DETAILED ACTION
Information Disclosure Statement
Applicant’s submission of Information Disclosure Statements on 8/1/2024, 8/2/2024, and 10/6/2025 have been received and considered.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
2019 PEG Analysis
Step 1: Are the claims directed to a statutory category (e.g., a process, machine, etc.)
Claims 1-13 are directed to a process. Claims 14-26 are directed to an apparatus.
Step 2A (Prong 1): Does the claim recite an abstract idea, law of nature or natural phenomenon?
Yes, the claims recite an abstract idea. The following specific limitations in the claims under examination recite an abstract idea:
Determining a target language and natural language of a user (e.g., claims 1, 5, 14, and 18)
Interacting with a target language and a natural language (e.g., claims 1 and 14)
Providing learning assistance (e.g., claims 2, 6, 7, 9-11, 15, 19, 20, and 22-24)
Determining a relevant score of a user response, i.e., a user assessment (e.g., claims 8 and 21)
The above listed identified limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG:
Mental Processes: concepts preformed in the human mind (including on observation, evaluation, judgement, opinion).
Certain Methods of Organizing Human Activity: managing personal behavior or relationships or interactions or relationships of interaction between people (including social activities, teaching, and following rules or instructions).
The claims are primarily directed to teaching a user a foreign/target language by providing assistance in the user’s native/natural language. The abstract ideas can be performed by mental processes using observation, evaluation, judgement and opinion to determine if the user is correctly speaking the foreign/target language. Furthermore, the abstract ideas are also associated with certain methods of organizing human activity including teaching and following rules (i.e., in this case language and grammar rules).
Step 2A (Prong 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
Overall, the following additional claim limitations appear to merely implement the abstract idea, add insignificant extra-solution activity to the judicial exception, or generally link the judicial exception to a particular environment or field of use, as outlined below:
Receiving user input/response (e.g., claims 1, 8, 11, 14, 21 and 24, insignificant extra-solution activity)
Avatars having different characteristics/displayed sizes (e.g., claims 3, 4, 13, 15, 16, and 26, particular environment or field of use)
Converting spoke interaction into written interaction (e.g., claims 11 and 24 insignificant extra-solution activity)
Outputting a spoken interaction (e.g., claims 12 and 25 insignificant extra-solution activity)
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
With regard to claims 1-26, the claims as a whole do not amount to significantly more than the exception itself. The above listed additional claim limitations asses language skills in a well-understood, routine, and conventional way. Further, the additional techonology/hardware of claim 1 (e.g., AI models, avatars, and a processor) are well-understood, routine, and conventional in the art.
In order to satisfy the Berkheimer factual determination of conventional elements in the art, U.S. Patent Application Publication No. 2021/0012766 to Kim is cited for disclosing the conventional features of language analysis methods including artificial intelligence and generic processors (e.g., see at least paragraph 139). Additionally, U.S. Patent Application Publication No. 2019/0340419 to Milman is cited for disclosing that avatars are conventional in machine-learning environments (e.g., see at least paragraphs 2 and 15 for discussion that avatars are conventional in machine-learning environments, “conventional avatar generation”). Therefore, claims 1-26 are not patent eligible under 101.
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 1-26 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2007/0015121 to Johnson in view of U.S. Patent Application Publication No. 2019/0385256 to Nahamoo.
