CTNF 18/951,420 CTNF 82197 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority Applicant claims the benefit Japanese Patent Application JP 2023-211201 filed on December 14, 2023. Claims 1-8 have been afforded the benefit of December 14, 2023 filing date. Information Disclosure Statement The IDS dated November 18, 2024 has been considered and placed in the application file. Claim Objections Claim 1 and Claim 5 is objected to because of the following informalities: Claim 1 and 5 state “inputting a thought for an inquiry” which should be written as “inputting a query for an inquiry” rather than using the word “thought”, since the use of “thought” implies that the system is able to input a mental “thought”, which is not an accurate reflection of the disclosed invention. 07-29-01 AIA Claim 4 is objected to because of the following informalities: Claim 4 states “wherein the inquiry is input by at least one of the control target system and a user of the control target system” which is interpreted as the inquiry being input by at least one control target system and at least one user of the control target system”. This should be written as “wherein the inquiry is input by at least one of the control target system or a user of the control target system”, in order to describe the two limitations as alternatives . Appropriate correction is required. Claim Rejections - 35 USC § 112 07-30-02 AIA 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. 07-34-01 Claim 3 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 3 recites “analyzes a reference pattern” in line 3 is unclear. A reference pattern is not defined clearly in the specification and therefore the scope of the term “reference pattern” in the claim is indefinite. More specifically, it isn’t clear as to whether the term “reference pattern” broadly covers any pattern, or if the term “reference pattern” is intended to cover a specific subset of patterns. Claim Rejections - 35 USC § 103 07-20-aia AIA The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made. Claim [ 1, 4, 5 ] are rejected under 35 U.S.C. 103 as being unpatentable over Galley (US 12,131,123 B2) in view of Feigner (US 6,246,404 B1). Regarding claim 1, Galley teaches A computer system that supports control of a control target system, wherein the computer system is connected to a text generation system including the control target system and a large language model, [Column 2, lines 42-44 "FIG. 1 schematically shows an example text generation computing system 100 implementing a machine learning model 102 "]; [Column 4, lines 20-23 "The machine learning model 102 of FIG. 1, the computing system of FIG. 14, and/or any other suitable text generation computing system may utilize framework 300 " where the framework 300 has the grounding interface 302 (agent) and control interface 304 (control target system]; [Column 4, lines 27-36 " As shown in FIG. 3, controllable grounded response generation framework 300 uses machine learning model 102 to output computer generated text 108 based on 1) human provided text 106, 2) grounding 302, and 3) control 304 . As such, controllable grounded response generation framework includes a grounding interface useable by the machine learning model to access a grounding source including information related to the human-provided text, and a control interface useable by the machine learning model to recognize a control signal”]. wherein the computer system includes an agent connected to the text generation system and a control tool for controlling the control target system, [Column 2, lines 42-44 "FIG. 1 schematically shows an example text generation computing system 100 implementing a machine learning model 102 "]; [Column 4, lines 27-36 " As shown in FIG. 3, controllable grounded response generation framework 300 uses machine learning model 102 to output computer generated text 108 based on 1) human provided text 106, 2) grounding 302, and 3) control 304 . As such, controllable grounded response generation framework includes a grounding interface useable by the machine learning model to access a grounding source including information related to the human-provided text, and a control interface useable by the machine learning model to recognize a control signal.”]; [Column 6, lines 20- 27 "In addition to generating text and/or as part of generating text, the text generation computing system 100 of FIG. 1 and/or the controllable grounded response generation framework 300 of FIG. 3 may perform computations, control other computers and/or hardware devices, (e.g., by invoking an API), communicate over networks (e.g., to invoke an API of a remote computing device), and/or perform other computing actions" where grounding interface acts as a component that performs functions on behalf of the text generation system and therefore corresponds to the agent and the control interface corresponds to control target system”]. wherein the computer system retains control manual information for managing a control manual of the control target system and link information for managing a relation between the control tool and partial text obtained by dividing the control manual , wherein, in the control manual information, first data for managing the partial text is stored, wherein, in the link information, second data associating the control tool with identification information of the partial text is stored, wherein the agent executes a first process of inputting a thought for an inquiry and control information for outputting response text including at least one of an answer and information regarding the control tool to be executed by the agent to the text generation system, [Column 12, lines 25- 28 " The CGRG framework (controllable grounded response generation framework) allows users to inject soft semantic control into the text generation process. The CGRG framework i ncorporates grounding to contextualize users' semantic intents as well as to boost information reliability" where the grounding framework (agent) is contextualizing users intent which essentially means executing a process of handling inquires]. [Column 12 " FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user . For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning model , as described above" where the CGRG (agent) is executing];[Column 12 " lines 52- 64 " At 1304, the method 1300 includes accessing, via a grounding interface useable by the machine learning model, a grounding source including information related to the human-provided text . In some examples, the grounding source may be a network-accessible grounding source, and the grounding interface may be configured to retrieve information from the grounding source via a network At 1306, the method 1300 includes recognizing a control signal with a control interface useable by the machine learning model . In some examples, the control signal may be human provided. In other examples, the control signal may be automatically computer generated , such as a by a content Planner" where control information and grounding interface (agent) is being used for a response text]; [Column 12, 65-67, Column 13, lines 1-2 "At 1308, the method 1300 includes outputting computer generated text based on the human-provided text, wherein the computer-generated text includes information from the grounding source and wherein the computer-generated text is focused based on the control signal " where the agent( grounding framework) executes to generate a text]. a second process of transmitting inquiry text corresponding to the inquiry to the text generation system when an inquiry of control about the control target system is received, [Column 12, lines 44-49 "FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning model, as described above" where the machine learning model includes the text generation system and the CRGC framework includes control interface(control target system). Transmitting of an inquiry is complete once the CGRG framework processing is finalized]. a third process of executing the control tool and transmitting inquiry text including an execution result of the control tool to the text generation system when the response text including information regarding the control tool is received from the text generation system, [Column 12, lines 44-49 "FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning mode l, as described above" where the machine learning model includes the text generation system and the CRGC framework includes control target system. Transmitting of an inquiry(input) to the text generation system happens when CGRG framework(control tool) processing is finalized]; [Column 12, lines 65-67, column 13, lines 1-2 "At 1308, the method 1300 includes outputting computer-generated text based on the human-provided text, wherein the computer-generated text includes information from the grounding source and wherein the computer-generated text is focused based on the control signa l" where executing of the control tool and transmitting of inquiry text happens together]. a fourth process of outputting an answer when the response text including the answer to the inquiry is received from the text generation system, and [Column 12, lines 65-67, column 13, lines 1-2 "At 1308, the method 1300 includes outputting computer-generated text based on the human-provided text , wherein the computer-generated text includes information from the grounding source and wherein the computer-generated text is focused based on the control signa l"] wherein, in the third process, the agent transmits the inquiry text including the execution result of the control tool and identification information of the partial text corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. [Column 12, lines 44-49 "FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning mode l, as described above" where the machine learning model includes the text generation system and the CRGC framework includes control target system. Transmitting of an inquiry is complete once the CGRG framework ( includes agent and control tool) processing is finalized]. However, Galley does not teach wherein the computer system retains control manual information for managing a control manual of the control target system and link information for managing a relation between the control tool and partial text obtained by dividing the control manual, wherein, in the control manual information, first data for managing the partial text is stored, wherein, in the link information, second data associating the control tool with identification information of the partial text is stored, wherein, in the third process, the agent transmits the inquiry text including the execution result of the control tool and identification information of the partial text corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. But Feigner teaches wherein the computer system retains control manual information for managing a control manual of the control target system and link information for managing a relation between the control tool and partial text obtained by dividing the control manual, [Column 2, lines 60-65 "A help editor operates upon a computer software application. The computer software application includes software components such as graphical control objects within dialog boxes. The help editor receives context-sensitive help information associated with the software components "]; [Column 3, lines 3-7 "According to the illustrative embodiment, the help editor creates header files describing the context-sensitive help information and creates map data structures associating software components with help information " where header files are related to the partial text (dividing the help information) and map data structures (control tool) are associated (linked) with the help information (control manual)];[Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " ]. wherein, in the control manual information, first data for managing the partial text is stored, [Column 3, lines 3-7 "According to the illustrative embodiment, the help editor creates header files describing the context-sensitive help information and creates map data structures associating software components with help information " where the header files is the first