DETAILED ACTION
Introduction
1. This office action is in response to Applicant's submission filed on 09/28/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-111 were cancelled before this first action on the merits. Thus, claims 112-141 are currently pending and examined below.
Drawings
2. The drawings filed on 09/28/2024 have been accepted and considered by the Examiner.
Information Disclosure Statement
3. The Information Statement (IDS) filed on 03/09/2026 has been accepted, considered and compliant with the provisions of 37 CFR 1.97.
Priority
4. The Applicants priority to U.S. Provisional Patent Application # 63/465,314 filed on 05/10/2023 has been accepted and considered in this office action.
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.
5. Claims 112-123, 125-127, 129, 132-134, 136, 138-141 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Wolverton (U.S. Patent Application Publication # 2014/0136187 A1).
With regards to claim 112, Wolverton teaches a method of assisting a user during a live conversation, comprising receiving, by an earpiece worn by the user and having a microphone and a speaker, audio of a conversation in which the user is participating (Paragraphs 97, 116, 125 and 127, teach a vehicle personal assistant using earpiece, microphone, speaker and headset to capture conversational audio from users in a vehicle including from driver and passengers);
processing, by a local artificial-intelligence (AI) system of the earpiece, the received audio to determine conversational context and to generate one or more real-time suggestions including at least one of what the user might say, how to say it, or how to respond (Paragraphs 38 and 99, teach that the vehicle personal assistant applies automated artificial intelligence classification and/or reasoning techniques to infer a likely current context of a dialog and proactively generates suggestions for the user based on the current vehicle context);
presenting the suggestion to the user through the earpiece during the conversation (See figure 3, blocks 312 and 226. Further, paragraphs 125-127, teach that the interaction between the user and the vehicle personal assistant can be carried out via an earpiece);
transmitting conversational data, including reactions or responses of other participants, from the earpiece to a remote Al server (Para 68, teaches use of a cloud service which could be an enterprise knowledge base system. Para 97, teaches that the method considers whether the user is the driver or a passenger in determining how to prompt the user for clarification. Para 133, teaches communicating audio data of user communication with the vehicle personal assistant to a server which could be an enterprise computer system, a network of computers, a combination of computers and other electronic devices, a mobile device, any of the aforementioned types of electronic devices, or other electronic devices);
retraining, by the remote Al server, a conversational model using the transmitted conversational data and updating a shared model used by multiple earpieces (Para 83, teaches that the vehicle context model may be continuously or periodically updated);
and adapting, by the local Al system, its conversational behavior over time to the user's individual speaking style, tone, or conversational preferences based on feedback received from the remote Al server (Paragraphs 48-49, teach that artificial intelligence machine learning techniques can be employed by the vehicle personal assistant to adapt the vehicle-specific conversation model to the user's personal language style over time. This speaking style can include tone, intonation, pace, accent, etc.);
wherein the local Al system operates during the conversation without requiring a continuous network connection and synchronizes its model-update data with the remote Al server after the conversation to improve subsequent conversational performance (Para 83, teaches that the vehicle context model may be continuously or periodically updated. Para 109, teaches the option of period modification or cancellation of a user conditional instruction. Para 133, teaches communicating audio data of user communication with the vehicle personal assistant to a server which could be a manufacturer enterprise computer system to receive information about the user's experience with the vehicle or the vehicle personal assistant).
With regards to claim 113, Wolverton teaches a method of assisting a user during a live conversation, comprising receiving, by an earpiece worn by the user and having a microphone and a speaker, audio of a conversation in which the user is participating (Paragraphs 97, 116, 125 and 127, teach a vehicle personal assistant using earpiece, microphone, speaker and headset to capture conversational audio from users in a vehicle including from driver and passengers);
processing, by a local artificial-intelligence (AI) system embedded in the earpiece and trained initially with simulated conversational data and retrained over time with real-world conversational data, the received audio to determine conversational context (Paragraphs 38 and 99, teach that the vehicle personal assistant applies automated artificial intelligence classification and/or reasoning techniques to infer a likely current context of a dialog and proactively generates suggestions for the user based on the current vehicle context);
generating, by the local Al system, one or more real-time suggestions related to the conversation, including at least one of what the user might say, how to say it, or how to respond (Paragraphs 38 and 99, teach that the vehicle personal assistant applies automated artificial intelligence classification and/or reasoning techniques to infer a likely current context of a dialog and proactively generates suggestions for the user based on the current vehicle context);
transmitting the suggestions from the local Al system to the user through the earpiece during the conversation (See figure 3, blocks 312 and 226. Further, paragraphs 125-127, teach that the interaction between the user and the vehicle personal assistant can be carried out via an earpiece);
and updating, by the local Al system, one or more parameters of its conversational model during or immediately after the conversation based on user feedback or detected conversational outcomes, thereby enabling continuous learning and adaptation to the user's communication style (Para 83, teaches that the vehicle context model may be continuously or periodically updated. Paragraphs 48-49, teach that artificial intelligence machine learning techniques can be employed by the vehicle personal assistant to adapt the vehicle-specific conversation model to the user's personal language style over time. This speaking style can include tone, intonation, pace, accent, etc.);
wherein the local Al system operates without requiring a continuous network connection and periodically synchronizes learned model-update data with a remote server to improve a shared conversational model across multiple device (Para 83, teaches that the vehicle context model may be continuously or periodically updated. Para 109, teaches the option of period modification or cancellation of a user conditional instruction. Para 133, teaches communicating audio data of user communication with the vehicle personal assistant to a server which could be a manufacturer enterprise computer system to receive information about the user's experience with the vehicle or the vehicle personal assistant).
