DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Claims 1-22 are pending in this application.
Applicant’s arguments on claim rejections 35 USC 101, filed 6/1/2026, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn.
Applicant’s arguments on claim rejections 35 USC 103, filed 6/1/2026, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Gao.
Response to Arguments
Applicant’s arguments with respect to claim rejections 35 USC 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 7-10, 13-16 and 19-22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gao et al. (US 2026/0003859, hereinafter “Gao”).
Regarding claim 1, Gao teaches One or more non-transitory computer readable media comprising instructions which, when executed by one or more hardware processors (Gao, [0063] and Fig. 5: discussing about instructions and data for the present module or process 505 for presenting a visualization of a query result obtained in response to an input prompt generated from a natural language request in accordance with a prompt mapping function (e.g., a software program comprising computer-executable instructions) can be loaded into memory 504 and executed by hardware processor element 502 to implement the steps, functions or operations), cause performance of operations comprising:
detecting, by a generative AI access platform, a first prompt directed to a generative artificial intelligence (AI) model (a generative AI model) to generate a visualization in a graphical user interface (GUI) based on a first set of attributes associated with a first metric (Gao, [0012]: Examples of the present disclosure may standardize database queries and output visualization processes using machine learning and generative artificial intelligence request/prompt enhancement as described herein. [0048] and Fig. 3: In various examples, field 310 may comprise a fillable field, a drop-down menu, and so forth. In one example, the UI 300 may present sample prompts, e.g., in region/box 320. For instance, the sample prompts may be provided by a system designer and may appear in response to the selection of the data source. For instance, different sample prompts may be presented to the user for different selected data sources. Alternatively, or in addition, different sample prompts may be suggested to different users, e.g., based upon the user's past requests/prompts, based upon the user's identity or role, e.g., within an organizational structure for communication network operations personnel, and so forth. In the present example, the UI 300 further includes a box/field 330 for the user's current prompt/request, e.g., “your prompt:” indicates that the user has provided the following prompt: “Show me distinct application ID for timeframe between May 1, 2024 and May 20, 2024 with pie chart.” [0052]: At step 410, the processing system obtains a natural language request associated with a database system. For instance, the database system may comprise data tables of communication network operational data, as described above.);
analyzing, by the generative AI access platform, first content in the request the first prompt to identify a first proprietary term in the first prompt associated with at least one of (a) the first metric and (b) the visualization (Gao, [0012]: Examples of the present disclosure may standardize database queries and output visualization processes using machine learning and generative artificial intelligence request/prompt enhancement as described herein. [0049] and Fig. 3: The UI 300 may further include a box/region 340 to present the structured query (e.g., an SQL query) generated based upon the user's prompt/request. For instance, in the example illustrated in FIG. 3, the automatically generated SQL query may be: “select distinct raw_record:tags:applicationID from eri_cmme_raw where sf_load_ts_utc between ‘2024 May 1 00:00:00’ and ‘2024 May 30 00:00:00’.” In addition, this box/region 340 may further provide summary data regarding the query results, e.g., “total records of query result: 5.” An additional node of “only show first 10 records” is also included.);
executing a prompt modification process responsive to identifying the first proprietary term in the first prompt, the prompt modification process comprising: accessing, by the generative AI access platform, a first mapping of the first proprietary term to a first set of content; (Gao, [0054]: In one example, an output of the prompt mapping function may be inserted into a prompt template to finalize the prompt for use in step 430.);
modifying the first prompt based on the first mapping to generate a second prompt that includes both (a) the first proprietary term and (b) the first mapping of the first proprietary term to the first set of content (Gao, [0055]: At step 430, the processing system applies the prompt as an input to a generative model to generate a structured query. In one example, the generative model may be implemented by the processing system. In other words, the instructions/code of the algorithm of the generative model may be executed by the processing system.);
transmitting the second prompt, with the first proprietary term and the first mapping, to the generative AI model to obtain visualization content from the generative AI model (Gao, [0012]: Examples of the present disclosure may standardize database queries and output visualization processes using machine learning and generative artificial intelligence request/prompt enhancement as described herein. [0056]: At step 440, the processing system applies the structured query to the database system to obtain a query result. In one example, the processing system may further comprise the database system such that it processes/executes the structured query over one or more data tables in the database system to generate the query result. [0057]: At optional step 450, the processing system generates at least one visualization of the query result. For instance, the at least one visualization may comprise one or more charts and/or one or more graphs, or the like. Alternatively, or in addition, the at least one visualization may comprise at least one result table. In still another example, the at least one visualization may alternatively or additionally comprise an animation, e.g., showing changes over time for one or more fields/columns of a result table, and so forth.),
wherein the generative AI model identifies the first set of content by mapping the first proprietary term to the first set of content based on the first mapping, wherein the generative AI model generates the visualization content based on the first set of content that is identified using the first mapping (Gao, [0049]-[0050] and Fig. 3: Below this box/region 340 may be a result of the SQL query, e.g., a query result or query table 350, which includes five records as shown. Moreover, the UI 300 may further include an output visualization 360, e.g., in this case in the form of a pie chart/graph. In particular, the pie chart/graph is divided into five regions corresponding to the five records in the query result. For instance, the relative proportions of each of the five sections in the pie chart/graph may correspond to the number of records indicating each of five distinct hosts in the first 100 records, as originally requested in the natural language request/prompt.); and
responsive to the first prompt, presenting a first visualization in the GUI based on the visualization content received from the generative AI model based on the second prompt (Gao, [0058]: At step 460, the processing system presents a visualization of the query result. For instance, step 460 may include presenting the visualization via a user interface (e.g., a GUI) that is used to submit the NL request at step 410. For instance, the visualization may be via a UI, such as UI 300 illustrated in FIG. 3 and described above. In one example, step 460 may include transmitting a file content of the visualization to a user device for presentation on a display screen.).
