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 .
Claim Rejections - 35 USC § 112
Claims 1-21 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.
Claims 1, 10, 19, 20 and 21 recite the term “environmental instructions” but it is unclear what would constitute such instructions as it is not a standard term in the industry and there is no definition provided in the specification.
The various dependent claims inherit this issue from their respective parents.
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) 1-4, 6-13 and 15-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Caporale et al. (pub. no. 20080235581) in view of Bean et al. (pub. no. 20240304174).
Regarding claim 1, Caporale discloses a method, comprising: accessing, by a computing system, prerequisite information associated with a user- interaction environment and a user (“FIG. 3 illustrates several components comprising action engine 222. These components are common to each of the alternative embodiments of the software-implemented system. In the specific instance of the standalone alone, as shown, subscriber registration component 302 is used for registering each new subscriber or user to enable a registered user to activate and use one or more avatars in the interactive environment 224. Avatar activation component 304 is used to activate each new avatar to be used in the in the interactive environment 224 by a registered user”, [0028]),
the prerequisite information comprising at least one of scheduling data, a behavioral pattern of an artifact in the user-interaction environment, or environmental instructions (“FIG. 7 illustrates a flowchart outlining a process for monitoring and controlling avatars in an interactive environment. This process commences at step 702 and proceeds with the monitoring of events in an interactive environment, as shown in step 704. While monitoring events in the interactive environment, user responses to these events will be monitored and measured, as shown as step 706, and these user responses will be compiled, analyzed and categorized according to one or more performance metrics, decision metrics and emotion metrics, as shown as step 708. The categorized user responses will be stored, as shown as step 710, for later searching and use by a knowledge engine in formulating requests to control the actions of one or more avatars in the interactive environment while operating an autonomous mode. The avatars perform actions in the autonomous mode when the subscriber to whom the avatars have registered is not logged-in or actively responding to monitored events in the interactive environment. The process concludes, as shown at step 712, after the user responses are categorized and stored in a knowledge base”, [0043]; a categorized response that is stored interpreted to be a behavioral pattern of an artifact in the user-interaction environment);
determining, by the computing system, situation information from the user-interaction environment, the user-interaction environment defined, at least in part, by a condition associated with the user-interaction environment; generating first content based on the situation information and the prerequisite information, the first content comprising at least one of text, audio, or video; transmitting, by the computing system, a request to an external server to provide the first content to a user device of the user such that the first content is displayed by the user device via an external service of the external server (“However, if action is required, an action alert will be sent to the, user as shown at step 1008, and the avatar await a response from the user, as shown at step 1010”, [0047];
“Referring back to step 1008, an action alert can be issued by one or more of a registered users avatars in the form of electronic mail messages or telephone calls from each of the user's avatars in which a speech synthesized voice of an avatar describes the events in the interactive environment to the user. The electronic mail message, also referred to as an "Emergency Action Message," can include a request for specific user input to enable the avatar to take action in the interactive environment in response to an imminent event or threat”, [0048]);
receiving, by the computing system, a response from the user device associated with the first content; providing a game service based on the response (“If a user responds to an action alert received on a telephone call with a command, the avatar will attempt to execute the command, as shown at step 1102 in FIG. 11”, [0049]);
and altering, by the computing system, at least a portion of the prerequisite information of the user-interaction environment based at least in part on the response (“Returning to FIG. 11, if a user response (i.e., a user action in the interactive environment) is received instead of a user command (i.e., an email or verbal directive to the avatar issuing the action alert), then the interactive environment will enter a real user operational mode, as shown at step 1114, and then return, as shown at step 1116. A user response involves a user logging into the interactive environment to take control of the one or more avatars in the interactive environment and to respond directly to monitored events in that environment”, [0051];
“While monitoring events in the interactive environment, user responses to these events will be monitored and measured, as shown as step 706, and these user responses will be compiled, analyzed and categorized according to one or more performance metrics, decision metrics and emotion metrics, as shown as step 708. The categorized user responses will be stored, as shown as step 710, for later searching and use by a knowledge engine in formulating requests to control the actions of one or more avatars in the interactive environment while operating an autonomous mode”, [0043]; a subsequent user response that is categorized and stored is interpreted to be altering of prerequisite information).
Regarding claim 1, it is noted that Caporale does not disclose AI generating content. Bean however, teaches an AI generating content (“After the parameters 214 are derived, the system 200 can input the text for synthesis 216 and the parameters 214 into a generative artificial intelligence (AI) model 218. In the particular example, the user 230 can be playing a learning game on the video game application 242 (i.e., the children's video game). The learning game can involve a non-play character (NPC) presenting questions for the user 230 to answer. Thus, the text for synthesis 216 input into the generative AI model 218 can be pre-canned speech of the questions for the learning game. In other examples, the text for synthesis 220 can be help information or accessibility content associated with the video game application 242. Additionally, in some examples, the text for synthesis 216 can be generated by the video game application 242 based on an action performed by the user 230 during video game play. For example, the user 230 can be playing a video game application 242 that involves a combat activity against an NPC, and the text for synthesis 216 can be generated in response to the player performing a particular move in the combat activity.
