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 TW112143768 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.
Information Disclosure Statement
The IDS dated 10/16/2024 and 4/17/2025 has been considered and placed in the application file.
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, 8-10, 12, 13, and 17 are rejected under 35 U.S.C. 103 as obvious over CN 109690526 A, (WU, Xian-chao.) in view of JP 2021501926 A, (Anshul Kotari.).
Claim 1 and 12
Regarding Claim 1 and 12, WU. teach
A method for introducing an intelligent dialogue, which is performed in a cloud server, the method comprising:
(page 3 paragraph 4 from the bottom " In the exemplary environment 100, the network 110 for the terminal device 120, the application server 130 and chatting robot server 140, 150, 160 are interconnected together.")
activating an online dialogue procedure,
(Page 4 paragraph 2 " chatting robot client 122 communication with chatting robot server 140. For example, chat robot client 122 sends the user input message to chatting robot server 140 and associated with the message received from the chatting robot server 140 response. chatting robot client 122 and chatting robot server 140 can be referred to as chat robot. Because the dialog between the user and the chat robot under normal condition by means of a query-response, the user input message is commonly known as query, chat robot output response is commonly known as response. the query-responding pair is recorded as a user log data. It should be understood that in some implementations, without interacting with the chatting robot server 140, the chat robot client 122 also can be locally generates a response to query the player input.")
introducing a first chatbot,
(Page 4 paragraph 5 from bottom "in addition to chatting robot 140 in the system 100 can have a variety of chatting robot, such as chat robot 150, 160. 150, 160 and so on can be special field chatting robot chatting robot, which provides various services, such as electronic commerce, travel assistance, location-based service such as restaurants, hospitals, shops and so on based on, and so on. the user can chat robot client 150, 160 of these field special chat robot installed on the terminal device of the user, and by using the corresponding chatting robot client end obtains the service. However, manually managing these special field chatting robot, is time consuming and difficult, especially when there is a large field of available private chat robot for the user. In one implementation of the present disclosure, chat robot 140 (usually a common chat robot) acting as a unified platform to manage or integrate all chat robot 150, 160, etc., (usually to the special chat robot), to provide various services to the user through the uniform platform.")
and receiving a user-input content via a dialogue interface;
(page 4 paragraph 2 from the bottom " System 200 comprises a user interface (UI) 210. UI 210 can be implemented at the client chat robot 122 that provides for user and chat window of the chat robot interaction.")
retrieving semantic features of the user-input content;
(Page 15 paragraph 5 " For a given query 1310, the query is mapped to vector space, which can generate the first vector 1410. may be recurrent neural network (RNN) to perform sentence code so that the query is mapped to the vector space. can use various techniques to RNN sentence code, such as long term memory (LSTM), the gating cycle unit (GRU) and the like. In one implementation, the first vector may be n-dimensional vector, one example, n is 100. the elements of the vector can be floating point numbers."
Page 15 paragraph 7 "can generate third vector 1310 for a given query 1414, to represent the topic query 1310-emotional. In one implementation, the associated with the query topic word mapped to (n-1) vector, then the corresponding emotion data in the vectors (n-1) of (n-1) elements and the element (as one of the group of elements) are combined can be obtained the n-dimensional third vector 1414. For example, tuple < learning related to the inquiry, a negative >, the words " learning" maps to (n-1) vector, the representative "negative" of "0" are combined to form a third vector 1414. In another implementation, the triad < topic, opinion, emotion >, mapping the words of the topic and opinion to the (n-1) - dimensional vector, with the "emotion" elements to form a third vector 1414."
Page 8 paragraph 5-7 "In one implementation, the GBDT ranking algorithm can use the characteristic may be the cumulative word between the trigger content query and candidate chatting robot to vector similarity score (word2vec). similarity scores by using the following equations (1) and (2), it can achieve the accumulation word to vector (word2vec).
similarity 1 = σ query w (word2vec (w, vy-vx)) (1)
wherein, w is the word in the inquiry, vx is the word in the trigger content of the candidate chatting robot, and such that word2vec (u, v) becomes the maximum value in all the word v in the trigger content."
Page 10 last paragraph and page 11 first paragraph " the chat robot A can send the information about the chat robot C of user experience problems, such as, " you feel how movie girl? my want to hear the feedback you ", as shown in FIG. after receiving the reply from the user (e.g., " yes, equivalent bar), the chat robot A can perform emotion analysis to the reply to the identification is positive or negative or neutral user feedback. and the user feedback can be used as trigger feature of the chat robot C, discussed above.")
obtaining user data
(Page 4 paragraph 2 "chatting robot client 122 communication with chatting robot server 140. For example, chat robot client 122 sends the user input message to chatting robot server 140 and associated with the message received from the chatting robot server 140 response. chatting robot client 122 and chatting robot server 140 can be referred to as chat robot. Because the dialog between the user and the chat robot under normal condition by means of a query-response, the user input message is commonly known as query, chat robot output response is commonly known as response. the query-responding pair is recorded as a user log data. It should be understood that in some implementations, without interacting with the chatting robot server 140, the chat robot client 122 also can be locally generates a response to query the player input."
