DETAILED ACTION
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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number TW112143775 dated 11/14/2023. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Response to Amendment and Arguments.
Applicant’s arguments, see page 9-10, filed 7/23/2026, with respect to claims 1, 3, 4, 6, 8, 10-16,18-20 rejection have been fully considered and are persuasive and in light of the current amendments. The 101 rejections of claims 1, 3, 4, 6, 8, 10-16,18-20 have been withdrawn.
The obvious double patent rejections of claims 1, 3, 4, 6, 8, 10-16,18-20 have been withdrawn.
Applicant’s arguments (103) with respect to claim(s) 1, 3, 4, 6, 8, 10-16,18-20 have been considered but are not persuasive. Applicants argue that
PNG
media_image1.png
267
643
media_image1.png
Greyscale
However, the examiner strongly disagrees, Maeder and Iriyama were not used for a dialogue linking icon. Beringer was brought in to teach this paragraphs 15 and 73. Therefore, applicants argument for claim 1 is not persuasive and rejection is till maintained.
Applicant further argues
PNG
media_image2.png
177
638
media_image2.png
Greyscale
PNG
media_image3.png
746
607
media_image3.png
Greyscale
However, examiner disagrees, the claim states “generate a dialogue content[[s]] that is consistent with the user preference and the real-time environmental information including at least one of real-time weather, real-time traffic, real-time news, and network information relating to a real-time location that is retrieved from one or more external systems in real time; and” The claim requires contents to be sent that aligns with user preference, and real time information. The claim does not indicate that the dialogue content needs to be an interactive natural language conversation. The reference (Iriyama), teaches these limitations please see (page 3 paragraph 4 and page 5 paragraph 2 and 3). These paragraphs show using Realtime information to output a dialogue. “Rain in 15 minutes” (being the Realtime information). Furthermore, the coupon acts as the initiator for real time information. Therefore, applicants’ argument for claim 1 is not persuasive and rejection is still maintained.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,3, 7, 10, 13,14,15, 16, 19, 20 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20230306205 A1, (MAEDER; Pascal.) in view of JP Patent JP 2014120049 A, (IRIYAMA TAKAMITSU.) in further view of US Patent US 20220358448 A1, (Beringer; Joerg.)
Claim 1 and 13
Regarding Claim 1 and 13, MAEDER teach
1. A method for processing natural language messages, which is performed in a cloud server, the method comprising:
(paragraph 103 "The communication system 200 comprises inter alia a plurality of client devices 210, a space-time service (STS) server 220, a conversational agent engine comprising a set of machine learning (ML) models 240, a training server 230, a database 235 and a knowledge database 270 communicatively coupled over a communications network 280."
The cloud server is not exactly phrased but the STS server architecture would be the cloud server. )
initiating an online dialogue procedure after actuation of a dialogue-linking icon displayed on a user interface initiated by the cloud server for receiving user-input contents via a dialogue interface;
(Paragraph 119 "The STS client platform 225 enables users to post and view messages on a graphical representation of a location on a map, which may be viewed at different moments in time by selecting a specific date and time range. The STS engine 222 may be accessed via the plurality of client devices 216, for example via a browser application or a stand-alone software application."
Paragraph 122 "The STS engine 222 enables augmenting said posts by further contributions from any user, in the form of answers to a post, or contribution to a thread of messages. Users can participate by viewing, creating of replying to posts. Anonymous users can enter in relation with unlimited amount of other anonymous users through relational posts."
The STS platforms messaging thread interface is an online dialogue. )
extracting semantic features of the user-input content;
(Paragraph 169-173 "[0160]Text Vectorization Model
[0170] The text vectorization model 258 is configured to inter alia: (i) receive a text portion including one or more words, sentences, and paragraphs; and (ii) generate an encoded feature vector based on the text portion.
[0171] In one or more embodiments, the text feature vector comprises semantic representation of the text portion in an n-dimensional feature space.
[0172] In the context of the present technology, the purpose of the text vectorization model 258 is to generate a vector or embedding of text portions such that they can be compared, and their similarity assessed.
[0173] In one or more embodiments, the text vectorization model 258 is used to generate text vectors of one or more messages posted by users of the STS engine 222 and textual background knowledge associated with historical characters such that they may be compared, and their similarity or dissimilarity may be quantified.")
determining contents that are consistent with the semantic features of the user-input content,
(Paragraph 17 "In accordance with a broad aspect of the present technology, there is provided a method for providing a relevant natural language response to a conversation thread by a trained conversational agent model, the method being executed by a processor. The method comprises: obtaining a conversation thread comprises at least one message, the conversation thread being associated with space-time coordinates, obtaining, based at least on the space-time coordinates, a character identity for the trained conversational agent model, obtaining, based on the character identity, a supportive knowledge text associated with the character, encoding the conversation thread and the supportive knowledge text to obtain an encoded conversation thread and encoded knowledge text portions, selecting, based on the encoded conversation thread, an encoded knowledge text portion, and generating, by the trained conversational agent model, based at least on the encoded knowledge text portion, a relevant natural language response to the conversation thread.")
processing the contents by a natural language model operated in the online dialogue procedure, so as to generate dialogue contents; and wherein the natural language model is operated in the cloud server that performs a vector operation on the user-input content, the user preference, and the real-time environmental information, annotates the text, calculates a vector for each of words, retrieves correlated contents based on vector distances between the words
(paragraph 173 "In one or more embodiments, the text vectorization model 258 is used to generate text vectors of one or more messages posted by users of the STS engine 222 and textual background knowledge associated with historical characters such that they may be compared, and their similarity or dissimilarity may be quantified."
