DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2021-200110, filed on 12/09/2021.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The claims are generally indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical errors.
Regarding claim 2, claim 2 recites “information related to the situation at the time of play by the user which situation is indicated by what is other than the game application”. In light of the specification, the most appropriate translation seems to be “information related to the situation at the time of play by the user obtained from sources external to the game application”. This interpretation of the claim language will henceforth be assumed for the purposes of compact prosecution.
Regarding claim 3, claim 3 recites “change operation at a time of an erroneous input”. Although the specification mentions “change operation” several times, it does not provide a definition for the term: thus, the definition of “change operation at a time of an erroneous input” will be assumed in light of paragraphs 101-102 and 111-112 of the specification to refer to a corrective input provided by the user to the gaming application to override or compensate for an input provided by the gameplay assistance unit which the user perceives to be erroneous. This interpretation of the claim language will henceforth be assumed for the purposes of compact prosecution.
Regarding claim 4, claim 4 recites “the assistance unit sets contents of the automatic operation according to a feature of the user at the time of the play which feature is indicated by a learning result of the correlation”. Although the specification mentions “features (at the time of play) of the user” several times, it does not provide an explicit definition for the term; however, examples are provided in paragraphs 76 and 83. In light of these examples, “a feature of the user at the time of the play which feature is indicated by a learning result of the correlation” will be assumed to refer to the behavior of a player in response to a particular in-game situation, such as attacking upon encountering a weaker opponent vs running away upon encountering a stronger opponent. This interpretation of the claim language will henceforth be assumed for the purposes of compact prosecution.
Regarding claims 5-8, as claims 5-8 depend on claims 2 and/or 4, these claims are rejected under the same reasoning as claims 2 and 4.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1- 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim 1 and dependent claim 2, analyzed as representative of the claimed subject matter, are reproduced below. The limitations determined to be abstract ideas are in italics. The additional elements recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
[Claim 1] An information processing device comprising:
a learning unit that learns a situation at a time of operation by a user on an application by machine learning; and
an assistance unit that executes, across one or more of the applications, operation assistance processing of automatically operating the application by using a learning result of the learning unit.
[Claim 2] The information processing device according to claim 1, wherein
the application is a game application, and
the situation at the time of operation includes information related to the situation at the time of the play by the user which situation is indicated by what is other than the game application,
the learning unit,
learns correlation of each of elements of the information related to the situation at the time of play and information related to the game application, and
the assistance unit
performs the automatic operation on behalf of the user by using a learning result of the correlation.
The claims are fundamentally directed to the abstract idea of using machine learning to perform automation of the operation of a computer application. Noting that a claim that requires a computer (a computer is implied by the use of machine learning) may still recite a mental process (MPEP 2106.04(a)(2)), the process of learning how to operate/play a computer/game application is fundamentally a mental process, which is classified as an abstract idea (MPEP 2106.04(a)). Furthermore, according to the interpretation of claim 2 discussed above, the Examiner notes that claim 2 (and its dependent claims 3 and 4) also directed to determining the statistical correlation between a set of variables (e.g., the correlation between variables external to the computer application and the user’s in-game activity in the case of claims 2-3 and the correlation between the current game state and the user’s in-game activity in the case of claim 4). This amounts to nothing more than a mathematical calculation, which is also classified as an abstract idea.
Regarding learning how to operate/play a computer/game application, the judicial exception is not integrated into a practical application because the claim” (MPEP 2106.04(d)). The claims merely recite generic computer components (in bold) without reciting any improvements that have been made to their functionality, and the claims generally link the use of the judicial exception, e.g., the abstract idea of learning the relationships between the inputs and outputs of a system, to a particular technological environment, e.g. wherein the system is a computer application or, more specifically, a game application. Regarding determining the statistical correlation between variables external to the application environment and the user’s in-game activity, the judicial exception is not integrated into a practical application because the limitation of a “learning unit” is merely reciting using a computer as a tool to perform an abstract idea, in this case a mathematical calculation (MPEP 2106.04(f)). Similarly, the limitation of an “assistance unit” automatically operating the game application also amounts to using a computer as a tool to perform an abstract idea, as this constitutes the assistance unit (a computer or computer component) following the “rules for playing a game”, which is classified as an abstract idea (see In re Marco Guldenaar Holding B.V., 911 F.3d 1157, 1161, 129 USPQ2d 1008, 1011 (Fed. Cir. 2018)), regardless of whether these rules are coded into the game application itself or are “high-level” play patterns (e.g., attacking an enemy with low health and avoiding enemies with high health): the latter could be considered “self-imposed” or “learned” rules for playing the game.