Page 10 last paragraph and page 11 first paragraph " the chat robot A can send the information about the chat robot C of user experience problems, such as, " you feel how movie girl? my want to hear the feedback you ", as shown in FIG. after receiving the reply from the user (e.g., " yes, equivalent bar), the chat robot A can perform emotion analysis to the reply to the identification is positive or negative or neutral user feedback. and the user feedback can be used as trigger feature of the chat robot C, discussed above.")
introducing a second chatbot according to information of one or any combination of the semantic features of the user-input content, a user preference obtained from the user data, and the real-time environmental information;
(Page 7 paragraph 3 "In an exemplary frame 500, the user 510 can perform conversation through intelligent auto-chatting with chatting robot A 520. chatting robot shown in FIG. 2, in search module 260 is implemented as a chat robot A of the learning sequence (LTR) model 522. LTR model 522 available and the best chat robot specific consumption and the intention of the user or emotional connection requirement is matched. In one implementation, the LTR model 522 the message received from the user and other artificial intelligent entity 530 (such as chat robot B532, C534, D536, E538, etc.) between the trigger content match rate to score. LTR model 522 based on the matching ratio, selecting other artificial intelligent entity 530 in an artificial intelligent entity, providing service to the user of the artificial intelligent entity is selected.")
generating a dialogue content by a natural language model that is operated by the second chatbot; and
(Page 18 paragraph 6 "1818, by switching to the second identity of the second artificial intelligent entity from the first identity of the first artificial intelligent entity, using the second identity of the second artificial intelligent entity performing the dialog with the user."
page 18 paragraph 8 "In one implementation, at 1818, establishing a dialog or communication between the first artificial intelligent entity and the second artificial intelligent entity, via the first artificial intelligent entity, received from the user of the message to the second artificial intelligent entity, and via the first artificial intelligent entity, received from the second artificial intelligent entity of the response to the user.")
importing the dialogue content to the online dialogue procedure, and
(Page 7 paragraph 5 " the chat robot A receives the query from the user conversation between the chat robot A and the user, and conversation between the chat robot A and the chat robot B transfers the inquiry to the chatting robot B. then, the chat robot A receives the corresponding response from the chat robot B conversation between the chat robot A and robot B in the chat, and conversation between the chat robot A and the user transmits the response to the user. providing the process service of the chat robot B to the user via the chat robot A, can be presented to the user the identity of the chat robot B to indicate it is the chat robot B is chatting with the user. However, the chat robot B without knowing the existence of user 510, and the chat robot B can only know the existence of the chat robot A (as the user). This is advantageous for the chat robot A search of existing chat robot to provide a specific AI service to the user as a uniform platform, and is also advantageous for independent from the chat robot A development chat robot 530. ")
outputting the dialogue content on the dialogue interface.
(Page 6 paragraph 3 "can be determined by the core processing module 220 of the response provided to the response queue, response cache 234. in response to queue the response cache response 234 may be further transmitted to the user interface 210, so that the response can be presented to the user in an appropriate order.")
WU do not explicitly teach all of and real-time environmental information;
However, Anshul teach
and real-time environmental information;
(Page 13 last paragraph and page 14 first paragraph "For example, an input query received in an input audio signal from computing device 104 could be "food bot, suggesting some good restaurants nearby." The chatbot component 114 can determine that the position component of the input query is present, based on the term "near". Instead of passing a query that could cause the data processing system 102 or the computing device 104 to query the third-party chatbot provider device 108 for location information, the chatbot component 114 is a second. You can build a query and enter the location information corresponding to the computing device 104. The data processing system 102 (eg, via the chatbot component 114) can locate the computing device 104. The data processing system 102 can determine the established location preference in the profile of the computing device 104. Location preferences are, for example, blocking the transmission of the location, or the permissible resolution of the location to transmit (eg, radius 100 meters, 200 meters, 300 meters, 500 meters, 1000 meters, 1 mile, zip code, city, town. , Addresses within the group) can be included. Based on location preference, the data processing system 102 identifies the current location of the computing device 104, such as "Identify a good restaurant near 123 Main Street, Anytown, United States" below. A second query can be constructed to include the information. Therefore, by preprocessing the input query to identify the missing information, then determining the missing information and generating a second query containing the determined information, the data processing system 102 can be a third party chatbot. Overall system efficiency can be improved by reducing excessive remote procedure calls made by provider device 108, thereby reducing the use of computing resources or battery consumption of computing device 104. ")
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 WU to incorporate the teachings of Anshul to provide a “and real-time environmental information;” Doing so would Improve or enhance the performance of the model, as recognized by Anshul. (page 13 paragraph 3).
Claim 2
Regarding Claim 2, WU in view of Anshul, further Anshul teach
2. The method according to claim 1, wherein, in addition to generating the dialogue content, the first chatbot or the second chatbot introduces one or more audiovisual contents to the dialogue interface according to the semantic features, the user preference, and the real-time environmental information.
(Page 7 paragraph 1 " The content data 130 is provided by, for example, content campaign information, content groups, content selection criteria, digital component objects, or content provider computing device 106, or is a data processing system to facilitate content selection. It may contain other information obtained or determined by. The content data 130 can include, for example, the past performance of the content campaign. Content data 128 may include digital components for audio output, display output, or associated metadata, as well as input audio messages that may be part of one or more communication sessions with the client computing device 104. it can. Digital components (or digital component objects) can include, for example, content items, online documents, audio, images, videos, multimedia content, or sponsored content."
Page 24 paragraph 3 "In ACT416, the data processing system can provide display output. The data processing system may send the generated display output to the computing device in order for the computing device to render the display output for display through the display device communicatively coupled to the computing device. it can.")
