed. Cir. 2009).
Here, the rationale (i.e., purpose) to combine Dawson is to adopt its sentiment identifier to determine sentiment and emotional state of participants in an activity (¶51). If a base reference implements a sentiment identifier to determine the sentiment and emotional state of individual participant in an activity, then one ordinarily skilled in the art would look to Dawson’s sentiment identifier because Dawson would predictable yield data indicative of each participant’s sentiment and emotional state.
Applicant’s citation to Dawson regarding deficiencies for delivering selection of music to a group of people does not undermine the rationale to adopt the sentiment identifier because the combination does not sought to deliver music according to group preferences of participants and nowhere in Dawson limits the sentiment identifier 56 to groups of participants such that the sentiment identifier 56 cannot be used (i.e., rendered unsatisfactory) to determine the sentiment / emotional state of individual participants.1
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made 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 1-3, 10-11, 14, 16-17, 19-20, and 22 are rejected under 35 USC 103(a) as being unpatentable over Costello et al. (US 2018/0122368 A1) in view of Dawson et al. (US 2020/0159487 A1).
Regarding Claims 1 and 14, Costello discloses electronic device (Fig. 2 and see ¶27, device 240 being an example of smartphone 140) comprising a processor (¶24, smartphone 140 includes processing circuit; ¶27, device 240 with neuromorphic processor), a memory (¶24, smartphone 140 having internal memory), and an interface (¶27, network connection for device 240 / smartphone 140), wherein the processor is configured to:
obtain one or more audio signals including a first audio signal (¶25 and ¶30, receive audio and video data in a multiparty communication), wherein the first audio signal is obtained from a call or conversation between a user of the electronic device and a caller, wherein the user is an agent (¶¶37-38, in a call center, guiding a user / speaker based on analyzing speech of the customer to make recommendations to the user / speaker);
determine, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶25 and ¶32, real-time speech recognition capabilities), one or more first sentiment metrics indicative of a first speaker state based on the first audio signal, the one or more first sentiment metrics including a first primary sentiment metric indicative of a primary sentiment state of the first speaker (¶30, provide pattern analysis of the audio and the video to produce analytics for determining sentiments of each speaker of the communication), wherein the first speaker is the caller (¶38, analyzing the speech of the customer), wherein to determine one or more first sentiment metrics comprises to extract one or more speaker features from the first audio signal (¶34, neuromorphic processor utilizing machine learning and deep learning to recognize participant’s sentiments through speech analysis and to contextualize specific sentiments with conversation parameters including but not limited to keywords, key phrases, and specific conversation phrases);
determine, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶25 and ¶32, real-time speech recognition capabilities), one or more first appearance metrics indicative of an appearance of the first speaker based on the first audio signal, the one or more first appearance metrics including a first primary appearance metric indicative of a primary appearance of the first speaker, wherein to determine one or more first appearance metrics indicative of an appearance of the first speaker comprises to extract one or more speaker appearance features from the first audio signal (¶34, apply machine learning and deep learning to recognize participants’ sentiments through speech analysis and to contextualize the detected sentiments with the content of the conversation on the multiparty communication), and wherein the one or more speaker appearance features comprise one or more of: a speaker tone feature (¶34, contextualize specific sentiments with conversation parameters including conversation tone to provide real-time insights into the emotional state of other participants; i.e., ¶38, providing a call center speaker with guidance based on caller’s sentiments and mental state), a speaker intonation feature, a speaker power feature, a speaker pitch feature, a speaker voice quality feature, a speaker rate feature, an acoustic feature, and a speaker spectral band energy feature;
determine, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶23 and ¶34, provide conference participants with real-time insights into the emotional state of other participants), a first speaker representation based on the first primary sentiment metric and the first primary appearance metric (¶21, determine the sentiments of one or more participants based on the analytics and generate a visual representation of the sentiments; ¶32 and Fig. 3, visualization program 285 generates icon 316a-316c representing facial expression of individual); and
output, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶23 and ¶34, provide conference participants with real-time insights into the emotional state of other participants), via the interface, the first speaker representation (¶21 and ¶36, display the sentiments during the course of the communication; Fig. 3 and ¶¶30-31, generate a graphical representation of the sentiments and display the representation on a device GUI), wherein the first speaker representation corresponds to the caller (¶38, call center implementation providing guidance to a speaker based on analyzing the speech of the customer).
Costello does not disclose wherein the one or more speaker features comprise paralinguistic features, and wherein the one or more first sentiment metrics are based on the paralinguistic features.