Paragraph 17 "In accordance with a broad aspect of the present technology, there is provided a method for providing a relevant natural language response to a conversation thread by a trained conversational agent model, the method being executed by a processor. The method comprises: obtaining a conversation thread comprises at least one message, the conversation thread being associated with space-time coordinates, obtaining, based at least on the space-time coordinates, a character identity for the trained conversational agent model, obtaining, based on the character identity, a supportive knowledge text associated with the character, encoding the conversation thread and the supportive knowledge text to obtain an encoded conversation thread and encoded knowledge text portions, selecting, based on the encoded conversation thread, an encoded knowledge text portion, and generating, by the trained conversational agent model, based at least on the encoded knowledge text portion, a relevant natural language response to the conversation thread.")
importing the dialogue content to the online dialogue procedure, and outputting the dialogue content via the dialogue interface.
(paragraph 138 "The dialogue generation model 252 is used to implement conversational agents taking the identity of characters which may create conversations and/or reply to conversations by providing natural language utterances (e.g., sentences) on a platform, such as the STS engine 222. The provided utterances may be in the form of informative speech statements or interrogative speech statements (e.g., in the form of questions) based on factual information related to a location, time, event, an ongoing conversation, the character's biographical details and other characters interest to users of a platform such as the STS engine 222.")
MAEDER do not explicitly teach all of (the bolded)
initiating an online dialogue procedure after actuation of a dialogue-linking icon displayed on a user interface initiated by the cloud server for receiving user-input contents via a dialogue interface;
obtaining user data and real-time environmental information;
a user preference obtained from the user data, and the real-time environmental information, and
wherein the natural language model is operated in the cloud server that performs a vector operation on the user-input content, the user preference, and the real-time environmental information, annotates the text, calculates a vector for each of words, retrieves correlated contents based on vector distances between the words
so as to generate a dialogue content[[s]] that is consistent with the user preference and the real-time environmental information including at least one of real-time weather, real-time traffic, real-time news, and network information relating to a real-time location that is retrieved from one or more external systems in real time; and
However, IRIYAMA teach obtaining user data and real-time environmental information;
(Page 4 paragraph 4 and 5 "As illustrated in FIG. 3, the user information DB 13 stores “user name, preference information, position log, behavior information, environment information, notification history” and the like. The information shown here is merely an example, and the present invention is not limited to this. For example, the user's name, user ID, address, telephone number, e-mail address, SNS (Social Networking Service) used by the user Other service information etc. are also stored.
The “user name” stored here is information for identifying the user such as the name and ID of the registered user, and is set and registered by the user such as “U001”, for example. “Preference information” is preference information registered by the user, for example, information such as “dislike alcohol” and “diet”. The “location log” is a history of location information acquired from the terminal device 1 by the location information acquisition unit 17 to be described later, and information such as “Chiyoda-ku ... Tokyo” is stored in time series. “Behavior information” is information indicating the user's behavior specified by the situation specifying unit 19 to be described later. For example, “train movement to just before”, “walking movement”, “running movement”, “stop”, “stay” It is information such as. “Environment information” is information about the environment at the current position of the user acquired by the environment acquisition unit 18 described later. For example, “rain, forecast that stops after 15 minutes”, “sunny”, “many ultraviolet rays”, “congestion” Information such as “medium”.")
a user preference obtained from the user data, and the real-time environmental information, and
(Page 4 paragraph 4 and 5 "As illustrated in FIG. 3, the user information DB 13 stores “user name, preference information, position log, behavior information, environment information, notification history” and the like. The information shown here is merely an example, and the present invention is not limited to this. For example, the user's name, user ID, address, telephone number, e-mail address, SNS (Social Networking Service) used by the user Other service information etc. are also stored.
The “user name” stored here is information for identifying the user such as the name and ID of the registered user, and is set and registered by the user such as “U001”, for example. “Preference information” is preference information registered by the user, for example, information such as “dislike alcohol” and “diet”. The “location log” is a history of location information acquired from the terminal device 1 by the location information acquisition unit 17 to be described later, and information such as “Chiyoda-ku ... Tokyo” is stored in time series. “Behavior information” is information indicating the user's behavior specified by the situation specifying unit 19 to be described later. For example, “train movement to just before”, “walking movement”, “running movement”, “stop”, “stay” It is information such as. “Environment information” is information about the environment at the current position of the user acquired by the environment acquisition unit 18 described later. For example, “rain, forecast that stops after 15 minutes”, “sunny”, “many ultraviolet rays”, “congestion” Information such as “medium”."
page 5 paragraph 2 and 3 from the bottom "
The environment acquisition unit 18 is a processing unit that acquires environmental information such as weather, ultraviolet rays, earthquakes, and traffic jams corresponding to the position information of the user. Specifically, when the environment acquisition unit 18 receives the current position of the terminal device 1 from the position information acquisition unit 17, the environment acquisition unit 18 acquires environment information corresponding to the current position from an external server or the like, and the terminal device in the user information DB 13 Stored in “environment information” of one record.
