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 .
Status of Claims
This action is in response to Application as filed on September 30, 2024. Claims 1-20 are pending.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 74 includes the language “The output from the asistant in this example may be.” This should read --The output from the assistant in this example may be --. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
A patent may be obtained for “any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof.” 35 U.S.C. § 101. The Supreme Court has held that this provision contains an important implicit exception: laws of nature, natural phenomena, and abstract ideas are not patentable. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014); Gottschalk v. Benson, 409 U.S. 63, 67 (1972) (“Phenomena of nature, though just discovered, mental processes, and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work.”). Notwithstanding that a law of nature or an abstract idea, by itself, is not patentable, the application of these concepts may be deserving of patent protection. Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1293-94 (2012). In Mayo, the Court stated that “to transform an unpatentable law of nature into a patent eligible application of such a law, one must do more than simply state the law of nature while adding the words ‘apply it.” Mayo, 132 S. Ct. at 1294 (citation omitted).
In Alice, the Supreme Court reaffirmed the framework set forth previously in Mayo “for distinguishing patents that claim laws of nature, natural phenomena, and abstract ideas from those that claim patent-eligible applications of these concepts.” Alice, 134 S. Ct. at 2355. The first step in the analysis is to “determine whether the claims at issue are directed to one of those patent-ineligible concepts.” Id. If the claims are directed to a patent-ineligible concept, then the second step in the analysis is to consider the elements of the claims “individually and ‘as an ordered combination” to determine whether there are additional elements that “transform the nature of the claim’ into a patent-eligible application.” Id. (quoting Mayo, 132 S. Ct. at 1298, 1297). In other words, the second step is to “search for an ‘inventive concept’-i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept] itself.” Id. (brackets in original) (quoting Mayo, 132 S. Ct. at 1294). The prohibition against patenting an abstract idea “cannot be circumvented by attempting to limit the use of the formula to a particular technological environment or adding insignificant post-solution activity.” Bilski v. Kappos, 561 U.S. 593, 610-11 (2010) (citation and internal quotation marks omitted). The Court in Alice noted that “[s]imply appending conventional steps, specified at a high level of generality,’ was not ‘enough’ [in Mayo] to supply an ‘inventive concept.” Alice, 134 S. Ct. at 2357 (quoting Mayo, 132 S. Ct. at 1300, 1297, 1294).
Examiners must perform a Two-Part Analysis for Judicial Exceptions. In Step 1, it must be determined whether the claimed invention is directed to a process, machine, manufacture or composition of matter.
Claims 1-20 are directed to method, non-transitory computer readable medium, and system. As such, the claimed invention falls into the broad categories of invention. However, even claims that fall within one of the four subject matter categories may nevertheless be ineligible if they encompass laws of nature, physical phenomena, or abstract ideas. See Diamond v. Chakrabarty, 447 U.S. at 309.
In Step 2A, it must be determined whether the claimed invention is ‘directed to’ a judicially recognized exception. According to the specification, “The present disclosure generally relates to video games, and more particularly to artificial intelligence assistants in video games.” (¶1) In particular, at ¶33 the specification explains that video game assistance is provided and configurable by a player based on a monitored game context.
Independent claim 1 recites the following (with emphasis):
A method, comprising:
receiving runtime data from a gameplay session by a player of a video game;
providing the runtime data from the gameplay session of the video game as an input to a gameplay model;
responsive to providing the input, receiving output data from the gameplay model;
using the output data, generating an audio stream describing a context of the gameplay session; and
providing the audio stream to the player of the video game during the gameplay session.
Claim 19 additionally adds to the method of claim 1:
A system, comprising: a processor; and a non-transitory computer-readable medium storing a set of instructions, which when executed by the processor, to implement the operations of the method of claim 1, wherein the runtime data comprises one or more of in-game images from the gameplay session, in-game video from the gameplay session, in-game audio from the gameplay session, audio conversations between players during the gameplay session, and chat messages between players during the gameplay session, and wherein the context comprises a live game status, wherein the live game status comprises one or more of a summary of a scene in the game, a position of the player in the scene, and a status of at least one NPC in the scene.
