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 .
DETAILED ACTION
Claims 1-18 are pending. Claim 1is independent.
Claims 2-18 depend from Claim 1.
This Application was published as U.S. 2024/0330598.
Response to Amendment
Examiner thanks Applicant for response filed on 10 Oct 2025 which has been correspondingly accepted and considered in this office action. Claims 1-18 are pending.
Response to Arguments
Applicant's arguments filed 10 Oct 2025 have been fully considered but they are not persuasive. Each argument of Applicant’s arguments will be addressed in turn.
With regards to objection to claims:
Applicant amended claim 2, and has provided remarks to this objection. Thus, the objection to claim 2 has been withdrawn. On the other hand, applicant has made a typo on claim 4 due to the amendment, and a new claim objection will be issued.
With regards to 35 USC § 112:
Applicant's arguments filed 10 Oct 2025 have been fully considered but they are not persuasive. Applicant amended claims 1, 2, 4, 14, and 18. For the most part, many of the § 112(b) issues have been resolved; however, new § 112(a) have emerged because new matter has been introduced in the amendments. As a result, § 112 rejections have been maintained.
With regards to 35 USC § 103:
Applicant's arguments filed 10 Oct 2025 have been fully considered but they are not persuasive. Applicant argues that features on pages 4-5 of applicant arguments make the claim 1 distinct from the prior art. First, the amendment includes new matter which makes the claim not allowable. Second, the amendments are not allowable because they are taught by prior art. Both of these points will be discussed in more detail in the office action below.
Thus, for all the reasons mentioned above, applicant arguments are not persuasive.
Claim Objections
Claim 4 objected to because of the following informalities:
Claim states “in the the artificial intelligence section.” The second “the” needs to be deleted.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-18 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With regards to claim 1,
Par 6 refers to a “4-tuple” which is not supported in the specification. Specification does not mention tuple or 4-tuple. A tuple is known in the art as an ordered sequence of elements. The original claim and specification does not have a requirement that the elements must be ordered in a certain manner. Instead, the specification states that “Sessions are created based on four conditions: USER, DEVICE, CHATBOT, and a specific time range.”(Par [0071]) MPEP 2163 states “new or amended claims which introduce elements or limitations that are not supported by the as-filed disclosure violate the written description requirement. See, e.g., In re Lukach, 442 F.2d 967, 169 USPQ 795 (CCPA 1971)” Thus, “4-tuple” is new matter.
Par 6 refers to “session key” which is not supported in the specification. Specification does not mention “session key.” Specification refers to “session control” (Par [0071]); however, the specification does not explain what is a “session control” and how the sessions are controlled. Thus, “session key” is not supported in the specification and is new matter.
Par 9 refers to “comprises” which is not supported in the specification or previous claims. Specification states the user intent understanding section “consists of an intent extraction section for conveying the intent extracted from the data received by the intent extraction section.” (Par [0027]) In the claims submitted 10 Oct 2023, claim 10 refers to “consists” Consists and comprises are not equivalent terms. MPEP 2111.03 Transitional phrases states:
The transitional term "comprising", which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. See, e.g., Mars Inc. v. H.J. Heinz Co., 377 F.3d 1369, 1376, 71 USPQ2d 1837, 1843 (Fed. Cir. 2004)
The transitional phrase "consisting of" excludes any element, step, or ingredient not specified in the claim. In re Gray, 53 F.2d 520, 11 USPQ 255 (CCPA 1931);
Changing the term from consists to “comprises” is impermissible broadening of the claimed limitation because it is not supported in the specification or claims.
Par 11 refers to “comprises” which is not supported in the specification or previous claims. Specification states the user answer selection/generation section “consists of an answer selection section for choosing the most optimal response based on the ranked answer candidates and an answer generation section for creating responses based on the selected answer.” (Par [0029]) In the claims submitted 10 Oct 2023, claim 12 refers to “consists” Consists and comprises are not equivalent terms. As discussed previously, changing the term from consists to “comprises” is impermissible broadening of the claimed limitation because it is not supported in the specification or claims.
Par 12 refers to “version-control” which is not supported in the specification or claims. Specification states the conversation agent system is “registered and executed through ADMI.” (Par [0073]). No mention of “version-control” is mentioned in the specification or claims, and is thus new matter.