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements constitute insignificant extra-solution activity. More specifically, the recitation of a “learning result” in claims 1-2, 4, and 9 amounts to “necessary data outputting, i.e., all uses of the recited judicial exception require such data gathering or data output” (MPEP 2106.05(g)), as all machine learning models which are deployed by humans for the purpose of learning a relationship between various system variables and autonomously controlling a system using the results of this learned relationship must necessarily output a result (i.e., a control signal) in order to perform their assigned function, which in this case is the control of an external computer/game application. Similarly, the collection of data from sources external to the game application recited in claims 2-4 amounts to “necessary data gathering”, as calculating a statistical correlation between a set of variables necessarily requires collecting numerical data that serve as quantitative representations of those variables. Regarding claims 5-8, as claims 5-8 depend on claims 2 and/or 4, these claims are rejected under the same reasoning as claims 2 and 4.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to a computer program per se (“A program causing a computer to realize […]”).
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.
Claim(s) 1 and 9-10 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Osman et al (U.S. Patent No 20180001205).
Regarding claim 1, Osman et al discloses “an information processing device (the automated artificial intelligence (AI) personal assistance system 129) comprising a learning unit (paragraph 54, lines 1-6, “profiler engine 145 is able to collect and analyze user data when a corresponding user 115A is playing any gaming application”) that learns a situation at a time of operation by a user on an application (see paragraph 61, lines 2-5, “for every response taken by the user 115S of client device 100 to address a task or subtask or action, the action state 121, action metrics 122, and action results 123 are collected by AI personal assistant 120”, paragraph 62, lines 1-3, “the action state 121 defines the action, sub-task, or task that is addressed within the context or gaming environment of the gaming application”, and paragraph 63, lines 1-4, “The collected data (e.g., action state 121, action metrics 122, and actions results 123) are delivered to profiler engine 145 for further analysis (e.g., generating a user response to tasks of certain type)”) by machine learning (see paragraph 42, lines 18-22, “the profiler engine 145 is able to learn (e.g., by applying deep learning or artificial intelligence techniques) how a particular gaming application should be played (generally, or within the context of a given level, sub-level, or given problem)”; see also paragraphs 70, lines 5-8 and 9-13, “the neural network 190 represents an example of an automated analysis tool for analyzing data sets used by the user game play profiler 145A to determine game play profiles […] an instance of the neural network 190 may be used to train the user game play profiler 145A to determine the game play profile […] which can be used to control the game play of the user 115S" and 74, lines 5-8 and 14-17, “the output nodes may identify problems, tasks, task types, difficult tasks, approaches to complete a specific task within a gaming application, […] provide appropriate responses to take regarding a presented task or task type according to a particular user's game play style, learn appropriate responses or approaches to take with respect to a particular task, etc.”) and an assistance unit that executes, across one or more of the applications, operation assistance processing of automatically operating the application by using a learning result of the learning unit” (see paragraph 68, lines 1-7 and 8-11, “the game play controller 171 is configured to control an instance of a gaming application based on the user game play style of a corresponding user game play profile for a corresponding user 115S. In particular, when automated game play control is requested by the user 115S, detection of a first task that is presented to the user 115S is effected in order to determine an appropriate response […] The first task that is presented and/or approached in the game play of the user 115S is automatically controlled based on a defined user first response”).
