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 .
Status of Claims
This is in response to applicant’s filing date of March 25, 2025, filed with preliminary amendment that amended claims 1-8 and 10-14 and cancelled claim 15. Claims 1-14 are currently pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority to Application JP2022-156758, filed on September 29, 2022. The certified copy of the application as required by 37 CFR 1.55 has been received.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/28/2026;4/20/2026;4/15/2026; and 3/25/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority Prior-Filed Application
Applicant’s claim for the benefit of a prior-filed application, PCT/JP2023/035067 filed on 9/27/2023, under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections -- 35 U.S.C. § 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.
Claims 1-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kaname HAYASHI (US-20190077021-A1)(“Hayashi”), provided by applicant in the IDS filed on 3/25/2025.
As per claim 1, Hayashi discloses a robot (Figures 1A-1B)comprising:
a controller including at least one circuit configured (Hayashi at Figure 4, processor 122, and Figure 5, data processor 136, and Para. [0071] discloses that the processors can perform certain functions as determined by an external stimulus and instructions in local storage/memory:” processor 122 selects an action of the robot 100 while communicating with the server 200 or the external sensor 114 via the communicator 126. Various kinds of external information obtained by the internal sensor 128 also affect the action selection. The drive mechanism 120 mainly controls the wheel 102 and the arm 106. The drive mechanism 120 changes a direction of movement and a movement speed of the robot 100 by changing the rotational speed and the direction of rotation of each of the two wheels 102. “) to:
acquire at least one of a captured image of a user or biological information of the user (Hayashi at Para. [0105] discloses using captured image and other sensors to recognize a user/person:” recognizing unit 156 analyzes external information obtained from the internal sensor 128. The recognizing unit 156 is capable of visual recognition (a visual unit), smell recognition (an olfactory unit), sound recognition (an aural unit), and tactile recognition (a tactile unit).”); and
instruct to perform a predetermined action that induces interaction with the user in accordance with a state of the user based on the at least one of the captured image or the biological information (Hayashi at Figure 5, operation determination 150, Figure 7, flow process specific action S26, and Para. [0104] disclosing as an example instructing the robot perform certain actions when encountering a familiar user:” action determining unit 140 can also perform a gesture of holding up both arms 106 as a gesture asking for “a hug” when a user with a high degree of familiarity is nearby, and can also perform a gesture of no longer wanting to be hugged by causing the wheel 102 to rotate in reverse in a housed state when bored of the “hug”. The action drive unit 146 causes the robot 100 to perform various gestures by driving the wheel 102 and the arm 106 in accordance with an instruction from the action determining unit 140.”).
As per claim 2, Hayashi discloses a robot as claimed in claim 1,wherein the controller is configured to estimate the predetermined action suitable for the state of the user (Hayashi at Figure 5, data processing 136, and Para. [0100] disclosing recognizing and action to be taken by the robot:” data processing unit 136 includes a recognizing unit 156 and an operation determining unit 150.”);
observe the state of the user based on the at least one of the captured image or the biological information (Hayashi at Para. [0143] disclosing recognizing the physical condition of the user:” robot 100 strongly expresses a feeling of affection by approaching a user (hereafter called an approaching action), and performing an affectionate gesture defined in advance as a gesture indicating goodwill toward a person.”); and
determine the predetermined action suitable for the state of the user based on a value of the predetermined action (Hayashi at Paras. [0151]-[0153] disclosing action that can be taken by the robot:” when the user A, who is a visitor, visits frequently, and speaks to and touches the robot 100, familiarity of the robot 100 toward the user A gradually rises, and the robot 100 ceases to perform an action of shyness (a withdrawing action) with respect to the user A. The user A can also feel affection toward the robot 100 by perceiving that the robot 100 has become accustomed to the user A.”).
As per claim 3, Hayashi discloses a robot as claimed in claim 1, wherein the captured image includes at least one of a face image or a full-body image of the user (Hayashi at Para. [0089] discloses a person recognizing unit 214 and physical characteristics:” person recognizing unit 214 recognizes a person from an image filmed by the camera incorporated in the robot 100, and extracts the physical characteristics and the behavioral characteristics of the person.”), and wherein the biological information includes information on at least one of a heartbeat, respiration, a blood pressure, or a body temperature of the user (Hayashi at Para. [0098] discloses determining at least body temperature:” internal sensor 128 includes a body temperature detection unit 152. The body temperature detection unit 152 measures a body temperature of a user. The body temperature detection unit 152 includes a remote detection unit 154 and a proximity detection unit 158. The remote detection unit 154 is a non-contact temperature sensor such as a radiation thermometer or thermography, and can measure the body temperature of a user, even from afar, by measuring radiant heat of the user. The proximity detection unit 158 is a contact temperature sensor such as a thermistor, bimetal, or a glass thermometer, and can measure body temperature more accurately than the remote detection unit 154 by coming into direct contact with a user.”).
