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 § 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 abstract ideas without significantly more. The claims are directed to providing audible video game player assistance that may take the form of a character giving advice. Observing game play, evaluating and judging how to improve, and orally expressing an opinion to a player are equivalent to human mental work that belongs to the enumerated abstract idea grouping of “Mental Processes.” And one person teaching another person how to improve their performance of an activity including through giving instructions is “Certain Methods of Organizing Human Activity”. The claims do not recite any technical details as to how computers are programmed to receive input data or what type of data is received, what type of learning model is used or how any particular computers are programmed to train the model, what steps the model uses to make inferences about appropriate outputs, or how output data is generated. There are also no details claimed as to what method steps constitute the model engaging as a non-player character. The claims are essentially a list of functionally-recited desired end results for applying generic, existing of machine learning language model training in a conceived field of use. Because the claims lack any description of novel hardware and/or software and because a language model is merely claimed as a field of use, no improvements to the performance of computers or to the field of neural networks can be identified.
A detailed discussion follows that is based on the guidance provided in the 2019 PEG and October 2019 update. Steps 1 and 2 of the Alice analysis have been conducted for pending claims 1-20.
Claim 1 is treated as representative; claims 13 and 19 are commensurate in scope and rise and fall with claim 1.
Claim 1 (a machine) is parsed into the following limitations:
L1: An apparatus, comprising: at least one processor system programmed with instructions;
L2: execute a video game to render a single-player game instance;
L3: while the video game is executing, receive input from a first player, the input indicating a task for a model to execute in support of the first player in the single-player game instance; and
L4: based on a trigger, present an audible output pursuant to the task.
Claim 13 is a method of use of the apparatus of claim and rises or falls with claim 1.
Claim 19 broadens claim 1 by reducing an audio output to “an in-game function” and appends a computer-readable storage medium to the generic computer and also rises and falls with claim 1.
Dependent claim overview:
Claims 2-5 and 17 recite an intended use of a data model of unknown type or function, that being providing a non-player ally or team character that “engages in the video game” in accordance with unspecified method steps.
Claims 6-11 recite ideas of in-game tasks (monitoring health, monitoring an enemy, etc.) without specifying any method steps delineating how these tasks are to be accomplished.
Claims 12, 20 recite that a model usable in the invention is some LLM (equivalent to “apply it” of the abstract idea using a generic prior art learning model).
Step 1: In this step of the Alice analysis, it is determined that all of the pending claims fall into statutory categories. The claims meet step 1 as follows:
Claims 1-12 are directed to a machine (apparatus).
Claims 13-18 are directed to a process (method).
Claims 19-20 are directed to a machine (apparatus).
Step 2A, Prong 1: In this step of the Alice analysis, judicial exception(s) that fall into abstract idea groupings enumerated in the 2019 PEG are identified and quoted.
Limitations directed to abstract ideas in the pending claims are L1:
L3 in claim 1: receive input from a player while a video game is executing, the input used for analysis by a model;
Observing video game play and making judgements and recommendations based on moves or actions a player is making is activity traditionally performed by human beings – a notoriously well-known scenario being a spectator watching another person play a video game and giving advice. This activity belongs to the grouping of “Mental Processes” (See MPEP § 2106.04(a)(2)(III)). The claims do not recite any particulars of how computers are programmed to process or analyze player inputs using a model or how machine learning models are used to generate outputs. The claims are essentially a list of functionally-recited desired end results for a conceived field of use of machine learning language model training. The claims do not delineate steps through which any particular language model achieves an improvement. Support for why the pending claimed invention is directed to abstract mental processes can be found in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), wherein "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, were held to be abstract.
L4 in claim 1: based on a trigger, present an audible output pursuant to a task.