Par 13 refers to “daily conversations” which is not supported in the specification or claims. Specification refers to “everyday conversations” (Par [0074]) In the claims submitted 10 Oct 2023, claim 1 refers to “everyday conversations.” Everyday means common, routine, or ordinary, and describes the content of a conversation. Based on the specification, this definition is reasonable because the specification is referring to various types of conversation to include common, routine, or ordinary conversations. On the other hand, daily refers to the frequency of an occurrence such as occurring every day, and does not place a limit on the content. Everyday is not synonymous with daily, and the change to “daily conversations” introduces new limitations that are not supported in the specification.
Par 13 refers to “slots and tasks based on SDS” which is not supported in the specification. Specification and prior presented claim refers to “slot and task definitions in SDS scenarios” (Par [0074], Claim 1) Removal of the word “definitions” broadens the scope of the claim because the claim is not limited by slot and task definitions.
With regards to claim 2,
Par 4 refers to “inherent knowledge” that is not supported in the specification or prior claims. Specification refers to “unique knowledge” (Par [0017]) and prior presented claim 2 refers to “proprietary knowledge.” Proprietary or unique knowledge is not equivalent to “inherent knowledge,” so “inherent knowledge” is not supported in the specification or prior claims and is deemed new matter.
With regards to claims 3-18
Claims are dependent on independent claim 1, and are thus rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-18 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regards to claim 1,
Par 6 refers to “Intent Finder” that is not definite. Specification does not provide a definition except to state a “session control is used to manage Intent Finder.” (Par [0071]) Since no definition is provided, it is not clear what the metes and bounds of the claimed limitation. Therefore, if the language of the claim is such that a person of ordinary skill in the art could not interpret the metes and bounds of the claim so as to understand how to avoid infringement, a rejection of the claim under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, is appropriate. See IBSA Institut Biochimique, S.A. v. Teva Pharm. USA, Inc., 966 F.3d 1374, 1378-81, 2020 USPQ2d 10865 (Fed. Cir. 2020) For the purpose of examination, “Intent Finder” will be treated as any mechanism of containing intent data that can be found in some way.
Par 6 refers to “most suitable” which is not definite. Specification does not provide a definition, and states “messages are delivered to the most suitable DIALOG AGENT.” (Par [0072]) Since no definition is provided and it is unknown how to determine the “most suitable” dialog agent, it is not clear what the metes and bounds of the claimed limitation such that a person of ordinary skill in the art would understand how to avoid infringement. For the purpose of examination, “most suitable” will be interpreted as any method that can identify the user’s intent to deliver the messages to a DIALOG AGENT.
With regards to claims 2-18
Claims are dependent on independent claim 1, and are thus rejected.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 4, 8-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Martin et al.(US2020/0274962 hereinafter Martin) in view of Sun et al. (Sun, Wenjuan, Paolo Bocchini, and Brian D. Davison. "Applications of artificial intelligence for disaster management." Natural Hazards 103.3 (2020): 2631-2689 hereinafter Sun), Chenny et al. (US2020/0065685 hereinafter Chenny), and Rasmussen et al. (US12271913 hereinafter Rasmussen), D'Agostino et al. (US2020/0099633 hereinafter D’Agostino), in further view of Kim et al.(US2019/0199658 hereinafter Kim)
With regards to claim 1, Martin teaches:
An AI-Enhanced Disaster Safety Knowledge Integration Management System, comprising: a disaster safety knowledge base integrated with a data network, [Martin Fig 1A item 120 is an Emergency Management System (EMS) for management of emergency and disaster safety knowledge base with data network (Fig 2) (Par [0005])]
the disaster safety knowledge base comprising a data collection section configured to gather and aggregate various information from external agencies; [Martin Fig 1A item 122, Par [0063]]
a data transmission section configured to transmit the aggregated information to a server via LTE, 5G, or WIFI; and a [Martin teaches server (Fig 1A item 123) of EMS system 220 connecting with communication devices 210 in Fig 2]
big data section configured to analyze and accumulate the transmitted data, wherein the big data section is configured to categorize, analyze, and accumulate the transmitted data as real-time data including slope sensors, water quantity and water level information, and disaster incident scene photos; [Martin Fig 8 Par [0086-87] teaches EMS system which analyzes and transmits data such as social media data (860), communication devices (810), emergency responders (842), and emergency service providers (830)]