The generative AI model 218 can be trained to predict how consonants, vowels, diagraphs etc. of the text for synthesis 216 may sound in the background voice 208 to generate the words, phrases, etc. of the text for synthesis 216. Therefore, the generative AI model 218 can output audio data 222 that can include synthesized speech 224 of the text for synthesis 216. The synthesized speech 224 can have features similar to the background voice 208, such as similar pitch, intensity, and cadence. The system 200 can then receive the audio data 222 including the synthesized speech 224 from the generative AI model 218. The system 200 can further play the audio data 222 in accordance with the video game application 242. In the particular example, the generative AI model 218 can output audio data 222 with the synthesized speech 224 of the questions and can play the audio data 222 to cause the NPC to sound like the mother when presenting the questions to the user 230”, [0040] & [0041]).
Exemplary rationales that may support a conclusion of obviousness include use of known technique to improve similar devices (methods, or products) in the same way. Here both Caporale and Bean are directed the interactive entertainment systems that provide voice communication from virtual characters. To modify Caporale so that the telephone call is generated by AI as taught by Bean would be to use a known technique to improve a similar device in the same way. Therefore, it would have been obvious to a person having ordinary skill in the art as of the claimed invention to add the Bean AI generation of voice content to Caporale. To do so would enhance the verisimilitude of the entertainment system.
Regarding claim 2, Caporale discloses generating of the first content comprises: obtaining first situation information about a situation faced by the user in the game service (“FIG. 10 depicts a process that enables an avatar to interact with a user when that user is not logged-in to an interactive environment. The process commences as step 1002 with the monitoring of events in an interactive environment while an avatar is in a protected wait state, as shown at step 1004. Depending on the monitored event, certain actions may be required by the avatar, as indicated by step 1006”, [0047]);
and generating the first content, based on the prerequisite information and the first situation information ([0047] & [0048]).
Regarding claim 3, Caporale discloses obtaining second situation information about a situation faced by the user in the game service; generating second content produced by setting a specific person as a recipient, based on the prerequisite information and the second situation information; requesting the external server to display the second content through the external service; obtaining, from the external server, a second answer response input by the user, based on the second content; and providing the game service controlled based on each of the first response and the second response ([0047] – [0049]; any other event occurring in the virtual environment).
Regarding claim 4, Caporale discloses obtaining sender information about a sender of the first content; obtaining first situation information about a situation faced by the user in the game service; and obtaining the first content transmitted from the sender to a recipient, based on the prerequisite information, the sender information, and the first situation information ([0047] & [0048]).
Regarding claim 6, Caporale discloses the first content comprises at least one of an image, video, text, and speech ([0048]).
Regarding claim 7, Caporale discloses receiving, from the external server, the first response obtained in the external service ([0049]).
Regarding claim 8, Caporale discloses the first response comprises at least one of text and speech ([0049]).
Regarding claim 9, Caporale discloses obtaining real life data related to a real life person corresponding to at least one character in the game service; and changing the prerequisite information, based on the real life data (“After updating, the interactive environment will be monitored to determine if a user has logged off, as shown in step 912. If a user has not logged off, a wait time will be checked to determine whether the length of time between the occurrence of the last action performed by the user and the present time exceeds a wait time threshold for receiving a response from a user to a monitored event in the interactive environment, as shown in step 914. If the wait time has been exceeded, then the one or more avatars for a registered user will enter into a protected wait state in the interactive environment and await commands or requests to take actions from the user, as shown at step 916.
Alternatively, if the user has logged off, as shown in step 912, then the user's avatars will enter into the protected wait state in the interactive environment, as shown in step 916. If the time in which a user takes an action has not exceeded the wait time, as shown at step 914, then the user responses will continue to be actively monitored and compared to the record of stored responses in the knowledge base, as shown at step 906”, [0045] & [0046]).
Claims 10-13 and 15-18 are directed to an apparatus that implements the methods of claim 1-4 and 6-9 respectively and are rejected for the same reasons as claims 1-4 and 6-9 respectively.