The rejection of device claim 1 above applies mutatis mutandis to the corresponding limitations of method claim 9 and computer program claim 10 while noting that the rejection above cites to both device and method disclosures. Claims 9 and 10 are mapped below for clarity of the record and to specify any new limitations not included in claim 1.
Regarding claims 9-10, claims 9-10 are identical in scope to claim 1 aside from the recitation of an “information processing method” instead of an “information processing device”, in the case of claim 9, and a recitation of a computer program that implements the information processing method of claim 9, in the case of claim 10: thus, as explained above, claims 9-10 are rejected under the same reasoning as claim 1.
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.
Claim(s) 2, 4, and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osman et al (U.S. Patent No 20180001205) in view of Wakeford et al (U.S. Patent No 10286322).
Regarding claim 2, and applying the interpretation of claim 2 discussed above, Osman et al, henceforth “Osman”, discloses “the information processing device according to claim 1, wherein the application is a game application (see paragraph 4, lines 1-3, “Embodiments of the present disclosure relate to systems and method for providing gaming control to a user playing a gaming application”) and the learning unit learns correlation of each of elements of the information related to the game application (the “action state”, “action metrics”, and “action results” described by Osman in paragraph 62 are analogous to some of the examples of “elements related to the game application” provided by Applicant’s specification, as discussed in the claim interpretation section above; see also paragraph 63, lines 1-4, “The collected data (e.g., action state 121, action metrics 122, and actions results 123) are delivered to profiler engine 145 for further analysis (e.g., generating a user response to tasks of certain type)”), and the assistance unit performs the automatic operation on behalf of the user by using a learning result of the correlation” (see paragraph 68, lines 1-7 and 8-11, “the game play controller 171 is configured to control an instance of a gaming application based on the user game play style of a corresponding user game play profile for a corresponding user 115S. In particular, when automated game play control is requested by the user 115S, detection of a first task that is presented to the user 115S is effected in order to determine an appropriate response […] The first task that is presented and/or approached in the game play of the user 115S is automatically controlled based on a defined user first response”).
However, Osman does not disclose “the situation at the time of operation includes information related to the situation at the time of the play by the user which situation is indicated by what is other than the game application” or “the learning unit learns correlation of each of the elements of the information related to the situation at the time of play”.
Wakeford et al, henceforth “Wakeford”, describes a system for learning the behaviors of a player of a mobile game application in response to in-game events so that the system may automatically execute certain game actions on behalf of the player in situations when the player is not available or is otherwise preoccupied. Wakeford teaches collecting data from external sources outside of the game application, such as sensors worn on the user’s body, to determine when the system should take action on behalf of the user (see column 12, lines 49-58, “actions may be determined by action component 120 based on user activity information regarding a users' current activity in the real-world by the given user. The user activity information may be obtained by action component 120 from user component 116 and/or other sources. The user activity information, for example, may indicate that the user is performing one or more real-world activities (e.g., driving, sleeping, etc.) and/or whether or not the user may be available to interact with the online game via a client game application” and column 10, lines 44-48, “In some implementations, the user component 116 may obtain and/or receive the user activity information from personal devices associated with the users, such as client computing platform(s) 104, wearable device 124, sensors, and/or any other type of personal device associated with the given user”).
Finally, Osman provides a motivation for a user to employ artificial intelligence to automate their own gameplay, namely allowing the AI assistant to temporarily “take over” for the user when the user wishes to take a break, “maintaining a presence” in the online game for the user while freeing them to attend other tasks external to the game (see paragraph 19, lines 21-29, “in a competitive multi-player game the user can allow the game play controller to take over game play of the user, so that the user is able to break away from the game (e.g., to eat, work, etc.). The game play controller trains an AI character to simulate the game play of the user (e.g., playing style), so that their online presence is maintained in the multi-player gaming session, and so that progress can continue in the gaming application”). It should be noted that this motivation aligns rather closely with the motivation provided by Wakeford, which also describes a user not being present in an online game as potentially being undesirable for the user (see column 1, lines 17-24, “if a user cannot or does not want to launch the game application, the user may not be able to respond and/or initiate actions in response to things that occur within the online game. As such, users that are not able to or do not want to access, launch, and/or run the game application may be disadvantaged as events and/or occurrences happen within the online game, and the user cannot respond”).