Audibly instructing a video game player on how to better play a game falls into the grouping of “certain methods of organizing human activity” through “managing personal behavior … or interactions between people” including “teaching” and “following rules or instructions”. See MPEP § 2106.04(a)(2)(II)(C) which expounds on this grouping as including communicating notifications to users using computing devices, considering historical usage information while a user inputs data, as well as automating activities traditionally performed as mental processes. And as instructed in MPEP § 2106.04(a)(II), conducting abstract activities between one or more persons and a computer(s) is covered activity. Additionally, gathering data through observing a human being playing a game and deciding what tips to publish to inform other game players is the equivalent of human mental work (See MPEP § 2106.04(a)(2)(III)).
Independent claims 13 (method) and 19 (apparatus comprising machine readable medium) are directed to the same abstract idea implemented by generic computers; they likewise fail step 2A prong 2 and Step 2B.
Step 2A, Prong 2: In this step, any additional elements beyond the identified abstract ideas are identified and evaluated for any integration into a practical application. In particular, any claimed technological improvement is considered.
Additional elements recited in the claims include:
L1: an apparatus comprising a processor (in claim 1); a CRSM (in claim 19);
L2: a single-player video game;
These hardware components and the implied-but-unclaimed software of a video game are recited at a high level of generality and merely outline a generic technological field in which to apply the abstract ideas for applying generic machine learning to providing video game hints or tips through routine and conventional use of generic hardware, and using some implied software of unclaimed programming. The instant disclosure’s silence as to any of the claimed hardware or software having any nonobvious structural or functional specifications or requirements or being used to solve any stated problems existing in computers per se or to provide any improvements to computers per se supports a finding that the hardware additional elements of the claimed apparatus comprises merely generic components and technologies used in their routine and conventional capacities in the art. “[T]he invocation of ‘already-available computers that are not themselves plausibly asserted to be an advance … amounts to a recitation of what is well-understood, routine, and conventional.” Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1366 (Fed. Cir. 2020). And “simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” Affinity Labs v. DirectTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)
L3(a): receiving input from a player;
“The receiving of input and storing steps represent the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” Affinity Labs v. DirectTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016 attributing particular generic computer functions for computer hardware to perform from well-known, routine, conventional functions performed by such hardware has been held to be insufficient to show an improvement to technology, Affinity Labs of Tex. v. DirecTV, LLC, 838 F.3d 1253, 1264, 120 USPQ2d 1201, 1208 (Fed. Cir. 2016).
L3(b), the input indicating a task for a model …
As an initial note, the intended use of data (the data being for a model) does not positively claim the existence or use of any particular data model. That said, applying existing machine learning models to a particular field of use has been held to be ineligible by the Federal Circuit in RECENTIVE ANALYTICS, INC. v. FOX CORP., FOX BROADCASTING CO., FOX SPORTS PRODUCTIONS, LLC (2023-2437, 04/18/2025). In RECENTIVE, the two patents at issue, ‘367 and ‘960, are “Machine Learning Training” patents that are used to learn from the scheduling of live events by iteratively collecting, training, outputting, and updating a model. The outputs of the model in RECENTIVE were, in one patent, used for recommending updates to optimize a live event schedule.
Limitations L3 and L4 of the pending application parallel RECENTIVE in that they concern collecting [game input data], training [“a model” through unspecified method steps], and outputting by a model [in the form of audio generated in accordance with unspecified method steps], and wherein the outputs are recommendations for optimizing [game play].
The Federal Circuit noted that in RECENTIVE, the specification teaches that the machine learning model “employs any suitable machine learning technique” such as a “random forest, a regression, a neural network, a decision tree” or “a Bayesian network [or] other type of technique”.
In the pending application on p. 5, the specification states that the machine learning model can “employ various machine learning models, including deep learning models … may use various algorithms trained in ways that include supervised learning, unsupervised learning, reinforcement learning … which can be implemented by computer circuitry, include one or more neural networks…” So the pending application, like the patents in RECENTIVE, is directed to training existing machine learning models for a conceived field of use.
In step 1 of the Alice test, the Federal Circuit found that RECENTIVE claimed an activity that pre-dated the existence of machine learning – event planners considering prior ticket sales, weather forecasts, etc. to determine when and where to schedule event(s). The Federal Circuit explained that applying machine learning to such a field of use was not patent eligible, citing that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.” Intell. Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1366 (Fed. Cir. 2015). The court also held that “the application of existing technology to a novel database does not create patent eligibility.” and cited SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018); Elec. Power, 830 F.3d at 1353 (“[W]e have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas.”