The underlined portions of claims 1 and 19 generally encompass the abstract idea, with substantially identical features in claim 13. Claims 2-12, 14-18 and 20 further define the abstract idea such as by defining the training material provided. Under prong 2, the claimed invention encompasses an abstract idea in the form of certain methods of organizing human activity and/or mental processes. The claims recite a method of providing assistance to players of a video game. This is a method of organizing human activity because it is drawn to a method of managing personal behavior during the playing of a game in addition to teaching/coaching user’s in the play of the game. Furthermore, the method can be performed in the mind of a human and/or with the aid of pencil and paper.
The evaluation of players, teaching of strategies, providing suggestions on play as a means of teaching, training, and imparting knowledge is basic to the learning or coaching process. The system, CRM and method in the instant application simply seek to automate this well-known activity using generic computers recited at a high level of generality, and, therefore, the claims are directed to the abstract concept sub-grouping of "managing personal behavior or relationships or interactions between people" including teaching and following rules or instructions, for example, an coach supervising a player during competition or game, including observing and evaluating play to provide assistance regarding strategy, gameplay in the form of suggestions.
In addition, the claims also recite a mental process (i.e., observations, evaluations, judgments, and opinions). Nothing in claim 1 limits the method to any environment computer implemented or otherwise. With regard to claims 13 and 19, but for the recitation of a processor and a computer-readable medium storing instructions executed a computer/processor, nothing in the claimed method or operations precludes the recitations from practically being performed in the mind. For example, receiving runtime data from a gameplay session by a player of a video game can be implemented by a coach watching a player play a video game; providing the runtime data from the gameplay session of the video game as an input to a gameplay model can be implemented by a coach observing and thinking about how well the player is playing; responsive to providing the input, receiving output data from the gameplay model can be implemented by a coach formulating advice for the player based on their observation and evaluation of play; using the output data, generating an audio stream describing a context of the gameplay session and providing the audio stream to the player of the video game during the gameplay session can be implemented by a coach speaking to the player to tell them based on their observation what strategy they should use to further the gameplay. If a claim, under its broadest reasonable interpretation, covers performance of recitations in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas.
Therefore, under prong 2, the claimed invention encompasses an abstract idea in the form of mental processes and/or certain methods of organizing human activity.
The above analysis demonstrates that the claimed invention encompasses an abstract idea in the form of mental processes and/or certain methods of organizing human activity. Moreover, under prong 2 the instant claims do not integrate the abstract idea into a practical application because they merely provide instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea, add only extra solution activity to the abstract idea, and/or generally link the use of the abstract idea to a particular technological environment or field of use. Under prong 2, the instant claims do not integrate the abstract idea into a practical application. In other words, the claims do not (1) improve the functioning of a computer or other technology, (2) effect a particular treatment or prophylaxis for a disease or medical condition (3) are not applied with any particular machine, (4) do not effect a transformation of a particular article to a different state, and (5) are not applied in any meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim, as a whole, is more than a drafting effort designed to monopolize the exception, the claims are directed to the judicially recognized exception of an abstract idea. See MPEP §§ 2106.05(a)-(c), (e)-(h).
Here, the abstract idea is not integrated into a practical application. According to 2019 PEG, a consideration indicative of integration into a practical application includes improvements to the functioning of a computer or to any other technology or technical field (MPEP 2106.05(a)) or adding a specific limitation other than what is well-understood, routine, conventional activity, or adding unconventional steps that confine the claim to a particular application (a non-conventional and non-generic arrangement of various computer components for filtering Internet content, as discussed in BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1350-51, 119 USPQ2d 1236, 1243 (Fed. Cir. 2016) (MPEP § 2106.05(d)).
