Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 8 recites the limitation "the at least one memory" in line 5. There is insufficient antecedent basis for this limitation in the claim.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to a program claim per se.
Claims 1-3, 5-10 and 12-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. Claim 8 reciting the steps comprising “providing/loading a machine learning model into a memory”, “detecting voice utterance using a microphone” “providing the utterance to the ML model” “prompting/running the ML model to generate output” and “providing the output on a speaker”, is directed to claims describing a series of mental steps.
In that regard the cited steps of “loading a ML model into memory”, “detecting a voice utterance of the person”, “providing voice received by a microphone to the ML model” and “prompting/running the ML model to generate output” are mental steps or actions that, under their broadest reasonable interpretations, can be practically performed in a human mind or by a person using a general purpose computer.
This judicial exception is not integrated into a practical application because, the claim describes generic software and hardware, such as microphone, speaker, memory, processor, server and network interface which are general purpose computer components used as a tool to implement the mental process.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, because, the claim does not include any additional limitation other than the use and the description of the ML model that is loaded into the memory such description including where “the ML, model is configured for generating contextually relevant and varied responses in natural language conversations”, and the providing steps includes “ loading the at least one ML model into the at least one memory from a storage medium (113) storing the at least one ML model; and/or connecting, via a communication connection (126), with a server (115) providing a conversation interface to the at least one ML model”.
Claim 1 is analogous to claim 8, therefore rejected for the same reason.
Dependent claims 2-3, 5-10 and 12-15 similarly describe the components of the ML model without significantly more, thus rejected for the reason set forth above.
Examiner’s Note
Examiner has cited particular columns and line numbers or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to
fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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)
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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. Wen et al. (US 2025/0335477).
“A computer-implemented method for managing conversation topics in an online conversation, comprising: performing a conversation comprising one or more interactions with a user via a user interface, each interaction comprising a natural language request received from the user and a reply to the natural language request generated using a machine learning based language model; generating metadata describing a set of conversation topics based on the conversation, wherein the metadata describes a particular conversation topic comprises a summary of interactions relevant to the particular conversation topic; and repeatedly performing: receiving a natural language request from the user via the user interface; generating one or more prompts for providing to a machine learning based language model, the one or more prompts comprising the natural language request and metadata describing the set of conversation topics, the one or more prompts requesting the machine learning based language model to generate a reply to the natural language request in relation to a conversation topic relevant to the natural language request; providing the one or more prompts to the machine learning based language model for execution; receiving a response generated by the machine learning based language model based on the one or more prompts; and sending a reply based on the response for display via the user interface.”
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