For example, if the environment acquisition unit 18 receives “Chiyoda-ku, Tokyo ...” as the current position, the environment acquisition unit 18 accesses the weather server 4 and the weather forecast for “Chiyoda-ku, Tokyo” “Rain, stops after 15 minutes” Is stored in the user information DB 13. In addition, the environment acquisition unit 18 acquires traffic jam information when the current position is “highway”, and acquires a delay situation or the like when the current position is “train”.")
wherein the natural language model is operated in the cloud server that performs a vector operation on the user-input content, the user preference, and the real-time environmental information, annotates the text, calculates a vector for each of words, retrieves correlated contents based on vector distances between the words
(page 4 paragraph 4 and 5 "As illustrated in FIG. 3, the user information DB 13 stores “user name, preference information, position log, behavior information, environment information, notification history” and the like. The information shown here is merely an example, and the present invention is not limited to this. For example, the user's name, user ID, address, telephone number, e-mail address, SNS (Social Networking Service) used by the user Other service information etc. are also stored.
The “user name” stored here is information for identifying the user such as the name and ID of the registered user, and is set and registered by the user such as “U001”, for example. “Preference information” is preference information registered by the user, for example, information such as “dislike alcohol” and “diet”. The “location log” is a history of location information acquired from the terminal device 1 by the location information acquisition unit 17 to be described later, and information such as “Chiyoda-ku ... Tokyo” is stored in time series. “Behavior information” is information indicating the user's behavior specified by the situation specifying unit 19 to be described later. For example, “train movement to just before”, “walking movement”, “running movement”, “stop”, “stay” It is information such as. “Environment information” is information about the environment at the current position of the user acquired by the environment acquisition unit 18 described later. For example, “rain, forecast that stops after 15 minutes”, “sunny”, “many ultraviolet rays”, “congestion” Information such as “medium”."
page 5 paragraph 2 and 3 from the bottom "
The environment acquisition unit 18 is a processing unit that acquires environmental information such as weather, ultraviolet rays, earthquakes, and traffic jams corresponding to the position information of the user. Specifically, when the environment acquisition unit 18 receives the current position of the terminal device 1 from the position information acquisition unit 17, the environment acquisition unit 18 acquires environment information corresponding to the current position from an external server or the like, and the terminal device in the user information DB 13 Stored in “environment information” of one record.
For example, if the environment acquisition unit 18 receives “Chiyoda-ku, Tokyo ...” as the current position, the environment acquisition unit 18 accesses the weather server 4 and the weather forecast for “Chiyoda-ku, Tokyo” “Rain, stops after 15 minutes” Is stored in the user information DB 13. In addition, the environment acquisition unit 18 acquires traffic jam information when the current position is “highway”, and acquires a delay situation or the like when the current position is “train”.")
so as to generate a dialogue content[[s]] that is consistent with the user preference and the real-time environmental information including at least one of real-time weather, real-time traffic, real-time news, and network information relating to a real-time location that is retrieved from one or more external systems in real time; and
(page 5 paragraph 2 and 3 from the bottom " The environment acquisition unit 18 is a processing unit that acquires environmental information such as weather, ultraviolet rays, earthquakes, and traffic jams corresponding to the position information of the user. Specifically, when the environment acquisition unit 18 receives the current position of the terminal device 1 from the position information acquisition unit 17, the environment acquisition unit 18 acquires environment information corresponding to the current position from an external server or the like, and the terminal device in the user information DB 13 Stored in “environment information” of one record.
Also see page 3 paragraph 4)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified MAEDER to incorporate the teachings of IRIYAMA to provide a “obtaining user data and real-time environmental information; a user preference obtained from the user data, and the real-time environmental information, and wherein the natural language model is operated in the cloud server that performs a vector operation on the user-input content, the user preference, and the real-time environmental information, annotates the text, calculates a vector for each of words, retrieves correlated contents based on vector distances between the words so as to generate a dialogue content[[s]] that is consistent with the user preference and the real-time environmental information including at least one of real-time weather, real-time traffic, real-time news, and network information relating to a real-time location that is retrieved from one or more external systems in real time; and ” Doing so would Efficiently provide information to the user, as recognized by IRIYAMA. (page 3 paragraph 3 from the bottom).
MAEDER in view of IRIYAMA do not explicitly teach all of initiating an online dialogue procedure after actuation of a dialogue-linking icon displayed on a user interface initiated by the cloud server for receiving user-input contents via a dialogue interface;
However, Beringer teach initiating an online dialogue procedure after actuation of a dialogue-linking icon displayed on a user interface initiated by the cloud server for receiving user-input contents via a dialogue interface;
(Paragraph 15 "In one embodiment, hybrid interface system 110 comprises server 112 and database 114. As explained in greater detail below, hybrid interface system 110 generates a graphical user interface (GUI) with touch- or cursor-based interactions side-by-side with a chatbot interface that uses natural language processing to support voice- or text-based interactions in a conversational chat format. Hybrid interface system 110 responds to users' chat-based requests by adopting a persona of one or more virtual assistants that are created as proxies for roles of human experts working in the target environments. In addition, or as an alternative, hybrid interface system 110 leverages data from workforce management system 130 to identify roles in the target environment, create virtual assistants for these roles, and route requests requiring human expert review to the on-duty expert."
Paragraph 73 " FIG. 4 illustrates startup display 400 of hybrid interface system 110, in accordance with an embodiment. Startup display 400 provides selectable icons for three virtual assistants (virtual assistant icons 402a-402c), a microphone icon 404 to initiate a voice command, and a password entry box 406. Although selectable icons 402a-402c are shown for three virtual assistants (chef, supervisor, and logistics expert), embodiments contemplate, as disclosed above, creating virtual persona for other expertise areas, which are relevant to other verticals, domains 238, other users of one or more interactive display devices 120, any one or more supply chain entities 150, and/or the like.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified MAEDER in view of IRIYAMA to incorporate the teachings of Beringer to provide a “initiating an online dialogue procedure after actuation of a dialogue-linking icon displayed on a user interface initiated by the cloud server for receiving user-input contents via a dialogue interface;” Doing so would Have an easily understandable presentation of the response, as recognized by Beringer. (paragraph 3,15 and 91).