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 information processing device of Osman to use data collected from external sources outside of the game application as inputs to the system, as taught by Wakeford, and they would have been motivated to do so to allow the AI personal assistant of Osman to automatically determine when the user is preoccupied with and engaging in tasks external to the game application and, in response to such a determination, execute gameplay actions on the user’s behalf, as taught by Wakeford, instead of relying on the less convenient and less efficient method of navigating through a menu and selecting an option to manually activate automatic control of their gameplay by an AI assistant, as taught by Osman, with reasonable expectation of success.
Regarding claim 4, and applying the interpretation of claim 4 discussed above, Osman (in view of Wakeford) discloses “the information processing device according to claim 2, wherein the assistance unit sets contents of the automatic operation according to a feature of the user at the time of the play which feature is indicated by a learning result of the correlation” (learning and emulation of the behavior of a player in response to a particular in-game situation by the AI assistant is disclosed by Osman in paragraphs 70, lines 9-13, “an instance of the neural network 190 may be used to train the user game play profiler 145A to determine the game play profile […] which can be used to control the game play of the user 115S" and paragraph 74, lines 5 and 14-16, “the output nodes may […] provide appropriate responses to take regarding a presented task or task type according to a particular user's game play style”).
Regarding claim 6, Osman (in view of Wakeford) discloses “the information processing device according to claim 4, wherein the assistance unit is provided in such a manner that the operation of the user can intervene interruptively during the execution of the automatic operation corresponding to the set contents” (see Osman paragraph 19, lines 14-16, “When the user wishes to return to active play, the user can stop the AI character, and allow the user to continue the game”).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osman in view of Wakeford as applied to claims 2, 4, and 6 above, and further in view of Mousavi et al.
Regarding claim 3, Osman (in view of Wakeford) discloses “the information processing device according to claim 2, wherein the elements include […] an input by the user during the play (the “action metrics” described by Osman in paragraph 62), an output in response to the input, (the output of the gaming application in response to a user’s input would be included in the “responses taken by the user” associated with Osman’s “action metrics”),” and “[…] position information of the user” (see Wakeford, column 8, lines 21-23, “a real-world location where the user took the action may be monitored, tracked, and/or stored by gameplay component 114”).
However, Osman (in view of Wakeford) does not disclose that the elements include “a game title of the game application, […] a mental and physical state of the user with respect to the input or output, […] a temperature around the user, and change operation at the time of erroneous input.”
Mousavi et al teaches a brain-computer interface system for controlling a cursor on a computer screen by employing machine learning to interpret the user’s brain activity. Particularly, Mousavi’s system is able to detect, from the user’s brain activity, whether the system has correctly or incorrectly interpreted the user’s intentions, and incorporates this into the output accordingly. Thus, Mousavi’s system can be said to employ machine learning to learn a correlation between “a mental state of the user with respect to the input or the output” of the system and is capable of changing the output of the system in response to the user’s perception of an erroneous input (“change operation at a time of an erroneous input”). Mousavi also teaches using error-related brain activity to improve the performance of a machine learning classifier in the future, essentially allowing the classifier to “learn from its mistakes” (“A second approach, based on error-driven learning, attempts to limit the occurrence of a future error by updating the classifiers upon the detection of error-related brain activity or to discard the unsupervised adaptation temporarily if an error is detected”). Furthermore, not only does Mousavi directly speak to the application of similar work to the field of video games (“For instance, the authors in [28] showed that error signals can be detected from human electrocorticography (ECoG) in a continuous task comprising a video game”), the use of brain-computer interface technology in the field of video games in general has been known in the art for many years.
