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 .
Priority
Receipt is acknowledged or paper submitted under 35 U.S.C. 119(a)-(d), which papers have been places of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/10/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings were submitted on 03/11/2025. These drawings are reviewed and accepted by the examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kawamura (US 20190248001 A1).
Regarding claims 1, 10, and 16, Kawamura teaches:
“acquire sensor data from a sensor, wherein the sensor is on an agent apparatus” (par. 0035; ‘For example, the information about the user of the robot 100 includes information such as information that the user has approached the robot 100 acquired from the human sensors 131, information that the user gazed at the robot 100 acquired from the camera 132, information that the user spoke to the robot 100 acquired from the microphones 133, and information that the user picked up the robot 100 acquired from the acceleration sensor or the tactile sensor.’);
“recognize a user based on the sensor data” (par. 0035; ‘For example, the information about the user of the robot 100 includes information such as information that the user has approached the robot 100 acquired from the human sensors 131, information that the user gazed at the robot 100 acquired from the camera 132, information that the user spoke to the robot 100 acquired from the microphones 133, and information that the user picked up the robot 100 acquired from the acceleration sensor or the tactile sensor.’);
“acquire related information associated with the recognized user via a network” (par. 0047, user profile, user situation; ‘The conversation output server 300 includes, as functional constituents, a LTE communicator 310, a scenario selector 321 (controller 320), a scenario storage 331 (storage 330), a user profile storage 332 (storage 330), and a user situation storage 333 (storage 330).’);
“generate interaction data based on the acquired related information” (par. 0059; ‘The controller 320 selects, on the basis of the information about the user stored in the user profile storage 332 and the situation information stored in the user situation storage 333, the scenario data stored in the scenario storage 331 to generate conversation data for creating an impression on the user that the robot 100 is having a conversation corresponding to the first information.’);
“store the related information in association with the recognized user” (par. 0059; scenario storage); and
“control the agent apparatus based on the interaction data” (par. 0059; ‘The controller 320 selects, on the basis of the information about the user stored in the user profile storage 332 and the situation information stored in the user situation storage 333, the scenario data stored in the scenario storage 331 to generate conversation data for creating an impression on the user that the robot 100 is having a conversation corresponding to the first information.’).
Regarding claim 2 (dep. on claim 1), Kawamura further teaches:
“wherein the related information is associated with people and animals of the user” (par. 0038; ‘The robot 100 can output electronic sounds or sounds that imitate animal sounds from the speaker 134.’).
Regarding claims 3 (dep. on claim 1) and 11 (dep. on claim 10), Kawamura further teaches:
“determine at least one of a state of the agent apparatus or a situation of the agent apparatus” (par. 0050; ‘As described later, when selecting the scenario data for creating an impression on the user that the robot 100 and the robot 100′ are conversing, the scenario selector 321 uses the situation information of the robot 100′ in addition to the situation information of the robot 100.’); and
“generate the interaction data based on at least one of the state of the agent apparatus or the situation of the agent apparatus” (par. 0050; ‘To distinguish between these pieces of situation information, the situation information of the robot 100 is referred to as “first situation information” and the situation information of the robot 100′ is referred to as “second situation information.”’).
Regarding claims 4 (dep. on claim 1) and 12 (dep. on claim 10), Kawamura further teaches:
“generate episode data based on the acquired related information; and store the generated episode data that corresponds to the user” (par. 0053; ‘In FIG. 3, the scenario data (the utterance content of the robot 100 and the corresponding response content of the robot 100′) is defined depending on an intimacy level of the robot 100 and an intimacy level of the robot 100′.’).
Regarding claims 5 (dep. on claim 1) and 13 (dep. on claim 10), Kawamura further teaches:
“extract topic information based on utterance data that is associated with the user; and generate the interaction data based on the extracted topic information” (par. 0055; ‘The middle table in FIG. 3 is an example of scenario data for a case in which the topic of conversation is the weather.’).
Regarding claims 6 (dep. on claim 5) and 14 (dep. on claim 13), Kawamura further teaches:
“acquire the related information based on the extracted topic information” (par. 0055; ‘In this example, when the weather included in the situation information of the robot 100 (the first situation information) is sunny and the weather included in the situation information of the robot 100′ (the second situation information) is rain, the utterance content of the robot 100 is defined as, “Today is nice and sunny!”, and the response content of the robot 100′ is defined as “That's nice. It's raining and there is nothing to do here.”’).
Regarding claims 7 (dep. on claim 1) and 15 (dep. on claim 10), Kawamura further teaches:
“generate the interaction data based on a character of the agent apparatus” (par. 0098; ‘In step S311, the robot 100 whose ID is GladDog and the robot 100′ whose ID is HappyCat are selected as conversation partners each other.’).
Regarding claim 8 (dep. on claim 1), Kawamura further teaches:
“wherein the agent apparatus is one of an autonomous mobile body, a smartphone, a tablet terminal, a game device, a home speaker, a home electrical appliance, or an automobile” (par. 0033; ‘The robot 100 is a pet-type robot that has an endearing shape, and includes a situation acquirer 110 and a short-range communicator 12.’).
Regarding claim 9 (dep. on claim 4), Kawamura further teaches:
“store the episode data in a storage apparatus that is external to the circuitry” (par. 0047; ‘The conversation output server 300 includes, as functional constituents, a LTE communicator 310, a scenario selector 321 (controller 320), a scenario storage 331 (storage 330), a user profile storage 332 (storage 330), and a user situation storage 333 (storage 330).’).
Conclusion
Other pertinent prior art are cited in the PTO-892 for the applicant's consideration.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK VILLENA whose telephone number is (571)270-3191. The examiner can normally be reached 10 am - 6pm EST Monday through Friday.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/ Examiner, Art Unit 2658
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