With regards to claim 114, Wolverton teaches the method of claim 113, wherein the earpiece includes a local Al component that processes the received audio and generates suggestions without requiring a continuous network connection (Para 83, teaches that the vehicle context model may be continuously or periodically updated. Para 109, teaches the option of period modification or cancellation of a user conditional instruction. Para 133, teaches communicating audio data of user communication with the vehicle personal assistant to a server which could be a manufacturer enterprise computer system to receive information about the user's experience with the vehicle or the vehicle personal assistant).
With regards to claim 115, Wolverton teaches the method of claim 113, wherein the earpiece communicates with a remote Al server that retrains the conversational model using data transmitted from real conversations (Para 83, teaches that the vehicle context model may be continuously or periodically updated);
With regards to claim 116, Wolverton teaches the method of claim 113, wherein the simulated conversational data comprises scripted or synthetic dialogue scenarios representing different tones, personalities, and social contexts (Para 39, teaches that inputs may also include involuntary or "passive" inputs that the user may not expressly intend to result in a system event such as a non-specific vocal or facial expression, tone of voice or loudness. Paragraphs 20-21, teach that the vehicle personal assistant may select a reply from a plurality of possible replies based on the current vehicle-related context. The vehicle personal assistant may select a presentation mode from a plurality of possible presentation modes based on the current vehicle-related context and present the reply using the selected presentation mode. The plurality of possible presentation modes may include machine-generated conversational spoken natural language, text, recorded audio, recorded video, and/or digital images).
With regards to claim 117, Wolverton teaches the method of claim 113, further comprising using reactions or responses from other participants in the conversation as feedback to retrain the Al system (Paragraphs 97-98, teach that the method considers whether the user is the driver or a passenger in determining how to prompt the user for clarification. The method may receive and store user feedback. The method may store or otherwise track the inquiries, requests, and/or responses of the user to the various outputs supplied by the vehicle personal assistant over the course of a dialog. The method may treat and interpret such feedback in the same manner as any of the human-generated inputs. The user's feedback may be in any of the forms that can be processed by the vehicle personal assistant. The method may store feedback-related templates or rules in the vehicle-specific conversation model which the method may use to interpret and determine the meaning of future inquiries, requests, and responses made by the user. The method then returns to block as described about. Thus, the method can continue the vehicle-related dialog with the user in response to the user's feedback).
With regards to claim 118, Wolverton teaches the method of claim 113, wherein the Al system adapts its suggestions to the user's individual speaking style, tone, or conversational goals (Paragraphs 48-49, teach that AI machine learning techniques can be employed by the vehicle personal assistant to adapt the vehicle-specific conversation model to the user's personal language style over time. This speaking style can include tone, intonation, pace, accent, etc.);
With regards to claim 119, Wolverton teaches the method of claim 113, wherein the suggestions include prompts for empathy, clarification, or phrasing adjustments to improve interpersonal effectiveness (Paragraphs 96-97, teach that if further clarification is needed, the vehicle personal assistant prompts the user for clarification or otherwise obtains additional inputs).
With regards to claim 120, Wolverton teaches the method of claim 113, wherein the Al system identifies characteristics of the conversation including time, location, and acoustic or visual cues obtained from one or more sensors associated with the earpiece or a companion device (Para 23, teaches that the vehicle personal assistant may determine a current vehicle-related context of the further human-generated input based on the real-time sensor input, and may interpret the further human-generated input based on the current vehicle-related context of the further human-generated input. The real-time sensor input may indicate a current status of a feature of the vehicle. The real-time sensor input may provide information about a current driving situation of the vehicle, including vehicle location, vehicle speed, vehicle acceleration, fuel status, and/or weather information. Para 50, teaches that gestures, gaze, touch and facial features or expressions can be captured and recorded by an in-vehicle camera or multiple such cameras, which may be mounted, for instance, to a rearview mirror, steering wheel, dashboard, or sun visor of the vehicle).