Conversely, considerations not indicative of integration include adding words “apply it” (or equivalent) with the judicial exception or mere instructions to implement the abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. (MPEP 2106.05(f)); adding insignificant extra-solution activity (MPEP 2106.05(g)), or generally linking the use of the abstract idea to a particular technological environment or field of use (MPEP 2106.05(h)).
Here, the claims recite a highly generalized computing environment consisting of computer implemented, processing and storage resources, and a machine learning model resulting in a generalized model output i.e., in the broadest claims no specifics as to form or function. There are also the elements of extra-solution accessing data for the abstract concept and outputting data from the abstract concept.
According to Applicant, steps and operations can be implemented on many different types of devices, e.g., the computer system 700 may be implemented with one or more processors 702. Processor 702 may be a general-purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information. [Spec. ¶82]. Similarly, the specification indicates “Computer system 700 can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them stored in an included memory 704, such as a Random Access Memory (RAM), a flash memory, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device.” [Spec. ¶83]. The term “machine-readable storage medium” or “computer-readable medium” as used herein refers to any. medium or media that participates in providing instructions to processor 702 for execution. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, and transmission media. [Spec. 90]
In addition, the specification provides “The systems, methods, and/or computing devices of the present disclosure can include machine learning modules. A ‘machine learning module’ is a software module and/or hardware module including computer-executable instructions to configure, train, and/or deploy (e.g., execute) one or more machine learning models. [Spec. ¶97]. Machine learning model implementations among these types include linear regression, logistic regression, evolution strategies (ES), convolutional neural networks (CNN), deconvolutional neural networks (DNN), generative adversarial networks (GAN), recurrent neural networks (RNN), large language models (LLM), transformers, and random forest, among others. One or more machine learning model implementations can be trained and configured for performing or automating one or more tasks or processes during runtime. [Spec. ¶98]
Applicant’s specification does not disclose that the processors, memory, or models are directed to a technological solution to a technological problem that “overcome some sort of technical difficulty.” See, e.g., ChargePoint, Inc. v. SemaConnect, Inc., 920 F.3d 759, 768 (Fed. Cir. 2019). Consequently, these devices are viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer or as a means to automate the steps. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014).
The claims also recite an application of models or machine learning. The Courts have held that claimed methods are not rendered patent eligible by the fact that (using existing machine learning technology) they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. See Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Apr. 18, 2025) (precedential decision), the court held that claims that do no more than apply established methods of machine learning to a new environment are patent ineligible, See also, Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333, in the context of computer-assisted methods, that such claims are not made patent eligible under 35 USC § 101 simply because they speed up human activity.
For there to be a practical application of machine learning, the disclosure and claims require meaningful technological improvements to the machine learning models themselves and not merely applying generic machine learning to new environments to be patent eligible. In Recentive, the claims were directed towards providing parameters to a machine learning model to train the model to identify relationships between different event parameters and target features using historical data corresponding to one or more previous series of live events, and changing conditions, and, using machine learning, dynamically generate optimized maps and schedules. However, the Court found that the technology described in the claims and specification was conventional. Neither the claims nor the specification described how such an improvement in machine learning was accomplished i.e., an articulated specific technological improvement to the underlying machine learning method not just claimed uses in new environments. Consequently, the Court held the claims failed to provide “significantly more” than the abstract idea of generating event schedules and network maps through the application of machine learning.
Applicant’s disclosure and it’s claims are similar to those of Recentive. Applicant uses trained models [Spec ¶¶96-100] to generate the claimed outcomes. However, there is no disclosure of how the generative model is itself a technological improvement in machine learning, and as such is merely a tool used conventionally to execute the operations of assistance in a video game. In fact Applicant’s admit “As known to a person of ordinary skill in the art, machine learning is commonly used for performing and/or automating one or more tasks such as identification, classification, determination, adaptation, grouping, and generation, among other things. Common types (e.g., classes or techniques) of machine learning include supervised, unsupervised, regression, classification, reinforcement, and clustering, among others.” Applicant’s do not purport to improve machine learning but rather use if for its intended purpose to automate gameplay assistance.