Regarding claim 2, Gao teaches wherein the first mapping of the first proprietary term to the first set of content includes a second mapping of the first proprietary term to a second set of non-proprietary terms (Gao, [0053]: At step 420, the processing system generates a prompt based upon the natural language request in accordance with a prompt mapping function. For instance, in one example, the prompt mapping function may comprise a term mapping function that matches terms in natural language requests to data fields of data tables of the database system.).
Regarding claim 3, Gao teaches wherein the first visualization includes: (a) a visualization shape based on the second set of non- proprietary terms, and (b) one or more labels specifying the first proprietary term (Gao, [0049]-[0050] and Fig. 3: Below this box/region 340 may be a result of the SQL query, e.g., a query result or query table 350, which includes five records as shown. Moreover, the UI 300 may further include an output visualization 360, e.g., in this case in the form of a pie chart/graph. In particular, the pie chart/graph is divided into five regions corresponding to the five records in the query result. For instance, the relative proportions of each of the five sections in the pie chart/graph may correspond to the number of records indicating each of five distinct hosts in the first 100 records, as originally requested in the natural language request/prompt.).
Regarding claim 4, Gao teaches wherein the first mapping of the first proprietary term to the first set of content includes a second mapping of the first proprietary term to a set of proprietary data corresponding to the first proprietary term (Gao, [0053]: Alternatively, or in addition, the prompt mapping function may comprise a thesaurus/controlled vocabulary and/or an ontology. In one example, such a thesaurus and/or ontology may be maintained as a graph, or graph database. In one example, the term mapping function may be updated via machine learning.).
Regarding claim 7, Gao teaches wherein analyzing the first prompt to identify the first proprietary term comprises: applying a semantic machine learning model to the first prompt, wherein the semantic machine learning model is trained to distinguish proprietary terms from non-proprietary terms (Gao, [0029]: In one example, DB(s) 136 may also store artificial intelligence (AI) models and/or machine learning models (MLMs) that may be trained by, activated, and/or deployed by server(s) 135 in connection with examples of the present disclosure. [0030]: It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service.).
Regarding claim 8, Gao teaches identifying, by the semantic machine learning model, a second proprietary term in the first prompt; determining that the second proprietary term is not included in the first mapping; and based on determining that the second proprietary term is not included in the first mapping: determining a second set of content to map to the second proprietary term; and modifying the first mapping to include a further mapping of the second set of content to the second proprietary term (Gao, [0036]: As noted above, examples of the present disclosure may automatically generate and run SQL queries based upon NL requests. In one example, and as illustrated in the example process 200 of FIG. 2, the generative MLM-based communication network knowledge platform may perform a prompt mapping 220 to create a prompt 230 to be used as an input/prompt for a generative model 250, e.g., a generative machine learning model (MLM). [0053]: Alternatively, or in addition, the prompt mapping function may comprise a thesaurus/controlled vocabulary and/or an ontology. In one example, such a thesaurus and/or ontology may be maintained as a graph, or graph database. In one example, the term mapping function may be updated via machine learning.).