The pending claimed activities of observing game play by a player and making recommendations on how to improve pre-dates the existence of machine learning and were traditionally conducted by human beings. Limiting the use of some unclaimed type of language model to the field of use of video game tips and hints is not patent eligible and furthermore collecting input data in a database does not create patent eligibility.
In Alice step two, the Federal circuit court in RECENTIVE agreed with the district court’s finding that the RECENTIVE patents were not directed to an “inventive concept” that would “amount[] to significantly more than a patent upon the [ineligible concept] itself,” id. at 456 (quoting Alice, 573 U.S. at 217–18), because the machine learning limitations were no more than “broad, functionally described, well-known techniques” and claimed “only generic and conventional computing devices,” id. at 457.
The pending claims, too, merely recite the training and use of a language model in broad, functionally described, well-known techniques and fail to provide an inventive concept for the same reason.
The Federal Circuit held in RECENTIVE that patents that merely claim applying existing machine learning models to a new field of use are not patent eligible. The Federal Circuit agreed with the District Court that claims to machine learning training were directed to abstract ideas when they “do not delineate steps through which the machine learning technology achieves an improvement.” The Federal circuit explained that patents to machine learning training were not eligible when “they appl[ied] machine learning to [a] new field of use.” The court held “that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
The same conclusion is reached in the pending claims. The description in the claims of “a language model” is limited to mere mention thereof. There is nothing about the model, any programming of computers using it, or any of the computer hardware claimed that reveals anything more than generic application of the model to a conceived new field of use. The claims are drafted using result-oriented language that lists desired end results of operating generic computers without describing in any detail how any of the desired end results are accomplished. There are also no details in the claims that reveal what specific types of learning model(s) are used or any details of computer programming that enables training or using the models. As such a broadest reasonable reading of the claims is that prior art existing learning models are being trained, at best, in a new field of use.
Dependent claims:
Claims 2-5 recite intended appearances of a game character that is an in-game manifestation of outputs of a data model. It is again noted that the claims do not in any way describe how any particular data model is trained, how it makes any inferences, or how recommendations are outputted or generated. And there are no technical details explaining how any of these functionally claimed end results (engages…, controls, is an ally, is a team character) are accomplished by any particular program instructions. As such how a character appears in a game are a) directed to how a message is conveyed to a human observer, which is equivalent to non-functional descriptive material which does not belong to a statutory category of invention and b) insignificant post-solution activity, neither of which can transform abstract ideas into practical applications or inventive concepts.
Claims 6-11 recite details of data gathering (monitoring health, enemies, etc), which is insignificant post-solution activity. Additionally, it has long been known for a human being looking over the shoulder of another playing a video game to observe details of game play, such as a player’s health and enemies encountered, and mentally evaluate, judge, and opine based on observations. Claims 6-10 are directed to further particulars of mental processes. Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract"); Genetic Techs. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (eligibility "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself."); Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)), “the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.”
Claims 11, 18 and 20 recite that the model of the parent claims comprises an LLM. Refer to the prior discussion of the relevance of RECENTIVE ANALYTICS, INC. v. FOX CORP. in reaching a conclusion that the pending claims to functionally-claimed use of some generic language model and data collection of a certain type of (game input) data for use in a certain (video game hints and tips) field did not make the claims that are rooted in pre-machine learning activity eligible.
The preceding additional elements, considered alone and in the context of the claims, do not integrate the abstract idea of generating audible game hints using a generic language model into a practical application that improves computer functionality or another technology. They:
Invoke generic computers, memories, and neural network(s). The specification admits that the instant invention seeks to apply existing machine learning models in a conceived new field of use or with new data, which is not an improvement to ML models or computers (see the instant specification on p. 5 which goes on to list a variety of model types such as decision trees, linear regressive, random forest, recurrent, etc. In essence, the specification is seen as an admission that the instant application seeks to train existing machine learning models using new data. RECENTIVE supports a conclusion that the instant claims merely identify a conceived field of use.)