Claim 13
Further MAEDER teach
13. A natural-language-message processing system, which is implemented by a computer system, the natural-language-message processing system comprising: a cloud server, wherein the cloud server uses a processing circuit to perform a method for processing natural language messages, and the method includes:
(Paragraph 28 "In accordance with a broad aspect of the present technology, there is provided a system for providing a relevant natural language response to a conversation thread by a trained conversational agent model. The system comprises: a non-transitory storage medium storing computer-readable instructions, and a processor operatively connected to the non-transitory storage medium. The processor, upon executing the computer-readable instructions, being configured for: obtaining a conversation thread comprises at least one message, the conversation thread being associated with space-time coordinates, obtaining, based at least on the space-time coordinates, a character identity for the trained conversational agent model, obtaining, based on the character identity, a supportive knowledge text associated with the character, encoding the conversation thread and the supportive knowledge text to obtain an encoded conversation thread and encoded knowledge text portions, selecting, based on the encoded conversation thread, an encoded knowledge text portion, and generating, by the trained conversational agent model, based at least on the encoded knowledge text portion, a relevant natural language response to the conversation thread."
Paragraph 100 "It will be appreciated that the electronic device 100 may be implemented as a server, a desktop computer, a laptop computer, a tablet, a smartphone, a personal digital assistant or any device that may be configured to implement the present technology, as it may be appreciated by a person skilled in the art.")
Claim 2, 14, and 16
Regarding Claim 2, 14, and 16, MAEDER in view of IRIYAMA, in view of Beringer, MAEDER further teach the method according to claim 1, wherein the online dialogue procedure implements a chatbot that applies the natural language model for chatting with a user via the dialogue interface and performing the method on each of the user-input contents.
(Paragraph 17 "In accordance with a broad aspect of the present technology, there is provided a method for providing a relevant natural language response to a conversation thread by a trained conversational agent model, the method being executed by a processor. The method comprises: obtaining a conversation thread comprises at least one message, the conversation thread being associated with space-time coordinates, obtaining, based at least on the space-time coordinates, a character identity for the trained conversational agent model, obtaining, based on the character identity, a supportive knowledge text associated with the character, encoding the conversation thread and the supportive knowledge text to obtain an encoded conversation thread and encoded knowledge text portions, selecting, based on the encoded conversation thread, an encoded knowledge text portion, and generating, by the trained conversational agent model, based at least on the encoded knowledge text portion, a relevant natural language response to the conversation thread."
Paragraph 18 "In one or more implementations of the method, the conversational agent model is based a transformer language model (TLM).")
Claim 3
Regarding Claim 3, MAEDER in view of IRIYAMA, in view of Beringer,, MAEDER further teach the method according to claim 1, wherein the dialogue content received via the dialogue interface is a text, a voice, or an audiovisual content; wherein, when the dialogue content is the voice or the audiovisual content, a textization process converts the voice or the audiovisual content into the text, and a semantic analysis process is performed on the text for retrieving semantic features of the text.
(Paragraph 120 "The STS client platform 225 enables selecting a set of coordinates using a graphical representation of a geographical location, in which the set of coordinates includes a geographical location, the area or dimension information, and a time or a time span. The STS client platform 225 enables attaching a post or message to the set of coordinates, the post containing text and optionally hyperlinks, image files, audio files or video files. In one or more alternative embodiments, the STS client platform 225 may be a component of another platform or service, such as a social media service, a metaverse, a mixed reality platform, a video game, etc. In this context, the STS client platform 225 enables users to interact in space and time with each other via avatars or other types of representations."
The claim is in alternative form therefore the second part of the claim is not necessary. Citation is for text only. )
Claim 7 and 15
Regarding Claim 7 and 15, MAEDER in view of IRIYAMA, in view of Beringer,, IRIYAMA further teach the method according to claim 1, wherein the real-time environmental information includes at least one of real-time weather, real-time traffic, real-time news, and network information relating to a real-time location that is retrieved from one or more external systems in real time.
(Page 5 paragraph 2 and 3 from the bottom "
The environment acquisition unit 18 is a processing unit that acquires environmental information such as weather, ultraviolet rays, earthquakes, and traffic jams corresponding to the position information of the user. Specifically, when the environment acquisition unit 18 receives the current position of the terminal device 1 from the position information acquisition unit 17, the environment acquisition unit 18 acquires environment information corresponding to the current position from an external server or the like, and the terminal device in the user information DB 13 Stored in “environment information” of one record. )
See claim one for rationale.
Claim 10 and 20
Regarding Claim 10 and 20, MAEDER in view of IRIYAMA in further view of Beringer, MAEDER further teach 10. (Currently Amended) The method according to claim [[9]]1, wherein the online dialogue procedure implements a chatbot that applies the natural language model for chatting with a user via the dialogue interface and performing the method on each of the user-input contents.
(Paragraph 17 "In accordance with a broad aspect of the present technology, there is provided a method for providing a relevant natural language response to a conversation thread by a trained conversational agent model, the method being executed by a processor. The method comprises: obtaining a conversation thread comprises at least one message, the conversation thread being associated with space-time coordinates, obtaining, based at least on the space-time coordinates, a character identity for the trained conversational agent model, obtaining, based on the character identity, a supportive knowledge text associated with the character, encoding the conversation thread and the supportive knowledge text to obtain an encoded conversation thread and encoded knowledge text portions, selecting, based on the encoded conversation thread, an encoded knowledge text portion, and generating, by the trained conversational agent model, based at least on the encoded knowledge text portion, a relevant natural language response to the conversation thread."
