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 .
Response to Arguments
Applicant’s arguments, with respect to the rejection of the claims under U.S.C. 101 as being directed to abstract idea, have been fully considered and are persuasive. The rejection has been withdrawn.
Applicant's arguments with respect to the rejection of claims 1and 8 as being anticipated by Wang, US 2023/0245651, have been fully considered but they are not persuasive for the following reasons.
In the non-final rejection, claims 1 and 8 were rejected over the teachings of Wang, in response applicant disagree with the rejection arguing that Wang doesn’t teach the claimed machine learning model configured for generating contextually relevant and varied response.
The examiner, respectfully, disagree with the applicant’s conclusion. Wang’s system is directed to an AI based conversational system implementing a plurality of models including natural language understanding model, natural language processing model and machine learning models, for conducting contextually relevant and therefore, varied responses/interactions with the user.
Wang teaches a conversational system where information received from the user utterance and plurality of contexts extracted are input to a context aware machine learning model to generate/determine the user intent and generate responses that varied according to the extracted context and the generated intent.
According to Wang the machine learning model 201 comprises a plurality of context aware models configured to analyze and provide contextually relevant conversational interaction with the user [0023] FIG. 16 depicts a flow chart illustrating the collaborative functioning of the environment context analysis and prediction (ECAP) model, a context matching (CM) method, and a context-aware model (CAM) to enhance the AI system in comprehending and anticipating the context within the environment.]
Wang teaches, Fig.18, where a context aware machine learning model is used to determine the intent and to deliver pertinent, contextually relevant and precise responses.
[0389] The process starts by receiving the user’s input and contextual information 1801, such as the user’s location, activity, or previous interaction history. User’s input can be in the form of voice commands, text input, or gesture recognition.
[0390] The AI system then applies natural language processing (NLP) techniques to analyze the user’s input, such as keywords or phrases that indicate the user’s intent or objective, and analyzes the available contextual information 1802, such as the user’s location, time of day, and relevant object attributes stored in the OKB.
[0391] The extracted information and analyzed contextual information are then classified and categorized using ML algorithms 1803, to generate a set of most likely intents and objectives 1804.”
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The examiner notes where Wang system includes the step wherein the user validates the contextual relevant response generated by the machine learning model, however this is simply an extra step performed by the system. For example, Figs.20- 21 show where the validation of the response by the user is not required.
Therefore, the examiner submits that subject matters claimed in claims 1 and 8 and argued by the applicant are anticipated by Wang, thus maintains the rejection of the claims for the foregoing reasons.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 5-10 and 12-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (US 2023/0245651).
As to claim 8, Wang teaches a computer implemented method performed by AI voice assistance system 100 (Figs.1, 6, 7, 18), for holding contextually relevant conversation with a person, comprising the steps of:
providing (making accessible) at least one machine learning (ML) model (201) configured for generating contextually relevant and varied responses in natural language conversations, by: loading the at least one ML model into the at least one memory from a storage medium storing the at least one ML model; and/or connecting, via a communication connection (109) of the system, with a server (107) providing a conversation interface to the at least one ML model;
detecting (601) a voice utterance of the person using the at least one microphone 122;
providing (602, 1802) the voice utterance as an input to the at least one ML (NLP) model;
prompting/applying the at least one ML model 201 to generate an output (1803-1805) based on the input and (available contextual information); and
providing (606, 1806-1808) the output to the at least one speaker 123 to be output to the person (Pars.5, 57, 196-199, 391-392; Figs.19-24)
As to claim 9, Wang teaches (Fig.2) wherein the at least one ML model 201 comprises: a Natural Language Understanding (NLU) module 204 for parsing a user input; a Context Management (CM) module 211 for maintaining a conversation context; and a Generative Language (GL) module 206 for producing coherent responses 216, ([0143] The Natural Language Generation (NLG) module 205 is also an important component of the AI system 200 designed for conversational interactions. The NLG module is responsible for creating coherent, human-like text responses based on the input and context provided by other components of the AI system, such as NLU and context-aware modules. The NLG module enables AI systems to generate responses that are easily understood by users, facilitating effective communication and improving the overall user experience), based on the input and the context (Pars.5, 32)
As to claim 10, Wang teaches wherein the CM module is configured to receive any or all previous conversations between the system and the person (Fig.22; Pars.55-58).
As to claim 12, Wang teaches wherein the steps further include: transforming the voice utterance into a textual representation using a speech-to-text engine; wherein the textual representation of the voice utterance is provided as the input to the one or more ML models (Pars.117, 165-168).
As to claim 13, Wang teaches wherein the steps further comprise pre-prompting the at least one ML model based on a predefined or dynamic pre-prompting instruction ([0079] As the AI system interacts with users and acquires additional information, it can refine and expand its OKB. During the training process, the seed knowledge is provided to a ML model, which enables it to learn and improve based on the initial data. By combining the seed knowledge 105 with newly acquired data, the AI system becomes more proficient in its domain, improving its ability to provide meaningful and accurate responses, recommendations, or solutions.).
As to claim 14, Wang teaches prior to providing the output to the at least one speaker, transforming the output from a textual representation to a sound format (0208 The generated response is presented to the user, either as text or through speech synthesis) using a text-to-speech engine.
Regarding claims 1-3 and 5-7 and 15, the corresponding instructions and system comprising the steps similar to the steps in the claims addressed above, are analogous therefore rejected as being anticipated by Wang for the foregoing reasons.
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.
Claim(s) 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 2023/0245651) as applied above and in view of Miyazawa et al. (US 5,983,186).
As to claims 4 and 11, it is noticed that Wang doesn’t include the use of wake-word. However, Miyazawa teaches a voice activated interactive speech recognition method Fig.4, comprising a wake/key-word detector for activating the system, comprising the steps of during a key-word detection state, detecting predetermined key-words s2-s3 in ambient sound recorded/received by the at least one microphone; if the predetermined key-word is detected s6, setting the system to enter an active state wherein the system is configured for detecting the voice utterance s8-s9 until the system enters the wake-word detection state again; and after a predetermined cooldown time duration has passed since the conversation or after satisfying an activity maintenance condition, setting the system to enter the wake-word detection state again s10-s14 (Figs.1-4).
The combination of the analogous arts would be obvious to one of ordinary skill in the art before the time of the applicant’s invention for the purpose of conserving the system energy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Vasylev (US 2024/0412720), Pars.12-13.
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Cohen et al. (US 2025/0363308), Figs.1-5.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL DEMELASH ABEBE whose telephone number is (571)272-7615. The examiner can normally be reached monday-friday 7-4.
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/DANIEL ABEBE/Primary Examiner, Art Unit 2657
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