data for managing the partial text (help information)]; wherein, in the link information, second data associating the control tool with identification information of the partial text is stored, [Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " where the second data is the graphical object identifiers which are associated with control object map and the help identification information]. wherein, in the third process, the agent transmits the inquiry text including the execution result of the control tool and identification information of the partial text corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. [Column 3, lines 3-7 "According to the illustrative embodiment, the help editor creates header files describing the context-sensitive help information and creates map data structures associating software components with help information "] [Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " where the second data is the graphical object identifiers which is the Identification information of the partial text]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine teachings of Feigner into the teachings of Galley because Galley teaches a large language model based on text generation system that generates responses and invokes control actions, while Feigner teaches maintaining associations between graphical control object identifiers and help information identifiers using mapping structures, header files, and identifier-based relationships. Combining them would allow the system to associate specific control tools with corresponding portions of stored manual and provide relevant information when generating responses. An advantage of the combination is that it improves the precision and consistency of control guidance by linking tool execution to identified manual information. It reduces retrieval of unrelated documentation and improves the relevance of information supplied to the text generation system. Regarding claim 4, Galley teaches the computer system according to claim 1, wherein the inquiry is input by at least one of the control target system and a user of the control target system. [Column 2 , lines 54-56 " In the example of FIG. 1, a human user 104 provides input text (also referred to herein as a human- or user-provided text or text seed) 106: "The United States."]; [Column 3, lines 5-15 " Text generation computing systems may be configured to generate text for a variety of different purposes and/or perform other suitable actions to assist a user. Using the example of FIG. 1, after human user 104 inputs the user provided text 106, the text generation computing system uses machine learning model 102, described in more detail below with reference to FIGS. 3 and 14, to output computer generated text 108. Computer-generated text 108 expands upon the "United States" topic of user-provided text 106, automatically writing a short paragraph pertaining to the user-provided text 106."]. Regarding claim 5, Galley teaches A control support method for a control target system executed by a computer system, [Column 12 , lines 44-49 FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning mode l, as described above"] wherein the computer system is connected to a text generation system including the control target system and a large language model, [Column 2, lines 42-44 "FIG. 1 schematically shows an example text generation computing system 100 implementing a machine learning model 102 "]; [Column 4, lines 20-23 "The machine learning model 102 of FIG. 1, the computing system of FIG. 14, and/or any other suitable text generation computing system may utilize framework 300 " where the framework 300 has the grounding interface 302 (agent) and control interface 304 (control target system]; [Column 4, lines 27-36 " As shown in FIG. 3, controllable grounded response generation framework 300 uses machine learning model 102 to output computer generated text 108 based on 1) human provided text 106, 2) grounding 302, and 3) control 304 . As such, controllable grounded response generation framework includes a grounding interface useable by the machine learning model to access a grounding source including information related to the human-provided text, and a control interface useable by the machine learning model to recognize a control signal”] wherein the computer system includes an agent connected to the text generation system and a control tool for controlling the control target system, [Column 2, lines 42-44 "FIG. 1 schematically shows an example text generation computing system 100 implementing a machine learning model 102 "]; [Column 4, lines 27-36 " As shown in FIG. 3, controllable grounded response generation framework 300 uses machine learning model 102 to output computer generated text 108 based on 1) human provided text 106, 2) grounding 302, and 3) control 304 . As such, controllable grounded response generation framework includes a grounding interface useable by the machine learning model to access a grounding source including information related to the human-provided text, and a control interface useable by the machine learning model to recognize a control signal.”]; [Column 6, lines 20- 27 "In addition to generating text and/or as part of generating text, the text generation computing system 100 of FIG. 1 and/or the controllable grounded response generation framework 300 of FIG. 3 may perform computations, control other computers and/or hardware devices, (e.g., by invoking an API), communicate over networks (e.g., to invoke an API of a remote computing device), and/or perform other computing actions" where grounding interface acts as a component that performs functions on behalf of the text generation system and therefore corresponds to the agent and the control interface corresponds to control target system”]. wherein the computer system retains control manual information for managing a control manual of the control target system and link information for managing a relation between the control tool and partial text obtained by dividing the control manual , wherein, in the control manual information, first data for managing the partial