Dawson teaches an electronic device comprising a processor, a memory, and an interface (¶21, computer system 12 comprising processor 16, memory 28, interface 22 such as a hand held or laptop device) configured to implement a sentiment identifier using vocal recognition techniques to analyze an audio feed from audio sensor to extract one or more paralinguistic speaker features from the audio feed and determine one or more sentiment metrics based on the one or more paralinguistic speaker features (¶53, sentiment identifier 56 uses vocal recognition techniques to analyze audio feed to identify paralanguage elements expressed by participants and to assign element values indicative of a participant’s mood).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to determine the one or more first sentiment metrics based on extracting paralinguistic features by weighing particular words or phrases spoken by the caller (Costello, ¶34, conversation parameters comprising keywords and key phrases for contextualizing sentiments) based on recognized paralanguage behavior accompanying a word or phrase (Dawson, ¶53) in order to use vocal recognition techniques on the first audio signal to identify paralanguage elements expressed by the caller and to assign indications of the caller’s mood (Dawson, ¶53).
Regarding Claim 2, Costello discloses wherein the one or more first sentiment metrics includes a first secondary sentiment metric indicative of a secondary sentiment state of the first speaker (Costello ¶34, apply machine learning speech analysis and to contextualize specific sentiments with conversation parameters to recognize participants’ sentiments; compare Dawsob ¶51, sentiment identifier 56 captures paralanguage sentiments such as mood, speaking style, rhythm, stress).
Regarding Claim 3, Costello discloses wherein the one or more first appearance metrics includes a first secondary appearance metric indicative of a secondary appearance of the first speaker (¶34, conversation parameters not limited to conversation tone).
Regarding Claim 10, Costello discloses wherein obtaining one or more audio signals comprises obtaining a second audio signal (¶30, receive audio and video data, perform pattern analysis of the audio and video data to produce analytics, utilize the analytics to determine the sentiment of each speaker in a multiparty communications session; i.e., this requires obtaining audio data of at least a second speaker); the method comprising:
determining one or more second sentiment metrics indicative of a second speaker state based on the second audio signal, the one or more second sentiment metrics including a second primary sentiment metric indicative of a primary sentiment state of a second speaker (¶34, apply machine learning and deep learning to recognize participants’ sentiments through speech analysis and to contextualize the detected sentiments with the content of the conversation on the multiparty communication);
obtaining one or more second appearance metrics indicative of an appearance of the second speaker, the one or more second appearance metrics including a second primary appearance metric indicative of a primary appearance of the second speaker (¶34, apply machine learning and deep learning to recognize participants’ sentiments through speech analysis and to contextualize the detected sentiments with the content of the conversation on the multiparty communication);
determining a second speaker representation based on the second primary sentiment metric and the second appearance metric (¶21, determine the sentiments of one or more participants based on the analytics and generate a visual representation of the sentiments; ¶32 and Fig. 3, visualization program 285 generates icon 316a-316c representing facial expression of individual); and
outputting, via the interface of the electronic device, the second speaker representation (¶21 and ¶36, display the sentiments during the course of the communication; Fig. 3 and ¶¶30-31, generate a graphical representation of the sentiments and display the representation on a device GUI).
Regarding Claim 11, Costello discloses wherein the second speaker representation is an agent representation (¶30, determine sentiments of each speaker; ¶38, the speaker at the call center de-escalating a complaint from caller / customer).
Regarding Claim 16, Costello discloses wherein the electronic device is selected from the group consisting of a mobile phone, a laptop computer, and a table computer (¶24, smartphone 140).
Regarding Claim 17, Costello discloses wherein the interface comprises a display (¶30, device 240 GUI).
Regarding Claim 19, Costello discloses wherein to obtain the one or more audio signals comprises to generate the one or more audio signals (¶42, receive communication data comprising audio data generated by a multiparty communication).
