DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This correspondence is in response to amendment filed on May 19, 2026. Claims 1-11 are amended. Claims 12-16 are new. Amendments to claim 3 obviate the claim objection set forth in the previous rejection and as such the objection is withdrawn. Amendments to claim 10 obviate the 112(b) rejection set forth in the previous rejection, and as such the 112(b) rejection is withdrawn. Examiner’s response to arguments are included below.
Response to Arguments
Applicant argues that Hasegawa does not include any such “identifiers of action files” as recited in amended claim 1 (see Remarks Page 11). Examiner disagrees. The Cambridge Dictionary defines an “identifier” as “a set of numbers, letters, or symbols that is used to represent a piece of data or a process in a computer program” (see https://dictionary.cambridge.org/us/dictionary/english/identifier). As such, Examiner ascertains tags “0-0”, “0-1”, “0-2”, etc. which are paired with the behavior identifications “basic behavior” and “character behavior” to be the corresponding “identifiers” as they are a set of numbers and symbols that are used to represent a piece of data or a process in the computer program. Each of these “identifiers” corresponds to a specific set of motion command column entries which are considered as “action files”. As identified by Examiner in the Interview with Attorney on May 7, 2026, “file” is defined as “a complete collection of data (such as text or a program) treated by a computer as a unit especially for purposes of input and output” (see https://www.merriam-webster.com/dictionary/file definition 5.2.C(2)). Thus, the three columns which are grouped based on the above identifier are considered as the complete collection of data treated by a computer as a unit especially for the purposes of action output. Therefore, there is a plurality of identifiers of action files stored in the motion table. Examiner has thus considered the argument but determines that it is NOT PERSUASIVE.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 15-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Although Examiner ascertains that any such processor could in theory or through implied teaching change the value entry of table data which calls upon another table such that the change to the value would not alter the function of the other table which is called upon, the disclosure offers no such function which the processor is configured to change said data. The disclosure is absent any mention of active data mutability or a change of input within the tables and as such Examiner cannot ascertain that support for claims 15 and 16 exists within Applicant’s disclosure.
NOTE: Examiner rejects claims 15-16 below at the broadest reasonable interpretation based on an ordinary skill of the art given the absence of an adequate written description. Claims will be reassessed upon amendment.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4 and 9-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hasegawa et al. (US 2022/0299999 A1; hereinafter “Hasegawa”; included in Applicant’s IDS which was submitted on March 21, 2025).
Regarding claim 1, Hasegawa discloses a robot (“robot 200”; Fig. 1) comprising:
at least one memory (“storage unit 120”; Fig. 8) storing:
a first table storing a plurality of values of a growth parameter indicating a growth of the robot and a plurality of behavior identifications corresponding to respective actions of the robot in association with each other (As shown in Fig. 12, there is a growth table 123 which stores a plurality of values of a growth parameter (growth values) which indicate a growth of the robot and a plurality of behavior identifications, i.e., “basic movement” and “character movement”, corresponding to actions, i.e., movements, of the robot in association with each other.); and
a second table storing the behavior identifications and a plurality of identifiers of action files defining the actions of the robot in association with each other (As shown in Fig. 14, there is a motion table 125 which stores the behavior identifications, i.e., “basic movement”, “character movement”, and “spontaneous movement”, with a plurality of identifiers, i.e., “2-0”, “2-0 HAPPY”, and “0-0”, of action files defining the actions of the robot in association with one another. As in the previous action, the “action file” is considered as the collection of three cells, i.e., “Time”, “Twist Motor”, and “Up-and-Down Motor”, as these three cells corresponding to each behavior identification and associated identifier are “a complete collection of data treated by a computer as a unit especially for purposes of input and output”.); and
at least one processor (“processing unit 110”; Fig. 8) configured to:
acquire a value of the growth parameter (“Next, the processing unit 110 calculates the largest numerical value among these character values as the growth value (step S202)” [0126]. Thus, the processing unit calculates, i.e., acquires, a value of the growth parameter.);