What remains of the claim limitations is accessing data for the model or outputting an audio stream based on the model input which is extra-solution activity of data gathering for the model and insignificant post solution activity of outputting the results. Thus, claims 1-20 lack a practical application.
In Step 2B, according to the 2019 PEG, in addition to the considerations discussed in Step 2A, an additional consideration indicative of an inventive concept (aka “significantly more”) is the addition of a specific limitation other than what is well-understood, routine, conventional activity in the field (MPEP 2106.05(d)). Conversely, an additional consideration not indicative of an inventive concept is simply appending well-understood, conventional activities previously known to the industry, specified at a high level of generality, to the abstract idea (MPEP 2106.05(d) and Berkheimer Memo, April 20, 2018). Thus, the additional elements evaluated under Step 2A are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field.
The additional elements involved in accessing data related to previous gameplay instances that also relate to the particular aspect of the video game is deemed to be extra-solution activity which is well-known (receiving or transmitting data over a network (MPEP 2106.05(d)(II)(i). Similarly, output of the model as an audio stream is insignificant post solution activity that is the result of the abstract process also recited at a high-level of generality.
The specification with regard to computer implementing devices admits, “By way of example, the computer system 700 may be implemented with one or more processors 702. Processor 702 may be a general-purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gated logic, discrete hardware components, or any other suitable entity that can perform calculations or other manipulations of information.” [spec. 82].
Additionally, the specification indicates with regard to storage resources “included memory 704, such as a Random Access Memory (RAM), a flash memory, a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device” [spec. 83].
Therefore, the specification describes the processing and storage resources in generic and functional terms, which illustrates that these are merely off-the-shelf computer components arranged in conventional ways. As a result, nothing in Applicant’s specification indicates the computer system performs anything other than well understood, routine, and conventional functions, such as receiving, storing, processing, and outputting. See, Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (ed. Cir. 2016) (“Nothing in the claims, understood in light of the [S]pecification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information.”); see also Alice, 573 US. at 224—26 (receiving, storing, sending information over networks insufficient to add an inventive concept); buySAFE, Inc. v. Google, Inc., 765 F.3d 1340, 1355 (ed. Cir, 2014) (That a computer receives and sends the information over a network-—with no further specification—is not even arguably inventive.”). At best, Applicant’s claimed subject matter simply uses generic processing circuitry recited at a high level of generality to perform the abstract idea of converting input data from one form to another (e.g., a gameplay state outputs into audible assistance to help as user play a game). As noted above, the use of a generic computer system does not alone transform an otherwise abstract idea into patent-eligible subject matter. As our reviewing court has observed, “after Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” DDR Holdings, 773 F.3d at 1256 (citing Alice, 573 U.S. at 223).
Taking the claimed elements individually yields no difference from taking them in combination because each element simply performs its respective function as discussed above. The claims do not purport to improve the functioning of a computer itself, nor do they effect an improvement in any other technology or technical field. Instead, the additional features merely amount to an instruction to apply the abstract idea using generic, functional, and conventional components well-known in the art. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. 208 (2014).
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 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, 3, 10-14, and 18 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by U.S. Publication No. 2020/0269136 by Gurumurthy et al. (“Gurumurthy”).