an artificial intelligence section configured to utilize the accumulated and analyzed data from the big data section to enable machine intelligence through rapid learning based on human cognitive abilities including language, speech, vision, and emotion, and learning and inference capabilities, [Martin Par [0120] teaches machine learning algorithms using emergency and social media data, where social media data is based on human cognitive abilities]
the artificial intelligence section functioning as a deep-query responding disaster safety bot comprising a user interface, a query/keyword input section, a problem analysis section, a user intent understanding section, an answer candidate search/inference section, an answer selection/generation section, and a response generation section; [Martin Fig 3-7B teaches chatbot that is implemented via “any appropriate form of artificial intelligence, such as deep learning” (Par [00146]) where Martin Fig 3-7B shows user interface, query/keyword input section and sections for problem analysis, user intent, answer search/inference, and selection/generation needed to devise communication with the autonomous communication session]
wherein the query/keyword input section is configured to perform in-depth query responses and keyword searches driven by chatbot devices, and comprises modules including a chatbot device and input device, a management system, machine learning tools, a query-response pre-management module, a user intent recognition module, a dialog agent, and a conversation management system; [Martin Figs 3-7B teaches various embodiments of chatbots that perform in-depth query responses and keyword searches by interaction with the user, where chatbot and input devices are depicted along with autonomous communication session that implements the communication between the user and the device “completely independently using any appropriate form of artificial intelligence, such as deep learning or natural language processing.” (Par [0146])]
wherein the problem analysis section comprises a query interpretation section configured to interpret a sentence structure and words of an input query, and a situational interpretation section configured to interpret context and situation included in the query; wherein the user intent understanding section comprises an intent extraction section configured to extract the user’s intent included in the query and an intent interpretation section configured to interpret the extracted intent and to route a message to the most suitable dialog agent; [Martin Fig 7A-7B Par [0082] teaches chatbot (793A) that interacts with the user via an autonomous communication that controls the interaction. Autonomous communication system uses “any appropriate form of artificial intelligence, such as deep learning or natural language processing” (Par [0146]) where natural language processing interprets user information, context, and intent to generate outputs. (see Adamopoulou E and Moussiades L. An Overview of Chatbot Technology. Artificial Intelligence Applications and Innovations. 2020 May, Chap 4 Page 377)]
wherein the dialog agent system is developed and deployed in a disaster safety data environment and is configured to register, version-control, and execute dialog agents through a domain-specific registration/execution management system (ADMI); and
wherein the conversation management system is configured to support a plurality of conversation types including daily conversation and news according to a Q&A engine and a knowledge base type, and to define and manage slots and tasks based on SDS (Spoken Dialogue System) scenarios generated by a conversation modeling tool to execute deep query answering and policy reporting scenarios.
With regards to claim 1, Martin fails to teach:
structured data like historical damage records and various facility safety information; and unstructured data such as disaster situation reports in text, disaster situation images, briefing materials, and the data categorized and analyzed in this way being archived under disaster incident damage status, disaster incident response history, and disaster safety policy information, and in the said Al section,
With regards to claim 1, Sun teaches:
structured data including historical damage records and facility safety information; and unstructured data including disaster situation report texts, disaster situation report images, and briefing materials, and to further classify the categorized and analyzed data into archived data under disaster incident damage status, disaster incident response history, and disaster safety policy information; [Sun teaches various AI techniques used for disaster management (Table 1-4, Pages 2639-51) that uses structured and unstructured data.
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System as taught by Martin with the AI applications as taught by Sun. The motivation to combine the teachings of Martin with Sun is because Sun teaches “example applications of different AI techniques and their benefits for supporting disaster management at different phases” (page 2631) which improves the capabilities of the invention of Martin to handle more AI applications.
With regards to claim 1, Martin in view of Sun fails to teach:
knowledge bot
With regards to claim 1, Chenny teaches:
knowledge bot [Chenny teaches “one or more process Bots to update the knowledge repository (e.g., local storage 106 and/or shared storage 124) with the details and/or features of the reported problem and/or solution to the reported problem” (Par [0053]) which are knowledge bots.