text is stored, wherein, in the link information, second data associating the control tool with identification information of the partial text is stored, wherein the control support method for the control target system comprises: a first step of inputting, by the agent, a thought for an inquiry and control information for outputting response text including at least one of an answer and information regarding the control tool to be executed by the agent to the text generation system, [Column 12, lines 25- 28 " The CGRG framework (controllable grounded response generation framework) allows users to inject soft semantic control into the text generation process. The CGRG framework i ncorporates grounding to contextualize users' semantic intents as well as to boost information reliability" where the grounding framework (agent) is contextualizing users intent which essentially means executing a process of handling inquires]. [Column 12 " FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user . For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning model , as described above" where the CGRG (agent) is executing];[Column 12 " lines 52- 64 " At 1304, the method 1300 includes accessing, via a grounding interface useable by the machine learning model, a grounding source including information related to the human-provided text . In some examples, the grounding source may be a network-accessible grounding source, and the grounding interface may be configured to retrieve information from the grounding source via a network. At 1306, the method 1300 includes recognizing a control signal with a control interface useable by the machine learning model . In some examples, the control signal may be human provided. In other examples, the control signal may be automatically computer generated , such as a by a content Planner" where control information and grounding interface (agent) is being used for a response text]; [Column 12, 65-67, Column 13, lines 1-2 "At 1308, the method 1300 includes outputting computer generated text based on the human-provided text, wherein the computer-generated text includes information from the grounding source and wherein the computer-generated text is focused based on the control signal " where the agent( grounding framework) executes to generate a text]. a second step of transmitting, by the agent, inquiry text corresponding to the inquiry to the text generation system when an inquiry of control about the control target system is received, [Column 12, lines 44-49 "FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning model, as described above" where the machine learning model includes the text generation system and the CRGC framework includes control interface(control target system). Transmitting of an inquiry is complete once the CGRG framework processing is finalized]. a third step of executing, by the agent, the control tool and transmitting inquiry text including an execution result of the control tool to the text generation system when the response text including information regarding the control tool is received from the text generation system, [Column 12, lines 44-49 "FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning mode l, as described above" where the machine learning model includes the text generation system and the CRGC framework includes control target system. Transmitting of an inquiry(input) to the text generation system happens when CGRG framework(control tool) processing is finalized]; [Column 12, lines 65-67, column 13, lines 1-2 "At 1308, the method 1300 includes outputting computer-generated text based on the human-provided text, wherein the computer-generated text includes information from the grounding source and wherein the computer-generated text is focused based on the control signa l" where executing of the control tool and transmitting of inquiry text happens together]. a fourth step of outputting, by the agent, an answer when the response text including the answer to the inquiry is received from the text generation system, and [Column 12, lines 65-67, column 13, lines 1-2 "At 1308, the method 1300 includes outputting computer-generated text based on the human-provided text , wherein the computer-generated text includes information from the grounding source and wherein the computer-generated text is focused based on the control signa l"] wherein the third process includes a step of transmitting, by the agent, the inquiry text including the execution result of the control tool and identification information of the partial text corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. [Column 12, lines 44-49 "FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning mode l, as described above" where the machine learning model includes the text generation system and the CRGC framework includes control target system. Transmitting of an inquiry is complete once the CGRG framework ( includes agent and control tool) processing is finalized]. However, Galley does not teach wherein the computer system retains control manual information for managing a control manual of the control target system and link information for managing a relation between the control tool and partial text obtained by dividing the control manual, wherein, in the control manual information, first data for managing the partial text is stored, wherein, in the link information, second data associating the control tool with identification information of the partial text is stored, wherein the third process includes a step of transmitting, by the agent, the inquiry text including the execution result of the control tool and identification information of the partial text corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. But Feigner teaches wherein the computer system retains control manual information for managing a control manual of the control target system and link information for managing a relation between the control tool and partial text obtained by dividing the control manual, [Column 2, lines 60-65 "A help editor operates upon a computer software application. The computer software application includes software