Finally, Osman discloses the use of a head-mounted display as a client device, which includes “Bio-sensors 442 […] to enable detection of physiological data from a user. In one embodiment, the bio-sensors 442 include one or more dry electrodes for detecting bio-electric signals of the user through the user's skin” (paragraph 129, lines 1-5). Dry electrodes, as would be known to one of ordinary skill in the art, are capable of detecting the electrical activity of a human brain when placed on the scalp, and the inclusion of such dry electrodes in a head-mounted display means they would already be in position for recording the user’s brain activity. Dry electrodes are also capable of detecting the muscle movements of a user by picking up on the electrical signals generated when muscles contract, as would be known to one of ordinary skill in the art, meaning that Osman’s device is further inherently capable of detecting the “physical activity” of the user, in conjunction with the cameras, gyroscopes, and accelerometers embedded in or used alongside the head-mounted display (see paragraphs 119-123).
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 information processing device of Osman to employ the sensors already included in the device to record the brain activity, muscle activity, and/or movements of the user so that the device could incorporate the mental and physical state of the user as input and provide the information collected by these sensors to the machine learning model, as taught by Mousavi, and also so that Osman’s AI assistant could learn to identify, based on the user’s brain activity, when it has made a “mistake” while performing autonomous control of the user’s gameplay, as taught by Mousavi, and learn from this feedback to reduce the occurrence of future “mistakes”, as taught by Mousavi, with reasonable expectation of success; it should be noted that recording the psychological/physiological data of the user from Osman’s “bio-sensors” would also improve the accuracy of the device in determining whether the user is preoccupied with or engaging in tasks external to the game application, as taught by Wakeford.
Finally, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the “game title of the game application” as an input to the machine learning model of Osman’s information processing device, as doing so would necessarily be required by any device which intends to employ artificial intelligence to automate gameplay of multiple games, as different games have different rules, control schemes, gameplay, etc., and so the assistance unit would necessarily need to be aware of which game application was currently running in order to perform its intended function; it also would have been obvious to incorporate any of a variety of supplementary data, such as the temperature around the user, as inputs to the device, as this data would serve to corroborate data from other sources regarding the user’s mental/physical state or location, which would improve the accuracy of the device in determining whether the user is preoccupied with or engaging in tasks external to the game application, as taught by Wakeford.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osman in view of Wakeford as applied to claims 2, 4, and 6 above, and further in view of Warnell et al.
Regarding claim 5, Osman (in view of Wakeford) discloses “the information processing device according to claim 4.”
However, Osman (in view of Wakeford) does not disclose “the assistance unit replays a play scene of the user, proposes contents of the automatic operation to the user according to the replayed scene and according to a feature at the time of the play, and sets the contents of the automatic operation in a form of being interactive with the user”.
In light of the specification, claim 5 seems to be reciting allowing a user to manually review the output of the assistance unit, such that the user can decide whether or not to approve a proposed course of action that the assistance unit should take in response to a particular game state or in-game event. Having a human being assess the output of a machine learning model and provide feedback to the model based on their assessment is well-known not only in the art of machine learning in general, but also in the art of teaching machine learning models to play video games in particular, as evidenced by Warnell et al, which describes the results of a study exploring using feedback from a human being to drive the behavior of an autonomous agent learning to perform a task in a video game through a framework called TAMER. Warnell concludes that the use of human feedback to train autonomous agents to play a video game is exceedingly lucrative, noting that “After just 15 minutes of real-time interaction with a human, Deep TAMER agents were able to achieve higher scores than agents trained using state-of-the-art deep reinforcement learning techniques and orders of magnitude more training data”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow a user of the information processing device taught by Osman et al to provide feedback to the AI assistant during the process of the AI assistant learning to emulate the user’s gameplay, as taught by Warnell, and the results of this modification (being an improvement in the performance of the AI assistant) would have been predictable as the technique of providing human feedback to an autonomous agent learning to play a video game to improve the performance of the autonomous agent had been known in art for several years at the time of the claimed invention, as evidenced by Warnell.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osman in view of Wakeford as applied to claims 2, 4, and 6 above, and further in view of Vange et al (U.S. Patent No 20220274023).