In re claims 1 and 13, Gurumurthy discloses a method, comprising: receiving runtime data from a gameplay session by a player of a video game [Fig. 4, #402, ¶¶18,34,35, 38, 43, among others, describe game data and player input data are received 402 or otherwise obtained. This can include pulling the data from an API of a game server, if exposed, or capturing image and input data for a player session]; providing the runtime data from the gameplay session of the video game as an input to a gameplay model [Fig. 4, 408, ¶¶12, 43, 44, among others, describe providing gameplay data to a model]; responsive to providing the input, receiving output data from the gameplay model [Fig. 4, 408, ¶¶12, 20-24, 27, 43, 44, among others, describe providing output from the model to the player, such as coaching, advice, based on the gameplay data]; using the output data, generating an audio stream describing a context of the gameplay session [Fig. 4, 408 ¶¶12, 24, 27, 31, 43, 44, among others, describe providing advice might be provided through graphical display, such as to provide an indication of a path to take or a location of an enemy character, while other types of device that might relate to strategy might better be delivered via audio communication, such as to indicate to a player that it might be advantageous to ask other members of the player's team to take specific actions. In the example, advice may be given via audio with respect to a specific door, and that door can be highlighted via a graphical overlay or other such display]; and providing the audio stream to the player of the video game during the gameplay session [Fig. 4, 412 ¶¶12, 27, 30, 31, 43, 44, among others, in-game for certain types of games, or can be provided via audio communications (such as through text-to-speech) during gameplay, so the player can receive the advice while still being able to concentrate on the game].
In re claims 3 and 14, Gurumurthy discloses the runtime data comprises one or more of in-game images from the gameplay session, in-game video from the gameplay session, in-game audio from the gameplay session, audio conversations during the gameplay session, and chat messages during the gameplay session [¶¶32-36, 38, 43, 52, among others, describe inputting gameplay data into the model including video or image data from the player playing the game, as well as player input data, text data, and audio data].
In re claims 10 and 18, Gurumurthy discloses receiving a plurality of in-game images and video from a plurality of previous game sessions of at least one video game; receiving a plurality of in-game player data from the plurality of previous game sessions of the at least one video game; and training the gameplay model using the plurality of in-game images and video and the plurality of in-game player data [¶¶32-36, 38, 43, 52, among others describe providing a plurality of prior gameplay image and video and player input data (both from the player and others) which is used to train the model to handle different gameplay and recommend corresponding actions].
In re claim 11, Gurumurthy discloses, wherein the plurality of in-game images and video depict one or more of images of player characters (PCs), images of non-player characters (NPCs), names of player characters (PCs), names of non-player characters (NPCs), actions of player characters (PCs), actions of non-player characters (NPCs), gameplay scenes, and game locations [¶¶18, 32, 43, among others, describe capture of gameplay data including player data, for example, obtaining image or video data for a game session and using computer vision to analyze the individual images for video frames to determine actions being taken, as well as the current state of the game including scene, location, character information, etc.].
In re claim 12, Gurumurthy discloses, wherein the plurality of in-game player data is associated with a plurality of players of the at least one video game during the plurality of previous game sessions, and the plurality of in-game player data comprises one or more of descriptions of actions of at least one player, descriptions of locations of the at least one player, communications between the plurality of players, and descriptions of gameplay strategy by at least one player [¶¶32-36, 38, 43, 45-60, describe in game data from prior gameplay used for training models including player characters (e.g., avatars), player actions, locations, and gameplay strategy, including labeling the inputs (i.e., descriptions)].
Claims 1-5, 8, 9, 13-15, 17, and 19 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by U.S. Publication No. 2026/0021390 by Grimm et al. (“Grimm”).
In re claims 1 and 13, Grimm discloses a method, comprising: receiving runtime data from a gameplay session by a player of a video game [Fig. 7 #720, 740, ¶¶3,54,56, 71-73, among others, describe AI assistant receiving gameplay data from user and game engine while playing a video game]; providing the runtime data from the gameplay session of the video game as an input to a gameplay model [Fig. 7 #720, 740, ¶¶3,54,56, 71-73, among others, describe AI assistant receiving gameplay data from user and game engine while playing a video game. The data being input to an AI model]; responsive to providing the input, receiving output data from the gameplay model [Fig. 7, 750, ¶¶3, 54,56, 71-73, among others, describe executing the model to identify outputs relevant to a game task or data]; using the output data, generating an audio stream describing a context of the gameplay session [Fig. 7, 770, ¶¶3, 54,56, 71-77,, among others, describe the AI model may be executed to, based on the input received at block 720 and the game engine data accessed at block 740, present an audible output]; and providing the audio stream to the player of the video game during the gameplay session [Fig. 7, 770, ¶¶3, 54,56, 71-77,, among others, the output may be provided in a voice of a video game character from the video game that is being executed].