Paragraph 18 "In one or more implementations of the method, the conversational agent model is based a transformer language model (TLM).")
Claim 19
Regarding Claim 19, MAEDER in view of IRIYAMA in view of Beringer, MAEDER further teach the method according to claim 5, wherein the cloud server further performs the vector operation on historical dialogue records recorded in the cloud server for generating the dialogue content.
(paragraph 22 "In one or more implementations of the method, the method further comprises, during a training procedure of the conversational agent model: obtaining a past conversation thread associated with space-time coordinates, the past conversation thread comprises a plurality of messages, obtaining a supportive knowledge text associated with a character, generating, based on the past conversation thread, a search query, embedding each of the search query and the supportive knowledge text to obtain a search query vector and supportive knowledge text vectors, determining a respective distance between the search query vector and each supportive knowledge text vector, selecting, based on the respective distances, a first set of knowledge sentences from the supportive knowledge text, extracting a set of keywords from the conversation thread, matching the set of keywords with a set of knowledge sentences from the supportive knowledge text, selecting, based on said matching, a second set of knowledge sentences from the supportive knowledge text, and training the conversational agent model to generate a response to a given conversation thread based on: the first set of knowledge sentences and the second set of knowledge sentences.")
Claims 3 is rejected under 35 U.S.C. 103 as obvious over US Patent US 20230306205 A1, (MAEDER; Pascal.) in view of JP Patent JP 2014120049 A, (IRIYAMA TAKAMITSU.) .) in view of US Patent US 20220358448 A1, (Beringer; Joerg.) in further view US Patent US 20250029192 A1 (Wheeler; Ross.)
Claim 3
Regarding Claim 3, MAEDER in view of IRIYAMA in view of Beringer do not explicitly teach all of 3. The method according to claim 1, wherein the dialogue content received via the dialogue interface is a text, a voice, or an audiovisual content; wherein, when the dialogue content is the voice or the audiovisual content, a textization process converts the voice or the audiovisual content into the text, and a semantic analysis process is performed on the text for retrieving semantic features of the text. (does not teach the bolded)
However, Wheeler teaches the method according to claim 1, wherein the dialogue content received via the dialogue interface is a text, a voice, or an audiovisual content; wherein, when the dialogue content is the voice or the audiovisual content, a textization process converts the voice or the audiovisual content into the text, and a semantic analysis process is performed on the text for retrieving semantic features of the text.
(Provisional and publication Paragraph 79 "Further, the GUI 300 may include an input interface 332. The GUI 300 may display a thread 302 in the input interface to prompt the user to input information into the GUI 300. The user may input information via text (e.g., by typing) and/or audio (e.g., by speaking), and/or uploading files (e.g., images, videos, etc.). In scenarios where the user responds by typing, the user may type in the input interface 332. In scenarios where the user responds by speaking, the user may interact with a selectable element 304 to begin speaking. The user device 102 may transcribe the audio data (e.g., using the ML module 140) and enter the transcribed audio into the chat interface 330."
Provisional and publication Paragraph 101 "Turning to FIG. 4C, in some embodiments, upon receiving the first prompt at block 410, the server may encode the first prompt to create a feature vector. Advantageously, a feature vector created based upon the first prompt may keep context information of the first prompt and thus be more effective for identifying relevant data and/or property metrics."
Provisional and publication Paragraph 103 "At block 415, the server may determine a feature vector of the first prompt based upon the semantic clusters. In some instances, the server may determine the feature vector by encoding the semantic clusters into the feature vector directly. Various techniques may be used to perform this step, such as Bag of Words. In other instances, the server may determine the feature vector by (1) encoding the semantic clusters as a set of vectors, and (2) determining a feature vector based upon the set of vectors associated with the semantic clusters"
Provisional and publication Paragraph 104 " Various techniques may be used to encode a semantic cluster into a vector, such as word2vec, which uses a machine model trained with a large corpus of text to learn word association. In some instances, a distance between the vectors reflects a semantic similarity between the corresponding semantic clusters, i.e., a smaller distance between two vectors corresponds to a greater similarity in semantic meanings between two corresponding semantic clusters. The distance between vectors may be a cosine distance, a Euclidean distance, or any other appropriate distance for vectors."
Provisional and publication Paragraph 105 " Various techniques may be used to determine a feature vector for a set of vectors. For example, a feature vector may be a mean vector or a weighted sum of the set of vectors. In another example, the server may combine the set of vectors into a matrix, calculate an eigenvector of the resulting matrix, and use the eigenvector as the feature vector. In yet another example, the server may use a trained machine learning model (such as Recurrent Neural Networks (RNN), Bidirectional Encoder Representations from Transformer (BERT), etc.) to determine a feature vector for the set of semantic clusters.")
Claims 4, 6 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20230306205 A1, (MAEDER; Pascal.) in view of JP Patent JP 2014120049 A, (IRIYAMA TAKAMITSU.) in further view of US Patent US 20220358448 A1, (Beringer; Joerg.) in view US Patent US 20250029192 A1 (Wheeler; Ross.) in further view of US Patent US 20240428008 A1, (ABRAHAM; Robin.)
Claim 4
Regarding Claim 4, MAEDER in view of IRIYAMA in view of Beringer in view of Wheeler do not explicitly teach all of 4. (Currently Amended) The method according to claim 3, wherein the natural language model operated in the cloud server employs a transformer model for conducting machine translation, document summarization, and document generation, so as to generate the dialogue content[[s]].