Regarding Claim 20, Costello discloses system (Fig. 7) comprising:
a server device (Fig. 7, cloud computing node 10 being server 12 of Fig. 6); and
an electronic device in communication with the server device (Fig. 7, computers 54 communicating with cloud computing node 10, computer 54 being cellular telephone 54 corresponding to the smartphone 140 / device 240 described in ¶24 and ¶27), the electronic device comprising a processor, a memory, and an interface (¶24 and ¶27, e.g., smartphone 140 including processing circuit (e.g., an example device 240 with neuromorphic processor) executing program code stored on an internal memory with network connection), wherein the processor is configured to:
obtain one or more audio signals including a first audio signal (¶25 and ¶30, receive audio and video data in a multiparty communication), wherein the first audio signal is obtained from a call or conversation between a user of the electronic device and a caller, wherein the user is an agent (¶¶37-38, in a call center, guiding a user / speaker based on analyzing speech of the customer to make recommendations to the user / speaker);
determine, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶25 and ¶32, real-time speech recognition capabilities), one or more first sentiment metrics indicative of a first speaker state based on the first audio signal, the one or more first sentiment metrics including a first primary sentiment metric indicative of a primary sentiment state of the first speaker (¶34, apply machine learning and deep learning to recognize participants’ sentiments through speech analysis and to contextualize the detected sentiments with the content of the conversation on the multiparty communication), wherein the first speaker is the caller (¶38, analyzing the speech of the customer), wherein to determine one or more first sentiment metrics comprises to extract one or more speaker features from the first audio signal (¶34, neuromorphic processor utilizing machine learning and deep learning to recognize participant’s sentiments through speech analysis and to contextualize specific sentiments with conversation parameters including but not limited to keywords, key phrases, and specific conversation phrases);
determine, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶25 and ¶32, real-time speech recognition capabilities), one or more first appearance metrics indicative of an appearance of the first speaker based on the first audio signal, the one or more first appearance metrics including a first primary appearance metric indicative of a primary appearance of the first speaker, wherein to determine one or more first appearance metrics indicative of an appearance of the first speaker comprises to extract one or more speaker appearance features from the first audio signal (¶34, apply machine learning and deep learning to recognize participants’ sentiments through speech analysis and to contextualize the detected sentiments with the content of the conversation on the multiparty communication), and wherein the one or more speaker appearance features comprise one or more of: a speaker tone feature (¶34, contextualize specific sentiments with conversation parameters including conversation tone to provide real-time insights into the emotional state of other participants; i.e., ¶38, providing a call center speaker with guidance based on caller’s sentiments and mental state), a speaker intonation feature, a speaker power feature, a speaker pitch feature, a speaker voice quality feature, a speaker rate feature, a linguistic feature, an acoustic feature, and a speaker spectral band energy feature;
determine, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶23 and ¶34, provide conference participants with real-time insights into the emotional state of other participants), a first speaker representation based on the first primary sentiment metric and the first primary appearance metric (¶21, determine the sentiments of one or more participants based on the analytics and generate a visual representation of the sentiments; ¶32 and Fig. 3, visualization program 285 generates icon 316a-316c representing facial expression of individual); and
output, substantially in real time with a delay of less than 10 seconds (¶22, “real time” means within a portion of a second or within a few seconds; ¶23 and ¶34, provide conference participants with real-time insights into the emotional state of other participants), via the interface, the first speaker representation (¶21 and ¶36, display the sentiments during the course of the communication; Fig. 3 and ¶¶30-31, generate a graphical representation of the sentiments and display the representation on a device GUI), wherein the first speaker representation corresponds to the caller (¶38, call center implementation providing guidance to a speaker based on analyzing the speech of the customer).
Costello does not disclose wherein the one or more speaker features comprise paralinguistic features, and wherein the one or more first sentiment metrics are based on the paralinguistic features.
Dawson teaches an electronic device comprising a processor, a memory, and an interface (¶21, computer system 12 comprising processor 16, memory 28, interface 22 such as a hand held or laptop device) configured to implement a sentiment identifier using vocal recognition techniques to analyze an audio feed from audio sensor to extract one or more paralinguistic speaker features from the audio feed and determine one or more sentiment metrics based on the one or more paralinguistic speaker features (¶53, sentiment identifier 56 uses vocal recognition techniques to analyze audio feed to identify paralanguage elements expressed by participants and to assign element values indicative of a participant’s mood).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to determine the one or more first sentiment metrics based on extracting paralinguistic features by weighing particular words or phrases spoken by the caller (Costello, ¶34, conversation parameters comprising keywords and key phrases for contextualizing sentiments) based on recognized paralanguage behavior accompanying a word or phrase (Dawson, ¶53) in order to use vocal recognition techniques on the first audio signal to identify paralanguage elements expressed by the caller and to assign indications of the caller’s mood (Dawson, ¶53).
Regarding Claim 22, Costello discloses wherein the server device is a cloud server (¶44, cloud computing node 10).
Claims 6-9 and 18 are rejected under 35 USC 103(a) as being unpatentable over Costello et al. (US 2018/0122368 A1) and Dawson et al. (US 2020/0159487 A1) as applied to claims 1 and 14, in view of Chen et al. (WO 2017092194 A1, see IP.com translation).