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System using chatbots as taught by Martin and Sun with the knowledge bot as taught by Chenny. The motivation to combine the teachings of Martin and Sun with Chenny is because Chenny teaches “update a knowledge repository with attributes of the identified one or more issues, wherein the one or more process Bots can cognitively learn from the data stored on the knowledge repository, and program instructions to output the one or more identified issues to a user” (Par [0004]) which improves the capabilities of the invention of Martin and Sun to better learn from the identified issues]
With regards to claim 1, Martin in view of Sun and Chenny fails to teach:
wherein the query-response pre-management module is configured to generate and control sessions by using a 4-tuple consisting of USER, DEVICE, CHATBOT, and a defined time window as a session key, and to manage contents delivered to a dialog agent selected by an Intent Finder based on the session key;
wherein the user intent recognition module is configured to determine the user’s intent based on multiple features including query context, user profile, device state, and session time window, and to route the message to the most suitable dialog agent according to the determination;
With regards to claim 1, Rasmussen teaches:
wherein the query-response pre-management module is configured to generate and control sessions by using a 4-tuple consisting of USER, DEVICE, CHATBOT, and a defined time window as a session key, and to manage contents delivered to a dialog agent selected by an Intent Finder based on the session key; (Rasmussen, Fig 1 teaches user (social media messages (102) from a user), device (106), chatbot (108, Col 4 lines 31-35) and defined time window (Col 2 lines 2-7) where session and contents are managed by manager of the chatbot and session keys using tuples to encrypt data is known in the art. (See Tang, Yi. "Sharing session keys in encrypted databases." 2006 IEEE International Conference on e-Business Engineering (ICEBE'06). IEEE, 2006.)
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System using chatbots and social media data as taught by Martin, Sun, and Chenny with the social media alert system as taught by Rasmussen. The motivation to combine the teachings of Martin, Sun, and Chenny with Rasmussen is because Rasmussen teaches “custom data-driven resource to help teams make smarter decisions through social media insights” (Col 2 lines 26-27) during a crisis event like a natural disaster which improves the capabilities of the invention of Martin, Sun, and Chenny to better use social media data]
With regards to claim 1, Martin in view of Sun, Chenny, and Rasmussen fails to teach
wherein the user intent recognition module is configured to determine the user’s intent based on multiple features including query context, user profile, device state, and session time window, and to route the message to the most suitable dialog agent according to the determination;
With regards to claim 1, D’Agostino teaches:
wherein the user intent recognition module is configured to determine the user’s intent based on multiple features including query context, user profile, device state, and session time window, and to route the message to the most suitable dialog agent according to the determination; [D’Agostino Fig 2 teaches user intent recognition module (206) to determine the user intent using “question, query, or information associated with the conversational input 204” (Par [0052]) and route the input to the most suitable chat bot or dialog agent by matching data with the user profile (Par [0054])
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System using chatbots and social media data as taught by Martin, Sun, Chenny and Rasmussen with the chatbot manager system as taught by D’Agostino. The motivation to combine the teachings of Martin, Sun, Chenny, and Rasmussen with D’Agostino is because D’Agostino teaches “matching to a particular user profile, the chat bot decision engine 212 may determine that an alert exists on or associated with the user's profile corresponding to the particular client device.” (Par [0055]) which improves the capabilities of the invention of Martin, Sun, Chenny, and Rasmussen to better authenticate the user and provide any other alerts associated with the user]
With regards to claim 1, Martin in view of Sun, Chenny, Rasmussen, and D’Agostino fails to teach:
wherein the answer candidate search/inference section comprises an answer candidate search section configured to search answer candidates based on the analyzed query, and an answer candidate inference section configured to rank and infer the answer candidates according to the user’s intent and context;
With regards to claim 1, Kim teaches:
wherein the answer candidate search/inference section comprises an answer candidate search section configured to search answer candidates based on the analyzed query, and an answer candidate inference section configured to rank and infer the answer candidates according to the user’s intent and context; [Kim Fig 4B teaches chatbot server (120) that searches for answer candidates by calculating ranking of answers (S.409) based on “context information of a chat between the user device 100 and the chatbot” (Par [0075]) and “which is most appropriate for a user's intent of question from among answers derived by multiple chatbots” (Par [0010])
wherein the answer selection/generation section comprises an answer selection section configured to select an optimal answer among the ranked answer candidates, and an answer generation section configured to generate an actual answer based on the selected answer; [Kim Par [0074] teaches polling model that uses multiple chatbots that calculates “rankings of answers derived from the respective chatbots, and select a chatbot for providing an answer to the user device 100 based on the calculated rankings.”