However, ABRAHAM teach 4. (Currently Amended) The method according to claim 3, wherein the natural language model operated in the cloud server employs a transformer model for conducting machine translation, document summarization, and document generation, so as to generate the dialogue content[[s]].
(paragraph 16 "Large language models (LLMs) have achieved significant advancements in various natural language processing (NLP) tasks. LLMs refer to machine learning artificial intelligence (AI) models that can generate natural language text based on the patterns they learn from processing vast amounts of data. LLMs use deep neural networks, such as transformers, to learn from billions or trillions of words, and to produce text on any topic or domain. LLMs can also perform various NLP tasks, such as classification, summarization, translation, generation, and dialogue.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified MAEDER in view of IRIYAMA in view of Beringer in view of Wheeler to incorporate the teachings of ABRAHAM to provide a “The method according to claim 3, wherein the natural language model operated in the cloud server employs a transformer model for conducting machine translation, document summarization, and document generation, so as to generate the dialogue contents.” Doing so would Improve the quality of the response, as recognized by ABRAHAM. (Paragraph 17).
Claim 5
Regarding Claim 5, MAEDER in view of IRIYAMA in view of Beringer in view of Wheeler in further view of ABRAHAM, MAEDER further teach
5. The method according to claim 4, wherein the cloud server performs a vector operation on the user-input content,
(paragraph 173 "In one or more embodiments, the text vectorization model 258 is used to generate text vectors of one or more messages posted by users of the STS engine 222 and textual background knowledge associated with historical characters such that they may be compared, and their similarity or dissimilarity may be quantified.")
the user preference, and the real-time environmental information, annotates the text, calculates a vector for each of words, retrieves correlated contents based on vector distances between the words, (doesn’t teach the bolded )
(paragraph 175 "In one or more alternative embodiments, the text vectorization model 258 may use techniques such as bag-of-words, tokenization, stop words removal, stemming, lemmatization, morphological segmentation, topic modeling, and the like to generate a text vector representation of a text portion."
Paragraph 178 "Non-limiting examples of embedding models include word2vec, sentence2vec, doc2vec, Stanford University's GloVe, AllenNLP's Elmo, fastText, as well as transformer-based language models such as XLNet, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformers (GPT) to generate semantic representations."
Paragraph 187 "The embedding similarity model 263 is a DNN having been trained to determine similarity of text portions based on a distance thereof in embedding space. In one or more embodiments, the embedding similarity model 263 may also generate the vectors of the text portions in embedding space before determining their similarity based on a distance.")
and generates the dialogue contents that are consistent with the user preference and the real-time environmental information. (doesn’t teach the bolded )
(paragraph 138 "The dialogue generation model 252 is used to implement conversational agents taking the identity of characters which may create conversations and/or reply to conversations by providing natural language utterances (e.g., sentences) on a platform, such as the STS engine 222. The provided utterances may be in the form of informative speech statements or interrogative speech statements (e.g., in the form of questions) based on factual information related to a location, time, event, an ongoing conversation, the character's biographical details and other characters interest to users of a platform such as the STS engine 222.")
MAEDER in view of IRIYAMA in view of Beringer in view of Wheeler in further view of ABRAHAM, IRIYAMA further teach The method according to claim 4, wherein the cloud server performs a vector operation on the user-input content, the user preference, and the real-time environmental information, annotates the text, calculates a vector for each of words, retrieves correlated contents based on vector distances between the words, and generates the dialogue contents that are consistent with the user preference and the real-time environmental information. ( teaches the bolded )
(Page 4 paragraph 4 and 5 "As illustrated in FIG. 3, the user information DB 13 stores “user name, preference information, position log, behavior information, environment information, notification history” and the like. The information shown here is merely an example, and the present invention is not limited to this. For example, the user's name, user ID, address, telephone number, e-mail address, SNS (Social Networking Service) used by the user Other service information etc. are also stored.
The “user name” stored here is information for identifying the user such as the name and ID of the registered user, and is set and registered by the user such as “U001”, for example. “Preference information” is preference information registered by the user, for example, information such as “dislike alcohol” and “diet”. The “location log” is a history of location information acquired from the terminal device 1 by the location information acquisition unit 17 to be described later, and information such as “Chiyoda-ku ... Tokyo” is stored in time series. “Behavior information” is information indicating the user's behavior specified by the situation specifying unit 19 to be described later. For example, “train movement to just before”, “walking movement”, “running movement”, “stop”, “stay” It is information such as. “Environment information” is information about the environment at the current position of the user acquired by the environment acquisition unit 18 described later. For example, “rain, forecast that stops after 15 minutes”, “sunny”, “many ultraviolet rays”, “congestion” Information such as “medium”."
page 5 paragraph 2 and 3 from the bottom "
The environment acquisition unit 18 is a processing unit that acquires environmental information such as weather, ultraviolet rays, earthquakes, and traffic jams corresponding to the position information of the user. Specifically, when the environment acquisition unit 18 receives the current position of the terminal device 1 from the position information acquisition unit 17, the environment acquisition unit 18 acquires environment information corresponding to the current position from an external server or the like, and the terminal device in the user information DB 13 Stored in “environment information” of one record.
For example, if the environment acquisition unit 18 receives “Chiyoda-ku, Tokyo ...” as the current position, the environment acquisition unit 18 accesses the weather server 4 and the weather forecast for “Chiyoda-ku, Tokyo” “Rain, stops after 15 minutes” Is stored in the user information DB 13. In addition, the environment acquisition unit 18 acquires traffic jam information when the current position is “highway”, and acquires a delay situation or the like when the current position is “train”.")