Regarding claims 6-7 and 18, Costello does not disclose wherein determining the first speaker representation comprises determining a first primary feature of a first avatar based on the first primary sentiment metric, and wherein the first speaker representation comprises the first avatar.
Chen discloses electronic device comprising a processor, a memory (p. 8, “Preferably, the device further comprises a storage unit…”), and an interface (p. 7, “an apparatus for animating a communication interface during communication, the apparatus being located in the terminal device of both users of the communication relationship”) configured to:
obtain audio signals from a call or conversation between a user of the electronic device and a caller (p. 9, “Step 11: Receive content generated by the user based on the communication during the communication. In this step, first, a communication relationship is established between the users, and the communication relationship may be established by performing a dial-up call or by using an instant messaging software for voice, text, or video communication”; p. 10, “if the user communicates using a voice call, the voice recognition unit in the device identifies the voice content generated by the user during the call”),
determine one or more first sentiment metrics indicative of a first speaker state based on the first audio signal, the one or more first sentiment metrics including a first primary sentiment metric indicative of a primary sentiment state of the first speaker (p. 10, “if the user communicates using a voice call, the voice recognition unit in the device identifies the voice content generated by the user during the call, and parses the semantics of the voice content, and then according to the semantics The corresponding state change instruction is generated”; e.g., p. 11, “the avatar on the communication interface changes the mouth shape, motion, shape, dress, body type, sound and/or expression of the avatar according to the state change instruction, for example, the user said during the voice call: " I am so happy. The expression of the avatar corresponding to the user on the interface starts to become a laugh according to the content of the call”; i.e., determine user’s sentiment is “happy” based on semantic analysis of the voice content),
determine one or more first appearance metrics indicative of an appearance of the first speaker based on the first audio signal, the one or more first appearance metrics including a first primary appearance metric indicative of a primary appearance of the first speaker (p. 11, “The first way is: the avatar on the communication interface changes the mouth shape, motion, shape, dress, body type, sound and/or expression of the avatar according to the state change instruction…” and “For example, the user said in the process of voice call: "Now I suddenly feel It’s so cold. The user’s avatar dress changes on the interface. Maybe the avatar was originally dressed in summer clothes, and now it’s thick clothes in winter”; i.e., determine user’s avatar appearance with thick winter cloth based on semantic analysis of the voice content);
determine, during the conversation, a first speaker representation based on the first primary sentiment metric and the first primary appearance metric (p. 7, “Preferably, the modifying the state of the user avatar on the communication interface according to the state change instruction comprises: The state of the mouth shape, motion, shape, dress, body type, sound, and/or expression of the user avatar is modified according to the state change command” and “The user avatar and/or virtual scene is an avatar and/or a virtual scene created based on the real image of the user and/or the real scene in which the user is located”);
wherein determining the first speaker representation comprises determining a first primary feature of a first avatar based on the first primary sentiment metric (p. 7, “Preferably, the user avatar and/or the virtual scene on the communication interface comprises an avatar and/or a virtual scene of the communication parties, the communication content being the communication content of the first party of the communication parties, according to the state The change instruction modifies the state of the user avatar and/or the virtual scene on the communication interface to cause the communication interface to produce an animation effect”), and wherein the first speaker representation comprises the first avatar (p. 7, “And changing a state of the first party's avatar and/or the virtual scene on the communication interface according to the state change instruction”);
wherein the first primary feature is selected from a mouth feature (p. 7, “The state of the mouth shape, motion, shape, dress, body type, sound, and/or expression of the user avatar is modified according to the state change command”), an eye feature (Fig. 5-2, relevant animation), a nose feature (Fig. 5-2, relevant animation), a forehead feature (Fig. 5-2, relevant animation), an eyebrow feature (Fig. 5-2, relevant animation), a hair feature (Fig. 5-2, relevant animation), an ear feature (Fig. 5-2, relevant animation), a beard feature, a gender feature (Fig. 5-2, relevant animation), a cheek feature (Fig. 5-2, relevant animation), an accessory feature (p. 11, “For example, the user said in the process of voice call: "Now I suddenly feel It’s so cold. The user’s avatar dress changes on the interface. Maybe the avatar was originally dressed in summer clothes, and now it’s thick clothes in winter”), a skin feature (Fig. 5-2, relevant animation), a body feature (p. 7, “The state of the mouth shape, motion, shape, dress, body type, sound, and/or expression of the user avatar is modified according to the state change command”), and a head dimension feature (Fig. 5-2, relevant animation).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to determine the first speaker representation comprising a first avatar with a first primary feature based on the first primary sentiment metric in order to enable a corresponding animation effect according to content of communication between users (Chen, Abstract).