determine, based on the value of the growth parameter and the first table, a behavior identification (“Next, the processing unit 110 selects the movement type using a random number based on the movement selection probability of each of the movement types acquired in step S203 (step S204)” [0127]. Thus, the processing unit selects the movement type, i.e., “basic movement” or “character movement” defined as the behavior identification, based on the value of the growth parameter as a probability distribution.);
determine, based on the behavior identification, an identifier of an action file (Each of the selected movement types which indicates the behavior identification additionally has an identifier “2-0”, “2-1”, “2-3”, etc. In the case of the “character movement” behavior identification, the processing unit further selects a character value “happy”, “active”, “shy”, or “wanted” based on an additional probability distribution (see [0129-0130]).);
loading the action file (As best understood in Applicant’s disclosure, such a “loading” process is merely a controller accessing associated data from a table to perform movement control. In other words, “loading” is best defined as “causing data to be copied or transferred into memory for use on a digital device”. In Fig. 23, S705 recites “read one row from motion table and acquire movement time and movement angle” and thus loads the action file one row at a time to the controller.); and
cause, based on the action file, the robot to perform at least one action (Also in Fig. 23, S709 reads “start motor movement” which causes the robot to perform the action associated with the one row of data read into the controller. As indicated by S711, for multiple movements in the action file, the controller continues to read each line of the action file until the reading of the motion table is finished.).
Regarding claim 2, Hasegawa discloses the robot according to claim 1,
wherein:
the first table further stores, in association with the values of the growth parameter and the behavior identifications, a plurality of probabilities (“As illustrated in FIG. 12, types of movements performed by the robot 200 according to movement triggers, such as external stimuli detected by the sensor unit 210, and a probability that each of the movements is selected according to a growth value (hereinafter referred to as a “movement selection probability”) are recorded in the growth table 123” [0081]. Thus, movement selection probabilities are additionally stored in the first table.); and
the at least one processor is further configured to determine the behavior identification based further on the probabilities (“The movement selection probabilities are set such that basic movements set according to the movement triggers are selected regardless of the character values while the growth value is small, and character movements set according to the character values are selected in the case where the growth value increases. Further, the movement selection probabilities are set such that types of the basic movements that can be selected increase as the growth value increases. Although one character movement is selected for each movement trigger in FIG. 12, types of the character movements to be selected can be increased as the character value increases similarly to the basic movement” [0081]. Thus, the processing unit is further configured to determine the behavior identification, i.e., “basic movement” or “character movement”, based on the probabilities which vary depending on the growth value.).
Regarding claim 3, Hasegawa discloses the robot according to claim 2,
wherein:
the first table further stores, in association with the values of the growth parameter, the behavior identifications, and the probabilities, a plurality of action triggers corresponding to external stimuli (“As illustrated in FIG. 12, types of movements performed by the robot 200 according to movement triggers, such as external stimuli detected by the sensor unit 210, and a probability that each of the movements is selected according to a growth value (hereinafter referred to as a “movement selection probability”) are recorded in the growth table 123” [0081]. Thus, movement triggers, i.e., plurality of action triggers corresponding to external stimuli, are additionally stored in the growth table 123.); and
the at least one processor is further configured to;
detect an external stimulus (“Next, the processing unit 110 determines whether there is an external stimulus detected by the sensor unit 210 (step S102)” [0093]. Thus, the processing unit detects an external stimulus via the sensor unit.);
determine, based on the external stimulus, an action trigger (“Then, the processing unit 110 executes the movement selection process using information on the external stimulus acquired in step S103 as the movement trigger (step S106), and then, proceeds to step S108. Note that details of the movement selection process will be described later, but the movement trigger is information such as an external stimulus serving as a trigger for a certain movement of the robot 200” [0110]. Thus, information on the external stimulus is used in the movement selection process as the movement trigger, i.e., action trigger.); and
determine the behavior identification based further on the action trigger (As can be seen in Fig. 12, the behavior identification corresponds to groupings which are further based on the movement triggers, i.e., action triggers.).
