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 .
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.
Claims 1, 3 and 9-12 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20250142138 A1) in view of Wang et al. (US 20220179888 A1).
Regarding claim 1, Zhang teaches a method for providing live streams in a live streaming platform, comprising:
generating a virtual chatbot via a machine learning model (generating a digital human/virtual character via machine learning model – see 0039-0042); and
setting the virtual chatbot in a live streaming room (establishing the virtual character with the second broadcasting style in a live streaming room – see abstract, 0039-0041, 0100).
Zhang lacks to teach determining an emotion of the virtual chatbot and feeding information of the emotion into the machine learning model. Wang discloses determining an emotion of the chatbot and inputting information associated with the emotion of the chatbot into the machine learning model. See 0032, 0065, 0074, 0093. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang by determining an emotion of the virtual chatbot and feeding information of the emotion into the machine learning model as disclosed by Wang to increase effectiveness of providing the relevant and emotional chatbot conversation.
Regarding claim 3, Zhang in combination with Wang teaches that wherein the emotion is determined according to a day in a week; the day in a week includes a week event prompt and a week score; and the week event prompt describes a sentence related to the day in a week and the week score indicates an emotion score of the sentence (determining the second broadcasting style according to time interval – see Zhang: 0056, 0062; determining emotion in a day and determining the emotion score of the sentence – see Wang: FIG. 1D, 11A-11B).
Regarding claim 9, Zhang teaches receiving a comment from a user in the live streaming room; generating a response on the comment via the machine learning model; and transmitting the response to the live streaming room (see FIGs. 2a-2b, 0062, 0066, 0068, 0069, 0076).
Regarding claim 10, Zhang in combination with Wang teaches that wherein the emotion is determined in a day (see Wang: FIG. 1d, 0093).
Regarding claim 11, see rejection of claim 1.
Regarding claim 12, see rejection of claim 1.
Claims 2, 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20250142138 A1) in view of Wang et al. (US 20220179888 A1) and further in view of Silverstein et al. (US 11165725 B1).
Regarding claim 2, Zhang lacks to teach the features as claimed. However, Silverstein
teaches determining emotion information by selecting a message describing one or more emotion categories and emotion levels indicating the scores of the emotion categories. See FIG. 3; col. 9, lines 9-21. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in combination with Wang by randomly selecting a context event prompt and a context score from an event list; and the event list includes one or more context event prompts describing events the virtual chatbot encounters and the event score indicates an emotion score of the event as taught or suggested by Silverstein for the purpose of generating chatbot responses appropriately.
Regarding claim 5, Zhang in combination with Wang teaches determining the emotion score and feeding emotion prompt into the machine learning model (see Wang: FIGs. 1B, 11B and 20A-B, 0088, 0098, 0185, 0193, 0194; Zhang: 0104). Both lack to teach calculating an overall emotion score for the emotion and determining the overall emotion prompt according to the overall emotion score. Silverstein teaches calculating an overall emotion score for the emotional categories and determining the overall emotion prompt according to the overall emotion score. See col. 7, line 65 to col. 8, line 14; col. 8, lines 33-36; col. 8, line 65 to col. 9, line 1; col. 10, lines 22-33 and 55-60. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in combination with Wang by calculating an overall emotion score for the emotional categories and determining the overall emotion prompt according to the overall emotion score as taught by Silverstein for the purpose of tailoring the chatbot response relative to the determined baseline emotive level.
Regarding claim 6, Zhang in combination with Wang teaches determining the emotion score and feeding emotion prompt into the machine learning model (see Wang: FIGs. 1B, 11B and 20A-B, 0088, 0098, 0185, 0193, 0194; Zhang: 0104). Both lack to teach calculating an overall emotion score for the emotion and determining the overall emotion prompt according to the overall emotion score. Silverstein teaches calculating an overall emotion score for the emotional categories and determining the overall emotion prompt according to the overall emotion score. See col. 7, line 65 to col. 8, line 14; col. 8, lines 33-36; col. 8, line 65 to col. 9, line 1; col. 10, lines 22-33 and 55-60. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in combination with Wang by calculating an overall emotion score for the emotional categories and determining the overall emotion prompt according to the overall emotion score as taught by Silverstein for the purposes of tailoring the chatbot response relative to the determined baseline emotive level.
Claims 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20250142138 A1) in view of Wang et al. (US 20220179888 A1) in view of Jin et al. (US 20220375467 A1).
Regarding claim 4, Zhang in view of Wang teaches determining the emotion score of the sentence (see Wang: FIG. 11A-11B) but lacks to teach the features associated with a change in a stock price and a stock event prompt as claimed. However, Jin teaches that AI agent 620 provide information related to a frequently changing price such as stock price to the user. See 0169. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in combination with Wang by using a change in a stock price and a stock event prompt as taught or suggested by Jin to provide the chatbot response accurately using the valid update information.
Regarding claim 8, Zhang in view of Jin teaches that wherein the stock event prompt is generated in response to the change satisfying a specific condition (providing stock information in response to the change of the stock price – see Jin: 0169).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US 20250142138 A1) in view of Wang et al. (US 20220179888 A1) and Jin et al. (US 20220375467 A1) and further in view of Silverstein et al. (US 11165725 B1).
Regarding claim 7, Zhang in combination with Wang teaches determining the emotion score and feeding emotion prompt into the machine learning model (see Wang: FIGs. 1B, 11B and 20A-B, 0088, 0098, 0185, 0193, 0194; Zhang: 0104). Both lack to teach calculating an overall emotion score for the emotion and determining the overall emotion prompt according to the overall emotion score. Silverstein teaches calculating an overall emotion score for the emotional categories and determining the overall emotion prompt according to the overall emotion score. See col. 7, line 65 to col. 8, line 14; col. 8, lines 33-36; col. 8, line 65 to col. 9, line 1; col. 10, lines 22-33 and 55-60. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in combination with Wang and Jin by calculating an overall emotion score for the emotional categories and determining the overall emotion prompt according to the overall emotion score as taught by Silverstein for the purposes of tailoring the chatbot response relative to the determined baseline emotive level.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wu (US 11580350 B2) teaches systems and methods for emotionally intelligent automated chatting.
Wu et al. (US 20200383623 A1) disclose method and apparatus for providing emotional care in a session between a user and an electronic conversational agent.
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/NGOC K VU/ Primary Examiner, Art Unit 2421