semantic features,
(Page 15 paragraph 2 from the bottom " Content selector component 118 can generate one or more keywords based on the response from chatbot component 118. Content selector component 118 can parse the response to identify one or more keywords. Content selector component 118 can use semantic analysis techniques, machine learning models, pattern matching techniques, or other keyword generation techniques to identify or generate one or more keywords. Content selector component 118 can identify the topic vertical, concept, category, product, service, or entity associated with the response to generate the keyword. For example, a response to the query "good restaurants nearby" can include the following three results: i) restaurant A, ii) restaurant B, and iii) restaurant C. The results can further include a description of the type of food offered at each of the restaurants. Food types can include burgers and french fries in Restaurant A, pizza and pasta in Restaurant B, and pancakes and waffles in Restaurant C. Content selector component 118 can parse these results to generate the following keywords, hamburgers, french fries, pizza, pasta, pancakes, and waffles. Content selector component 118 can enter these automatically generated keywords into the real-time content selection process to select sponsored digital components. ")
the user preference,
(Page 6 last paragraph "Historical data 126 may be stored in one or more data structures. Historical data 126 includes historical network activity associated with computing device 104, chatbot identifiers used by computing device 104, computing device 104 configuration, device capabilities, preferences, or content selection, or digital configuration. It can contain other information associated with the compute device 104 that can facilitate the selection of positions between chatbot results where elements are inserted…")
and the real-time environmental
(Page 13 last paragraph and page 14 first paragraph "For example, an input query received in an input audio signal from computing device 104 could be "food bot, suggesting some good restaurants nearby." The chatbot component 114 can determine that the position component of the input query is present, based on the term "near". Instead of passing a query that could cause the data processing system 102 or the computing device 104 to query the third-party chatbot provider device 108 for location information, the chatbot component 114 is a second. You can build a query and enter the location information corresponding to the computing device 104. The data processing system 102 (eg, via the chatbot component 114) can locate the computing device 104. The data processing system 102 can determine the established location preference in the profile of the computing device 104. Location preferences are, for example, blocking the transmission of the location, or the permissible resolution of the location to transmit (eg, radius 100 meters, 200 meters, 300 meters, 500 meters, 1000 meters, 1 mile, zip code, city, town. , Addresses within the group) can be included. Based on location preference, the data processing system 102 identifies the current location of the computing device 104, such as "Identify a good restaurant near 123 Main Street, Anytown, United States" below. A second query can be constructed to include the information. Therefore, by preprocessing the input query to identify the missing information, then determining the missing information and generating a second query containing the determined information, the data processing system 102 can be a third party chatbot. Overall system efficiency can be improved by reducing excessive remote procedure calls made by provider device 108, thereby reducing the use of computing resources or battery consumption of computing device 104. ")
See claim one for rationale.
Claim 3
Regarding Claim 3, WU. In view of Anshul, further Anshul teach
The method according to claim 2, wherein one or more location-based recommended contents associated with a location of a user are shown on the dialogue interface.
(Page 13 last paragraph and page 14 first paragraph "For example, an input query received in an input audio signal from computing device 104 could be "food bot, suggesting some good restaurants nearby." The chatbot component 114 can determine that the position component of the input query is present, based on the term "near". Instead of passing a query that could cause the data processing system 102 or the computing device 104 to query the third-party chatbot provider device 108 for location information, the chatbot component 114 is a second. You can build a query and enter the location information corresponding to the computing device 104. The data processing system 102 (eg, via the chatbot component 114) can locate the computing device 104. The data processing system 102 can determine the established location preference in the profile of the computing device 104. Location preferences are, for example, blocking the transmission of the location, or the permissible resolution of the location to transmit (eg, radius 100 meters, 200 meters, 300 meters, 500 meters, 1000 meters, 1 mile, zip code, city, town. , Addresses within the group) can be included. Based on location preference, the data processing system 102 identifies the current location of the computing device 104, such as "Identify a good restaurant near 123 Main Street, Anytown, United States" below. A second query can be constructed to include the information. Therefore, by preprocessing the input query to identify the missing information, then determining the missing information and generating a second query containing the determined information, the data processing system 102 can be a third party chatbot. Overall system efficiency can be improved by reducing excessive remote procedure calls made by provider device 108, thereby reducing the use of computing resources or battery consumption of computing device 104. "
page 15 paragraph 2 from the bottom " Content selector component 118 can generate one or more keywords based on the response from chatbot component 118. Content selector component 118 can parse the response to identify one or more keywords. Content selector component 118 can use semantic analysis techniques, machine learning models, pattern matching techniques, or other keyword generation techniques to identify or generate one or more keywords. Content selector component 118 can identify the topic vertical, concept, category, product, service, or entity associated with the response to generate the keyword. For example, a response to the query "good restaurants nearby" can include the following three results: i) restaurant A, ii) restaurant B, and iii) restaurant C. The results can further include a description of the type of food offered at each of the restaurants. Food types can include burgers and french fries in Restaurant A, pizza and pasta in Restaurant B, and pancakes and waffles in Restaurant C. Content selector component 118 can parse these results to generate the following keywords, hamburgers, french fries, pizza, pasta, pancakes, and waffles. Content selector component 118 can enter these automatically generated keywords into the real-time content selection process to select sponsored digital components. ")
See claim one for rationale.
Claim 8 and 13
Regarding Claim 8 and 13, WU. In view of Anshul, further Anshul 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 or network information relating to a present location that are retrieved in real time from one or more external systems.
(Page 14 first paragraph "…The data processing system 102 can determine the established location preference in the profile of the computing device 104. Location preferences are, for example, blocking the transmission of the location, or the permissible resolution of the location to transmit (eg, radius 100 meters, 200 meters, 300 meters, 500 meters, 1000 meters, 1 mile, zipcode, city, town. , Addresses within the group) can be included. Based on location preference, the data processing system 102 identifies the current location of the computing device 104, such as "Identify a good restaurant near 123 Main Street, Anytown, United States" below…")
See claim one for rationale.
Claim 9 and 17
Regarding Claim 9 and 17, WU. In view of Anshul, further WU teach
The method according to claim 1, wherein the first chatbot is a default main chatbot in the online dialogue procedure, and the second chatbot is a domain chatbot to be determined by a software process operated in the cloud server for introducing a chatbot based on one or any combination of the semantic features, the user preference, and the real-time environmental information.