Regarding claim 9, Gao teaches identifying a set of criteria in the first mapping, wherein including the first mapping in the first prompt of the first proprietary term to the first set of content is based at least on determining the first prompt meets the set of criteria (Gao, [0048] and Fig. 3: In the present example, the UI 300 further includes a box/field 330 for the user's current prompt/request, e.g., “your prompt:” indicates that the user has provided the following prompt: “Show me distinct application ID for timeframe between May 1, 2024 and May 20, 2024 with pie chart.” [0049] and Fig. 3: The UI 300 may further include a box/region 340 to present the structured query (e.g., an SQL query) generated based upon the user's prompt/request. For instance, in the example illustrated in FIG. 3, the automatically generated SQL query may be: “select distinct raw_record:tags:applicationID from eri_cmme_raw where sf_load_ts_utc between ‘2024 May 1 00:00:00’ and ‘2024 May 30 00:00:00’.”).
Regarding claim 10, Gao teaches wherein determining the first prompt meets the set of criteria comprises: determining at least one additional term in the first prompt corresponds to a type specified in the set of criteria (Gao, [0048] and Fig. 3: In the present example, the UI 300 further includes a box/field 330 for the user's current prompt/request, e.g., “your prompt:” indicates that the user has provided the following prompt: “Show me distinct application ID for timeframe between May 1, 2024 and May 20, 2024 with pie chart.” [0050]: Moreover, the UI 300 may further include an output visualization 360, e.g., in this case in the form of a pie chart/graph. In particular, the pie chart/graph is divided into five regions corresponding to the five records in the query result. For instance, the relative proportions of each of the five sections in the pie chart/graph may correspond to the number of records indicating each of five distinct hosts in the first 100 records, as originally requested in the natural language request/prompt.)
Claim 13 is rejected under the same rationale as claim 1.
Claim 14 is rejected under the same rationale as claim 2.
Claim 15 is rejected under the same rationale as claim 3.
Claim 16 is rejected under the same rationale as claim 4.
Claim 19 is rejected under the same rationale as claim 7.
Claim 20 is rejected under the same rationale as claim 1. Gao also teaches A system comprising: at least one device including a hardware processor (Gao, [0063] and Fig. 5: discussing about instructions and data for the present module or process 505 for presenting a visualization of a query result obtained in response to an input prompt generated from a natural language request in accordance with a prompt mapping function).
Regarding claim 21, Gao teaches wherein the first proprietary term is a term that is either (i) not included in a training dataset for the generative AI model or (ii) included in the training dataset with insufficient frequency to train the generative AI model as to a meaning of the first proprietary term (Gao, [0014]: In one example, AL/ML elements of the present disclosure may be trained/updated on an ongoing basis via self-training/learning for optimized structured query generation, e.g., via user feedback or other feedback. [0030]: It should be noted that as referred to herein, a machine learning model (MLM) (or machine learning-based model) may comprise a machine learning algorithm (MLA) that has been “trained” or configured in accordance with input training data to perform a particular service.).).
Regarding claim 22, Gao teaches wherein presenting the first visualization in the GUI comprises, based on the first mapping, including the first proprietary term in the first visualization (Gao, [0053]: At step 420, the processing system generates a prompt based upon the natural language request in accordance with a prompt mapping function. For instance, in one example, the prompt mapping function may comprise a term mapping function that matches terms in natural language requests to data fields of data tables of the database system. [0058]: At step 460, the processing system presents a visualization of the query result. For instance, step 460 may include presenting the visualization via a user interface (e.g., a GUI) that is used to submit the NL request at step 410. For instance, the visualization may be via a UI, such as UI 300 illustrated in FIG. 3 and described above. In one example, step 460 may include transmitting a file content of the visualization to a user device for presentation on a display screen.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5-6 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Gao in view of Courcelle et al. (US 2025/0293998, hereinafter “Courcelle”).
Regarding claim 5, Gao teaches the computer readable media of claim 1 as discussed above. Gao does not explicitly teach wherein the first mapping of the first proprietary term to the first set of content includes a second mapping of the first proprietary term to a non-proprietary visualization type.
Courcelle teaches wherein the first mapping of the first proprietary term to the first set of content includes a second mapping of the first proprietary term to a non-proprietary visualization type ([0027]: As a result, from any prior chatbot response or visualization that a user selects, the system restore or reuse the relevant context, including the interpretations of terms (e.g., whether “profit” refers to “gross profit” or “net profit”) and data mappings (e.g., “profit” is mapped to values in particular column of a particular data set).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the mapping prompt of Gao with the teaching about the data mappings of Courcelle because it can provide users features that enable the user to select chatbot responses or visualizations output by the chatbot to be saved or exported for later use (Courcelle, [0005]).