components such as graphical control objects within dialog boxes. The help editor receives context-sensitive help information associated with the software components "]; [Column 3, lines 3-7 "According to the illustrative embodiment, the help editor creates header files describing the context-sensitive help information and creates map data structures associating software components with help information " where header files are related to the partial text (dividing the help information) and map data structures (control tool) are associated (linked) with the help information (control manual)];[Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " ]. wherein, in the control manual information, first data for managing the partial text is stored, [Column 3, lines 3-7 "According to the illustrative embodiment, the help editor creates header files describing the context-sensitive help information and creates map data structures associating software components with help information " where the header files is the first data for managing the partial text (help information)]; wherein, in the link information, second data associating the control tool with identification information of the partial text is stored, [Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " where the second data is the graphical object identifiers which are associated with control object map and the help identification information]. wherein the third process includes a step of transmitting, by the agent, the inquiry text including the execution result of the control tool and identification information of the partial text corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. [Column 3, lines 3-7 "According to the illustrative embodiment, the help editor creates header files describing the context-sensitive help information and creates map data structures associating software components with help information "] [Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " where the second data is the graphical object identifiers which is the Identification information of the partial text]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine teachings of Feigner into the teachings of Galley because Galley teaches a large language model based on text generation system that generates responses and invokes control actions, while Feigner teaches maintaining associations between graphical control object identifiers and help information identifiers using mapping structures, header files, and identifier-based relationships. Combining them would allow the system to associate specific control tools with corresponding portions of stored manual and provide relevant information when generating responses. An advantage of the combination is that it improves the precision and consistency of control guidance by linking tool execution to identified manual information. It reduces retrieval of unrelated documentation and improves the relevance of information supplied to the text generation system. Regarding claim 8, Galley in view of Feigner teach the control support method for the control target system according to claim 5, wherein the inquiry is input by at least one of the control target system and a user of the control target system. Claim 8 is rejected for the same reasons as claim 4. Claim [ 2, 6 ] are rejected under 35 U.S.C. 103 as being unpatentable over Galley (US 12,131,123 B2) in view of Feigner (US 6,246,404 B1) and in further view of Houchin (US 6,263,333 Bl). Regarding claim 2, Galley in view of Feigner do no teach The computer system according to claim 1, wherein the second data includes a reference count, wherein, in the third process, the agent selects the second data to be used based on the reference count when there are a plurality of the pieces of second data corresponding to the control tool to be executed, wherein the agent stores the inquiry text and the response text as status information, and wherein the computer system includes an update unit that updates the reference count of the second data based on the status information. However, Houchin teaches The computer system according to claim 1, wherein the second data includes a reference count, [Column 4 , lines 44- 49 "One or more tables are then populated during the initialization process. These include: a dictionary table 20 for use by a matching algorithm to locate keyword identifiers (IDs), a keyword table 22 that identifies each keyword , its ID and other identifying information including a usage count" where Identifying information with keyword is the second data here which includes the usage count(reference count)]. wherein, in the third process, the agent selects the second data to be used based on the reference count when there are a plurality of the pieces of second data corresponding to the control tool to be executed, [Column 6, lines 39-43 "At step 58, ADL ranks the set of solutions, for example, based on how many times the respective solutions have been used (i.e. based on the usage count) and, optionally, based on how may matching keywords are associated with the respective solution " where ADL (Adaptive learning program functionality (computer program) is the process that selects/ranks solution(data keywords) based on usage count(reference) and how many matching keywords shows the plurality of pieces of second data]. wherein the agent stores the inquiry text and the response text as status information, and [Column 6, lines 9-14 "Referring now briefly to FIG. 5, the keyword solution hash table 22 stores keywords . Each keyword contains a keyword ID 32, and a list 34 of solution objects. Each solution object, in turn, stores its solution ID 36, a usage count 38, a security mask 40, and a SCIM 42”]; [Column 3, lines 63-65 "When that solution is found, it is then indexed into the knowledge base , together with the original user-entered”]. wherein the computer system includes an update unit that updates the reference count of the second data based on the status information. [Column 9 lines 34-43 "According to the present invention, once the set of matching keywords are found, a set of solutions associated with