DETAILED ACTION
This is a Final Office Action on the merits in response to communications filed by Applicant on June 18th, 2026. Claims 1-13 are currently pending and examined below.
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 Amendment
The amendments to the Claims filed on June 18th, 2026, have been entered. Claims 1-11 are currently amended and pending and claims 12 and 13 are new and pending.
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.
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.
Claim(s) 1-7 and 10-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10898999 B1 ("Cohen") in view of US 2018/0333862 A1 ("Hayashi").
Regarding claim 1, Cohen teaches an information processing device comprising (Cohen: Abstract, “Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selective human-robot interaction. In some implementations, sensor data describing an environment of a robot is received, and a person in the environment of the robot is detected based on the sensor data. Scores indicative of properties of the detected person are generated based on the sensor data and processed using a machine learning model. Processing the scores can produce one or more outputs indicative of a likelihood that the detected person will perform a predetermined action in response to communication from the robot. Based on the one or more outputs of the machine learning model, the robot initiates communication with the detected person.”):
a memory (Cohen: Column 10 lines 19-30, “The processing indicated for the interaction prediction model 130, the selection module 140, and the communication module 150 may reside locally on the robot 110, may be performed by a remote computing system in communication with the robot 110, or may be performed in part by the robot 110 and in part by a remote system. The robot 110 can include one or more computing devices, e.g., hardware processors and memory storing instructions executable by the processors. Software corresponding to the elements 130, 140, 150 and other functionality of the robot 110 can be stored locally at the robot 110 and may be updated from time to time by a remote computing system.”); and
a processor configured to (Cohen: Column 10 lines 19-30, “The processing indicated for the interaction prediction model 130, the selection module 140, and the communication module 150 may reside locally on the robot 110, may be performed by a remote computing system in communication with the robot 110, or may be performed in part by the robot 110 and in part by a remote system. The robot 110 can include one or more computing devices, e.g., hardware processors and memory storing instructions executable by the processors. Software corresponding to the elements 130, 140, 150 and other functionality of the robot 110 can be stored locally at the robot 110 and may be updated from time to time by a remote computing system.”):
store, in the memory, a piece of correspondence information in which a detection result by a sensor at a certain time point(Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 38-44, “The robot 110 includes a variety of sensors 220 which enabled the robot 110 to obtain information regarding in the environment of the robot 110. Examples of these sensors 220 include a microphone, camera, and LIDAR module, a radar module, and infrared detector. Other sensors, such as a GPS receiver, accelerometers, force sensors, can indicate the current context of the robot 110.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the system is configured to save, in memory, sensor data corresponding to human-robot interaction),
is correlated with presence or absence of a predetermined action from outside to a robot within a predetermined period including the certain time point, the sensor being included in a robot for detecting at least one action from a user (Cohen: Column 3 lines 61-67, “Receiving the sensor data includes receiving sensor data for a time period before the robot performs the action and a time period after the robot performs the action.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 13 lines 11-30, “For example, features may be determined based on data captured in a particular amount of time, e.g., the previous second, previous 5 seconds, previous minute, etc., or based on a number of measurements, e.g., the previous 5 measurements, the previous 50 measurements, etc.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the sensor data is associated with whether a human performed an action to the robot and that this sensor data is correlated to time, and can be take over a predetermined time period. The cited passages further show that the robot includes sensors configured to detect human actions.), and
based on a plurality of pieces of correspondence information stored in the memory and corresponding to a plurality of time points different from one another, predicts whether the robot receives an action from the user at a specific time point after the plurality of time points, the pieces of correspondence information each being the piece of correspondence information (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 7 lines 22-41, “Not only can the machine learning model indicate a user's disposition to communication, the machine learning model can learn the capability to predict a likelihood that interaction initiated by the robot 110 will result in a specific type of action by a person, e.g., orienting the robot, loading an object onto or unloading an object from the robot, providing a desired type of information, and so on.”, Column 17 lines 10-14, “A predictive model is trained based on the sensor data and the result data (506). The predictive model is trained to indicate, in response to input data describing a person near a robot, a likelihood that the human will performing the action if the robot initiates communication with the person.”. The cited passages clearly shows that the system is configured to make a prediction on whether or not a human will interact with the robot based on the correspondence data.);
in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion (Cohen: Column 4 lines 9-31, “In some implementations, determining the direction of travel includes determining a direction of travel that moves the robot closer to the detected person based on determining that the one or more outputs of the machine learning model indicate at least a threshold likelihood that the detected person will perform a predetermined action in response to communication from the robot. For example, the robot can travel in a direction that brings the robot closer to a current position of the detected person, or closer to an estimated future position of the detected person inferred from the detected person's current or recent movement. Other types of travel can also be set for the robot.”, Column 9 lines 22-37, “The selection module 140 can also determine whether the likelihood indicated for a particular person satisfies at least a minimum threshold, for example, a minimum 50% likelihood of success. If the likelihood does not satisfy the minimum threshold, the robot 110 may decline to communicate with the person 145, for example, waiting until a person having a higher likelihood score is identified.”, Column 16 lines 25-35, “Based on the one or more outputs of the machine learning model, one or more computing devices cause the robot to initiate communication with the detected person (410). The computing device may also compare the score to one or more thresholds to determine whether at least a minimum likelihood of success is indicated, and initiate communication with the person in response.”. The cited passages clearly shows that the robot is configured to make a motion when it is determined that the probability that a human will interact with the robot is above a predetermined threshold.); and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion (Cohen: Column 4 lines 9-31, “In some implementations, determining the direction of travel includes determining a direction of travel that moves the robot closer to the detected person based on determining that the one or more outputs of the machine learning model indicate at least a threshold likelihood that the detected person will perform a predetermined action in response to communication from the robot. For example, the robot can travel in a direction that brings the robot closer to a current position of the detected person, or closer to an estimated future position of the detected person inferred from the detected person's current or recent movement. Other types of travel can also be set for the robot.”, Column 9 lines 22-37, “The selection module 140 can also determine whether the likelihood indicated for a particular person satisfies at least a minimum threshold, for example, a minimum 50% likelihood of success. If the likelihood does not satisfy the minimum threshold, the robot 110 may decline to communicate with the person 145, for example, waiting until a person having a higher likelihood score is identified.”, Column 16 lines 25-35, “Based on the one or more outputs of the machine learning model, one or more computing devices cause the robot to initiate communication with the detected person (410). The computing device may also compare the score to one or more thresholds to determine whether at least a minimum likelihood of success is indicated, and initiate communication with the person in response.”. The cited passages clearly shows that the robot is configured to make a motion when it is determined that the probability that a human will interact with the robot is above a predetermined threshold. One of ordinary skill in the art would recognize that when the robot predicts that the human will not perform an action to the robot, the robot clearly does not perform a motion.).