As per claim 4, Hayashi discloses a robot as claimed in claim 1 , wherein the state of the user includes an emotional state of the user classified based on at least one of a face image of the user or information on at least one of a heartbeat, respiration, a blood pressure, or a body temperature of the user (Hayashi at Para. [0242] discloses determining the condition such a user’s mood:” classifies the physical condition of the user into a multiple of categories such as “in good condition”, ‘in bad condition”, “has a fever”, “feeling lethargic”, and “in a bad mood”. For example, the physical condition determining unit 226 may track the behavior of a user P1, and determine a time for which the user P1 is asleep by detecting a time at which the user P1 goes to bed and a time at which the user P1 gets up.”).
As per claim 5, Hayashi discloses a robot as claimed in claim 1, wherein the state of the user includes an action state of the user classified based on a motion of a skeleton estimated from a full-body image of the user (Hayashi at Para. [0118] discloses acquiring certain motion and physical characteristics of a user:” the robot 100 regularly carries out image capturing, and the person recognizing unit 214 recognizes a moving object from the images, and extracts characteristics of the moving object. When a moving object is detected, physical characteristics and behavioral characteristics are also extracted from the smell sensor, the incorporated highly directional microphone, the temperature sensor, and the like. For example, when a moving object appears in an image, various characteristics are extracted, such as having a beard, being active early in the morning, wearing red clothing, smelling of perfume, having a loud voice, wearing spectacles, wearing a skirt, having gray hair, being tall, being plump, being suntanned, or being on a sofa.”).
As per claim 6, Hayashi discloses a robot as claimed in claim 1 , wherein the state of the user is a predetermined state classified based on a combination of an emotion and an action of the user estimated from the at least one of the captured image or the biological information (Hayashi at Figure 5, operation determination 150, Figure 7, flow process specific action S26, and Para. [0104] disclosing as an example instructing the robot perform certain actions when encountering a familiar user:” action determining unit 140 can also perform a gesture of holding up both arms 106 as a gesture asking for “a hug” when a user with a high degree of familiarity is nearby, and can also perform a gesture of no longer wanting to be hugged by causing the wheel 102 to rotate in reverse in a housed state when bored of the “hug”. The action drive unit 146 causes the robot 100 to perform various gestures by driving the wheel 102 and the arm 106 in accordance with an instruction from the action determining unit 140.”).
As per claim 7, Hayashi discloses a robot as claimed in claim 2, wherein the controller is configured to generate a learning model by machine learning, the learning model being configured to receive the state of the user and output the value of the action of the robot (Hayashi at Para. [0123] discloses using and generating a learning model or neural network:” person recognizing unit 214 of the server 200 extracts characteristics from sensing information of an image or the like obtained from the robot 100, and determines which cluster a moving object near the robot 100 corresponds to using deep learning (a multilayer neural network). For example, when a moving object that has a beard is detected, the probability of the moving object being the father is high.”).
As per claim 8, Hayashi discloses a robot as claimed in claim 7, wherein the controller is configured to acquire information on a result of the interaction established with the user as a result of the action of the robot, and update the learning model based on the result of the interaction established with the user (Hayashi at Para. [0123] discloses that when an object has not been previously analyzed an update is performed with the newly acquired classification:” when a moving object that wears spectacles is detected, there is a possibility of the moving object being the mother. When the moving object has a beard, the moving object is neither the mother nor the father, because of which the person recognizing unit 214 determines that the moving object is a new person who has not been cluster analyzed.”).
As per claim 9, Hayashi discloses a robot as claimed in claim 7, wherein the learning model is an action value table or a neural network (Hayashi at Para. [0123] discloses using a learning model or neural network:” person recognizing unit 214 of the server 200 extracts characteristics from sensing information of an image or the like obtained from the robot 100, and determines which cluster a moving object near the robot 100 corresponds to using deep learning (a multilayer neural network).”).