With regard to claim 1, Johnson discloses method for artificial intelligence-based (e.g., see at least paragraph 128 that discussions that “learner model update module 111 may utilize a Bayesian knowledge tracking algorithm”, wherein a BKT algorithm is known AI model) language skill assessment (e.g., see at least paragraph 55 for discussion of interactive simulations being used to develop and assess communication skills) and development using avatars (e.g., see at least Figs. 9-11 that show various virtual characters that serve as avatars, with virtual character representing the user, an agent, and natural/native people), comprising:
determining, by an electronic processor (e.g., see at least paragraph 61 that discusses the use of “processing components and data stores used in an interactive social simulation module”), a target language and a natural language of a user (e.g., see at least paragraph 42 that discusses a user learning a target/foreign language and the user has existing mastery of their native language); generating, by the electronic processor, a first avatar corresponding to the target language (e.g., see at least paragraphs 79 and 89 that discuss that the learner/user interacts with other characters 94 or 95, see also Fig. 9, wherein the other characters are native speakers of the user’s target language) and a second avatar corresponding to the natural language (e.g., see at least Fig. 9, virtual aide 91; see also paragraphs 89 and 90 for discussion of the virtual aide 91) on a graphical user interface (e.g., see screenshot on Fig. 9); generating, by the electronic processor, a first interaction for the first avatar using the target language (e.g., see at least paragraph 92 that discusses characters 96 and 98 providing communication functions, which include speaking with the user in the user’s target language); receiving, by the electronic processor, a user input to select the second avatar (e.g., see at least paragraph 89 that discusses that when a user take action in a game, the user may receive help and assistance); and in response to the user input, generating, by the electronic processor, a second interaction for the second avatar using the natural language (e.g., see at least paragraph 89 that discusses the virtual aide’s interaction with the user “in the native language of the learner”), the second interaction being associated with the first interaction (e.g., see at least paragraph 89 that discusses the virtual aide 91 providing guidance to the user to communicate with the other characters), the second avatar being associated with a second generative artificial intelligence model (e.g., see at least paragraph 128 that discussions that “learner model update module 111 may utilize a Bayesian knowledge tracking algorithm”, wherein a BKT algorithm is known AI model), the second generative artificial intelligence model communicating with the first generative artificial intelligence model to produce the second interaction (e.g., see at least paragraph 90 that discusses two agent models; see also paragraph 111 that discusses the BKT/AI algorithm);
[claim 2] wherein the second interaction comprises one or more assistance interactions (e.g., see at least paragraph 47 that discloses the virtual aide providing assistance and guidance to be tailored to each learner’s individual skills), and wherein the generating of the second interaction comprising: receiving the one or more assistance interactions from the second generative artificial intelligence model, which generates the one or more assistance interactions based on the first interaction (e.g., see at least paragraph 47 that discloses the virtual aide providing assistance and guidance to be tailored to each learner’s individual skills);
[claim 3] wherein a first characteristic of the first avatar is different from a second characteristic of the second avatar being associated with the second generative artificial intelligence model (e.g., see at least paragraph 89 and 90 that discuss the first avatar being “other characters” that are foreign/target language speakers and the second avatar being a virtual aide for the learner that speaks to the user in their native/natural language);
[claim 4] further comprising: assigning, by the electronic processor, the first characteristic being associated with the first avatar; and assigning, by the electronic processor, the second characteristic to the second generative artificial intelligence model being associated with the second avatar (e.g., see at least paragraph 89 and 90 that discuss the first avatar being “other characters” that are foreign/target language speakers and the second avatar being a virtual aide for the learner that speaks to the user in their native/natural language);
[claim 5] wherein the determining of the target language and the natural language comprises: selecting, by the electronic processor, the first avatar and the second avatar from a plurality of available avatars for the target language and the natural language, respectively, each avatar of the plurality of available avatars corresponding to one of a plurality of available generative artificial intelligence models (e.g., see at least paragraph 42 that discusses a user learning a target/foreign language and the user has existing mastery of their native language; further the “other characters” speak the user’s target language and the virtual aide speaks the user’s native language);
[claim 6] wherein the second interaction comprises a first assistance interaction in the target language, the first assistance interaction comprising one or more possible answers to respond to the first interaction (e.g., see at least paragraph 47 that discloses the virtual aide providing assistance and guidance to be tailored to each learner’s individual skills);