[T]he invocation of ‘already-available computers that are not themselves plausibly asserted to be an advance … amounts to a recitation of what is well-understood, routine, and conventional.” Customedia Techs., LLC v. Dish Network Corp., 951 F.3d 1359, 1366 (Fed. Cir. 2020). And “simply adding a general-purpose computer or computer components after the fact to an abstract idea […] does not integrate a judicial exception into a practical application or provide significantly more.” Affinity Labs v. DirectTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016)
Do not recite a specific improvement to the functioning of a computer (e.g., no particular improvements to a server or a machine learning model). As no particular programming steps are claimed or disclosed that would describe how video game input data is encoded, received, used to train a model, how the model functions, or how gaming strategy outputs are generated or rendered, no evidence of improvements to a language model or computers can be found.
Do not effect a transformation of an article.
Are drafted as applying the abstract idea in the field of computer software involving LLM’s (field-of-use) using generic computing operations (execute, receive input, present, with no description of how these operations are accomplished.)
With regard to interpreting results-oriented claim language when performing a 35 USC §101 analysis, see Beteiro LLC v. DraftKings Inc., (Fed. Cir 2024) when "the claims are drafted using largely (if not entirely) result-focused functional language, containing no specificity about how the purported invention achieves these results. Claims of this nature are almost always found to be ineligible for patenting under Section 101." See also Interval Licensing LLC v. AOL Inc. (896 F.3d 1335) wherein the court found that claims to a computer software "attention manager" that displays content on unused portions of a screen were result-oriented and invalid under 35 U.S.C. § 101 because they did not recite a specific technological method for achieving the claimed result; Contour IP Holding LLC v. GoPro, Inc., 2024 U.S. App. LEXIS 22825 (Fed. Cir. 2024): The court held that claims must not only describe desired outcomes but also include a specific process or machinery for achieving that result; In re Killian, 45 F.4th 1373 (Fed. Cir. 2022): The court reaffirmed that claims simply reciting a desired result without specifying how to achieve it are directed to an abstract idea and are ineligible under 35 U.S.C. § 101. The claims at issue were directed to analyzing data from two databases. In the Step 2 of the Alice test, the court determined that there was no inventive concept because the additional elements merely involved generic and routine data gathering and analysis steps that could have been performed with or without a computer.
MPEP § 2106.05(f) explains that, “The recitation of claim limitations that attempt to cover any solution to an identified problem with no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it"”.
The pending claims do not include any technical description of mechanisms for accomplishing the claimed results. Instead, the claims use some unspecified computer and unspecified programming to conduct generic, result-oriented steps such as “execute,” “receive input”, “present”,” for performing abstract certain methods of organizing human activity and performing mental-equivalent work. The claims seek to cover any system and any method (such as any programming instructions, any computer, any language models) for applying the abstract rules and instructions for analyzing game play and generating game play suggestions. As such the claims are found to be directed to ineligible subject matter.
Step 2A Prong 2 concludes in a determination that the additional elements do not amount to a practical application of the claimed abstract ideas.
Step 2B: In this step of the Alice analysis, it is assessed whether additional elements amount to significantly more than abstract ideas. Any well-understood, routine, conventional (“WURC”) activity is also discussed along with evidentiary considerations.
Absent integration into a practical application, the claims lack “significantly more” than the abstract idea.