In re claim 2, Grimm discloses the gameplay model comprises a large-language model (LLM) [¶¶4, 22, 55, 73, among others, describe the model is an LLM].
In re claims 3 and 14 the runtime data comprises one or more of in-game images from the gameplay session, in-game video from the gameplay session, in-game audio from the gameplay session, audio conversations during the gameplay session, and chat messages during the gameplay session [¶¶56-61, among others, describe game runtime data including audio conversations, e.g., between user and AI assistant].
In re claims 4 and 15, Grimm discloses the context comprises a live game status, wherein the live game status comprises one or more of a summary of a scene in the video game, a position of the player in the scene, and a status of at least one NPC in the scene [Fig. 4, ¶¶58-60, among others, describe live game status including a context of a summary (sniper shot at you from hill behind you), position (behind), and status of NPC (a sniper is shooting at player)].
In re claim 5, Grimm discloses, further comprising receiving a selection by the player to configure the output data [Figs, 2-3, 9, ¶¶23, 54, 83, among others, describe player output settings, including player selection In game assistant to provide audible guidance/information and/or haptic and visual information, for example, an audible selection of input to configure output to indicate when a fellow player’s health drops to a certain level (both audible and visual)].
In re claim 8, Grimm discloses the selection comprises enabling recommendations, and the context comprises gameplay recommendations to the player during the gameplay session [¶¶23, 68, among others, describe the player selection to prompt the AI assistant for in game recommendations, for example, the player 200’s audible input asks, “Hey console, tell me what strategy and weapons to use for the next level” and the AI assistant recommends 1) a strategy for player 200 to follow, and 2) suggests a weapon loadout for the player 200].
In re claim 9, Grimm discloses the context further comprises predictive gameplay information to assist the player during the gameplay session [¶¶23, 68 , among others, describe predicting what strategy and weapons will work for the next level].
In re claim 17, Grimm discloses receive a selection by the player to configure the output data[Figs, 2-3,¶¶23, 54, among others, describe player using an audible selection of input to configure output to indicate when a fellow player’s health drops to a certain level], the selection comprising enabling recommendations [¶¶23, 68 , among others, describe the player selection to prompt the AI assistant, for example, the player 200’s audible input asks, “Hey console, tell me what strategy and weapons to use for the next level” and the AI assistant recommends 1) a strategy for player 200 to follow, and 2) suggests a weapon loadout for the player 200], and the context comprising gameplay recommendations to the player during the gameplay session and predictive gameplay information to assist the player during the gameplay session [¶¶23, 68 , among others, describe predicting what strategy and weapons will work for the next level].
In re claim 19, Grimm discloses a system [Fig. 1, #10], comprising: a processor [Fig. 1 #54,¶3]; and a non-transitory computer-readable medium storing a set of instructions, which when executed by the processor [Fig. 1 #56¶3, 13], configure the system to: receive runtime data from a gameplay session by a player of a video game[Fig. 7 #720, 740, ¶¶3,54,56, 71-73, among others, describe AI assistant receiving gameplay data from user and game engine while playing a video game]; provide the runtime data from the gameplay session of the video game as an input to a gameplay model [Fig. 7 #720, 740, ¶¶3,54,56, 71-73, among others, describe AI assistant receiving gameplay data from user and game engine while playing a video game. The data being input to an AI model]; responsive to providing the input, receive output data from the gameplay model [Fig. 7, 750, ¶¶3, 54,56, 71-73, among others, describe executing the model to identify outputs relevant to a game task or data]; using the output data, generate an audio stream describing a context of the gameplay session [Fig. 7, 750, ¶¶3, 54,56, 71-73, among others, describe executing the model to identify outputs relevant to a game task or data]; and provide the audio stream to the player of the video game during the gameplay session [Fig. 7, 770, ¶¶3, 54,56, 71-77,, among others, the output may be provided in a voice of a video game character from the video game that is being executed], wherein the runtime data comprises one or more of in-game images from the gameplay session, in-game video from the gameplay session, in-game audio from the gameplay session, audio conversations between players during the gameplay session, and chat messages between players during the gameplay session [¶¶56-61, among others, describe game runtime data including audio conversations, e.g., between user and AI assistant], and wherein the context comprises a live game status, wherein the live game status comprises one or more of a summary of a scene in the game, a position of the player in the scene, and a status of at least one NPC in the scene [Fig. 4, ¶¶58-60, among others, describe live game status including a context of a summary (sniper shot at you from hill behind you), position (behind), and status of NPC (a sniper is shooting at player)].