[claim 7] wherein the second interaction further comprises a second assistance interaction in the natural language, the second assistance interaction corresponding to the first assistance interaction (e.g., see at least paragraph 47 that discloses the virtual aide providing assistance and guidance to be tailored to each learner’s individual skills);
[claim 8] further comprising: receiving, by the electronic processor, a user response from the user, the user response being responsive to the first interaction; and determining, by the electronic processor, whether a relevance score of the user response is higher than a predetermined threshold (e.g. see at least paragraph 117 that states that the “learner’s utterance may be matched against the expected utterances, and the skill builder may give feedback according to whether or not the learner’s response is correct”, which equates to measuring a relevance score against a threshold);
[claim 9] further comprising: in response to determining the relevance score is higher than the predetermined threshold, generating, by the electronic processor, an avatar response from the first avatar, the avatar response being responsive to the user response (e.g., see at least paragraphs 92-94 for discussion of the first avatar’s response to the user’s response);
[claim 10] further comprising: in response to determining the relevance score is equal or lower than the predetermined threshold, providing, by the electronic processor, an avatar response from the first avatar, the avatar response being indicative of irrelevance to the user response (e.g., see at least paragraphs 92-94 for discussion of the first avatar’s response to the user’s response);
[claim 11] wherein the user response is a spoken interaction, wherein the method further comprises: converting, by the electronic processor, the spoken interaction to a written interaction (e.g., see at least paragraphs 86-90 for discussion of spoken interaction and see Fig. 9 for an image including written interaction “Introduce yourself to the man”, element 93); inputting, by the electronic processor, the written interaction to the first generative artificial intelligence model for an avatar response in the target language; receiving, by the electronic processor, the avatar response from the first generative artificial intelligence model; and providing, by the electronic processor, the avatar response by the first avatar to the user (e.g., see at least paragraphs 86-90 for discussion of spoken interaction and see Fig. 9 for an image including written interaction “Introduce yourself to the man”, element 93);
[claim 12] wherein each of the first interaction and a second interaction are a spoken interaction (e.g., see at least paragraphs 86-90 for discussion of spoken interaction);
[claim 13] further comprising: when the first interaction is provided from the first avatar, displaying, by the electronic processor, the first avatar at a larger scale than the second avatar on the graphical user interface (e.g., see at least Figs. 9-11 that show different avatars/characters/aide/learner in slightly different sizes).
With regard to at least claims 1 and 2, Johnson is silent regarding the type of model that manages the interactions of “other characters”/first avatar (e.g., characters 94 and 95 of Fig. 10), and more specifically it is not clear if the “other characters” are managed by an additional AI model. As set forth above, Johnson discloses the use of an AI model (e.g., see paragraph 128, BKT algorithm) and notes that the virtual aide may be driven from two different models (e.g., see paragraph 90). While, the Examiner believes that the “other characters” are also driven by an AI model, it’s not expressly stated. In order practice compact prosecution, the Examiner relies upon a secondary prior art reference to teach one than one AI model for additional entities.
Reasonably pertinent to the problem faced, Nahamoo teaches that more than one AI model may be used in a system related to human-machine interactions (e.g., see at least paragraph 85 discusses “employing more than one AI entity, each with such features as natural language processing, reasoning rules, and machine learning”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the current invention to modify Johnson with the multiple AI models for different entities as taught by Nahamoo in order to use a known technique to improve similar devices (methods, or products) in the same way. In this case, providing Johnson’s “other characters” with their own AI model will provide a more robust interaction for the learner to engage.
With regard to claims 14-26, Johnson in combination with Nahamoo make obvious all of the recited features based on the analysis set forth above for claims 1-13, which are similar in claim scope.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication No. 2024/0221721 to Hiray discusses using a neural network to convert spoken language to a target language (e.g., see at least Fig. 3)
U.S. Patent Application Publication No. 2021/0319893 to DeOrchis discusses an avatar assisted telemedicine platform with avatars being difference sizes (e.g., see at least Fig.1)
U.S. Patent Application Publication No. 2014/0272821 to Pitschel discusses user training by intelligent digital assistant (e.g., see at least Fig. 4A)
U.S. Patent Application Publication No. 2010/0100907 to Chang discusses a presentation of an adaptive avatar (e.g., see at least Fig. 7).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES S MCCLELLAN whose telephone number is (571)272-7167. The examiner can normally be reached Monday-Friday (8:30AM-5:00PM).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kang Hu can be reached at 571-270-1344. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James S. McClellan/Primary Examiner, Art Unit 3715