at least one of the matching keywords is then generated by the solution identifying mechanism 23 . The ranking mechanism 25 is then used to rank the set of solutions based on some given criteria. Thus, for example, the set of solutions may be ranked based on how many times the respective solutions have been used before, or how many matching keywords are associated with a respective solution, or both . Other ranking criteria may be implemented as well" where solutions are ranked based on how many times(usage count) have been used which implied increment of usage data every time a solution is applied]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine teachings of Galley in view of Feigner with the teaching of Houchin because Houchin teaches maintaining usage counts for stored associations and selecting among multiple entries based on those counts as well as updating the counts. Combining these would enable the system to select the manual information most frequently referenced for a particular control tool and continuously refine the associations based on prior interactions. An advantage would be improved relevancy and efficiently by automatically prioritizing the most useful manual information. Regarding claim 6, Galley in view of Feigner and in further view of Houchin teach the control support method for the control target system according to claim 5, wherein the computer system includes an update unit, wherein the second data includes a reference count, wherein the third process includes a step of selecting, by the agent, the second data to be used based on the reference count when there are a plurality of the pieces of second data corresponding to the control tool to be executed, wherein the control support method for the control target system comprises: a fifth step of storing, by the agent, the inquiry text and the response text as status information; and a sixth step of updating, by the update unit, the reference count of the second data based on the status information. Claim 6 is rejected for the same reasons as claim 2. Claim [ 3, 7 ] are rejected under 35 U.S.C. 103 as being unpatentable over Galley (US 12,131,123 B2) in view of Feigner (US 6,246,404 B1) and in further view of Houchin (US 6,263,333 Bl) and in further view of Liebald (US 8,688, 706 B2 ) Regarding claim 3, Galley in view of Feigner and in further view of Houchin do teach the computer system according to claim 2, wherein the update unit analyzes a reference pattern of the partial text based on the status information, and adds the identification information of the partial text referred to along with the partial text to the second data of the arbitrary partial data based on a result of the analysis , Galley teaches wherein, in the third process, the agent transmits the inquiry text including the execution result of the control tool [Column 12, lines 44-49 "FIG. 13 shows an example method 1300 for providing computer-generated text in response to input from a user. For example, the method may be performed by a computing system configured to train and/or execute a controllable grounded response generation framework (CGRG) 300 utilizing a machine learning mode l, as described above" where the machine learning model includes the text generation system and the CRGC framework includes control target system. Transmitting of an inquiry is complete once the CGRG framework ( includes agent and control tool) processing is finalized]. Feigner also teaches the second data of the arbitrary partial data based on a result of the analysis, [Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " where the graphical control identifier is the second data and the help information identifier is also part of the partial text]. Feigner teaches identification information of a plurality of the pieces of partial data corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information [Column 3, lines 3-7 "According to the illustrative embodiment, the help editor creates header files describing the context-sensitive help information and creates map data structures associating software components with help information "]; [Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " where the second data is the graphical object identifiers which is the Identification information of the partial text]; [Column 3 , lines 9-13 "For example, the help editor receives a control object map associating graphical control object identifiers with help information identifiers . The help editor filters the control object map to create one or more map data structures " where the help editor receives a control object map linking graphical object identifiers (second data) to help information identifiers (first data). The identifier of the partial data(help information ID) corresponds to the second data(graphical object ID) and are linked]; [Column 6, lines For example, a computer software application may have a dialog box with multiple graphical control objects for which context sensitive help is displayed"]; [ Column 13, lines 24-34 "The dialog-box-help-editor 46 uses a dialog-box-code-generation-module 60 to generate source code for a context-sensitive help function at step 44. The dialog-box-code-generation-module 60 generates header files and help data structures (e.g., arrays of the Help IDs generated) required for implementing context-sensitive help in an application. Since the dialog-box-help-editor 46 stores the properties for the controls in a control object map, it is relatively easy for it to generate help header files and help data structures in multiple formats by simply filtering the data in the control object map using the dialog-box-code-generation-module 60" where each help item is associated with a help identifier thus implying multiple help ID's]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine teachings of Galley with the teachings Feigner because Galley teaches an agent that transmits text and tool execution information to a text generation system while Feigner teaches associating control objects with help information identifiers. Combining them would allow the agent to transmit identifier