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System using chatbots and social media data as taught by Martin, Sun, Chenny , Rasmussen, and D’Agostino with the multiple chatbot system as taught by Kim. The motivation to combine the teachings of Martin, Sun, Chenny, Rasmussen, and D’Agostino with Kim is because Kim teaches “instant messaging service and more particularly, technologies for providing an instant messaging service using a relay chatbot linked to multiple chatbots.” (Par [0002]) which improves the capabilities of the emergency management invention of Martin, Sun, Chenny , Rasmussen, and D’Agostino to handle instant messaging]
With regards to claim 2, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the artificial intelligence section is configured to: derive meanings and recognize patterns from raw data as objective facts; convert the derived results into information by understanding associations and correlations; [Martin Par 120 teaches “machine learning algorithms are used for training prediction models and/or making predictions such as predicting a dispatch category based on available emergency data” where meaning and pattern recognition is extracted from the emergency data or objective facts which leads to training of a model which is information transformation based on understanding association and correlations, and the resulting information is internalized in the model that is used for prediction]
internalize the information as inherent knowledge such that the results are structured into knowledge; and ultimately derive a structured knowledge representation. [Kim teaches bots that have “a knowledgebase of each Bot, via a knowledge repository (e.g., local storage 108 and/or shared storage 124), is refreshed in every interaction using machine learning process” (Par [0033]) which derives a knowledge representation]
With regards to claim 4, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the data accumulated and analyzed in the aforementioned big data section are utilized to enable machine intelligence through rapid learning based on human cognitive abilities including language, speech, and emotion and based on learning and inference capabilities in the the artificial intelligence section, and [Martin Par [0120] teaches machine learning via emergency data as well as social media content which is based on human cognitive abilities]
this process involves the interpretation of queries and problem analysis based on situational analysis, leading to the understanding of user intent; [Martin Fig 7A-7B Par [0082] teaches chatbot (793A) that interacts with the user via an autonomous communication that controls the interaction. Autonomous communication system uses “any appropriate form of artificial intelligence, such as deep learning or natural language processing” (Par [0146]) where natural language processing interprets user information, context, and intent to generate outputs. (see Adamopoulou E and Moussiades L. An Overview of Chatbot Technology. Artificial Intelligence Applications and Innovations. 2020 May, Chap 4 Page 377)]
the search and inference of answer candidates; [Kim Fig 4B teaches chatbot server (120) that searches for answer candidates by calculating ranking of answers (S.409) based on “context information of a chat between the user device 100 and the chatbot” (Par [0075]) and “which is most appropriate for a user's intent of question from among answers derived by multiple chatbots” (Par [0010])
self-learning and growth, judgment, and anticipation, leading to the selection/production of answers; and ultimately, the deep query response or automatic report generation process. [Martin teaches autonomous communication using a chatbot for query and answer response using “deep learning or natural language processing.” (Par [0146])]
With regards to claim 8, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the query/keyword input section is composed of a query/keyword collection section for collecting queries/keywords from users and a query identification section for identifying contextual errors or typos in the queries/keywords themselves, requesting re- entry, or forwarding them to the problem analysis section. [Martin Par [0146] teaches autonomous communication system that uses “any appropriate form of artificial intelligence, such as deep learning or natural language processing” which includes collecting data from the user, storing the data, and using the data]
With regards to claim 9, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the problem analysis section is composed of a query interpretation section for interpreting the sentence structure and words of the entered query and a situational interpretation section for interpreting the context and situation contained in the query. [Martin Fig 7A-7B Par [0082] teaches chatbot (793A) that interacts with the user via an autonomous communication that controls the interaction. Autonomous communication system uses “any appropriate form of artificial intelligence, such as deep learning or natural language processing” (Par [0146]) where natural language processing interprets user information, context, and intent to generate outputs. (see Adamopoulou E and Moussiades L. An Overview of Chatbot Technology. Artificial Intelligence Applications and Innovations. 2020 May, Chap 4 Page 377)]
With regards to claim 10, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the user intent understanding section consists of an intent extraction section that extracts the user's intent contained in the query and forwards it to the intent interpretation section, and an intent interpretation section that interprets the user's intent contained in the data received from the intent extraction section. [Martin Fig 7A-7B Par [0082] teaches chatbot (793A) that interacts with the user via an autonomous communication that controls the interaction. Autonomous communication system uses “any appropriate form of artificial intelligence, such as deep learning or natural language processing” (Par [0146]) where natural language processing interprets user information, context, and intent to generate outputs. (see Adamopoulou E and Moussiades L. An Overview of Chatbot Technology. Artificial Intelligence Applications and Innovations. 2020 May, Chap 4 Page 377)]
With regards to claim 11, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the answer candidate search and inference section consists of an answer candidate search section that searches for answer candidates based on the analyzed query and an answer candidate inference section that ranks and infers the optimal answers from the answer candidate list based on the user's intent and context. [Kim Fig 4B teaches chatbot server (120) that searches for answer candidates by calculating ranking of answers (S.409) based on “context information of a chat between the user device 100 and the chatbot” (Par [0075]) and “which is most appropriate for a user's intent of question from among answers derived by multiple chatbots” (Par [0010])
With regards to claim 12, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the answer selection/generation section consists of an answer selection section that chooses the best answer based on the ranked answer candidates and an answer generation section that generates responses based on the selected answer. [Kim Par [0074] teaches polling model that uses multiple chatbots that calculates “rankings of answers derived from the respective chatbots, and select a chatbot for providing an answer to the user device 100 based on the calculated rankings.”]