With regards to claim 121, Wolverton teaches the method of claim 113, wherein the earpiece transmits audio, video, or other environmental data from the conversation to the remote Al to support retraining. (Para 21, teaches that the plurality of possible presentation modes may include machine-generated conversational spoken natural language, text, recorded audio, recorded video, and/or digital images. Para 49, teaches that the vehicle specific conversation model also includes an acoustic model that is appropriate for the in-vehicle environment as well as other possible environments in which the vehicle personal assistant may be used such as a garage, a car wash, a line at a drive-thru restaurant, etc. As such, the acoustic model is configured to account for noise and channel conditions that are typical of these environments, including road noise as well as background noise e.g., radio, video, or the voices of other vehicle occupants or other persons outside the vehicle).
With regards to claim 122, Wolverton teaches the method of claim 113, wherein retraining of the Al model comprises adjusting parameters, weights, or algorithms based on analysis of user feedback or conversational outcomes (Para 62, teaches a vehicle-specific user's guide knowledge base which is a computer-accessible data structure that may include one or more indexed or otherwise searchable stores of vehicle-related knowledge e.g., databases, lookup tables, or the like, each of which contains or references data, arguments, parameters, and/or machine-executable algorithms that can be applied by the informational retrieval engine to a search request e.g., processed input or a generated search query. The knowledge base may include all of the content typically found in a vehicle owner's manual, including text, graphics, video, as well as conversational spoken natural-language representations of the text, graphics, and/or video found in the vehicle owner's manual. The spoken natural-language representations may include a collection of answering sentences, which correspond to common vehicle-related inquiries, and may be ranked or otherwise scored for relevancy in determining an appropriate response to an input. Para 81, teaches that as a result of analysis, the vehicle input monitor may associate a higher weight value or ranking with inputs whose values have recently changed, so that those inputs receive greater consideration by the context analyzer in determining the current context).
With regards to claim 123, Wolverton teaches the method of claim 113, wherein the Al system generates the suggestions in natural-language form and converts them into audible signals communicated through the earpiece (Paragraphs 38, 99 and 102-103, teach that the vehicle personal assistant applies automated artificial intelligence classification and/or reasoning techniques to infer a likely current context of a dialog and proactively generates audible suggestions for the user based on the current vehicle context).
With regards to claim 125, Wolverton teaches the method of claim 113, wherein the Al system learns user preferences for conversational style based on accumulated prior conversations (Para 58, teaches a reasoner including a standard spoken dialog manager module, which keeps track of the current state and flow of each conversation or dialog that occurs between the user and the vehicle personal assistant. The dialog manager module may interface with a dialog portion of the vehicle-specific conversation model, to apply dialog-managing rules, templates or task flows to the input that are appropriate for the vehicle context. The dialog model may include vehicle-specific rules for determining when a conversation has started or ended, or for determining whether a current input is related to other inputs. Such other inputs may include inputs that have been received in one or more prior rounds of the same dialog and/or inputs that have been received around the same time as the current input and may include one or more non-verbal inputs).
With regards to claim 126, Wolverton teaches the method of claim 113, wherein the Al system provides post-conversation feedback or summary statistics to the user regarding conversational performance (Para 66, teaches that the dialog manager may refer to real-time vehicle-related inputs to determine if the user has performed the task correctly, and interrupt the dialog if the inputs indicate that user has performed the task incorrectly).
With regards to claim 127, Wolverton teaches the method of claim 113, wherein the Al system retrains iteratively using both simulated and real-world conversational data to improve future suggestions (Para 39, teaches that inputs may also include involuntary or "passive" inputs that the user may not expressly intend to result in a system event such as a non-specific vocal or facial expression, tone of voice or loudness. Paragraphs 20-21, teach that the vehicle personal assistant may select a reply from a plurality of possible replies based on the current vehicle-related context. The vehicle personal assistant may select a presentation mode from a plurality of possible presentation modes based on the current vehicle-related context and present the reply using the selected presentation mode. The plurality of possible presentation modes may include machine-generated conversational spoken natural language, text, recorded audio, recorded video, and/or digital images).
With regards to claim 129, Wolverton teaches the method of claim 113, wherein the conversational Al system utilizes machine-learning architectures trained on a combination of synthetic and real dialogue (Para 39, teaches that inputs may also include involuntary or "passive" inputs that the user may not expressly intend to result in a system event such as a non-specific vocal or facial expression, tone of voice or loudness. Paragraphs 20-21, teach that the vehicle personal assistant may select a reply from a plurality of possible replies based on the current vehicle-related context. The vehicle personal assistant may select a presentation mode from a plurality of possible presentation modes based on the current vehicle-related context and present the reply using the selected presentation mode. The plurality of possible presentation modes may include machine-generated conversational spoken natural language, text, recorded audio, recorded video, and/or digital images).