Cohen does not teach in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action.
Hayashi, in the same field of endeavor, teaches in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction.); and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction. One of ordinary skill in the art would recognize that when the user is predicted to not perform an action the would elicit a response from the robot (e.g. such as determining that the human is not leaving the house) the robot would clearly not perform the associated action.).
Cohen teaches an information processing device comprising: a memory; and a processor configured to: store, in the memory, a piece of correspondence information in which a detection result by a sensor at a certain time point, is correlated with presence or absence of a predetermined action from outside to a robot within a predetermined period including the certain time point, the sensor being included in a robot for detecting at least one action from a user, and based on a plurality of pieces of correspondence information stored in the memory and corresponding to a plurality of time points different from one another, predicts whether the robot receives an action from the user at a specific time point after the plurality of time points, the pieces of correspondence information each being the piece of correspondence information; in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion. Cohen does not teach in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action. Hayashi teaches in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action. A person of ordinary skill in the art would have the technological capabilities required to have modified the device taught in Cohen with in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action taught in Hayashi. Furthermore, the device taught in Cohen is configured to perform a predetermined motion based on whether or not it is predicted that the human will perform an action to the robot, however the device taught in Cohen does not teach that the predetermined motion performed by the robot responds to the action the human is predicted to make. As such, because the device taught in Cohen is already configured to cause the robot to make a motion based on the prediction that a human will make an action, a person of ordinary skill in the art would have been able to modify the device taught in Cohen such that the motion the robot makes responds to the predicted action of the human as taught in Hayashi according to methods known in the art. Such a modification would not have changed or introduced new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an information processing device comprising: in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the device taught in Cohen with in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action taught in Hayashi with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 2, Cohen in view of Hayashi teaches wherein based on the plurality of pieces of correspondence information and a detection result by the sensor at a time point corresponding to the specific time point, the processor is configured to determine the prediction for the action that the robot receives from the user at the specific time point (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 7 lines 22-41, “Not only can the machine learning model indicate a user's disposition to communication, the machine learning model can learn the capability to predict a likelihood that interaction initiated by the robot 110 will result in a specific type of action by a person, e.g., orienting the robot, loading an object onto or unloading an object from the robot, providing a desired type of information, and so on.”, Column 10 lines 38-44, “The robot 110 includes a variety of sensors 220 which enabled the robot 110 to obtain information regarding in the environment of the robot 110. Examples of these sensors 220 include a microphone, camera, and LIDAR module, a radar module, and infrared detector. Other sensors, such as a GPS receiver, accelerometers, force sensors, can indicate the current context of the robot 110.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the system is configured to make a prediction that a human will interact with a user based on sensor data that corresponds to results data (the result data being whether the human interacted with the robot or not)).
Regarding claim 3, Cohen in view of Hayashi teaches wherein the processor is configured to: determine, based on the detection result by the sensor, whether the robot has received the action from the outside (Cohen: Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 38-44, “The robot 110 includes a variety of sensors 220 which enabled the robot 110 to obtain information regarding in the environment of the robot 110. Examples of these sensors 220 include a microphone, camera, and LIDAR module, a radar module, and infrared detector. Other sensors, such as a GPS receiver, accelerometers, force sensors, can indicate the current context of the robot 110.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly show that the system uses a detection results by a sensor to determine if a human has interacted with the robot.); and
in response to determining that the robot has received the action, store, in the memory, the piece of correspondence information in which information indicating that the robot has received the action is correlated with the detection result by the sensor at the certain time point in the predetermined period including a time point at which the robot received the action (Cohen: Cohen: Column 3 lines 61-67, “Receiving the sensor data includes receiving sensor data for a time period before the robot performs the action and a time period after the robot performs the action.”, Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 13 lines 11-30, “For example, features may be determined based on data captured in a particular amount of time, e.g., the previous second, previous 5 seconds, previous minute, etc., or based on a number of measurements, e.g., the previous 5 measurements, the previous 50 measurements, etc.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the sensor data is associated with whether a human performed an action to the robot and that this sensor data is correlated to time, and can be take over a predetermined time period.).
Regarding claim 4, Cohen in view of Hayashi teaches wherein based on the plurality of pieces of correspondence information corresponding to the time points, the processor is configured to predict a probability that the robot receives the action from the user at the specific time point (Cohen: Column 8 lines 55-67, “The model 130 may provide outputs 135 indicative of a likelihood that each person will successfully assist the robot. For example, when each individual person is detected, the sensor data 120 collected by the robot 110 maybe segmented or pre-processed to isolate data sets that each represent properties of an individual person. Scores or portions of the sensor data 120 corresponding to a specific person can be provided to the model 130 to generate a score 135 for that person. In FIG. 1, the scores 135 are illustrated as probability measures, for example, that the person 145 has a 70% likelihood of successfully performing the target action if requested by the robot 110.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the system is configured to determine a probability that a human will interact with the robot based on the correspondence information.).
Regarding claim 5, Cohen in view of Hayashi teaches wherein the processor is configured to control the robot to make the predetermined motion in response to the probability derived being equal to or more than a predetermined threshold value (Cohen: Column 4 lines 9-31, “In some implementations, determining the direction of travel includes determining a direction of travel that moves the robot closer to the detected person based on determining that the one or more outputs of the machine learning model indicate at least a threshold likelihood that the detected person will perform a predetermined action in response to communication from the robot. For example, the robot can travel in a direction that brings the robot closer to a current position of the detected person, or closer to an estimated future position of the detected person inferred from the detected person's current or recent movement. Other types of travel can also be set for the robot.”, Column 9 lines 22-37, “The selection module 140 can also determine whether the likelihood indicated for a particular person satisfies at least a minimum threshold, for example, a minimum 50% likelihood of success. If the likelihood does not satisfy the minimum threshold, the robot 110 may decline to communicate with the person 145, for example, waiting until a person having a higher likelihood score is identified.”, Column 16 lines 25-35, “Based on the one or more outputs of the machine learning model, one or more computing devices cause the robot to initiate communication with the detected person (410). The computing device may also compare the score to one or more thresholds to determine whether at least a minimum likelihood of success is indicated, and initiate communication with the person in response.”. The cited passages clearly shows that the robot is configured to make a predetermined motion when it is determined that the probability that a human will interact with the robot is above a predetermined threshold. Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”).