Regarding claim 4, Hasegawa discloses the robot according to claim 1, wherein:
the second table further stores, in association with the behavior identifications, a plurality of animal sounds (As seen in Fig. 14, the motion table 125 additionally includes sound data, i.e., plurality of animal sounds, in association with the behavior identifications.); and
the at least one processor is further configured to;
determine, based on the behavior information, an animal sound; and cause the robot to perform the animal sound (“In parallel with the above-described driving of the twist motor 221 and the up-and-down motor 222, the processing unit 110 reproduces a bleep sound from the speaker 231 based on sound data of the bleep sound” [0088]. Thus, in this example, the bleep sound is the animal sound associated with the behavior information and the processing unit causes the robot to perform the animal sound from the speaker.).
Regarding claim 9, Hasegawa discloses the robot according to claim 1, wherein the growth parameter corresponds to an elapsed time from a reference date and time (“In the embodiment, the largest value among these four character values is used as the growth degree data (growth value) indicating the simulated growth degree of the robot 200” [0080]. “Assuming that the first period is, for example, a period of 50 days since the simulated birth of the robot 200 (for example, at the time of the first activation by the user after purchase), the processing unit 110 determines that it is in the first period if the days-of-growth data 126 is 50 or less. If it is not in the first period (step S111; No), the processing unit 110 executes the character correction value adjustment process (step S112), and proceeds to step S115. Note that details of the character correction value adjustment process will be described later” [0115]. “For example, as this predetermined condition, a condition that “the largest value among the four character values is a predetermined value or more as the growth degree data representing the simulated growth degree of the robot 200” may be used instead of a condition that “the days-of-growth data 126 is equal to or more than a predetermined value” or together with this condition (under the OR condition). Further, this growth degree data may be set according to the number of days, may be set according to the number of times the external stimulus has been detected, may be set according to the character value, or may be set according to a value obtained by combining them (for example, the sum, an average value, or the like of them)” [0146]. Thus, the growth degree data, i.e., the growth parameter, is influenced by the character data which is adjusted based on an elapsed days-of-growth or number of days metric. Thus, the growth degree data contains data indicating an elapsed time from a reference date and time (simulated birth of the robot, [0070]). See additionally Fig. 15, S110-115 which indicates a determined character correction value adjustment process.).
Regarding claim 10, Hasegawa discloses a method of controlling a robot (Fig. 15 displays a movement control process, i.e., method, for controlling the robot 200.), the method comprising:
acquiring a value of a growth parameter indicating a growth of the robot (“Next, the processing unit 110 calculates the largest numerical value among these character values as the growth value (step S202)” [0126]. Thus, the processing unit calculates, i.e., acquires, a value of the growth parameter.);
referencing a first table storing a plurality of values of the growth parameter and a plurality of behavior identifications corresponding to respective actions of the robot in association with each other (As shown in Fig. 12, there is a growth table 123 which stores a plurality of values of a growth parameter (growth values) which indicate a growth of the robot and a plurality of behavior identifications, i.e., “basic movement” and “character movement”, corresponding to actions, i.e., movements, of the robot in association with each other. Such a table is referenced throughout the disclosed method.) and
a second table storing the behavior identifications and a plurality of identifiers of action files defining the actions of the robot in association with each other (As shown in Fig. 14, there is a motion table 125 which stores the behavior identifications, i.e., “basic movement”, “character movement”, and “spontaneous movement”, with a plurality of identifiers, i.e., “2-0”, “2-0 HAPPY”, and “0-0”, of action files defining the actions of the robot in association with one another. As in the previous action, the “action file” is considered as the collection of three cells, i.e., “Time”, “Twist Motor”, and “Up-and-Down Motor”, as these three cells corresponding to each behavior identification and associated identifier are “a complete collection of data treated by a computer as a unit especially for purposes of input and output”. Such a table is referenced throughout the disclosed method.);
determine, based on the value of the growth parameter and the first table and the second table, an identifier of an action file (“Next, the processing unit 110 selects the movement type using a random number based on the movement selection probability of each of the movement types acquired in step S203 (step S204)” [0127]. Thus, the processing unit selects the movement type, i.e., “basic movement” or “character movement” defined as the behavior identification, based on the value of the growth parameter as a probability distribution. Each of the selected movement types which indicates the behavior identification additionally has an identifier “2-0”, “2-1”, “2-3”, etc. In the case of the “character movement” behavior identification, the processing unit further selects a character value “happy”, “active”, “shy”, or “wanted” based on an additional probability distribution (see [0129-0130]).);
loading the action file (As best understood in Applicant’s disclosure, such a “loading” process is merely a controller accessing associated data from a table to perform movement control. In other words, “loading” is best defined as “causing data to be copied or transferred into memory for use on a digital device”. In Fig. 23, S705 recites “read one row from motion table and acquire movement time and movement angle” and thus loads the action file one row at a time to the controller.); and
causing, based on the action file, the robot to perform at least one action (Also in Fig. 23, S709 reads “start motor movement” which causes the robot to perform the action associated with the one row of data read into the controller. As indicated by S711, for multiple movements in the action file, the controller continues to read each line of the action file until the reading of the motion table is finished.).