With regards to claim 132, Wolverton teaches the method of claim 113, wherein the Al system employs explainable-Al techniques to provide human-interpretable reasoning for its conversational suggestions (Para 54, teaches a reasoner that considers the current context as determined by the context analyzer and incorporates the most likely relevant aspects of the current context into the interpretation of the input to infer the user's most probable intended meaning of the input. To do this, the reasoner applies a probabilistic or statistical model using, e.g., Bayesian modeling, which may be stored in the rules/templates portion of the vehicle context model. The probabilistic or statistical model includes data relating to the likelihood, probability, or degree of confidence or certainty with which particular inputs are associated with particular meanings based on the context).
With regards to claim 133, Wolverton teaches the method of claim 113, wherein a plurality of earpieces each having a local Al system communicate with one another or with a shared network service to exchange model-update information derived from respective user conversations (Para 124, also teaches that a portion of the vehicle personal assistant is local to the computing system, while another portion is distributed across one or more other computing systems or devices that are connected to the networks. The portion may include portions of the vehicle- specific conversation model, the vehicle-specific user's guide knowledge base, and/or the vehicle context model).
With regards to claim 134, Wolverton teaches the method of claim 113, wherein the conversational Al system employs federated learning, such that model parameters from a plurality of users' earpieces are aggregated by a remote server to update a shared conversational model without transferring underlying conversational data (Para 55, teaches that certain inputs may have a different meaning depending on whether the vehicle is turned on or off, whether the user is situated in the driver's seat or a passenger seat, whether the user is inside the vehicle, standing outside the vehicle, or simply accessing the vehicle personal assistant from a computer located inside a home or office, or even whether the user is driving the vehicle at relatively high speed on a freeway as opposed to being stuck in traffic or on a country road. Para 61, teaches that the system is domain-independent and as such, can accommodate new or updated information about the vehicle e.g., if the vehicle-specific user's guide knowledge base is updated or new third party sources are added to the vehicle-related search realm. Para 124, also teaches that a portion of the vehicle personal assistant is local to the computing system, while another portion is distributed across one or more other computing systems or devices that are connected to the networks. The portion may include portions of the vehicle- specific conversation model, the vehicle-specific user's guide knowledge base, and/or the vehicle context model).
With regards to claim 136, Wolverton teaches the method of claim 113, wherein the Al system performs continuous online learning, updating its conversational parameters during or immediately after each conversation based on real-time user feedback or detected conversational outcomes (Para 83, teaches that the vehicle context model may be continuously or periodically updated. Para 61, teaches that the information retrieval engine uses a combination of term frequency and inverse document frequency algorithms e.g., a modified tf-idf algorithm, and/or semantic similarity computational techniques, to create and continuously update an index of words that appear to be most important to or favored by the user, and stores that index in the knowledge base. The system is domain-independent and as such, can accommodate new or updated information about the vehicle e.g., if the vehicle-specific user's guide knowledge base is updated or new third party sources are added to the vehicle-related search realm).
With regards to claims 138-139, please see the rejection of claims 112-113 and 120 above.
With regards to claims 140-141, these are system claims for the corresponding method claims 112-113, 115-116, 127 and 134. These two sets of claims are related as method and apparatus of using the same, with each claimed system element's function corresponding to the claimed method step. Accordingly, claims 140-141 are similarly rejected under the same rationale as applied above with respect to method claims 112-113, 115-116, 127 and 134.
Allowable Subject Matter
6. Claims 124, 128, 130-131, 135 and 137 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The prior art of record, alone or in combination, does not currently suggest or teach the invention as outlined in these claims. More detailed reasons for allowance will be outlined as and when the Application proceeds to allowability.
Conclusion
7. The following prior art, made of record but not relied upon, is considered pertinent to applicant's disclosure: Miller (U.S. Patent Application Publication # 2019/0005021 A1), Can (U.S. Patent # 12592246 B2). These references are also included in the PTO-892 form attached with this office action.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to NEERAJ SHARMA whose contact information is given below. The examiner can normally be reached on Monday to Friday 8 am to 5 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Louis-Desir can be reached on 571-272-7799 (Direct Phone). The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
/NEERAJ SHARMA/
Primary Examiner, Art Unit 2659
571-270-5487 (Direct Phone)
571-270-6487 (Direct Fax)
neeraj.sharma@uspto.gov (Direct Email)