As per claim 10, Hayashi discloses a robot as claimed in claim 8, wherein the information on the result of the interaction established with the user includes whether the user approaches, an emotional level of the user, and a duration of the interaction with the user (Hayashi at Para. [0242] discloses determining the condition such a user’s mood:” classifies the physical condition of the user into a multiple of categories such as “in good condition”, ‘in bad condition”, “has a fever”, “feeling lethargic”, and “in a bad mood”. For example, the physical condition determining unit 226 may track the behavior of a user P1, and determine a time for which the user P1 is asleep by detecting a time at which the user P1 goes to bed and a time at which the user P1 gets up.”), and wherein the controller is configured to acquire a reward for the action of the robot based on the result of the interaction established with the user, and updates the value of the action for the state of the user based on the reward (Hayashi at Para. [0191] discloses dispensing a positive reaction which under broadest reasonable interpretation is a reward based on the interaction such as touching and the like:” positive reaction may be an explicit action such as hugging, touching a specific place (the head or the tip of the nose) on the robot 100, or saying positive words such as “you're cute” or “thank you”, or may be an implicit action such as a smile.”).
As per claim 11, Hayashi discloses a learning device communicably (Figure 5, robot 100) connected to a robot, comprising:
a processor (Hayashi at Figure 4, processor 122, and Figure 5, data processor 136, and Para. [0071].); and
a memory storing program instructions that cause the processor (Hayashi at Figure 4, storage device 124, and Para. [0099] discloses that the storage devices contain instructions for performing a specific action by the robot:” data storage unit 148 includes an operation pattern storage unit 224 that defines various kinds of operations of the robot 100. The operation pattern storage unit 224 stores a specific action selection table of the robot 100. Details of the specific action selection table will be described hereafter.”) to:
observe, based on at least one of a captured image of a user or biological information of the user, a state of the user (Hayashi at Para. [0105] discloses using captured image and other sensors to recognize a user/person:” recognizing unit 156 analyzes external information obtained from the internal sensor 128. The recognizing unit 156 is capable of visual recognition (a visual unit), smell recognition (an olfactory unit), sound recognition (an aural unit), and tactile recognition (a tactile unit).”); and
(Hayashi at Para. [0123] discloses using and generating a learning model or neural network:” person recognizing unit 214 of the server 200 extracts characteristics from sensing information of an image or the like obtained from the robot 100, and determines which cluster a moving object near the robot 100 corresponds to using deep learning (a multilayer neural network). For example, when a moving object that has a beard is detected, the probability of the moving object being the father is high.”), the learning model being configured to receive the state of the user and output a value of an action of the robot (Hayashi at Figure 5, operation determination 150, Figure 7, flow process specific action S26, and Para. [0104] disclosing as an example instructing the robot perform certain actions when encountering a familiar user:” action determining unit 140 can also perform a gesture of holding up both arms 106 as a gesture asking for “a hug” when a user with a high degree of familiarity is nearby, and can also perform a gesture of no longer wanting to be hugged by causing the wheel 102 to rotate in reverse in a housed state when bored of the “hug”. The action drive unit 146 causes the robot 100 to perform various gestures by driving the wheel 102 and the arm 106 in accordance with an instruction from the action determining unit 140.”).
As per claim 12. Hayashi discloses a learning device as claimed in claim 11, wherein the program instructions cause the processor to determine the action of the robot suitable for the state of the user based on the value of the action (Hayashi at Figure 5, operation determination 150, Figure 7, flow process specific action S26, and Para. [0104] disclosing as an example instructing the robot perform certain actions when encountering a familiar user:” action determining unit 140 can also perform a gesture of holding up both arms 106 as a gesture asking for “a hug” when a user with a high degree of familiarity is nearby, and can also perform a gesture of no longer wanting to be hugged by causing the wheel 102 to rotate in reverse in a housed state when bored of the “hug”. The action drive unit 146 causes the robot 100 to perform various gestures by driving the wheel 102 and the arm 106 in accordance with an instruction from the action determining unit 140.”); and
transmit, to the robot, a command to perform the action (Hayashi at Figure 5, operation determination 150, Figure 7, flow process specific action S26, and Para. [0104] disclosing as an example instructing the robot perform certain actions when encountering a familiar user:” action determining unit 140 can also perform a gesture of holding up both arms 106 as a gesture asking for “a hug” when a user with a high degree of familiarity is nearby, and can also perform a gesture of no longer wanting to be hugged by causing the wheel 102 to rotate in reverse in a housed state when bored of the “hug”. The action drive unit 146 causes the robot 100 to perform various gestures by driving the wheel 102 and the arm 106 in accordance with an instruction from the action determining unit 140.”).