Regarding claim 11, Hasegawa discloses a non-transitory computer-readable recording medium storing a program (“The ROM stores the program to be executed by the CPU of the processing unit 110 and data necessary for executing the program in advance” [0052]. Thus, the ROM, i.e., non-transitory computer-readable recording medium, stores a program which is executed by a CPU and causes the computer to perform the methods of the program as will be described below.) causing a computer to:
acquire a value of a growth parameter indicating a growth of a robot (“Next, the processing unit 110 calculates the largest numerical value among these character values as the growth value (step S202)” [0126]. Thus, the processing unit calculates, i.e., acquires, a value of the growth parameter.);
reference a first table storing a plurality of values of the growth parameter and a plurality of behavior identifications corresponding to respective actions of the robot in association with each other (As shown in Fig. 12, there is a growth table 123 which stores a plurality of values of a growth parameter (growth values) which indicate a growth of the robot and a plurality of behavior identifications, i.e., “basic movement” and “character movement”, corresponding to actions, i.e., movements, of the robot in association with each other. Such a table is referenced throughout the disclosed method.) and
a second table storing the behavior identifications and a plurality of identifiers of action files defining the actions of the robot in association with each other (As shown in Fig. 14, there is a motion table 125 which stores the behavior identifications, i.e., “basic movement”, “character movement”, and “spontaneous movement”, with a plurality of identifiers, i.e., “2-0”, “2-0 HAPPY”, and “0-0”, of action files defining the actions of the robot in association with one another. As in the previous action, the “action file” is considered as the collection of three cells, i.e., “Time”, “Twist Motor”, and “Up-and-Down Motor”, as these three cells corresponding to each behavior identification and associated identifier are “a complete collection of data treated by a computer as a unit especially for purposes of input and output”. Such a table is referenced throughout the disclosed method.);
determine, based on the value of the growth parameter and the first table and the second table, an identifier of action file (“Next, the processing unit 110 selects the movement type using a random number based on the movement selection probability of each of the movement types acquired in step S203 (step S204)” [0127]. Thus, the processing unit selects the movement type, i.e., “basic movement” or “character movement” defined as the behavior identification, based on the value of the growth parameter as a probability distribution. Each of the selected movement types which indicates the behavior identification additionally has an identifier “2-0”, “2-1”, “2-3”, etc. In the case of the “character movement” behavior identification, the processing unit further selects a character value “happy”, “active”, “shy”, or “wanted” based on an additional probability distribution (see [0129-0130]).);
load the action file (As best understood in Applicant’s disclosure, such a “loading” process is merely a controller accessing associated data from a table to perform movement control. In other words, “loading” is best defined as “causing data to be copied or transferred into memory for use on a digital device”. In Fig. 23, S705 recites “read one row from motion table and acquire movement time and movement angle” and thus loads the action file one row at a time to the controller.); and
cause, based on the action file, the robot to perform at least one action (Also in Fig. 23, S709 reads “start motor movement” which causes the robot to perform the action associated with the one row of data read into the controller. As indicated by S711, for multiple movements in the action file, the controller continues to read each line of the action file until the reading of the motion table is finished.).