(Page 4 paragraph 5 from the bottom "… In one implementation of the present disclosure, chat robot 140 (usually a common chat robot) acting as a unified platform to manage or integrate all chat robot 150, 160, etc., (usually to the special chat robot), to provide various services to the user through the uniform platform."
Page 5 paragraph 3 from the bottom "an index entry in the index database 250 may also include the application index 256 and index chat robot 258 which can be chatting robot search module 260 uses to determine whether to recommend the user the other chat robot. "
Page 8 paragraph 5-7 "In one implementation, the GBDT ranking algorithm can use the characteristic may be the cumulative word between the trigger content query and candidate chatting robot to vector similarity score (word2vec). similarity scores by using the following equations (1) and (2), it can achieve the accumulation word to vector (word2vec).
similarity 1 = σ query w (word2vec (w, vy-vx)) (1)
wherein, w is the word in the inquiry, vx is the word in the trigger content of the candidate chatting robot, and such that word2vec (u, v) becomes the maximum value in all the word v in the trigger content.")
Claim 10
Regarding Claim 10, WU. In view of Anshul, further WU teach
The method according to claim 9, wherein the cloud server provides a multi-domain robot database, and the domain chatbot is provided from the multi-domain robot database.
(Page 3 paragraph 4 from bottom "In the exemplary environment 100, the network 110 for the terminal device 120, the application server 130 and chatting robot server 140, 150, 160 are interconnected together."
Page 5 paragraph 4 from bottom "index database 250 may include a plurality of index items. an index entry in the index database 250 may include a pure index set 252 and the question-answer pair index set 254, which may be a core processing module 220 as the response…"
Page 5 paragraph 3 from the bottom "an index entry in the index database 250 may also include the application index 256 and index chat robot 258 which can be chatting robot search module 260 uses to determine whether to recommend the user the other chat robot. "
Page 2 paragraph 2 "chatting robot into inlet of many online services, such as electronic commerce, travel assistance, location based service such as restaurants, hospitals, shops and so on based on, and so on. downloading each chat robot and using them in different application scenes, it is difficult for the user."
index database is being interpreted as Multi domain robot database )
Claims 4 and 15 are rejected under 35 U.S.C. 103 as obvious over CN 109690526 A, (WU, Xian-chao.) in view of JP 2021501926 A, (Anshul Kotari.) in further view of US 20170295114 A1, (Goldberg; Jeremy Harrison.)
Claim 4 and 15
Regarding Claim 4 and 15, WU. In view of Anshul do not explicitly teach all of the method according to claim 1, wherein the cloud server operates a social media, a corresponding social media application is executed in a user device, and a user clicks on a dialogue linking icon shown on a page of the social media application to enter the online dialogue procedure.
However, Goldberg teach
4. The method according to claim 1, wherein the cloud server operates a social media,
(Paragraph 41 "The consumer-to-business service 110 may comprise a social networking service 130. The social networking service 130 may maintain a social graph data structure representing a social graph. The social graph may represent relationships between entities, such as user entities, commerce entities, and any other sort of entity. The social graph may represent the relationships as graph relationships, in which all information is encoded as either being attached to a particular node in the graph or attached to a particular edge between two nodes in the graph. A messaging system 140 may be an element of a social networking service 130, with the social graph containing, at least in part, social-networking information. The whole of the consumer-to-business service 110 may be an element or composed of elements of a social networking service.")
a corresponding social media application is executed in a user device,
(Paragraph 44 "A user may participate in the consumer-to-business messaging system 100 and interact with the consumer-to-business service 110 using a messaging endpoint 125 software application executing on a client device 120. The client device 120 may typically be a smartphone—a mobile phone capable of executing software applications that provide functionality beyond that of a conventional telephone—such as an iPhone®, Android® phone, or other smartphone. The messaging endpoint 125 may be specifically associated with a particular messaging system 140 that forms part of the consumer-to-business service 110 or may be a general-purpose messaging client operative to interact with a plurality of messaging services. The messaging endpoint 125 may interact with one or both of the consumer portal 150 and the messaging system 140 for the performance of messaging tasks and commerce tasks.")
and a user clicks on a dialogue linking icon shown on a page of the social media application
(Paragraph 66 "The user interface 300 for the message thread may comprise a message interaction display 310. The message interaction display 310 may comprise the messages exchanged within the message thread. The message interaction display 310 may be iteratively updated as additional messages are added to the message thread by the participants in the message thread. Messages may be displayed in association with an avatar for the user."
Paragraph 68 "The extracted element 312, or any other content or context of a message thread, may invoke the display of messaging bot invocation controls 315. The messaging client may retrieve and display a plurality of suggested services, such as may correspond to suggests messaging bots, in a plurality of triggered messaging bot invocation controls 315. In some embodiments, the plurality of suggested services may be automatically displayed on response to the messaging client and/or messaging system 140 detecting a relevant context, such as based on an extracted element 312 or a plurality of extracted elements. The messaging bot invocation controls 315 may comprise a plurality of messaging bot options. A messaging bot invocation control may comprise a particular messaging bot associated with a particular service."
Paragraph 74 "Selecting a messaging bot invocation control of the plurality of messaging bot invocation controls 315 may invoke an interface for interaction with that particular selected messaging bot."
Message bot invocation control is being interpreted as dialogue linking icon )
to enter the online dialogue procedure.
(Paragraph 78 "A messaging bot menu 335 for a particular messaging bot may be displayed in response to a messaging bot invocation control for that messaging bot being selected by a user of a client device 320. The messaging bot menu 335 may be displayed in association with the messaging thread from which it is invoked."