Regarding claim 6, Cohen in view of Hayashi teaches wherein the processor is configured to store, in the memory, the piece of correspondence information in which the detection result by the sensor at the certain time point corresponding to a time point at which the robot started to make the predetermined motion is correlated with the presence or absence of the action from the outside to the robot within, of the predetermined period, a predetermined time from a start of the predetermined motion (Cohen: Column 4 lines 32-45, “In some implementations, the method includes, before causing the robot to initiate communication with the detected person, repeating a set of operations comprising: obtaining additional sensor data, generating additional scores indicating properties of the detected person based on the additional sensor data, processing the additional scores using the machine learning model to generate additional output corresponding to the detected person, and evaluating the additional output of the machine learning model. The method also includes determining that one or more of the additional outputs of the machine learning model satisfies a threshold. Causing the robot to initiate communication with the detected person is performed in response to determining that the additional output satisfies the threshold.”, Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 13 lines 11-30, “For example, features may be determined based on data captured in a particular amount of time, e.g., the previous second, previous 5 seconds, previous minute, etc., or based on a number of measurements, e.g., the previous 5 measurements, the previous 50 measurements, etc.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the sensor data is associated with whether a human performed an action to the robot and that this sensor data is correlated to time, and can be take over a predetermined time period. Additionally, the cited passages shows that the robot can be configured to perform the process again after it has moved to a person and prior to interacting with said person.).
Regarding claim 7, Cohen in view of Hayashi teaches wherein the processor is configured to: derive an evaluation value of the predetermined motion based on the presence or absence of the action from the outside to the robot within the predetermined time from the start of the predetermined motion, and based on the derived evaluation value, adjusts a content of the motion (Cohen: Column 4 lines 32-45, “In some implementations, the method includes, before causing the robot to initiate communication with the detected person, repeating a set of operations comprising: obtaining additional sensor data, generating additional scores indicating properties of the detected person based on the additional sensor data, processing the additional scores using the machine learning model to generate additional output corresponding to the detected person, and evaluating the additional output of the machine learning model. The method also includes determining that one or more of the additional outputs of the machine learning model satisfies a threshold. Causing the robot to initiate communication with the detected person is performed in response to determining that the additional output satisfies the threshold.”. The cited passages clearly shows that the robot is configured to calculate additional scores after motion has begun, based on the sensor and result data, and can further adjust the motion (i.e. perform the interaction of not) based on this additional score.).
Regarding claim 10, Cohen teaches an information processing method that is performed by a computer, comprising (Cohen: Abstract, “Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selective human-robot interaction. In some implementations, sensor data describing an environment of a robot is received, and a person in the environment of the robot is detected based on the sensor data. Scores indicative of properties of the detected person are generated based on the sensor data and processed using a machine learning model. Processing the scores can produce one or more outputs indicative of a likelihood that the detected person will perform a predetermined action in response to communication from the robot. Based on the one or more outputs of the machine learning model, the robot initiates communication with the detected person.”):
storing, in a memory, a piece of correspondence information in which a detection result by a sensor at a certain time point (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 38-44, “The robot 110 includes a variety of sensors 220 which enabled the robot 110 to obtain information regarding in the environment of the robot 110. Examples of these sensors 220 include a microphone, camera, and LIDAR module, a radar module, and infrared detector. Other sensors, such as a GPS receiver, accelerometers, force sensors, can indicate the current context of the robot 110.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the system is configured to save, in memory, sensor data corresponding to human-robot interaction. The cited passages further show that the robot includes sensors configured to detect human actions.),
is correlated with presence or absence of a predetermined action from outside to a robot within a predetermined period including the certain time point, the sensor being included in a robot for detecting at least one action from a user (Cohen: Column 3 lines 61-67, “Receiving the sensor data includes receiving sensor data for a time period before the robot performs the action and a time period after the robot performs the action.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 13 lines 11-30, “For example, features may be determined based on data captured in a particular amount of time, e.g., the previous second, previous 5 seconds, previous minute, etc., or based on a number of measurements, e.g., the previous 5 measurements, the previous 50 measurements, etc.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the sensor data is associated with whether a human performed an action to the robot and that this sensor data is correlated to time, and can be take over a predetermined time period.); and
based on a plurality of pieces of correspondence information stored in the memory and corresponding to a plurality of time points different from one another, predict whether the robot receives an action from the user at a specific time point after the plurality of time points, the pieces of correspondence information each being the piece of correspondence information (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 7 lines 22-41, “Not only can the machine learning model indicate a user's disposition to communication, the machine learning model can learn the capability to predict a likelihood that interaction initiated by the robot 110 will result in a specific type of action by a person, e.g., orienting the robot, loading an object onto or unloading an object from the robot, providing a desired type of information, and so on.”, Column 17 lines 10-14, “A predictive model is trained based on the sensor data and the result data (506). The predictive model is trained to indicate, in response to input data describing a person near a robot, a likelihood that the human will performing the action if the robot initiates communication with the person.”. The cited passages clearly shows that the system is configured to make a prediction on whether or not a human will interact with the robot based on the correspondence data.);
in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction.); and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion (Cohen: Column 4 lines 9-31, “In some implementations, determining the direction of travel includes determining a direction of travel that moves the robot closer to the detected person based on determining that the one or more outputs of the machine learning model indicate at least a threshold likelihood that the detected person will perform a predetermined action in response to communication from the robot. For example, the robot can travel in a direction that brings the robot closer to a current position of the detected person, or closer to an estimated future position of the detected person inferred from the detected person's current or recent movement. Other types of travel can also be set for the robot.”, Column 9 lines 22-37, “The selection module 140 can also determine whether the likelihood indicated for a particular person satisfies at least a minimum threshold, for example, a minimum 50% likelihood of success. If the likelihood does not satisfy the minimum threshold, the robot 110 may decline to communicate with the person 145, for example, waiting until a person having a higher likelihood score is identified.”, Column 16 lines 25-35, “Based on the one or more outputs of the machine learning model, one or more computing devices cause the robot to initiate communication with the detected person (410). The computing device may also compare the score to one or more thresholds to determine whether at least a minimum likelihood of success is indicated, and initiate communication with the person in response.”. The cited passages clearly shows that the robot is configured to make a motion when it is determined that the probability that a human will interact with the robot is above a predetermined threshold. One of ordinary skill in the art would recognize that when the robot predicts that the human will not perform an action to the robot, the robot clearly does not perform a motion.).