As per claim 13. Hayashi discloses a learning device as claimed in claim 11 wherein the program instructions cause the processor to acquire information on a result of interaction established with the user as a result of the action of the robot (Hayashi at Para. [0242] discloses determining the condition such a user’s mood:” classifies the physical condition of the user into a multiple of categories such as “in good condition”, ‘in bad condition”, “has a fever”, “feeling lethargic”, and “in a bad mood”. For example, the physical condition determining unit 226 may track the behavior of a user P1, and determine a time for which the user P1 is asleep by detecting a time at which the user P1 goes to bed and a time at which the user P1 gets up.”), update the learning model based on the result of the established interaction (Hayashi at Para. [0123] discloses that when an object has not been previously analyzed an update is performed with the newly acquired classification:” when a moving object that wears spectacles is detected, there is a possibility of the moving object being the mother. When the moving object has a beard, the moving object is neither the mother nor the father, because of which the person recognizing unit 214 determines that the moving object is a new person who has not been cluster analyzed.”).
As per claim 14, Hayashi discloses a control method of a robot (See at least Figure 7), the control method comprising:
acquiring, by the robot, at least one of a captured image of a user or biological information of the user (Hayashi at Para. [0105] discloses using captured image and other sensors to recognize a user/person:” recognizing unit 156 analyzes external information obtained from the internal sensor 128. The recognizing unit 156 is capable of visual recognition (a visual unit), smell recognition (an olfactory unit), sound recognition (an aural unit), and tactile recognition (a tactile unit).”); and
performing (Hayashi at Para. [0143] disclosing recognizing the physical condition of the user:” robot 100 strongly expresses a feeling of affection by approaching a user (hereafter called an approaching action), and performing an affectionate gesture defined in advance as a gesture indicating goodwill toward a person.”), by the robot, a predetermined action that induces interaction with the user in accordance with a state of the user based on the at least one of the captured image or the biological information (Hayashi at Figure 5, operation determination 150, Figure 7, flow process specific action S26, and Para. [0104] disclosing as an example instructing the robot perform certain actions when encountering a familiar user:” action determining unit 140 can also perform a gesture of holding up both arms 106 as a gesture asking for “a hug” when a user with a high degree of familiarity is nearby, and can also perform a gesture of no longer wanting to be hugged by causing the wheel 102 to rotate in reverse in a housed state when bored of the “hug”. The action drive unit 146 causes the robot 100 to perform various gestures by driving the wheel 102 and the arm 106 in accordance with an instruction from the action determining unit 140.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
TAKAHASHI; KEI et al. (US-20240367065-A1) AUTONOMOUS MOBILE BODY, INFORMATION PROCESSING METHOD, AND PROGRAM;
HAYASHI; Kaname (US-20190143528-A1) AUTONOMOUSLY ACTING ROBOT THAT UNDERSTANDS PHYSICAL CONTACT;
Kerzner; Daniel (US-20190011909-A1) ROBOTIC ASSISTANCE IN SECURITY MONITORING;
Kamiya; Tsuyoshi et al. (US-6175772-B1) User adaptive control of object having pseudo-emotions by learning adjustments of emotion generating and behavior generating algorithms;
Faridi; Fardad et al. (US-10357881-B2) Multi-segment social robot;
LIU, Xiao-feng et al. (CN-106648054-B) multi-mode interactive method of a companion robot based on RealSense;
Korean Publication (KR-20210131646-A) Robot Structure for Performing Emotion Recognition;
ZHENG, Yun-jing (CN-114393596-A) A method for intelligent interaction of robot;
UNANNOUNCED INVENTOR (CN-106239533-A) A robot controlled by the emotion;
XIE, Qiao-jing et al. (CN-109176535-A) Interactive method and system based on intelligent robot;
KAWAUCHI YASUHIRO et al. (JP-2021019966-A) Personal assistant control system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELLIS B. RAMIREZ whose telephone number is (571)272-8920. The examiner can normally be reached 7:30 am to 5:00pm.
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/ELLIS B. RAMIREZ/Primary Examiner, Art Unit 3658
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