Paragraph 79 "The messaging bot menu 335 is a display element specifically associated with a particular messaging bot and empowers a user to interact with an interface for the messaging bot within the context of the message thread from which it is invoked. The user of the client device 320 may move into the messaging bot menu 335, interact with the messaging bot menu 335 to interact with the messaging bot, back out of the messaging bot menu 335 to re-engage with the messaging conversation in the message thread, move back into the messaging bot menu 335 to re-engage with the messaging bot, move between different messaging bot menus, and generally dynamically interact with messaging bot menus within the context of the message thread so as to select and interact with a messaging bot while retaining a connection to an ongoing messaging conversation.")
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 WU. In view of Anshul to incorporate the teachings of Goldberg to provide a “The method according to claim 1, wherein the cloud server operates a social media, a corresponding social media application is executed in a user device, and a user clicks on a dialogue linking icon shown on a page of the social media application to enter the online dialogue procedure.” Doing so would Allow the users to engage with network services within the similar experience of messaging client, as recognized by Goldberg. (Paragraph 32).
Claims 5 and 16 are rejected under 35 U.S.C. 103 as obvious over CN 109690526 A, (WU, Xian-chao.) in view of JP 2021501926 A, (Anshul Kotari.) in view of US 20170295114 A1, (Goldberg; Jeremy Harrison.) in further view of US 20090003659 A1, (Forstall; Scott.)
Claim 5 and 16
Regarding Claim 5 and 16, WU. In view of Anshul, in further view of Goldberg, do not explicitly teach all of the method according to claim 4, wherein the page is a map interface, multiple linking points associated with a geographical location are marked on the map interface, and the multiple linking points include one or more audiovisual linking points.
However, Forstall teach
The method according to claim 4, wherein the page is a map interface, multiple linking points associated with a geographical location are marked on the map interface, and the multiple linking points include one or more audiovisual linking points.
(paragraph 6 "In some implementations, a method includes: presenting a map of a geographic region on a touch-sensitive display; receiving touch input selecting a geographic location; determining geographic positioning information of the geographic location; receiving data in response to an input received by a touch-sensitive display; associating the data with the geographic positioning information of the geographic location to produce geographically tagged data; and storing the geographically-tagged data."
Paragraph 63 "As shown in FIG. 8, in some implementations, multiple indicators 406, 800, 802, 804 and 806 can be placed on the map display area 402 to indicate multiple locations of interest. In some implementations, for each geographic location of interest, the user can capture data such as, pictures, notes, audio and video and save it to the mobile device 100 with an association to the geographic location of interest as described above with regard to indicator 406. In the example interface of FIG. 8, data associated with Palo Alto, Calif. (indicator 800) and San Francisco, Calif. (indicators 802, 804 and 806) is saved on the mobile device 100."
Paragraph 67 "In some implementations, the multimedia presentation begins by displaying the indicator 406 on the map display area 402 as shown in FIG. 9. The presentation continues by showing selected, a predetermined portion, or all pictures, notes, audio and/or video associated with the geographic location specified by the indicator 406. For example, the user interfaces of FIGS. 4 and 5 can be displayed in response to a selection of the indicator 406 such that users can step through the pictures, notes and/or videos using the navigation objects 802 and 804.")
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 WU. In view of Anshul, in further view of Goldberg to incorporate the teachings of Forstall to provide a “The method according to claim 4, wherein the page is a map interface, multiple linking points associated with a geographical location are marked on the map interface, and the multiple linking points include one or more audiovisual linking points.” Doing so would Allow a review of experiences at that location , as recognized by Forstall. (Paragraph 64).
Claims 6, 7, and 14 are rejected under 35 U.S.C. 103 as obvious over CN 109690526 A, (WU, Xian-chao.) in view of JP 2021501926 A, (Anshul Kotari.) in view of US 20240428008 A1, (ABRAHAM; Robin.)
Claim 6
Regarding Claim 6, WU. In view of Anshul, do not explicitly teach all of 6. The method according to claim 1, wherein the natural language model operated in the cloud server uses a transformer model to conduct machine translation, document summarization, and document generation, so as to generate the dialogue content.
However, ABRAHAM teach the method according to claim 1, wherein the natural language model operated in the cloud server uses a transformer model to conduct machine translation, document summarization, and document generation, so as to generate the dialogue content.
(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 WU. In view of Anshul, to incorporate the teachings of ABRAHAM to provide a “6. The method according to claim 1, wherein the natural language model operated in the cloud server uses a transformer model to conduct machine translation, document summarization, and document generation, so as to generate the dialogue content.” Doing so would Improve the quality of the response , as recognized by ABRAHAM. (Paragraph 17).
Claim 7 and 14
Regarding Claim 7 and 14, WU. In view of Anshul, further WU teaches the method according to claim 6, wherein the cloud server further performs a vector operation on the user-input content, the user preference, and the real-time environmental information, (doesn’t teach the bolded )
(Page 15 paragraph 5 "For a given query 1310, the query is mapped to vector space, which can generate the first vector 1410. may be recurrent neural network (RNN) to perform sentence code so that the query is mapped to the vector space. can use various techniques to RNN sentence code, such as long term memory (LSTM), the gating cycle unit (GRU) and the like. In one implementation, the first vector may be n-dimensional vector, one example, n is 100. the elements of the vector can be floating point numbers.")
annotates texts, calculates a vector for each of words, retrieves correlated contents based on vector distances between the words,
(Page 12 paragraph 5 "1112, information 1110 performs keyword/phrase extraction can chat robot to the target so as to obtain keywords or phrases. can be obtained by using an existing text processing technique (such as sentence segmentation and word segmentation, part of speech (POS) tag, extracting noun phrases, named entity recognition (NER), predicate-argument analysis) to realize the keyword/phrase extraction. e.g., from 22 century-help one name is greater than for the sentence " A", can extract the keyword " A", "greater than", "youth", "22". it should be understood that this is only illustrative examples, and the extraction result can be various."