Cohen does not teach in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action.
Hayashi, in the same field of endeavor, teaches in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction.); and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction. One of ordinary skill in the art would recognize that when the user is predicted to not perform an action the would elicit a response from the robot (e.g. such as determining that the human is not leaving the house) the robot would clearly not perform the associated action.).
Cohen teaches an information processing method that is performed by a computer, comprising: storing, in a memory, a piece of correspondence information in which a detection result by a sensor at a certain time point, is correlated with presence or absence of a predetermined action from outside to a robot within a predetermined period including the certain time point, the sensor being included in a robot for detecting at least one action from a user, and based on a plurality of pieces of correspondence information stored in the memory and corresponding to a plurality of time points different from one another, predicts whether the robot receives an action from the user at a specific time point after the plurality of time points, the pieces of correspondence information each being the piece of correspondence information; in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion. Cohen does not teach in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action. Hayashi teaches in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action. A person of ordinary skill in the art would have the technological capabilities required to have modified the method taught in Cohen with in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action taught in Hayashi. Furthermore, the method taught in Cohen is configured to perform a predetermined motion based on whether or not it is predicted that the human will perform an action to the robot, however the method taught in Cohen does not teach that the predetermined motion performed by the robot responds to the action the human is predicted to make. As such, because the method taught in Cohen is already configured to cause the robot to make a motion based on the prediction that a human will make an action, a person of ordinary skill in the art would have been able to modify the device taught in Cohen such that the motion the robot makes responds to the predicted action of the human as taught in Hayashi according to methods known in the art. Such a modification would not have changed or introduced new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an information processing method performed by a computer, comprising: in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method taught in Cohen with in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action taught in Hayashi with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 11, Cohen teaches a non-transitory computer-readable storage medium storing a program causing a computer to (Cohen: Abstract, “Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selective human-robot interaction. In some implementations, sensor data describing an environment of a robot is received, and a person in the environment of the robot is detected based on the sensor data. Scores indicative of properties of the detected person are generated based on the sensor data and processed using a machine learning model. Processing the scores can produce one or more outputs indicative of a likelihood that the detected person will perform a predetermined action in response to communication from the robot. Based on the one or more outputs of the machine learning model, the robot initiates communication with the detected person.”):
storing, in a memory, a piece of correspondence information in which a detection result by a sensor at a certain time point (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 38-44, “The robot 110 includes a variety of sensors 220 which enabled the robot 110 to obtain information regarding in the environment of the robot 110. Examples of these sensors 220 include a microphone, camera, and LIDAR module, a radar module, and infrared detector. Other sensors, such as a GPS receiver, accelerometers, force sensors, can indicate the current context of the robot 110.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the system is configured to save, in memory, sensor data corresponding to human-robot interaction. The cited passages further show that the robot includes sensors configured to detect human actions.),
is correlated with presence or absence of a predetermined action from outside to a robot within a predetermined period including the certain time point, the sensor being included in a robot for detecting at least one action from a user (Cohen: Column 3 lines 61-67, “Receiving the sensor data includes receiving sensor data for a time period before the robot performs the action and a time period after the robot performs the action.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 13 lines 11-30, “For example, features may be determined based on data captured in a particular amount of time, e.g., the previous second, previous 5 seconds, previous minute, etc., or based on a number of measurements, e.g., the previous 5 measurements, the previous 50 measurements, etc.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the sensor data is associated with whether a human performed an action to the robot and that this sensor data is correlated to time, and can be take over a predetermined time period.); and
based on a plurality of pieces of correspondence information stored in the memory and corresponding to a plurality of time points different from one another, predict whether the robot receives an action from the user at a specific time point after the plurality of time points, the pieces of correspondence information each being the piece of correspondence information (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 7 lines 22-41, “Not only can the machine learning model indicate a user's disposition to communication, the machine learning model can learn the capability to predict a likelihood that interaction initiated by the robot 110 will result in a specific type of action by a person, e.g., orienting the robot, loading an object onto or unloading an object from the robot, providing a desired type of information, and so on.”, Column 17 lines 10-14, “A predictive model is trained based on the sensor data and the result data (506). The predictive model is trained to indicate, in response to input data describing a person near a robot, a likelihood that the human will performing the action if the robot initiates communication with the person.”. The cited passages clearly shows that the system is configured to make a prediction on whether or not a human will interact with the robot based on the correspondence data.);
in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction.); and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion (Cohen: Column 4 lines 9-31, “In some implementations, determining the direction of travel includes determining a direction of travel that moves the robot closer to the detected person based on determining that the one or more outputs of the machine learning model indicate at least a threshold likelihood that the detected person will perform a predetermined action in response to communication from the robot. For example, the robot can travel in a direction that brings the robot closer to a current position of the detected person, or closer to an estimated future position of the detected person inferred from the detected person's current or recent movement. Other types of travel can also be set for the robot.”, Column 9 lines 22-37, “The selection module 140 can also determine whether the likelihood indicated for a particular person satisfies at least a minimum threshold, for example, a minimum 50% likelihood of success. If the likelihood does not satisfy the minimum threshold, the robot 110 may decline to communicate with the person 145, for example, waiting until a person having a higher likelihood score is identified.”, Column 16 lines 25-35, “Based on the one or more outputs of the machine learning model, one or more computing devices cause the robot to initiate communication with the detected person (410). The computing device may also compare the score to one or more thresholds to determine whether at least a minimum likelihood of success is indicated, and initiate communication with the person in response.”. The cited passages clearly shows that the robot is configured to make a motion when it is determined that the probability that a human will interact with the robot is above a predetermined threshold. One of ordinary skill in the art would recognize that when the robot predicts that the human will not perform an action to the robot, the robot clearly does not perform a motion.).
Cohen does not teach in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action.
Hayashi, in the same field of endeavor, teaches in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction.); and
in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction. One of ordinary skill in the art would recognize that when the user is predicted to not perform an action the would elicit a response from the robot (e.g. such as determining that the human is not leaving the house) the robot would clearly not perform the associated action.).