See claim one for rationale.
Claim 6
Regarding Claim 6 and 19, MAEDER in view of IRIYAMA in view of Beringer in view of Wheeler in further view of ABRAHAM, MAEDER further teach 6. (Currently Amended) The method according to claim [[5]] 1, wherein the cloud server further performs the vector operation on historical dialogue records recorded in the cloud server for generating the dialogue content.
(paragraph 22 "In one or more implementations of the method, the method further comprises, during a training procedure of the conversational agent model: obtaining a past conversation thread associated with space-time coordinates, the past conversation thread comprises a plurality of messages, obtaining a supportive knowledge text associated with a character, generating, based on the past conversation thread, a search query, embedding each of the search query and the supportive knowledge text to obtain a search query vector and supportive knowledge text vectors, determining a respective distance between the search query vector and each supportive knowledge text vector, selecting, based on the respective distances, a first set of knowledge sentences from the supportive knowledge text, extracting a set of keywords from the conversation thread, matching the set of keywords with a set of knowledge sentences from the supportive knowledge text, selecting, based on said matching, a second set of knowledge sentences from the supportive knowledge text, and training the conversational agent model to generate a response to a given conversation thread based on: the first set of knowledge sentences and the second set of knowledge sentences.")
Claim 18 contains limitations similar to those found in claims 4 and 5 and therefore are not patent eligible for the same reasons.
Claims 8 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20230306205 A1, (MAEDER; Pascal.) in view of JP Patent JP 2014120049 A, (IRIYAMA TAKAMITSU.) in view of US Patent US 20220358448 A1, (Beringer; Joerg.) in further view of US Patent US 20250078676 A1, (Zhang; Qingquan.)
Claim 8
Regarding Claim 8, MAEDER in view of IRIYAMA in view of Beringer do not explicitly teach all of 8. (Currently Amended) The method according to claim 1, wherein the dialogue content[[s]] includes texts generated by the natural language model and audiovisual contents obtained by querying an audiovisual database.
However, Zhang teach the method according to claim 1, wherein the dialogue contents include texts generated by the natural language model and audiovisual contents obtained by querying an audiovisual database.
(Paragraph 91 "This may further include, based on the understood content, searching for related knowledge points in the knowledge database, and using one or more scoring or matching algorithms to select the best-matched learning materials corresponding to the knowledge points to reply to the natural language information."
paragraph 99 "Step 5 is generating multiple forms of replies, including but not limited to text, audio, video, and images, and outputting them according to corresponding needs. This may include:")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified MAEDER in view of IRIYAMA in view of Beringer to incorporate the teachings of Zhang to provide a “8. The method according to claim 1, wherein the dialogue contents include texts generated by the natural language model and audiovisual contents obtained by querying an audiovisual database.” Doing so would Make the response easier to understand than pure text, as recognized by Zhang. (Paragraph 174).
Claims 11 and 12 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20230306205 A1, (MAEDER; Pascal.) in view of JP Patent JP 2014120049 A, (IRIYAMA TAKAMITSU.) in further view of US Patent US 20220358448 A1, (Beringer; Joerg.) in further view of US Patent US 20250029192 A1, (Wheeler; Ross.).
Claim 11
Regarding Claim 11, MAEDER in view of IRIYAMA in further view of Beringer do not explicitly teach all of 11. (Currently Amended) The method according to claim 10, wherein the dialogue interface provides an input field for the user-input contents and a dialogue display area that displays the dialogue content[[s]] outputted by the chatbot and the user-input contents.
However, Wheeler teaches 11. (Currently Amended) The method according to claim 10, wherein the dialogue interface provides an input field for the user-input contents and a dialogue display area that displays the dialogue content[[s]] outputted by the chatbot and the user-input contents.
(Provisional and publication paragraph 79 "Further, the GUI 300 may include an input interface 332. The GUI 300 may display a thread 302 in the input interface to prompt the user to input information into the GUI 300. The user may input information via text (e.g., by typing) and/or audio (e.g., by speaking), and/or uploading files (e.g., images, videos, etc.). In scenarios where the user responds by typing, the user may type in the input interface 332. In scenarios where the user responds by speaking, the user may interact with a selectable element 304 to begin speaking. The user device 102 may transcribe the audio data (e.g., using the ML module 140) and enter the transcribed audio into the chat interface 330."
Provisional and publication paragraph 81 " Upon the user entering an input, the GUI 300 may present the user's input in the chat interface 330, such as the inputs 310 and 316. The user's input may be a question associated with a property, such as the input 310. The application may generate a response to the question and present the response in the chat interface 330, such as the responses 312 and 318.")
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified MAEDER in view of IRIYAMA in further view of Beringer to incorporate the teachings of Wheeler to provide a “the method according to claim 10, wherein the dialogue interface provides an input field for the user-input contents and a dialogue display area that displays the dialogue contents outputted by the chatbot and the user-input contents.” Doing so would Provide more accurate and effective responses, as recognized by Wheeler. (Paragraph 4 &5).
Claim 12
Regarding Claim 12, MAEDER in view of IRIYAMA in further view of Beringer , further IRIYAMA teach the method according to claim 10, wherein the dialogue content[[s]] generated by the natural language model operated in the chatbot according to the semantic features of the user-input content, the user preference, and the real-time environmental information include at least one of multiple recommendation options, multiple recommended audiovisual contents, and multiple friend suggestion links. (teaches the bolded)
(Page 4 paragraph 4 and 5 "As illustrated in FIG. 3, the user information DB 13 stores “user name, preference information, position log, behavior information, environment information, notification history” and the like. The information shown here is merely an example, and the present invention is not limited to this. For example, the user's name, user ID, address, telephone number, e-mail address, SNS (Social Networking Service) used by the user other service information etc. are also stored.