Regarding claim 12, Hasegawa discloses the robot according to claim 2, wherein:
the first table stores, for each value of the growth parameter, one or more groups of one or more behavior identifications (The first table stores one or more groups of one or more behavior identifications by identifying behavior identifications associated with a specified external stimulus (see Fig. 12). Groups are identified based on the first number of the identifier. For example, identifiers “0-0” and “0-1” belong to the same group corresponding to the “body is stroked” movement trigger. As the growth parameter increases, more behavior identifications are included with the group.);
the probabilities are associated with the groups of behavior identifications (As can be seen in Fig. 12, for each group, there is a corresponding probability associated with each behavior identification included with the group.); and
the at least one processor is further configured to determine the behavior identification by:
selecting, based on the value of the growth parameter and the probabilities, a group of the groups (As can be seen in the growth table 123, the group of the groups selected is those behavior identifications which have a probability of being selected greater than 0, i.e., have a chance of occurrence, based on the value of the growth parameter. For example, with a growth value of 3 which results from the stroking of the body, the group of groups selected would be “0-0” and “0-1” of group 0.); and
select the behavior identification from the behavior identifications of the group (“Next, the processing unit 110 selects the movement type using a random number based on the movement selection probability of each of the movement types acquired in step S203 (step S204). For example, in a case where the calculated growth value is 8 and the movement trigger is “hearing a loud sound”, the “basic movement 2-0” is selected at the probability of 20%, the “basic movement 2-1” is selected at the probability of 20%, the “basic movement 2-2” is selected at the probability of 40%, and the “character movement 2-0” is selected at the probability of 20% (see FIG. 12)” [0127]. Thus, as demonstrated, the behavior identification selected is from the behavior identifications of the group.).
Regarding claim 13, Hasegawa discloses the robot according to claim 12, wherein the group includes a plurality of behavior identifications (Fig. 12 shows examples of the group including a plurality of behavior identifications which are either “basic movement” or “character movement”.).
Regarding claim 14, Hasegawa discloses the robot according to claim 13,
wherein the at least one processor is further configured to select the behavior identification randomly from the group (As identified in the example of [0127], the behavior identification is selected from the group based on a random number generated from the probabilities.).
Claim Rejections - 35 USC § 103
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.
Claims 5 and 7-8 are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa in view of Saito (US 2002/0016128 A1).
Regarding claim 5, Hasegawa teaches robot according to claim 1,
with Hasegawa further teaching …the at least one processor is further configured to:
determine… an emotion change amount(“Then, the processing unit 110 acquires the emotion change data 122 to be added to or subtracted from the emotion data 121 in response to the external stimulus acquired in step S103 (step S104)” [0094]. Thus, the processor is configured to determine an emotion change amount.);
update, based on the emotion change, an emotion parameter (““Then, the processing unit 110 sets the emotion data 121 according to the emotion change data 122 acquired in step S104 (step S105)” [0095]. Thus, the emotion data, i.e., emotion parameter, is set, i.e., updated, based on the emotion change.);
update, based on the emotion parameter, a personality parameter indicating a pseudo-personality of the robot (By the process of Fig. 17, there is a character value, i.e., personality parameter, indicating a pseudo-personality of the robot, i.e., shy/happy/active/wanted, is corrected, i.e., updated, based on the emotion map. As identified in [0068], the emotion map corresponds to the emotion data, i.e., emotion parameter.); and
set, based on the personality parameter, a pseudo-personality of the robot (Growth values are determined as the maximum character value, i.e., personality parameter, and each such personality is then weighted to determine the pseudo-personality for character movements (see [0128-0130]).).