Cohen teaches a non-transitory computer-readable storage medium storing a program causing a computer to: storing, in a memory, a piece of correspondence information in which a detection result by a sensor at a certain time point, is correlated with presence or absence of a predetermined action from outside to a robot within a predetermined period including the certain time point, the sensor being included in a robot for detecting at least one action from a user, and based on a plurality of pieces of correspondence information stored in the memory and corresponding to a plurality of time points different from one another, predicts whether the robot receives an action from the user at a specific time point after the plurality of time points, the pieces of correspondence information each being the piece of correspondence information; in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion. Cohen does not teach in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action. Hayashi teaches in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action. A person of ordinary skill in the art would have the technological capabilities required to have modified the method taught in Cohen with in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action taught in Hayashi. Furthermore, the method taught in Cohen is configured to perform a predetermined motion based on whether or not it is predicted that the human will perform an action to the robot, however the method taught in Cohen does not teach that the predetermined motion performed by the robot responds to the action the human is predicted to make. As such, because the method taught in Cohen is already configured to cause the robot to make a motion based on the prediction that a human will make an action, a person of ordinary skill in the art would have been able to modify the device taught in Cohen such that the motion the robot makes responds to the predicted action of the human as taught in Hayashi according to methods known in the art. Such a modification would not have changed or introduced new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of non-transitory computer-readable storage medium storing a program causing a computer to: in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method taught in Cohen with in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action; and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action taught in Hayashi with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 12, Cohen in view of Hayashi teaches wherein the processor is further configured to, in response to the robot receiving the action from the user after the processor predicts that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot to start the predetermined motion that responds to the action (Hayashi: ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”. The cited passages shows that the system is configured to predict when a user will make a specific action (e.g. the human returning to the house) and give priority to monitoring the entryway such that the robot can perform a predetermined action that responds to the human returning. One of ordinary skill in the art would recognize from the cited passages would recognize that the robot is configured to perform the predetermined motion that responds to the action even if the human wasn’t predicted to make said action, as the robot is configured to give priority to monitoring the human/location when it is predicted to make an action and does not prevent the robot from performing said action when the human is not predicted to make said action. The robot would clearly still perform the motion when it predicts the human performing the associated action.).
Regarding claim 13, Cohen in view of Hayashi teaches where: the action includes a plurality of types of actions (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 38-44, “The robot 110 includes a variety of sensors 220 which enabled the robot 110 to obtain information regarding in the environment of the robot 110. Examples of these sensors 220 include a microphone, camera, and LIDAR module, a radar module, and infrared detector. Other sensors, such as a GPS receiver, accelerometers, force sensors, can indicate the current context of the robot 110.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the correspondence information generated and saved by the system includes a plurality of different actions performed by the human.);
the motion includes a plurality of types of motions (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction.); and
the processor is further configured to: in response to the robot receiving, from the outside, any one of the plurality of types of actions, store, in the memory, the piece of correspondence information according to a type of the received action (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 10 lines 9-18, “After requesting input and assistance from the selected person, the robot 110 may provide and store data indicating the results of the attempted interaction. The sensor data 120 and the data indicating whether the target action was successfully completed, as well as the type of interaction requested, may be used to further refine the interaction prediction model 130. This data may be used to update the model 130 for the particular robot 110, and/or maybe provided to a server system to update one or more models used by other robots.”, Column 10 lines 38-44, “The robot 110 includes a variety of sensors 220 which enabled the robot 110 to obtain information regarding in the environment of the robot 110. Examples of these sensors 220 include a microphone, camera, and LIDAR module, a radar module, and infrared detector. Other sensors, such as a GPS receiver, accelerometers, force sensors, can indicate the current context of the robot 110.”, Column 10 lines 45-62, “The sensor data for the various sensors may be time-stamped or synchronized so that different types of sensor data can be mapped together to indicate different observed parameters that occur at the same time.”, Column 16 line 62 – Column 17 line 6, “Sensor data corresponding to human-robot interactions is received (502). The sensor data can indicate interactions in which a robot attempted to obtain assistance from a person to perform an action. The sensor data can be accompanied by data indicating other information related to the interaction, such as the particular action that was targeted, the type of communication initiated by the robot, and so on. In addition to or instead of sensor data, one or more scores derived from sensor data can be obtained. For example, scores indicating how many people were present, activities and attributes of the people present, environmental factors, and other data can be obtained.”, Column 17 lines 6-9, “Result data is obtained, where the result data indicates whether each of the human-robot interactions resulted in a person assisting a robot to perform the action (504).”. The cited passages clearly shows that the correspondence information generated and saved by the system includes a plurality of different actions performed by the human.);
based on the plurality of pieces of correspondence information stored by type of action, predict a type of action that the robot receives from the user (Cohen: Column 4 lines 51-67, “Another innovative aspect of the subject matter described in this specification is embodied in methods that include the actions of: receiving, by the one or more computing devices, sensor data corresponding to human-robot interactions in which a robot attempted to obtain assistance from a human to perform an action; receiving, by the one or more computing devices, result data indicating whether each of the human-robot interactions resulted in a human assisting a robot to perform the action; training, by the one or more computing devices, a predictive model based on the sensor data and the result data to indicate, in response to input data describing a human near a robot, a likelihood that the human will perform the action if the robot initiates communication with the human; and providing, by the one or more computing devices, the predictive model to a robot, the robot being configured to use the predictive model to select people to interact with to perform the action.”, Column 7 lines 22-41, “Not only can the machine learning model indicate a user's disposition to communication, the machine learning model can learn the capability to predict a likelihood that interaction initiated by the robot 110 will result in a specific type of action by a person, e.g., orienting the robot, loading an object onto or unloading an object from the robot, providing a desired type of information, and so on.”, Column 17 lines 10-14, “A predictive model is trained based on the sensor data and the result data (506). The predictive model is trained to indicate, in response to input data describing a person near a robot, a likelihood that the human will performing the action if the robot initiates communication with the person.”. The cited passages clearly shows that the system is configured to make a prediction on whether or not a human will interact with the robot based on the correspondence data.); and