With regards to claim 13, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the response generation section consists of a response implementation section that generates responses in user-friendly colloquial sentences and a response presentation section for delivering responses to the user interface. [Martin Fig 7A-7B Par [0146] teaches autonomous communication system that uses “any appropriate form of artificial intelligence, such as deep learning or natural language processing” where natural language processing generates user-friendly responses]
With regards to claim 14, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the artificial intelligence section further comprises a report generation section, a knowledge-based language generation section, and a content processing and generation section configured to assist decision-making and to reduce time and labor costs through automated policy planning and report generation. [Martin Fig 1A item 120 is an Emergency Management System (EMS) which includes policy planning, and report generation (Fig 24, Par [0118]) using natural language processing based on knowledge from the autonomous communication session (Par [0146])]
With regards to claim 15, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 14
wherein the report generation section is composed of a template creation and content synthesis section, which ensures that the report content is generated in accordance with the template format, and a template and output interface section that provides the generated report content in a user-friendly manner aligned with the template format. [Martin Fig 24 Par [0118] teaches template for report generation]
With regards to claim 16, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 14
wherein the knowledge base-based language generation section consists of a data extraction and support search section for extracting and searching the necessary data from the disaster safety knowledge base, and a content composition and data catalog section for structuring content based on the extracted and searched data. [Martin Fig 3 Par [0073] teaches EMS extracting emergency information using natural language processing based on knowledge from the autonomous communication session (Par [0146])]
With regards to claim 18, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 4
wherein the report design and report server part are configured to generate reports through data links and provide a process for report integration/distribution and report utilization, and various reports, which are created based on report sources including knowledge bases and which are integrated with report agents, are distributed on the distribution server and the report contents reflected in the content management system of the application server are finally transferred to the reporting server and exposed to users. [Martin Fig 1A item 120 is an Emergency Management System (EMS) includes a report generation (Fig 24, Par [0118]) and report can be transmitted to an Emergency Service provider]
Claims 3, 5-7, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Martin et al.(US2020/0274962) in view of Sun et al. (Sun, Wenjuan, Paolo Bocchini, and Brian D. Davison. "Applications of artificial intelligence for disaster management." Natural Hazards 103.3 (2020): 2631-2689), Chenny et al. (US2020/0065685), Rasmussen et al. (US12271913), D'Agostino et al. (US2020/0099633), and Kim et al.(US2019/0199658) in further view of Li et al. (Li et al. "DI-DAP: an efficient disaster information delivery and analysis platform in disaster management." Proceedings of the 25th ACM international on conference on information and knowledge management. 2016 hereinafter Li.)
With regards to claim 3, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
With regards to claim 3, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim fails to teach:
wherein collected overseas data, the unstructured data, and the structured data are accumulated in the big data section of the disaster safety knowledge base after undergoing knowledge resource collection/management or knowledge learning based on human cognitive abilities and natural language understanding knowledge learning in the data collection section of the disaster safety knowledge base, and in addition to natural language understanding knowledge learning, knowledge curation and composite inference knowledge augmentation are also provided in the big data section.