information associated with a selected control tool to the text generation system. An advantage would be improved retrieval of relevant manual/help information, resulting in more accurate and relevant responses. However, Galley (US 12,131,123 B2) in view of Feigner (US 6,246,404 B1) and in further view of Houchin (US 6,263,333 Bl) do not teach The computer system according to claim 2, wherein the update unit analyzes a reference pattern of the partial text based on the status information, and adds the identification information of the partial text referred to along with the partial text to the second data of the arbitrary partial data based on a result of the analysis, and wherein, in the third process, the agent transmits the inquiry text including the execution result of the control tool and identification information of a plurality of the pieces of partial data corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. But Liebald teaches The computer system according to claim 2, wherein the update unit analyzes a reference pattern of the partial text based on the status information, [Column 1, lines 38-42 " A user profile is created which represents the selected topics. In one embodiment, the topics associated with the content items have associated topic strengths and the user analysis module selects the topics for user's profiles based on the topic strengths" ]; [Column 4 , lines 58-64 "The user analysis module 120 determines and stores a user profile based on the videos accessed by the user , and is one means for performing this function. FIG. 2 illustrates method executed by the user analysis module 120 for determining and storing the topics for a user profile. To determine the topics, the user analysis module 120 queries the user access log 160 and determines 202 videos accessed by the user" where user access log indicates a status information is stored ]. and adds the identification information of the partial text referred to along with the partial text [Column 4, lines 51-56 " The selected topic's web page includes content related to the selected topic , like related multimedia content or textual content. Additionally, the topic's web page may includes links to other related topics' web pages. These related topics may be displayed as topics related to the selected topic or recommended topics for a user visiting the selected topic's web page" where it adds related topics (partial text) based on occurrence]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine teachings of Galley in view of Feigner and in further view of Houchin with the teaching of Liebald teaches identifying related items based on co-occurrence. Combining them would enable the system to learn additional related manual information associated with control tool and provide those identifiers to the text generation framework. An advantage would be improved retrieval of relevant documentation resulting in more complete and contextually relevant responses. Regarding claim 7, Galley in view of Feigner (US 6,246,404 B1) and in further view of Houchin and in further view of Liebald teach the control support method for the control target system according to claim 6, wherein the sixth step includes a step of analyzing, by the update unit, a reference pattern of the partial text based on the status information, and a step of adding, by the update unit, the identification information of the partial text referred to along with the partial text to the second data of the arbitrary partial data based on the result of the analysis, and wherein the third process includes a step of transmitting, by the agent, the inquiry text including the execution result of the control tool and identification information of a plurality of the pieces of partial data corresponding to the second data to the text generation system when there is the second data corresponding to the control tool to be executed in the link information. Claim 7 is rejected for the same reasons as claim 3. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHEZA ABDUL AZIZ whose telephone number is (571)272-9610. The examiner can normally be reached Monday-Friday 7:30am-5pm Alternate Fridays off. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at (571) 272-5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /SHEZA ABDUL AZIZ/Examiner, Art Unit 2657 /DANIEL C WASHBURN/Supervisory Patent Examiner, Art Unit 2657 Application/Control Number: 18/951,420 Page 2 Art Unit: 2657 Application/Control Number: 18/951,420 Page 3 Art Unit: 2657 Application/Control Number: 18/951,420 Page 4 Art Unit: 2657 Application/Control Number: 18/951,420 Page 5 Art Unit: 2657 Application/Control Number: 18/951,420 Page 6 Art Unit: 2657 Application/Control Number: 18/951,420 Page 7 Art Unit: 2657 Application/Control Number: 18/951,420 Page 8 Art Unit: 2657 Application/Control Number: 18/951,420 Page 9 Art Unit: 2657 Application/Control Number: 18/951,420 Page 10 Art Unit: 2657 Application/Control Number: 18/951,420 Page 11 Art Unit: 2657 Application/Control Number: 18/951,420 Page 12 Art Unit: 2657 Application/Control Number: 18/951,420 Page 13 Art Unit: 2657 Application/Control Number: 18/951,420 Page 14 Art Unit: 2657 Application/Control Number: 18/951,420 Page 15 Art Unit: 2657 Application/Control Number: 18/951,420 Page 16 Art Unit: 2657 Application/Control Number: 18/951,420 Page 17 Art Unit: 2657 Application/Control Number: 18/951,420 Page 18 Art Unit: 2657 Application/Control Number: 18/951,420 Page 19 Art Unit: 2657 Application/Control Number: 18/951,420 Page 20 Art Unit: 2657 Application/Control Number: 18/951,420 Page 21 Art Unit: 2657 Application/Control Number: 18/951,420 Page 22 Art Unit: 2657 Application/Control Number: 18/951,420 Page 23 Art Unit: 2657 Application/Control Number: 18/951,420 Page 24 Art Unit: 2657 Application/Control Number: 18/951,420 Page 25 Art Unit: 2657 Application/Control Number: 18/951,420 Page 26 Art Unit: 2657 Application/Control Number: 18/951,420 Page 27 Art Unit: 2657 Application/Control Number: 18/951,420 Page 28 Art Unit: 2657 Application/Control Number: 18/951,420 Page 29 Art Unit: 2657
Prosecution Insights