Regarding claim 6, Gao in view of Courcelle teaches wherein the first visualization includes a visualization shape based on the non-proprietary visualization type (Courcelle, [0027]: As a result, from any prior chatbot response or visualization that a user selects, the system restore or reuse the relevant context, including the interpretations of terms (e.g., whether “profit” refers to “gross profit” or “net profit”) and data mappings (e.g., “profit” is mapped to values in particular column of a particular data set). [0080]: The AI/ML models 132 can then be used to specify the parameters for the visualization, such as the type of visualization (e.g., line chart, bar chart, line graph, geographical map, heat map, etc.), and identification of which data items are shown on different axes or dimensions of the visualization, the ranges to show, the labels to use, the color scheme, and or other properties.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the mapping prompt of Gao with the teaching about the data mappings of Courcelle because it can provide users features that enable the user to select chatbot responses or visualizations output by the chatbot to be saved or exported for later use (Courcelle, [0005]).
Claim 17 is rejected under the same rationale as claim 5.
Claim 18 is rejected under the same rationale as claim 6.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Gao in view of Basheer et al. (US 2019/0273701, hereinafter “Basheer”).
Regarding claim 12, Gao teaches the computer readable media of claim 1 as discussed above. Gao does not explicitly teach further limitations as claimed.
Basheer teaches wherein the first prompt is directed to the generative AI model to generate a reconciliation visualization based on the first set of attributes associated with the first metric, wherein the first metric is a first reconciliation metric, wherein the first prompt includes first reconciliation content, the first proprietary term is a first proprietary reconciliation term, and the first set of content includes a first set of reconciliation content, wherein the first mapping maps the first proprietary reconciliation term to the first set of reconciliation content, wherein generating the second prompt comprises: generating the second prompt to the generative AI model to generate the reconciliation visualization at least by: including in the second prompt the first mapping of the first proprietary reconciliation term to the first set of reconciliation content, wherein the visualization content is based on the first mapping of the first proprietary reconciliation term to the first set of reconciliation content, and wherein presenting the first visualization in the GUI comprises presenting a first reconciliation visualization in the GUI ([0043]: The chat survey may prompt the user to answer various questions about the chat, such as whether their request was resolved, a service rating, and/or the like. Chat orchestrator 110 may parse a chat survey response from user terminal 105 to add the survey data to the user chat data. Chat orchestrator 110 may transmit the user chat data to historical chat database 115 (step 407). Historical chat database 115 may store the user chat data based on the user identifier from user terminal 105, such that historical chat database 115 maintains a historical chat record of interactions the user identifier had with multi-profile chat environment 101. [0044]: In various embodiments, and as a further example, profile updating engine 150 may transmit the chat profile to be updated to an update queue for manual updating by a user. For example, and in accordance with various embodiments, in response to determining that a chat profile leads to a 95% chat failure rate (e.g., the user's request was not resolved), profile updating engine 150 may mark (e.g., via metadata or the like) the chat profile as “inactive” and may transmit a request for a manual review of the chat profile.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the mapping prompt of Gao with the teaching about the service rating of Basheer because it would reduce the need for human intervention during the chat session, thus improving the overall resource utilization of the computer based system (Basheer, [0015]).
Allowable Subject Matter
Claim 11 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Regarding claim 11, the prior arts of made record fail to teach wherein the first prompt is directed to the generative AI model to generate a scheduling visualization based on the first set of attributes associated with the first metric, wherein the first metric is a first scheduling metric, wherein the first prompt includes first scheduling content, the first proprietary term is a first proprietary scheduling term, and the first set of content includes a first set of scheduling content, wherein the first mapping maps the first proprietary scheduling term to the first set of scheduling content, wherein generating the second prompt comprises: generating the second prompt to the generative AI model to generate the scheduling visualization at least by: including in the second prompt the first mapping of the first proprietary scheduling term to the first set of scheduling content, wherein the visualization content is based on the first mapping of the first proprietary scheduling term to the first set of scheduling content, and wherein presenting the first visualization in the GUI comprises presenting a first scheduling visualization in the GUI.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Addanki et al. (US 2025/0103625) discloses that an inverted index is an index data structure that stores mappings from content, such as words or character strings, extracted from documents to locations of the documents within the corpus of documents. In some embodiments, the mappings can be implemented as a hash table that uses different words or character strings extracted from each document of the corpus of documents as keys.
Barton et al. (US 2026/0044673) discloses that generative AI is a type of artificial intelligence where models are used to create (or “generate”) new content based on inputs, often in the form of prompts from users.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHONG H NGUYEN whose telephone number is (571)270-1766. The examiner can normally be reached Monday-Friday, 8:30am-5pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at (571) 272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHONG H NGUYEN/ Primary Examiner, Art Unit 2156
July 15, 2026
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