Additional elements that are generic computer implementation and conventional components are:
p. 5 of the specification states that the machine learning model can “employ various machine learning models, including deep learning models … may use various algorithms trained in ways that include supervised learning, unsupervised learning, reinforcement learning … which can be implemented by computer circuitry, include one or more neural networks…”
Conclusion:
Claims 1-20 are found to be ineligible under 35 U.S.C. § 101. Although step 1 is satisfied (the claims recite manufacture/process/machine), in Step 2A Prong 1, the claims are found to recite an abstract idea—outputting game strategy information based on observed and analyzed game play of a player, which is a method of organizing human activity and the equivalent of mental processes. And as found in Step 2A Prong 2, the abstract ideas are not integrated into a practical application; only generic computer implementation and field-of-use limitations are claimed such as some “model” desired to perform a step of “receive input” to result in “present an audible output”. There are no technical details in the claims that reveal how any of the claimed result-oriented language is to be accomplished such as how some generic “model” is trained or how inferences might be used to generate insights or outputs. And performing Step 2B, there is nothing “significantly more” found beyond WURC elements as evidenced by the specification.
Possible remedies:
To improve subject matter eligibility under 35 USC § 101, it is recommended to anchor the claims to concrete, non-generic technical mechanisms (such as particular software processes or nonobvious system architectures) in a way that there is evidence in the claims of certain improvements to computer or network operations or to another technology. Examples might include to:
Tie abstract steps to a specific, non-generic technological implementation that improves computer functionality or another technology with technical mechanisms claimed.
Replace high-level “receive input”, “a task for a model to execute,” “present an audible output” result-oriented steps with concrete algorithmic operations and data structures with constraints that provide a computer-functionality improvement (faster lookup, reduced cache misses, deterministic scheduling, etc.).
Provide evidence of improvements to computers or network operations in the claims by claiming certain network nonobvious server-side architecture that is also claimed as solving problems existing in the art, or claiming a certain improvement in rendering such as a GPU-accelerated improvement that provides measurable improvements to game functionality.
Provide specification support (benchmarks, comparative studies) establishing that the recited computer improvements are not WURC (per Berkheimer), enabling either Step 2A integration or Step 2B “significantly more.”
Add claim elements showing a particular machine or a transformation of an article, beyond mere data manipulation or display functions.
Limit scope to a specific technological field and architecture (e.g., “a distributed game server cluster employing [named protocol] with defined message cadence and buffer management”) and claim the architecture itself, avoiding broad “apply it on a computer” formulations.
Provide specification support demonstrating the asserted improvements are not well-understood, routine and conventional:
Implementation details: algorithms with stepwise operations, data structures with constraints, hardware configurations, protocol diagrams.
Performance evidence: benchmarks, latency/throughput graphs, memory usage comparisons versus baselines.
Engineering rationale: why existing approaches fail and how your mechanism achieves measurable gains.
Recite in the claims a technical solution to a technological problem (e.g., secure hardware-backed attestations, nonobvious protocol flows, improved cryptographic operations, sensor fusion pipelines).
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.
Claims 1-15, 17-20 are rejected under 35 U.S.C. 102(a)(1) as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over US 2024/0424405 A1 to Rao et al.
Re claim 1, Rao teaches:
An apparatus, comprising: at least one processor system programmed with instructions
[0058] states that the invention of the disclosure of Rao, including video game graphics generation and execution of a generative machine learning model, is enabled by hardware and software such as software executed by general-purpose processors.
[0059]-[0064] discuss illustrative embodiments of hardware and software used to practice the invention of Rao
to: execute a video game to render a single-player game instance;
[0016], one or more players 112 interact with game 110 which comprises NPC’s and other interactive game assets.
while the video game is executing, receive input from a first player, the input indicating a task for a model to execute in support of the first player in the single-player game instance;
[0016], “Once the game 110 is deployed, one or more players 112 can interact with the game 110. These interactions may include, in particular, interactions with non-player characters (NPCs) … or other interactive game assets (e.g., objects or machines that react in certain active ways to different manipulations, e.g., by opening, blowing up, playing music, etc.). The NPCs and other assets may be implemented with grounded computer-controlled agents 114, that is, sub-programs of the game that process player input with a generative machine learning model 116 … to affect the operation of the game in some manner.”
and based on a trigger, present an audible output pursuant to the task.