control the robot to make one of the plurality of types of motions corresponding to the predicted type of action as the predetermined motion that responds to the action (Hayashi: Abstract, “Empathy toward a robot is increased by the robot emulating human-like or animal-like behavior. A robot includes a movement determining unit that determines a direction of movement, an action determining unit that selects a gesture from multiple kinds of gesture, and a drive mechanism that executes a specified movement and gesture. When a user enters a hall, an external sensor installed in advance detects a return home, and notifies the robot via a server that the user has returned home. The robot heads to the hall, and welcomes the user home by performing a gesture indicating goodwill, such as sitting down and raising an arm.”, ¶ 0093, “The action determining unit 140 decides a gesture of the robot 100. Multiple gestures are defined in advance in the data storing unit 148. Specifically, a gesture of sitting by housing the wheel 102, a gesture of raising the arm 106, a gesture of causing the robot 100 to carry out a rotating action by causing the two wheels 102 to rotate in reverse or by causing only one wheel 102 to rotate, a gesture of shaking by causing the wheel 102 to rotate in a state in which the wheel 102 is housed, and the like are defined.”, ¶ 0094, “The 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.”, ¶ 0100, “In at least one embodiment, when the front door opens and a user returns home, the robot 100 greets the user in the hall. The robot 100 sits down in the hall, and performs the gesture of asking for a hug by raising both arms 106. Also, the robot 100 may express a feeling of pleasure at the user's return home by performing a rotating action in the hall. Alternatively, the robot 100 may emit a peculiar peeping "call" from the incorporated speaker.”, ¶ 0101, “When a user goes out, the robot 100 heads toward the hall to see the user off. At this time, the robot 100 expresses "see you" by raising one arm 106 and causing the arm 106 to oscillate. The robot 100 may also express with an action a feeling of sadness at the user going out by emitting a peculiar call.”, ¶ 0141, “The robot 100 may move near the hall when the time at which one user returns home is near, and perform a gesture of waiting for the user to return home, for example, a sitting or a rotating movement. Also, when the time at which one user leaves home is near, the robot 100 may carry out tracking of the user with priority, promptly detect the user heading toward the hall, and perform an action of following the user.”, ¶ 0152, “The path predicting unit 404 determines whether or not a user moving toward an entrance exists. The path predicting unit 404 tracks and monitors a position of a user in a periphery of the robot 100, and determines whether or not any user is heading in a direction of an entrance. In at least one embodiment, the path predicting unit 404 determines whether or not any user has entered within a predetermined range from an entrance. When the external sensor 114 is installed in an entrance vicinity, the path predicting unit 404 may determine whether or not a user moving toward an entrance exists from a detection signal from the external sensor 114.”, ¶ 0165, “The existence determining unit 402 may refer to user's lifestyle pattern information, and predict a time at which the user will return home. When the time at which the user is predicted to return home approaches, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410, and "greet" the user in advance. According to this kind of control method, greeting is easily realized with high accuracy. Alternatively, the robot 100 need not perform a greeting when the time at which the user returns home is not near.”, ¶ 0170, “The path predicting unit 404 may refer to user's lifestyle pattern information, and predict a time at which the user will leave home. When the user P3 actually moves in a direction toward the entrance 410 in a time range including the time at which the user P3 is predicted to leave home, the movement determining unit 138 may cause the robot 100 to move in the direction of the entrance 410 and "see off" the user. For example, when the time at which the user P3 is predicted to leave home is 7 o'clock in the evening, one hour before and after that, which is 6.30 to 7.30, is set as a preparation time range for a seeing off, and the user P3 is monitored with priority in the preparation time range. Specifically, settings such as increasing the frequency of detecting the position of the user P3, and regularly directing the camera at the user P3, are carried out. According to this kind of control method, rather than performing a seeing off action every time a user passes through the entrance 410, a seeing off action is easily effectively performed at a timing at which a possibility of going out is high.”, ¶ 0171, “The robot 100 may perform a "seeing off" or a "greeting" with respect to another user when performing a predetermined communication action with respect to the user Pl. In at least one embodiment, a communication action in this case is an action expressing interest or attention toward the user Pl, such as being hugged by the user Pl, staring at the user Pl, or circling in the vicinity of the user Pl.”. The cited passages clearly shows that the robot is configured to predict whether or not the human will make a specific action (e.g. such a leaving the house) and cause the robot to perform a predefined action based on this prediction.).
Cohen teaches a system and method for controlling a robot. The system is configured to store correspondence data between sensor data, actions performed by a human, and time data when the sensor data was collected. Based on this correspondence data, the system is configured to determine the probability the human will interact with the robot (i.e. perform an action to the robot). The system then controls the robot to perform a predetermined motion based on this probability. Hayashi teaches a system and method for controlling a robot. The system is configured with a plurality of predetermined motions that the robot can perform in response to different actions performed by the human. The system is further configured to predict what action the human will make, and based on this prediction, cause the robot to perform one of the plurality of predefined motions that responds to the predicted action of the human. Because Cohen already teaches that the system is configured to collect correspondence data on a plurality of actions performed by humans to the robot, and controls the robot to perform an action based on the probability predicted from the correspondence information that the human will interact with the robot, a person of ordinary skill in the art would have been able to modify the system taught in Cohen such that the robot can perform a plurality of actions and perform one of the plurality of actions based on the type of action the human is to perform as taught in Hayashi. Such a modification would not have changed or introduced new functionality. No inventive effort would have been required. Therefore, the combination of Cohen in view of Hayashi clearly teaches the limitations of claim 13.
Claim(s) 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10898999 B1 ("Cohen") in view of US 2018/0333862 A1 ("Hayashi") in further view of US 2022/0009103 A1 ("Buerkle").
Regarding claim 1, Cohen does not teach wherein the processor is configured to: perform a predetermined regression analysis using, as explanatory variables, detection results by sensors each being the sensor, the detection results being included in each of the plurality of pieces of correspondence information, thereby deriving a regression formula for the probability that the robot receives the action, and
derive the probability based on the derived regression formula and detection results by the sensors at a time point corresponding to the specific time point.