Regarding Claims 8-9, Costello does not disclose wherein determining the first speaker representation comprises determining a first secondary feature of a first avatar based on the first primary appearance metric.
Chen discloses wherein determining the first speaker representation comprises determining a first secondary feature of a first avatar based on the first primary appearance metric (pp. 11-12, “For example, the user said in the process of voice call: "Now I suddenly feel It’s so cold. The user’s avatar dress changes on the interface. Maybe the avatar was originally dressed in summer clothes, and now it’s thick clothes in winter. The avatar action may become shivering”);
wherein the first secondary feature is different from a first primary feature of the first avatar (pp. 11-12, “For example, the user said in the process of voice call: "Now I suddenly feel It’s so cold. The user’s avatar dress changes on the interface. Maybe the avatar was originally dressed in summer clothes, and now it’s thick clothes in winter. The avatar action may become shivering”), wherein the first primary feature is based on the first primary sentiment metric (p. 11, “for example, the user said during the voice call: "I am so happy. The expression of the avatar corresponding to the user on the interface starts to become a laugh according to the content of the call”), and wherein the first secondary feature is selected from a mouth feature (p. 7, “The state of the mouth shape, motion, shape, dress, body type, sound, and/or expression of the user avatar is modified according to the state change command”), an eye feature (Fig. 5-2, relevant animation), a nose feature (Fig. 5-2, relevant animation), a forehead feature (Fig. 5-2, relevant animation), an eyebrow feature (Fig. 5-2, relevant animation), a hair feature (Fig. 5-2, relevant animation), an ear feature (Fig. 5-2, relevant animation), a beard feature, a gender feature (Fig. 5-2, relevant animation), a cheek feature (Fig. 5-2, relevant animation), an accessory feature (p. 7, “The state of the mouth shape, motion, shape, dress, body type, sound, and/or expression of the user avatar is modified according to the state change command”), a skin feature (Fig. 5-2, relevant animation), a body feature (p. 7, “The state of the mouth shape, motion, shape, dress, body type, sound, and/or expression of the user avatar is modified according to the state change command”), and a head dimension feature (Fig. 5-2, relevant animation).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to determine the first speaker representation comprises determining a first secondary feature of a first avatar based on the first primary appearance metric in order to enable a corresponding animation effect according to content of communication between users (Chen, Abstract).
Claim 12 is rejected under 35 USC 103(a) as being unpatentable over Costello et al. (US 2018/0122368 A1) and Dawson et al. (US 2020/0159487 A1) as applied to claim 1, in view of Kawakami et al. (US 2022/0232191 A1).
Regarding Claim 12, Costello does not disclose detecting a termination of speech, and in accordance with detecting the termination of speech, storing a speaker record in the memory and/or transmitting a speaker record to a server device of the system, the speaker record comprising a first speaker record indicative of one or more of first appearance metric data and first sentiment metric data of the first speaker.
Kawakami teaches a system generating avatar / speaker record for users engaging in conversation in virtual space (¶41 and ¶43), detecting a termination of speech / conversation (¶48, a trigger generator 162 generates a conversation end trigger corresponding to an end of started conversation by a user), and storing a speaker record / avatar data corresponding to a speaking user in a memory and transmitting the speaker record to a server device of the system (¶64, avatar arrangement information includes a display mode of the avatar; ¶69, in response to detection of conversation end trigger, avatar controller 333a updates avatar arrangement information and stores the arrangement data in storage 32; per Fig. 4 and ¶58, storage 32 being at a content distribution server 3).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to detect a termination of speech and storing a speaker record indicative of one or more of first appearance metric data and first sentiment metric data of the first speaker (compare Costello, Fig. 3 and ¶32, display mode / icon arrangement information includes facial expression icons and sentiments of the participants) in memory / transmitting the speaker record to server / cloud computing node 10 in accordance with detecting the termination of speech in order to faithfully reflect the operations of users (Kawakami ¶69).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 05/01/2026
1 Although not relevant to the rationale to combine, Dawson et al. (US 2020/0159487 A1) at ¶48 determines cognitive music preferences profiles 210A-N for each participant 110N, which means Dawson does determine preferences of each individual participants.
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