page 12 paragraph 5"1114, can be based on expansion of the extracted word/phrase word/phrase to obtain a synonym thereof. In one implementation, may be performed based on word to word/phrase extension vector (word2vec) to obtain synonym of the extracted word/phrase. then, collecting synonyms of the word/phrase and their extracted as seed stock 1116."
page 7 paragraph 3 "In an exemplary frame 500, the user 510 can perform conversation through intelligent auto-chatting with chatting robot A 520. chatting robot shown in FIG. 2, in search module 260 is implemented as a chat robot A of the learning sequence (LTR) model 522. LTR model 522 available and the best chat robot specific consumption and the intention of the user or emotional connection requirement is matched. In one implementation, the LTR model 522 the message received from the user and other artificial intelligent entity 530 (such as chat robot B532, C534, D536, E538, etc.) between the trigger content match rate to score. LTR model 522 based on the matching ratio, selecting other artificial intelligent entity 530 in an artificial intelligent entity, providing service to the user of the artificial intelligent entity is selected."
Page 8 paragraph 3 from the bottom "wherein, v is the candidate word the trigger content chatting robot, wx is the words in the query, and it makes the word2vec (u, v) is the maximum in all words w in the query.")
and generates the dialogue content that is consistent with the user preference and the real-time environmental information. (doesn’t teach the bolded)
(Page 15 paragraph 2 "Then, the query 1310 can be speaking style data of the query 1320 and the topic of the query-emotion data 1318 as input, response of the response generating module 1320 generates model 1320 can be output for a query 1310 of response 1322. In one implementation, the response generating module 1320 can use encoder-decoder algorithm for automatically generating query response.")
WU. In view of Anshul, further Anshul teaches the method according to claim 6, wherein the cloud server further performs a vector operation on the user-input content, the user preference, and the real-time environmental information, (teaches the bolded )
(Page 6 last paragraph "Historical data 126 may be stored in one or more data structures. Historical data 126 includes historical network activity associated with computing device 104, chatbot identifiers used by computing device 104, computing device 104 configuration, device capabilities, preferences, or content selection, or digital configuration. It can contain other information associated with the compute device 104 that can facilitate the selection of positions between chatbot results where elements are inserted…"
page 14 first paragraph "…The data processing system 102 can determine the established location preference in the profile of the computing device 104. Location preferences are, for example, blocking the transmission of the location, or the permissible resolution of the location to transmit (eg, radius 100 meters, 200 meters, 300 meters, 500 meters, 1000 meters, 1 mile, zipcode, city, town. , Addresses within the group) can be included. Based on location preference, the data processing system 102 identifies the current location of the computing device 104, such as "Identify a good restaurant near 123 Main Street, Anytown, United States" below…")
(Page 6 last paragraph "Historical data 126 may be stored in one or more data structures. Historical data 126 includes historical network activity associated with computing device 104, chatbot identifiers used by computing device 104, computing device 104 configuration, device capabilities, preferences, or content selection, or digital configuration. It can contain other information associated with the compute device 104 that can facilitate the selection of positions between chatbot results where elements are inserted…"
page 14 first paragraph "…The data processing system 102 can determine the established location preference in the profile of the computing device 104. Location preferences are, for example, blocking the transmission of the location, or the permissible resolution of the location to transmit (eg, radius 100 meters, 200 meters, 300 meters, 500 meters, 1000 meters, 1 mile, zipcode, city, town. , Addresses within the group) can be included. Based on location preference, the data processing system 102 identifies the current location of the computing device 104, such as "Identify a good restaurant near 123 Main Street, Anytown, United States" below…")
see claim one for rationale.
Claims 11 and 18 are rejected under 35 U.S.C. 103 as obvious over CN 109690526 A, (WU, Xian-chao.) in view of JP 2021501926 A, (Anshul Kotari.) in view of US 20240144921 A1, (SINGH; Pranav.)
Claim 11
Regarding Claim 11, WU. In view of Anshul, do not explicitly teach all of 11. The method according to claim 10, wherein the multi-domain robot database includes multiple domain models that are trained by learning professional knowledge in various domains through multiple machine-learning algorithms, and the multiple domain models achieve multiple domain chatbots capable of processing natural languages by a natural language processing technology and a generative artificial intelligence technology.
However, SINGH teach
The method according to claim 10, wherein the multi-domain robot database includes multiple domain models that are trained by learning professional knowledge in various domains through multiple machine-learning algorithms, and the multiple domain models achieve multiple domain chatbots capable of processing natural languages by a natural language processing technology and a generative artificial intelligence technology.
(paragraph 46 "The resource provider environment 206 can provide virtual assistant interpretation services 221 for virtual assistants that can support applications or domains (e.g., smart homes, e-commerce, travel, etc.) These services can, for example, train a model that can enable virtual assistants to respond to a broad range of requests addressed by different domains or may configure them to handle a specific set of requests from one or a small number of domains, such as restaurant domains. A virtual assistant can be a software agent with a voice-enabled user interface, which can perform tasks or services for a user based on his/her queries or spoken inputs. It can be integrated into different types of devices and platforms. For example, a virtual assistant can be incorporated into smart speakers, voice-enabled applications, and the like. In certain embodiments, the virtual assistant interpretation services 221 can be offered by a service provider to enable companies to easily create their own application-specific virtual assistants. In various embodiments, the virtual assistant interpretation services can be performed in hardware or software, or in combination thereof."
Paragraph 61 "Training system 246 is operable to train neural network language models to generate such phrases, utterances, or sentences via unsupervised learning. In various embodiments, training system 246 is operable to train classifier models to compute correctness scores for sentences and select one or more sentences with correctness scores satisfying a threshold (e.g., higher than the threshold.) Training system 246 can receive training data from intake system 242."