Buerkle, in the same field of endeavor, teaches wherein the processor is configured to: perform a predetermined regression analysis using, as explanatory variables, detection results by sensors each being the sensor, the detection results being included in each of the plurality of pieces of correspondence information, thereby deriving a regression formula for the probability that the robot receives the action (Buerkle: ¶ 0152, “The systems and methods of the disclosure may utilize one or more machine learning models to perform corresponding functions of the agent (or other functions described herein). The term “model” as, for example, used herein may be understood as any kind of algorithm, which provides output data from input data (e.g., any kind of algorithm generating or calculating output data from input data). A machine learning model may be executed by a computing system to progressively improve performance of a specific task. According to the disclosure, parameters of a machine learning model may be adjusted during a training phase based on training data. A trained machine learning model may then be used during an inference phase to make predictions or decisions based on input data.”, ¶ 0154, “In supervised learning, the model may be built using a training set of data that contains both the inputs and corresponding desired outputs. Each training instance may include one or more inputs and a desired output. Training may include iterating through training instances and using an objective function to teach the model to predict the output for new inputs. In semi-supervised learning, a portion of the inputs in the training set may be missing the desired outputs.”, ¶ 0157, “he systems and methods of the disclosure may utilize one or more classification models. In a classification model, the outputs may be restricted to a limited set of values (e.g., one or more classes). The classification model may output a class for an input set of one or more input values. An input set may include road condition data, event data, sensor data, such as image data, radar data, LIDAR data and the like, and/or other data as would be understood by one of ordinary skill in the art. A classification model as described herein may, for example, classify certain driving conditions and/or environmental conditions, such as weather conditions, road conditions, and the like. References herein to classification models may contemplate a model that implements, e.g., any one or more of the following techniques: linear classifiers (e.g., logistic regression or naive Bayes classifier), support vector machines, decision trees, boosted trees, random forest, neural networks, or nearest neighbor.”. The cited passages clearly shows that the system is configured to use a linear regression model to make prediction based on sensor data captured by sensors mounted on the robot. Additionally, one of ordinary skill in the art would have recognized that training a logistic regression model involves providing the model with inputs and desired outputs in order for the model to determine the proper weight of the logistic regression model. Therefore, the cited passages clearly teaches deriving a logistic regression formula.), and
derive the probability based on the derived regression formula and detection results by the sensors at a time point corresponding to the specific time point (Buerkle: ¶ 0090, “Another example (e.g. example 8) relates to a previously-described example (e.g. one or more of examples 1-7), wherein: the processor is configured to estimate a risk of harm to the human based on a collision probability of the one or more other autonomous agents with the human; the collision probability is determined based on a distance of the human to the one or more other autonomous agents and a behavior certainty score; and the behavior certainty score is determined based on a current movement of the human and a planned path of the autonomous agent through the environment.”, ¶ 0152, “The systems and methods of the disclosure may utilize one or more machine learning models to perform corresponding functions of the agent (or other functions described herein). The term “model” as, for example, used herein may be understood as any kind of algorithm, which provides output data from input data (e.g., any kind of algorithm generating or calculating output data from input data). A machine learning model may be executed by a computing system to progressively improve performance of a specific task. According to the disclosure, parameters of a machine learning model may be adjusted during a training phase based on training data. A trained machine learning model may then be used during an inference phase to make predictions or decisions based on input data.”, ¶ 0157, “he systems and methods of the disclosure may utilize one or more classification models. In a classification model, the outputs may be restricted to a limited set of values (e.g., one or more classes). The classification model may output a class for an input set of one or more input values. An input set may include road condition data, event data, sensor data, such as image data, radar data, LIDAR data and the like, and/or other data as would be understood by one of ordinary skill in the art. A classification model as described herein may, for example, classify certain driving conditions and/or environmental conditions, such as weather conditions, road conditions, and the like. References herein to classification models may contemplate a model that implements, e.g., any one or more of the following techniques: linear classifiers (e.g., logistic regression or naive Bayes classifier), support vector machines, decision trees, boosted trees, random forest, neural networks, or nearest neighbor.”. The cited passages clearly show that the system is configured to use a logistic regression model to determine a probability.).
Cohen teaches an information processing device comprising a processor that stores, in a storage, a piece of correspondence information in which a detection result by a sensor at a certain time point, the sensor being included in a robot for detecting at least one action from a user, is correlated with presence or absence of a predetermined action from outside to the robot within a predetermined period including the certain time point, and based on pieces of correspondence information stored in the storage and corresponding to time points different from one another, the pieces of correspondence information each being the piece of correspondence information, makes a prediction for an action that the robot receives from the user at a specific time point after the time points. Cohen does not teach wherein the processor is configured to: perform a predetermined regression analysis using, as explanatory variables, detection results by sensors each being the sensor, the detection results being included in each of the plurality of pieces of correspondence information, thereby deriving a regression formula for the probability that the robot receives the action, and derive the probability based on the derived regression formula and detection results by the sensors at a time point corresponding to the specific time point. Buerkle teaches wherein the processor is configured to: perform a predetermined regression analysis using, as explanatory variables, detection results by sensors each being the sensor, the detection results being included in each of the plurality of pieces of correspondence information, thereby deriving a regression formula for the probability that the robot receives the action, and derive the probability based on the derived regression formula and detection results by the sensors at a time point corresponding to the specific time point. A person of ordinary skill in the art would have had the technological capabilities required to have modified the device taught in Cohen with wherein the processor is configured to: perform a predetermined regression analysis using, as explanatory variables, detection results by sensors each being the sensor, the detection results being included in each of the plurality of pieces of correspondence information, thereby deriving a regression formula for the probability that the robot receives the action, and derive the probability based on the derived regression formula and detection results by the sensors at a time point corresponding to the specific time point taught in Buerkle. Furthermore, the device taught in Cohen already teaches using a machine learning algorithm to calculate a probability that a human will interact with the robot. Additionally, logistic regression is a common and known algorithm that would have been well within the technological knowledge of a person of ordinary skill in the art. As such, a person of ordinary skill in the art would have been able to modify the machine learning algorithm taught in Cohen to use logistic regression as taught in Buerkle according to known methods. Such a modification would not have changed or introduced new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an information processing device comprising wherein the processor is configured to: perform a predetermined regression analysis using, as explanatory variables, detection results by sensors each being the sensor, the detection results being included in each of the plurality of pieces of correspondence information, thereby deriving a regression formula for the probability that the robot receives the action, and derive the probability based on the derived regression formula and detection results by the sensors at a time point corresponding to the specific time point.