[0016], “the computer-controlled agents associated with NPCs are usually conversational agents that process natural-language player input to generate natural-language NPC output, thereby facilitating a conversation between the player character and the NPC. By using the player utterances, optionally along with contextual information … as input to the generative model 116, the possible natural-language output of the conversational agent are virtually limitless.”
[0028], “the user interface 204 may utilize a camera and/or microphone to allow the designer 100 to … speak in a voice desired for an NPC”
An NPC who speaks in a voice created by game designers used to convey natural-language NPC output to a player through a conversation is obviously, if not inherently, audible.
Re claims 13 and 19, refer to the rejection of claim 1.
Re claims 2-3, 17, [00016], [0028], the generative learning model is used to generate natural-language voice output for NPC’s. NPC’s can aid the first player by suggesting a side quest to pursue, see [0017], [0020], suggest part of the game world to explore, [0021], bias conversations in a manner that pushes a player towards preferred game paths or provides other valuable information, see [0022].
Re claims 4-11, these claims recite 1) characteristics or attributes intended for a second character, but lacking any additional method steps required to provide them and 2) the content of data representing “tasks” that are usable “for a model” in parent claim 1. Parent claim 1 does not require these “tasks” to be executed in accordance with any particular software instructions, but merely identifies they are capable of use for some model itself having unclaimed hardware and/or software. "[A]pparatus claims cover what a device is, not what a device does." Hewlett-Packard Co. v. Bausch & Lomb Inc., 909 F.2d 1464, 1469, 15 USPQ2d 1525, 1528 (Fed. Cir. 1990) (emphasis in original). A claim containing a "recitation with respect to the manner in which a claimed apparatus is intended to be employed does not differentiate the claimed apparatus from a prior art apparatus" if the prior art apparatus teaches all the structural limitations of the claim. Ex parte Masham, 2 USPQ2d 1647 (Bd. Pat. App. & Inter. 1987).
Additionally, these claims are seen as being directed to the particular payload or attributes of data (equivalent to claims to “data per se”), namely character attributes and types of tasks that are merely claimed functionally but not required to be used in any particular steps assigned to a programmed apparatus. As indicated in MPEP § 2106.03, data per se and software per se do not belong to a statutory category of invention. When considered as an additional element under the Alice analysis, then, matters of data per se cannot impart eligibility to judicial exceptions.
Restricting data in a claim to a particular type or content is also seen as filtering gathered data, which has been held in Bascom Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349, 119 USPQ2d 1236, to represent an abstract idea of the grouping of “certain methods of organizing human activity” because interacting with a database and selecting certain data by generic computers is not fundamentally different from human beings interacting with printed content. “[f]iltering software, apparently composed of filtering schemes and filtering elements, was well-known in the prior art” and “using ISP servers to filter content was well-known to practitioners.”
Re claim 12, 18, 20, see “LLM” in [0013].
Re claims 14-15, see [0016], [0028], an NPC character is both audible by virtue of having a voice created by game creators that is used to provide natural language conversational outputs to a player and visible in the game.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over US 2024/0424405 A1 to Rao et al. in view of US 20200269136 A1 to Gurumurthy et al.
Re claim 16, Although Rao teaches substantially the same inventive concept including outputting audiovisual suggestions to a game player generated using machine learning, Rao is silent as to whether haptic outputs can be generated.
Gurumurthy is an analogous prior art reference for gamer training using neural networks. Gurumurthy teaches, see [0012], [0019], that it was known in this art for “haptic feedback or guidance in near real time during game play” to be an alternative to or addition to “providing visual, audio” feedback or guidance as “the recommended next action for the player to take in the game.”
It would have been obvious to one having ordinary skill in the art before the effective filing date of the instant invention that Rao’s machine learning gamer assistance could have outputted haptic (tactile) output instead of or in addition to visual and audio guidance as taught by Gurumurthy without causing any unexpected results. An expected advantage would be that the human body is highly sensitive to haptic feedback.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STEVEN J HYLINSKI whose telephone number is (571)270-1995. The examiner can normally be reached Mon-Fri 10-530.
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/STEVEN J HYLINSKI/ Primary Examiner, Art Unit 3715
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