Paragraph 98 " Sentence generation model 408 can produce correct and meaningful sentences for the intent based on the provided keywords. According to some embodiments, the sentence generation model can be a general-purpose natural language generation (NLG) model that is fine-tuned by associated keywords combined with corresponding sentences. According to some embodiments, the natural language generation model can be fine-tuned by domain identifiers. Finetuning is the procedure of training a general language model using customized or specific data. As a result of the finetuning procedure, the weights of the original model can be updated to account for the characteristics of the domain data and the task the system is interested in.")
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 WU. In view of Anshul, to incorporate the teachings of SINGH to provide a “11. The method according to claim 10, wherein the multi-domain robot database includes multiple domain models that are trained by learning professional knowledge in various domains through multiple machine-learning algorithms, and the multiple domain models achieve multiple domain chatbots capable of processing natural languages by a natural language processing technology and a generative artificial intelligence technology.” Doing so would Improve the accuracy and effectiveness of the assistant, as recognized by SINGH. (Paragraph 22).
Claim 18 contains limitations similar to those found in claims 10 and 11 and therefore are not patent eligible for the same reasons.
Claims 19 are rejected under 35 U.S.C. 103 as obvious over CN 109690526 A, (WU, Xian-chao.) in view of JP 2021501926 A, (Anshul Kotari.) in view of US 20110213642 A1, (MAKAR; Michael G..)
Claim 19
Regarding Claim 19, WU. In view of Anshul, further WU teaches the system according to claim 17, wherein the cloud server counts a quantity of introducing the domain chatbot,
(Page 3 paragraph 4 from bottom "In the exemplary environment 100, the network 110 for the terminal device 120, the application server 130 and chatting robot server 140, 150, 160 are interconnected together."
Page 5 paragraph 4 from bottom "index database 250 may include a plurality of index items. an index entry in the index database 250 may include a pure index set 252 and the question-answer pair index set 254, which may be a core processing module 220 as the response…"
Page 5 paragraph 3 from the bottom "an index entry in the index database 250 may also include the application index 256 and index chat robot 258 which can be chatting robot search module 260 uses to determine whether to recommend the user the other chat robot. "
Page 2 paragraph 2 "chatting robot into inlet of many online services, such as electronic commerce, travel assistance, location based service such as restaurants, hospitals, shops and so on based on, and so on. downloading each chat robot and using them in different application scenes, it is difficult for the user.")
WU. In view of Anshul, do not explicitly teach all of an operating time, a quantity of dialogues, and a clicking number and a clicking time of a content recommended by the domain chatbot, so as to provide a domain client report.
However, MAKAR teach
an operating time,
(Paragraph 148-149 "Start and End--Date and Time
[0149] Enter the dates and hours of the day the campaign will be active. The start dates are important as they imply the order in which the campaigns are applied. To confirm the changes the web retailer must click the "OK" button."
Paragraph 138 "TSA can support multiple campaigns. At least one campaign, titled "Default" must be present and active for any chat session to launch. Campaigns may be made active or inactive using the check boxes located at the left side of the window. They will also become active or inactive depending on the campaign start and end dates. TSA will always establish a Global campaign, which handles responses to most undesirable and improper language.")
a quantity of dialogues,
(Paragraph 176 "FIG. 22 is an example screen of the TSA management console for viewing user questions reports, according to the present invention. This report provides a dissected list of user chats aligned by keyword triggers for any period. Agent responses to keywords along with frequency counts are also provided.")
and a clicking number
(Paragraph 174 "FIG. 21 is an example screen of the TSA management console for viewing campaign performance reports, according to the present invention. This report tracks the number of TSA launches, customer engagements, customer clicks, & conversions. Percentages are also provided for convenience.")
and a clicking time of a content recommended by the domain chatbot,
(Paragraph 172 "FIG. 20 is an example screen of the TSA management console for viewing reports, according to the present invention. All reports can be viewed online, printed or exported to a spreadsheet. First select the Start and End Dates, then Select the Site and Campaign. Note the Default for Site and Campaign is "All". A few of the example reports are described below:"
Paragraph 114-116 "FIG. 11 is an example screen of the TSA management console for managing links, according to the present invention. Links to different areas, pages and documents on web retailer's site are entered so the agent when asked by web retailer's customer can direct the customer to the appropriate information. One of the most important links to create is a link directing the abandoned customer back to the order page, shopping cart or registration page. All links should contain affiliate IDs to provide tracking and credit to the TSA system.
[0115] Additional links to consider including are: Privacy Policy, Shipping Information, FAQs, Contact Information, Contest Rules, Return Policy, Refer a Friend, if you like "that" then you might like "this", and more.
[0116] Links will be inserted into TSA Responses as they these responses are built in the Manage Campaigns section. As an example of a link being used, TSA may respond with "This is a limited offer to take advantage now click here".")
so as to provide a domain client report.
(Paragraph 57 "Prior to the present invention, publishers had to primarily rely on exit pops and follow-up emails to attempt to recover lost customers or cross-sell or up-sell them. The present invention is designed to effectively reduce shopping cart, lead and registration abandonment. The present invention had to be capable of up-selling and cross-selling as well while providing the customer with a satisfactory experience. The present invention is customer friendly and provides real-time campaign management and reporting for publishers. Powered by a self-learning artificial intelligence engine, the present invention assists publishers in increasing their revenue opportunities. The present invention has been successfully deployed and continually enhanced and improved to meet the changes and needs of a growing market.")
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 WU. In view of Anshul, to incorporate the teachings of MAKAR to provide a “an operating time, a quantity of dialogues, and a clicking number and a clicking time of a content recommended by the domain chatbot, so as to provide a domain client report.” Doing so would Provide real time reporting, to improve the agent, as recognized by MAKAR. (Paragraph 57 & 109).