With regards to claim 3, Li teaches:
wherein collected overseas data, the unstructured data, and the structured data are accumulated in the big data section of the disaster safety knowledge base after undergoing knowledge resource collection/management or knowledge learning based on human cognitive abilities and natural language understanding knowledge learning in the data collection section of the disaster safety knowledge base, and in addition to natural language understanding knowledge learning, knowledge curation and composite inference knowledge augmentation are also provided in the big data section. [Li teaches Disaster management system (Fig 1) that uses vertical search engine to collect data of different types and classify via taxonomy generation and knowledge learning and curation (Page 1595-97)
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System using chatbots and social media data as taught by Martin, Sun, Chenny , Rasmussen, and D’Agostino with the disaster management system as by Li. The motivation to combine the teachings of Martin, Sun, Chenny, Rasmussen, D’Agostino, and Kim with Li is because Li teaches web crawling that gets information public sources such as “news feeds and announcements from government sites” (Par 3.2, page 1596) which improves the capabilities of the invention of Martin, Sun, Chenny, Rasmussen, and D’Agostino to get more data from a variety of sources]
With regards to claim 5, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 1
wherein the disaster safety knowledge base, as disaster safety knowledge data that supports efficient information sharing in disaster situations using big data and AI technologies for data-driven decision support, [Martin Fig 1A item 120 is an Emergency Management System (EMS) for management of emergency, which consists of disaster safety knowledge based, artificial intelligence section, and big data (Fig 2) Par [0005]]
a natural language understanding knowledge learning section, an artificial intelligence knowledge learning section, and a perception resource collection/management section. [Martin Par [0129] teaches “emergency data and/or communications (e.g., automated chat messages) undergoes natural language processing using one or more machine learning algorithms” which is controlled by the EMS and manages resources]
With regards to claim 5, Martin in view of Sun fails to teach
is composed of a complex inference knowledge augmentation section,
With regards to claim 5, Li teaches:
is composed of a complex inference knowledge augmentation section, [Li teaches using feature vectors to determine context used for disaster storyline generation. (Par 4.1 pages 1597-98)
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System using chatbots and social media data as taught by Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim with the disaster system management system as taught by Li. The motivation to combine the teachings of Martin, Sun, Chenny, Rasmussen, D’Agostino, and Kim with Li is because Li teaches “a method of disaster management that “summarizes and organizes the extracted events into a meaningful storyline.” (Par 2.1, page 1595) which improves the capabilities of the emergency management invention of Martin, Sun, Chenny , Rasmussen, D’Agostino, and Lim]
With regards to claim 6, Martin in view of Sun and Li teaches:
All the limitations of claim 5
wherein the said complex inference knowledge augmentation section is configured to extract rules and knowledge relationships from structured and unstructured documents, and based on this, new facts are explored and inferred to generate knowledge. [Li teaches event extraction (Par 4.1, page 1597-98) used to form disaster storyline generation (Par 4.1 pages 1597-98)
With regards to claim 7, Martin in view of Sun teaches:
All the limitations of claim 5
wherein the natural language understanding knowledge learning section is configured to analyze the structure, context, and intent of queries received from users, and the results of this analysis are accumulated to enhance natural language understanding. [Martin Par [0146] teaches autonomous communication system that uses “any appropriate form of artificial intelligence, such as deep learning or natural language processing” and chatbot for communicating with users]
With regards to claim 17, Martin, Sun, Chenny , Rasmussen, D’Agostino, and Kim teaches:
All the limitations of claim 14
wherein the content processing and generation section consists of a table/graph and text processing section for generating statistical data (tables/graphs) based on text and a title and content automatic generation section for creating content in a user-friendly format and extracting/summarizing key information. [Li Fig 7a teaches tables/graphs of generating statistical data used for case studies. (Par 6.2.2, page 1601)
It would be obvious to one of ordinary skill in the art at the time of applicant’s filing to combine the EMS System as taught by Martin and Sun with the disaster management system as taught by Li. The motivation to combine the teachings of Martin and Sun with Li is because Li teaches a method of disaster management that “summarizes and organizes the extracted events into a meaningful storyline.” (Par 2.1, page 1595) which improves the capabilities of the emergency management invention of Martin and Sun]
Conclusion
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.
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JOSEPH J. YAMAMOTO
Examiner
Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656