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the information processing device taught in Cohen with wherein the processor is configured to: perform a predetermined regression analysis using, as explanatory variables, detection results by sensors each being the sensor, the detection results being included in each of the plurality of pieces of correspondence information, thereby deriving a regression formula for the probability that the robot receives the action, and derive the probability based on the derived regression formula and detection results by the sensors at a time point corresponding to the specific time point taught in Buerkle with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 9, Cohen in view of Buerkle teaches wherein the processor is configured to: derive the regression formula in response to a predetermined minimum number of pieces of correspondence information or more being stored in the memory (¶ 0152, “The systems and methods of the disclosure may utilize one or more machine learning models to perform corresponding functions of the agent (or other functions described herein). The term “model” as, for example, used herein may be understood as any kind of algorithm, which provides output data from input data (e.g., any kind of algorithm generating or calculating output data from input data). A machine learning model may be executed by a computing system to progressively improve performance of a specific task. According to the disclosure, parameters of a machine learning model may be adjusted during a training phase based on training data. A trained machine learning model may then be used during an inference phase to make predictions or decisions based on input data.”, ¶ 0154, “In supervised learning, the model may be built using a training set of data that contains both the inputs and corresponding desired outputs. Each training instance may include one or more inputs and a desired output. Training may include iterating through training instances and using an objective function to teach the model to predict the output for new inputs. In semi-supervised learning, a portion of the inputs in the training set may be missing the desired outputs.”, ¶ 0157, “he systems and methods of the disclosure may utilize one or more classification models. In a classification model, the outputs may be restricted to a limited set of values (e.g., one or more classes). The classification model may output a class for an input set of one or more input values. An input set may include road condition data, event data, sensor data, such as image data, radar data, LIDAR data and the like, and/or other data as would be understood by one of ordinary skill in the art. A classification model as described herein may, for example, classify certain driving conditions and/or environmental conditions, such as weather conditions, road conditions, and the like. References herein to classification models may contemplate a model that implements, e.g., any one or more of the following techniques: linear classifiers (e.g., logistic regression or naive Bayes classifier), support vector machines, decision trees, boosted trees, random forest, neural networks, or nearest neighbor.”. One of ordinary skill in the art would have recognized that, in order to solve for the weights of a logistic regression equation, at least one data point for each input into the logistic regression equation is necessary to solve for said weights. Therefore, because the system teaches training a logistic network based on multiple sensor inputs, this limitation is taught.), and
after deriving the regression formula, each time the processor stores a new piece of correspondence information in the memory, update the regression formula based on the plurality of pieces of correspondence information stored in the memory including the new piece of correspondence information most recently stored (Cohen: Column 15 lines 33-52, “In some implementations, the interaction estimation model 130 includes or is generated using set of heuristics 320. The heuristics 320 may represent rules or policies for interpreting sensor data. As an example, one heuristic may assess motion of a robot and motion of a user. It may indicate that if a person backs away as the robot approaches, a decreased likelihood of successful interaction should be provided. This type of determination based on known for expected signals maybe used to initially operate the model 130. As more training data is acquired, examples may prove or disprove the predictive value of individual heuristics. As a result, data acquired from various robots in different locations maybe used to incrementally update the model 130 and learn which heuristics 320 are most accurately predicting outcomes and which are not. The heuristics 320 may be altered over time based on the increased learning of the system, for example, to remove or alter low-performing heuristics, or to decrease the influence of the heuristics 320 as the model 130 training proceeds and provides more accurate output.”. The cited passages clearly shows that the machine learning model is continuously updated with newly acquired training data.).
Response to Arguments
Applicant’s arguments, see Pages 8-10, filed June 18th, 2026, with respect to the 35 U.S.C. § 101 rejection of claims 1-4 and 6-11 have been fully considered and are persuasive. The independent claims 1, 10, and 11 have been amended to recite the limitation “in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion that responds to the action”. Such a limitation is clearly an active control step of the system using the information generated by the abstract idea and clearly integrates the abstract idea into a practical application. Therefore, the 35 U.S.C. § 101 rejection of claims 1-4 and 6-11 has been withdrawn.
Applicant’s arguments with respect to claim(s) 1, 10, and 11, specifically on Page12 of Applicant’s arguments, where Applicant argues that the primary reference Cohen does not teach the limitation “in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion that responds to the action”, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's arguments filed June 18th, 2026 have been fully considered but they are not persuasive.
On Pages 10-12 Applicant argues that the primary reference fails to teach the limitations of the independent claims.
Specifically on Page 12, Applicant argues that the primary reference fails to teach the limitations “predicts whether the robot receives an action from the user at a specific time point after the plurality of time points” and “in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion”. The Examiner respectfully disagrees. As was stated in the previous Non-Final Office Action mailed on March 18th, 2026, and stated above in the 35 U.S.C. § 103 rejection section, the primary reference Cohen teaches an information processing device comprising (Cohen: Abstract): a memory (Cohen: Column 10 lines 19-30); and a processor configured to (Cohen: Column 10 lines 19-30): store, in the memory, a piece of correspondence information in which a detection result by a sensor at a certain time point(Cohen: Column 4 lines 51-67, Column 10 lines 9-18, Column 10 lines 38-44, Column 10 lines 45-62, Column 16 line 62 – Column 17 line 6, Column 17 lines 6-9), is correlated with presence or absence of a predetermined action from outside to a robot within a predetermined period including the certain time point, the sensor being included in a robot for detecting at least one action from a user (Cohen: Column 3 lines 61-67, Column 10 lines 45-62, Column 13 lines 11-30, Column 16 line 62 – Column 17 line 6, Column 17 lines 6-9), and based on a plurality of pieces of correspondence information stored in the memory and corresponding to a plurality of time points different from one another, predicts whether the robot receives an action from the user at a specific time point after the plurality of time points, the pieces of correspondence information each being the piece of correspondence information (Cohen: Column 4 lines 51-67, Column 7 lines 22-41, Column 17 lines 10-14); in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion (Cohen: Column 4 lines 9-31, Column 9 lines 22-37); and in response to predicting that the robot does not receive the action from the user at the specific time point after the plurality of time points, control the robot not to start the predetermined motion (Cohen: Column 4 lines 9-31, Column 9 lines 22-37). The cited passages of Cohen clearly teaches a system and method for controlling a robot. The system is configured to store correspondence data between sensor data, actions performed by a human, and time data when the sensor data was collected. Based on this correspondence data, the system is configured to determine the probability the human will interact with the robot (i.e. perform an action to the robot). The system then controls the robot to perform a predetermined motion based on this probability. One of ordinary skill in the art would recognize that determining a probability that a human will interact with the robot, is clearly a form of determining a probability that the human receives an action from the human. Additionally, one of ordinary skill in the art would recognize that causing the robot to initiate an interaction with the human based on the predicted probability is clearly a form of causing the robot to perform a predetermined motion in response to the prediction. Therefore, Cohen clearly teaches the limitations “predicts whether the robot receives an action from the user at a specific time point after the plurality of time points” and “in response to predicting that the robot receives the action from the user at the specific time point after the plurality of time points, control the robot to start a predetermined motion”.
Therefore, for the reasons stated above an in the 35 U.S.C. § 103 rejection section, the 35 U.S.C. § 103 rejection of claims 1, 10, and 11 are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/N.W.S./ Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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