Claims 20 are rejected under 35 U.S.C. 103 as obvious over CN 109690526 A, (WU, Xian-chao.) in view of JP 2021501926 A, (Anshul Kotari.) in view of US 20220035869 A1, (Beck; Benjamin)
Claim 20
Regarding Claim 20, WU. In view of Anshul, further Anshul teaches and the real-time environmental information,
(Page 13 last paragraph and page 14 first paragraph "For example, an input query received in an input audio signal from computing device 104 could be "food bot, suggesting some good restaurants nearby." The chatbot component 114 can determine that the position component of the input query is present, based on the term "near". Instead of passing a query that could cause the data processing system 102 or the computing device 104 to query the third-party chatbot provider device 108 for location information, the chatbot component 114 is a second. You can build a query and enter the location information corresponding to the computing device 104. The data processing system 102 (eg, via the chatbot component 114) can locate the computing device 104. The data processing system 102 can determine the established location preference in the profile of the computing device 104. Location preferences are, for example, blocking the transmission of the location, or the permissible resolution of the location to transmit (eg, radius 100 meters, 200 meters, 300 meters, 500 meters, 1000 meters, 1 mile, zip code, city, town. , Addresses within the group) can be included. Based on location preference, the data processing system 102 identifies the current location of the computing device 104, such as "Identify a good restaurant near 123 Main Street, Anytown, United States" below. A second query can be constructed to include the information. Therefore, by preprocessing the input query to identify the missing information, then determining the missing information and generating a second query containing the determined information, the data processing system 102 can be a third party chatbot. Overall system efficiency can be improved by reducing excessive remote procedure calls made by provider device 108, thereby reducing the use of computing resources or battery consumption of computing device 104. ")
See claim one for rationale.
WU. In view of Anshul, do not explicitly teach all of the system according to claim 17, wherein the cloud server provides an audiovisual database, and the domain chatbot queries the audiovisual database according to the semantic features, the user preference, so as to introduce one or more audiovisual contents to the dialogue interface.
However, Beck teaches
20. The system according to claim 17, wherein the cloud server provides an audiovisual database, and the domain chatbot queries the audiovisual database according to the semantic features,
(paragraph 41 "A static or dynamic set of possible suggested videos, images, or similar content—or identifiers, pointers, or links to such content—may be stored or indexed in a library (e.g., video library) or database. The library or database may be customizable. Suggested videos, images, or similar content may be stored, indexed, and/or searched from a certain source(s), such as websites, video libraries, or databases related to or provided by a particular topic, subject, field, industry, resource, entity, goods or service provider, company, person, or university."
Paragraph 43 "A suggested video or similar content may be identified or selected based on a correlation, matching, or mapping of data from a user's processed entry to data or properties associated with a suggested video. Data or properties associated with a suggested video may be metadata, a description of the video's content, a title, a keyword, a source or author, a category, NER data (e.g., NER data processed from text associated with the video), intent classification data (e.g., intent classification data processed from text associated with the video), or sentiment analysis data. The correlation may be based, for example, on a keyword, intent, entity, sentiment, or category. For example, a user's entry may be processed to identify NER data and/or intent classification data for the entry, and a video library may be searched to identify one or more suggested videos with data that correlates to the NER data and/or intent classification data of the user's entry.")
the user preference,
(Paragraph 40 "In one embodiment, the communication interface comprises features related to suggested content. Suggested content, for example, may be videos, images, links to webpages or other content, suggested inquiries, and/or other types of content. Suggested content may be identified based on particular information in a user's present entry or previous activity (e.g., previous entry or previously selected suggested content item) during a present session or previous session. Suggested content may be identified based on a context determined from a user's previous activity during a present session or a previous session. A context for a particular user may be stored or associated with the user by a user profile, user authentication, or database. A suggested content item may not be identified or displayed to a user if the suggested content item was previously displayed to a user or selected by a user. One or more suggested content items may be displayed to a user before a user entry, for example, when the communication interface is first launched. For example, one or more suggested content items may be provided along with the prompt, may relate to a preset or predetermined context, and/or may be based on previous activity. A user's initial entry or input may comprise selection of such an initial suggested content item.")
so as to introduce one or more audiovisual contents to the dialogue interface.(paragraph 55 "In one embodiment, when a user selects a suggested video, the selected suggested video is displayed and/or played in the communication interface (e.g., in a conversation field or a suggested content display area). In one embodiment, a selected suggested video can be displayed and/or played outside of the communication interface, such as on a separate webpage, in a separate browser tab, in a separate browser, or on a website on which the communication interface is hosted or embedded. The communication interface may stay open or may be hidden or closed when a suggested video is selected or played."
Paragraph 89 "In one embodiment, an implementation of the present invention (e.g., a web application) can be hosted on a cloud platform, for example, to provide flexibility, reliability, and security. The cloud platform may be AWS, Google, or Azure. An end user may be connected to a cloud provider via a content delivery network (CDN), such as CloudFront. For example, FIG. 7 shows one embodiment of a system architecture where an implementation of a chatbot is hosted on a cloud provider platform (AWS) 104 that is provided to an end user 100, and accessed by the end user 100, via CloudFront (content delivery network or CDN) 102.")
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 WU. In view of Anshul, to incorporate the teachings of Beck to provide a “The system according to claim 17, wherein the cloud server provides an audiovisual database, and the domain chatbot queries the audiovisual database according to the semantic features, the user preference, so as to introduce one or more audiovisual contents to the dialogue interface.” Doing so would Provide users with more relevant and targeted information, as recognized by Beck. (Paragraph 3).
Conclusion
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.
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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.
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/ALI M HASSAN/ Examiner, Art Unit 2653
/Paras D Shah/ Supervisory Patent Examiner, Art Unit 2653
06/08/2026
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