However, Hasegawa does not explicitly teach …wherein:
the second table further stores, in association with the behavior identifications, a plurality of emotion change amounts; and
the at least one processor is further configured to:
determine, based on the behavior identification, an emotion change amount…
Saito, pertinent to the problem at hand, teaches the at least one processor is further configured to:
determine, based on the behavior identification, an emotion change amount (“A reaction behavior pattern of the dog type robot 1 is determined based on a stimulus signal from the stimulus sensors 5. Then, the control unit controls the actuators 3 or the speaker 4 so that the dog type robot 1 will act according to the determined reaction behavior pattern. The character state of the dog type robot 1 (the character determined by later-described character parameter XY), which specifies the character or the degree of growth of the dog type robot 1, changes by what reaction behavior the dog type robot 1 takes to the received stimulus. The reaction behavior of the dog type robot 1 changes according to the character state” [0041]. Therefore, there is a character state determined by a character parameter, i.e., emotion parameter, which changes depending on the reaction behavior that the dog robot takes with respect to the received stimulus. In other word, the change of the character parameter, i.e., emotion change amount, is determined based on the behavior identification.)…
Examiner ascertains that Saito implicitly teaches …the second table further stores, in association with the behavior identifications, a plurality of emotion change amounts…
Provided the direct dependency of the change amount on which reaction behavior the dog robot takes, it would additionally be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the tables of Figs. 5-7 in the teachings of Saito to further include the change amount with the behavior identification, i.e., Output No., such that the change may be determined directly when calling the corresponding action outputs with a reasonable expectation of success. Such a modification would have been obvious to one of ordinary skill in the art as the inclusion of this value in the table rather than by direct computation would increase computational efficiency of the processor by minimizing the required computational power to recall such a value. Examiner ascertains that such a modification to the table would be a mere combination of prior art elements according to known methods to yield predictable results (see MPEP 2143.I(A)).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the data storage and methods of Hasegawa to further determine the emotion change amount based on the behavior identification as taught by Saito with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make such a modification because such a modification leads to a rich variation in user interactions, thus giving the user an impression that they are communicating with the dog type robot and thus feel connected to the robotic pet (Saito, [0041]).
Regarding claim 7, Hasegawa as modified by Saito teaches the robot according to claim 5, with Hasegawa further teaching wherein:
the personality parameter includes a plurality of personality values corresponding to degrees of mutually different personalities (Fig. 11 shows the set character values, i.e., personality parameters, which includes the plurality of character values that express the degrees of mutually different personalities such as happy, shy, active, and wanted.); and
the at least one processor is further configured to set the pseudo-personality of the robot based on the personality values (As described in [0128-0130], the pseudo-personality of the robot is set based on a probabilistic weighting of character values, for setting the pseudo-personality of the robot during character movements.).
Regarding claim 8, Hasegawa as modified by Saito teaches the robot according to claim 5, with Hasegawa further teaching wherein:
the first table further stores the behavior identifications depending on the pseudo-personality of the robot (The growth table, i.e., first table, stores “character movement” behavior identifications which depend on the pseudo-personality of the robot as seen in Fig. 13.); and
the at least one processor is further configured to select the behavior identification based further on the pseudo-personality of the robot (“If the character movement has been selected (step S205; Yes), the processing unit 110 acquires a selection probability of each character based on the magnitude of each of the character values (step S206). Specifically, for each character, a value obtained by dividing the character value corresponding to the character by a total value of the four character values is defined as the selection probability of the character” [0129]. Thus, the processing unit is further configured to select the character behavior identification based further on the pseudo-personality of the robot.).
Claims 6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Hasegawa.