The “user name” stored here is information for identifying the user such as the name and ID of the registered user, and is set and registered by the user such as “U001”, for example. “Preference information” is preference information registered by the user, for example, information such as “dislike alcohol” and “diet”. The “location log” is a history of location information acquired from the terminal device 1 by the location information acquisition unit 17 to be described later, and information such as “Chiyoda-ku ... Tokyo” is stored in time series. “Behavior information” is information indicating the user's behavior specified by the situation specifying unit 19 to be described later. For example, “train movement to just before”, “walking movement”, “running movement”, “stop”, “stay” It is information such as. “Environment information” is information about the environment at the current position of the user acquired by the environment acquisition unit 18 described later. For example, “rain, forecast that stops after 15 minutes”, “sunny”, “many ultraviolet rays”, “congestion” Information such as “medium”."
page 5 paragraph 2 and 3 from the bottom "
The environment acquisition unit 18 is a processing unit that acquires environmental information such as weather, ultraviolet rays, earthquakes, and traffic jams corresponding to the position information of the user. Specifically, when the environment acquisition unit 18 receives the current position of the terminal device 1 from the position information acquisition unit 17, the environment acquisition unit 18 acquires environment information corresponding to the current position from an external server or the like, and the terminal device in the user information DB 13 Stored in “environment information” of one record.
For example, if the environment acquisition unit 18 receives “Chiyoda-ku, Tokyo ...” as the current position, the environment acquisition unit 18 accesses the weather server 4 and the weather forecast for “Chiyoda-ku, Tokyo” “Rain, stops after 15 minutes” Is stored in the user information DB 13. In addition, the environment acquisition unit 18 acquires traffic jam information when the current position is “highway”, and acquires a delay situation or the like when the current position is “train”.")
MAEDER in view of IRIYAMA in view of Beringer in further view of Wheeler, Wheeler further teaches the method according to claim 10, wherein the dialogue contents generated by the natural language model operated in the chatbot according to the semantic features of the user-input content, the user preference, and the real-time environmental information include at least one of multiple recommendation options, multiple recommended audiovisual contents, and multiple friend suggestion links. (does not teach the bolded)
(Provisional and publication paragraph 49 "In some embodiments, the voice bots or chatbots 150 discussed herein may be configured to utilize artificial intelligence (AI) and/or ML techniques. For instance, the voice bot or chatbot 150 may be a GPT bot (e.g., may use ChatGPT or InstructGPT) or another type of AI or large language model (LLM) bot (e.g., may use Codex or a Google Bard). The voice bot or chatbot 150 may employ supervised or unsupervised ML techniques, which may be followed by, and/or used in conjunction with, reinforced or reinforcement learning techniques. The voice bot or chatbot 150 may employ the techniques utilized for ChatGPT, InstructGPT, Codex, Google Bard, or other similar AI or ML models or platforms."
Provisional and publication paragraph 103 "At block 415, the server may determine a feature vector of the first prompt based upon the semantic clusters. In some instances, the server may determine the feature vector by encoding the semantic clusters into the feature vector directly. Various techniques may be used to perform this step, such as Bag of Words. In other instances, the server may determine the feature vector by (1) encoding the semantic clusters as a set of vectors, and (2) determining a feature vector based upon the set of vectors associated with the semantic clusters."
Provisional and publication Paragraph 105 " Various techniques may be used to determine a feature vector for a set of vectors. For example, a feature vector may be a mean vector or a weighted sum of the set of vectors. In another example, the server may combine the set of vectors into a matrix, calculate an eigenvector of the resulting matrix, and use the eigenvector as the feature vector. In yet another example, the server may use a trained machine learning model (such as Recurrent Neural Networks (RNN), Bidirectional Encoder Representations from Transformer (BERT), etc.) to determine a feature vector for the set of semantic clusters."
Provisional and publication Paragraph 116 " In some embodiments, there may be more than one recommendation in the chatbot's response. The server may process each of the recommendations in a similar manner described above."
Provisional and publication Paragraph 117 " If there are more than one valid recommendation in the response, at block 438, the server may determine the recommendation(s) to be presented to the user. For example, the server may select one or more recommendations based upon their corresponding changes of metric values, such as selecting the recommendations with greatest metric increases, or selecting the recommendations with metric increases exceeding a threshold (which may be different from the threshold used in some embodiments of block 436).")
See claim 11 for rationale.
Claims 18 are rejected under 35 U.S.C. 103 as obvious over US Patent US 20230306205 A1, (MAEDER; Pascal.) in view of JP Patent JP 2014120049 A, (IRIYAMA TAKAMITSU.) in view of US Patent US 20220358448 A1, (Beringer; Joerg.) in further view of US 20240428008 A1, (ABRAHAM; Robin.)
Claim 18 contains limitations similar to those found in claims 4 and 5 and therefore are not patent eligible for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent US 20230350929 A1 to Hasan; Asif discloses a chatbot that uses external knowledge user context data, or conversation history.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 ALI M HASSAN whose telephone number is (571)272-5331. The examiner can normally be reached Monday - Friday 8:00am - 4:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras Shah can be reached at (571)270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALI M HASSAN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
10/02/2026