Regarding claim 19, Caporale discloses a server comprising: a memory storing instructions; and at least one processor functionally connected to the memory and configured to execute the instructions to (“FIGS. 5A and 5B depict alternative embodiments of the software-implemented system in a client/server configuration. In particular, these figures represent client devices 104 and server devices 102. FIG. 5A represents a server device 102 comprising one or more input devices 502, a communication interface 504, a read only memory 506, a storage device 518, a processor 512, a program memory 514 and one or more output devices 516. Each of these components of the server device 102 is communicatively coupled to communication bus 510. Program memory 514 includes a knowledge base 518 and a server knowledge engine 520. Server knowledge engine 520 continuously monitors and analyses user responses and forms associations between events monitored by client knowledge engine 540 in the interactive environment 536 and the user responses. Knowledge engine 520 also applies processes to categorize user responses according to various metrics, including a decision metric, a performance metric and an emotion metric. Knowledge based 518 stores associations between monitored events and monitored user responses and permits the organized storage of the associated events and responses according to the categories in which user responses have been categorized based on the applicable metric (i.e., decision, performance or emotion)”, [0038]):
accessing, by a computing system, prerequisite information associated with a user-interaction environment and a user, the prerequisite information comprising at least one of scheduling data, a behavioral pattern of an artifact in the user-interaction environment, or environmental instructions ([0028], [0043]; a categorized response that is stored interpreted to be a behavioral pattern of an artifact in the user-interaction environment);
determining, by the computing system, situation information from the user-interaction environment, the user-interaction environment defined, at least in part, by a condition associated with the user-interaction environment; generating first content based on the situation information and the prerequisite information, the first content comprising at least one of text, audio, or video; transmitting, by the computing system, a request to an external server to provide the first content to a user device of the user such that the first content is displayed by the user device via an external service of the external server; receiving, by the computing system, a response from the user device associated with the first content ([0047] – [0049]);
providing a game service based on the response; and altering, by the computing system, at least a portion of the prerequisite information of the user-interaction environment based at least in part on the response ([0051]; [0043]; a subsequent user response that is categorized and stored is interpreted to be altering of prerequisite information).
Regarding claim 20, Caporale discloses a user terminal for providing a game service, the user terminal comprising: a user input unit configured to receive a user input; a memory storing instructions; and at least one processor functionally connected to the user input unit and the memory, and configured to execute the instructions to (“FIG. 5B illustrates a client device 104 comprised of one or more input devices 522, a communication interface for communicating to and from server device 102, as illustrated in the FIG. 5A, a read only memory 526, a storage device 528, a processor 532, a program memory 534 and one or more output devices 530. Each of these components is coupled to communication bus 525 to facilitate inter-component communication. Program memory 534 includes a client knowledge engine 540, an action engine 542, an interactive environment 536 and an operating system 538. Each of the components included in program memory 534 are used by processor 532 for execution of the interactive environment 536. The interactive environment 536 is a computer-generated environment that controls the autonomous execution of computer-generated events. In an embodiment the interactive environment 536 is a computer-generated simulation environment, while on a different embodiment the interactive environment 536 is a computer-generated video game environment. Client knowledge engine 540 continuously monitors events in the interactive environment 536 and user responses to those events when a registered user is logged-in and actively responding to events in the interactive environment 536. Client knowledge engine 540 compiles a profile of the user responses and actively reports those responses to server knowledge engine 520 for analysis, association and categorization. User responses are categorized by several different metrics, including a decision metric, a performance metric and an emotion metric. After categorization, the responses are stored in knowledge base 518 according to one or more associations and applicable metrics”, [0039]):
accessing, by the user terminal, prerequisite information associated with a user-interaction environment and a user, the prerequisite information comprising at least one of scheduling data, a behavioral pattern of an artifact in the user-interaction environment, or environmental instructions ([0028], [0043]; a categorized response that is stored interpreted to be a behavioral pattern of an artifact in the user-interaction environment);
transmitting, by the user terminal, a request to an external server to provide first content to a user device of the user such that the first content is displayed by the user device via an external service of the external server; receiving, by the user terminal, a response from the user input unit, the response associated with the first content; providing, by the user terminal a game service based on the response ([0047] – [0049]);
and altering, by the user terminal, at least a portion of the prerequisite information of the user- interaction environment based at least in part on the response ([0051]; [0043]; a subsequent user response that is categorized and stored is interpreted to be altering of prerequisite information).
Regarding claims 19 and 20, it is noted that Caporale does not disclose AI generating content. Bean however, teaches an AI generating content ([0040] & [0041]).
Exemplary rationales that may support a conclusion of obviousness include use of known technique to improve similar devices (methods, or products) in the same way. Here both Caporale and Bean are directed the interactive entertainment systems that provide voice communication from virtual characters. To modify Caporale so that the telephone call is generated by AI as taught by Bean would be to use a known technique to improve a similar device in the same way. Therefore, it would have been obvious to a person having ordinary skill in the art as of the claimed invention to add the Bean AI generation of voice content to Caporale. To do so would enhance the verisimilitude of the entertainment system.
Claim 21 is directed an article of manufacture containing code that implements the method of claim 1 and is rejected for the same reasons as claim 1.
Response to Arguments
Applicant’s arguments filed on June 24, 2026 have been fully considered but they are not entirely persuasive.
On pages 13-15 applicant argues that the amended claims are not an abstract idea because they are an automated computational loop that alters operational parameters of a software environment. Examiner agrees. The rejections based on 101 have been withdrawn.
On pages 16-19 applicant argues that the amended claims overcome the prior art of record because Caporale fails to disclose generating the first content using AI or altering the perquisite information based on the response. Examiner agrees in part. Caporale does not disclose generating content using AI. However, it would have been obvious to do so given the teachings of Bean as detailed above. Examiner disagrees that Caporale fails to disclose altering the prerequisite information based on the response. If the user responds by directly controlling the avatar and such control is categorized and stored it would amount to altering the prerequisite information based on the response.
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.
/LAWRENCE S GALKA/Primary Examiner, Art Unit 3715
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