Regarding claim 6, Hasegawa teaches the robot according to claim 1, wherein:
…the at least one processor is further configured to;
detect an external stimulus (“If there is an external stimulus (step S102; Yes), the processing unit 110 acquires the external stimulus from the sensor unit 210 (step S103)” [0093]. Thus, an external stimulus is detected via the sensor unit through the processing unit.);
determine, based on the external stimulus, an action trigger (“Then, the processing unit 110 executes the movement selection process using information on the external stimulus acquired in step S103 as the movement trigger (step S106)” [0110]. Thus, there is a determination of a movement trigger, i.e., action trigger, based on the external stimulus.);
determine… an emotion change amount (“Then, the processing unit 110 acquires the emotion change data 122 to be added to or subtracted from the emotion data 121 in response to the external stimulus acquired in step S103 (step S104)” [0094]. Thus, the processor is configured to determine an emotion change amount.);
update, based on the emotion change amount, an emotion parameter (““Then, the processing unit 110 sets the emotion data 121 according to the emotion change data 122 acquired in step S104 (step S105)” [0095]. Thus, the emotion data, i.e., emotion parameter, is set, i.e., updated, based on the emotion change.);
update, based on the emotion parameter, a personality parameter indicating a pseudo-personality of the robot (By the process of Fig. 17, there is a character value, i.e., personality parameter, indicating a pseudo-personality of the robot, i.e., shy/happy/active/wanted, is corrected, i.e., updated, based on the emotion map. As identified in [0068], the emotion map corresponds to the emotion data, i.e., emotion parameter.); and
set, based on the personality parameter, a pseudo-personality of the robot (Growth values are determined as the maximum character value, i.e., personality parameter, and each such personality is then weighted to determine the pseudo-personality for character movements (see [0128-0130]).).
However, Hasegawa does not explicitly teach …the first table further stores emotion change amounts corresponding to respective triggers; and
the at least one processor is further configured to:
…determine, based on the action trigger, an emotion change amount…
Examiner ascertains that such teachings of Hasegawa would render the limitations obvious to one of ordinary skill in the art. In the disclosure, the emotion change data directly corresponds to the external stimulus ([0094]). The external stimulus additionally directly corresponds to the action trigger ([0110]). The behavior identification which is selected in the first table as corresponding to the action trigger (see Fig. 12) is further determined based on the growth value which depends from the emotion change amount which is calculated. One of ordinary skill in the art would have been motivated to make such a modification to Hasegawa to store such an emotion change amount with the corresponding action trigger such that in the performance of S104 and S105 which lead to S201 and S209 the calculation may be simplified to only one of the action trigger which calls the value associated with the emotion change amount additionally determined from the same external stimulus data. This simplification in the calculation would increase program efficiency by decreasing the computational power and time which is required to output the result of the emotion change data.
Regarding claim 15, Hasegawa teaches the robot according to claim 1,
wherein the at least one processor is further configured to update the second table by changing an action file associated with a behavior identification while maintaining an association between the value of the growth parameter and the behavior identification stored in the first table (Although not explicit, due to the nature of the data tables which calls on growth table 123 and motion table 125 separately, the processor is configured to update the action file in the motion table 125 associated with a behavior identification while maintaining an association between the growth parameter and behavior identification in the growth table 123 because manipulation of data in the motion table 125 specific to the action file will not influence the data of the first table. Such teachings are implied rather than directly stated in the disclosure.).
Regarding claim 16, Hasegawa teaches the robot according to claim 1,
wherein the at least one processor is further configured to update the first table by changing the behavior identification associated with the value of the growth parameter while maintaining the action file associated with the behavior identification in the second table (Although not explicit, due to the nature of the data tables which calls on growth table 123 and motion table 125 separately, the processor is configured to update the behavior identification of the growth table 123 associated with the value of the growth parameter while maintaining the action file associated with the behavior identification in the motion table 125 because the change to data entries in the behavior identification column of the growth table 123 will not influence any of the data in the motion table associated with the action file. Such teachings are implied rather than directly stated in the disclosure.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Each of US 2003/0045203 A1, US 2023/0028871 A1, and US 2021/0303964 A1 teach similar emotion change data but teach such emotion change data being utilized prior to determining the behavior of the robot similar to Hasegawa and thus cannot read on the disclosed limitations of amended claim 5. US 2020/0030706 A1 teaches emotion data as well, but this emotion data is related to a location proximity of the robot.
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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/S.L.M./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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