Regarding claim 7, Osman (in view of Wakeford) discloses “the information processing device according to claim 6.”
However, Osman (in view of Wakeford) does not disclose “the assistance unit is provided in such a manner that intervention by or cancellation of the operation of the user can be performed with utterance of a predetermined wake word by the user as a trigger.”
Vange et al teaches a “game engine powered by an artificial intelligence system” that employs an artificial intelligence to automatically perform “low-order” aspects of gameplay in response to “higher order” instructions inferred from inputs issued by the user (see paragraphs 39-41, particularly paragraph 40 lines 1-3, “The artificial intelligence powered game engine 208 provides the player with the ability to perform any task, skill, or action during gameplay with only minimal input”). As Applicant’s and Osman’s inventions are also directed toward the use of artificial intelligence to automate gameplay, Vange is considered to belong to the same field of art: as Vange teaches the use of voice commands as player inputs to the system (paragraph 36, lines 5-8, “player inputs may comprise active gestures, motions, touch commands, voice commands, or other like actions taken on the user device 210 intended to express an intent during gameplay”), it would have been known in the art at the time of the claimed invention that systems employing artificial intelligence to automate aspects of gameplay could be designed to accept voice commands from a user. It should also be noted that, although Osman is silent on whether voice commands may be used to initiate or interrupt automated gameplay, the information processing device disclosed by Osman is, at least, inherently capable of receiving vocal commands from a user (see Osman paragraph 125, lines 2-5, “a microphone 422 may be included for capturing audio from the real environment, including sounds from the ambient environment, speech made by the user, etc.” and paragraph 136, lines 13 and 16, “Input devices of Clients 510 may include, for example, […] a voice recognition system”).
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 information processing device of Osman to allow the user to initiate or interrupt automatic control of the user’s gameplay by the AI assistant by issuing voice commands, as taught by Vange, instead of using an in-game menu, and the results of this modification would have been predictable as not only was Osman’s information processing device already inherently capable of receiving voice commands, the use of voice commands as an input to consumer electronic devices and systems designed for processing video game applications, and in particular systems featuring AI-automated gameplay, was already a known technique in the art at the time of the claimed invention, as evidenced by Vange.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Osman in view of Wakeford as applied to claims 2, 4, and 6 above, and further in view of World of Warcraft, as evidenced by Wowpedia and WoW AFK Mode Example Video.
Regarding claim 8, Osman (in view of Wakeford) discloses “the information processing device according to claim 2.”
However, Osman (in view of Wakeford) does not disclose “the assistance unit notifies another user other than the user, via a network, that the user is assisted by the operation assistance processing”.
World of Warcraft, a popular massively-multiplayer online game, allows players to enter an “Away/AFK [Away From Keyboard] mode” by entering a command in the in-game chat. When a player enters Away/AFK mode, the word “Away” will appear next to their username in angle brackets, as seen in WoW AFK Mode Example Video and confirmed by Wowpedia. Thus, displaying a visual/textual indicator next to the username of the in-game avatar of an unavailable player in a multiplayer video game to inform other players that the player is not present to control their avatar or respond to in-game messages in cases where their in-game avatar is physically present in an in-game environment was known in the art at the time of the claimed invention.
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 information processing device of Osman to include a visual/textual indicator next to the username of the in-game avatar when the user associated with that avatar is not presently issuing control inputs to their avatar, as taught by World of Warcraft, including when that user’s avatar is being controlled by an artificial intelligence, as taught by Osman, and the results of this modification would have been predictable as it would merely require appending a short message such as “<Away>” or “<AFK>” to the avatar’s username, as taught by World of Warcraft, and the technique had also been known in the art for several years at the time of the claimed invention, as evidenced by WoW AFK Mode Example Video.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH HAROLD JOHANSSON whose telephone number is (571)272-5755. The examiner can normally be reached Monday-Thursday from 8:30 to 6:30.
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/K.H.J./
Examiner, Art Unit 3715
/PETER S VASAT/ Supervisory Patent Examiner, Art Unit 3715
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