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 6, 7, 16, and 20 are rejected under 35 U.S.C. § 103 as being unpatentable over Grimm in view of US Publication No. 2019/0259240 by Watkeys et al. (“Watkeys”).
In re claim 6, Grimm discloses wherein the selection comprises enabling player character updates, the context comprises a plurality of player character updates during the gameplay session [Figs, 2-3, 9, ¶¶23, 54, 83, among others, describe player output settings, including player selection In game assistant to provide audible guidance/information and/or haptic and visual information, for example, an audible selection of input to configure output of character updates, for example, to indicate when a fellow player’s health drops to a certain level (both audible and visual), and locations of teammates, etc.].
Grimm teaches enabling the AI assistant and configuring outputs about player character updates including audible voice communications and/or text and symbols to convey game information. Grimm doesn’t explicitly teach disabling game audio output. However, Watkeys teaches options related to audible outputs of a gaming device (e.g., enabling/disabling some or all audible outputs, sound levels, selections of songs or other audio provided by the gaming device).
Grimm and Watkeys are both considered to be analogous to the claimed invention because they are in the same field of electronic gaming. 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 automated video game assistance of Grimm to include disabling game audio output, as taught by Watkeys, in order to increase user enjoyment, for example, by providing increased control over the game environment such as disabling audio they might find distracting or that may disturb others in the surrounding environment.
In re claim 7, Grimm discloses the plurality of player character updates comprise one or more of summaries of positions of player characters during the gameplay session, summaries of actions by player characters during the gameplay session, and summaries of communications by player characters during the gameplay session [¶23, among others, describe play character updates including providing updated information of the location of fellow teammates with regard to a player character and/or common enemy].
In re claims 16 and 20, Grim discloses receive a selection by the player to configure the output data, the selection comprising enabling player character updates, and the context comprising a plurality of player character updates during the gameplay session wherein the plurality of player character updates comprise one or more of summaries of positions of player characters during the gameplay session, summaries of actions by player characters during the gameplay session, and summaries of communications by player characters during the gameplay session [Figs, 2-3, 9, ¶¶23, 54, 83, among others, describe player output settings, including player selection In game assistant to provide audible guidance/information and/or haptic and visual information, for example, an audible selection of input to configure output of character updates, for example, to indicate when a fellow player’s health drops to a certain level (both audible and visual), and locations of teammates, etc.].
Grimm teaches enabling the AI assistant and configuring outputs about player character updates including audible voice communications and/or text and symbols to convey game information. Grimm doesn’t explicitly teach disabling game audio output. However, Watkeys teaches options related to audible outputs of a gaming device (e.g., enabling/disabling some or all audible outputs, sound levels, selections of songs or other audio provided by the gaming device).
Grimm and Watkeys are both considered to be analogous to the claimed invention because they are in the same field of electronic gaming. 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 automated video game assistance of Grimm to include disabling game audio output, as taught by Watkeys, in order to increase user enjoyment, for example, by providing increased control over the game environment such as disabling audio they might find distracting or that may disturb others in the surrounding environment.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed on the attached Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew Bodendorf whose telephone number is (571) 272-6152. The examiner can normally be reached M-F 9AM-5PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached on (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW BODENDORF/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
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