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 .
This action is responsive to application filed on 4/9/25. Claims 1-20 are presented for examination.
This application has a provisional priority of 4/5/24. This application is further a CIP of 18/628,553 with an earlier filing dated 4/7/23. The CIP however does not teach or fairly suggest the subject matter of the pending application and the priority date is therefore NOT effective against the pending claims. For purposes of this application, the earlies effective date is 4/5/24.
Abstract analysis:
Claims 1 and 20 process a natural language input using a language model to generate strategy map nodes, process the strategy map nodes and a strategy map to determine a mapping of the strategy map nodes to the strategy map, process user input to generate an updated mapping of the strategy map nodes, and apply the updated mapping to the strategy map to generate an updated strategy map comprises practical application in the field of improved querying using a language model.
Claim 11 claims an application executing on a computer processor comprising the same steps as method claim 1 and is therefore also statutory and comprises practical application in the field of improved querying using a language model.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Baldua USPN. 2025/0110957.
Regarding claims 1, 11 and 20, Baldua discloses a method, system comprising a processor and application, and non-transitory medium performing the method comprising (fig. 1A, par. 45, applications 102, query 104, and plan generatioin using large language model 116):
receiving natural language input (fig. 1, query 104-106 and par. 48, Baldua);
processing the natural language input using a language model to generate strategy map nodes (fig. 1, LLM 116 and query planning system 110, pars. 48-49, context with classification prompt used by the LLM to determine input classification 118 and intent. Note that the instant application teaches the strategy map nodes may be objects at par. 38);
processing the strategy map nodes and a strategy map to determine a mapping of the strategy map nodes to the strategy map (fig. 1, item 120, par. 54, “configures a plan generation prompt 122 based on the input classification 118 produced by the (LLM) 116”. Note that graphs and portions of graphs are automatically update based on time or changes to data, see par. 145);
processing user input to generate an updated mapping of the strategy map nodes (figs. 3, 6 and 7A, item 710 and 712, pars. 175, translate the intent into a set of functions…”, note the intent is the outcome of the user query/context updating) and
applying the updated mapping to the strategy map to generate an updated strategy map (fig. 7A, item 74-716 and 712, pars. 175, second prompt generated comprises updated instructions thus updated mapping of the strategy map nodes).
2. The method of claim 1, Baldua discloses further comprising: processing the updated strategy map to identify an off track node using the language model and transmitting an off track message based on the off track node (figs. 6 and 7, pars. 174 and 192, performance metric associated with LLM and threshold value, wherein a prompt is submitted based on the performance metric value and context. The different mappings may comprise different nodes of a graph, see par. 144. Messages are exchanged, par. 148. Note off track node is a strategy map node that meets a desired threshold, see par. 64 instant publication).
3. The method of claim 1, Baldua discloses further comprising: processing the updated strategy map to identify a regular cadence node using the language model; and transmitting a regular cadence message based on the regular cadence node (figs. 6 and 7 , the different mappings may comprise different nodes of a graph, see par. 144. Messages are exchanged, par. 148. In addition, type/different version or large language models comprise different maps).
4. The method of claim 1, Baldua discloses further comprising: processing the updated strategy map to identify an overdue node using the language model and transmitting an overdue message based on the overdue node (graphs and portions of graphs are automatically updated based on time or changes to data hence update may be overdue, see par. 145).
5. The method of claim 1, Baldua discloses further comprising: using a retrieval augmented generation system to detect a duplicate between the strategy map nodes and existing strategy map nodes within the strategy map (pars. 147-148, subset and superset entity graph structures comprise duplicate/overlapping graphs, the graphs/subgraphs are used for similarity measurements and statistical concepts); sending an update request when the duplicate does not have an update within an update threshold and sending an update notification when the update is within the update threshold (figs. 6 and 7, pars. 174 and 192, performance metric associated with LLM and threshold value, wherein a prompt is submitted based on the performance metric value and context. Messages are exchanged, par. 148).
6. The method of claim 1, Baldua discloses further comprising: storing the strategy map nodes in a graph database and in a relational database (par. 159, the strategy plans are stored in graph structures and relational databases); processing a query using the graph database when the query does not identify a project node in the strategy map and processing the query using the relational database when the query identifies the project node in the strategy map (fig. 7A, item 710 Yes and No, pars. 171 and 173, data graphs are processed based on the need to generate a plan, and are processed differently).
7. The method of claim 1, Baldua discloses wherein processing the natural language input further comprises extracting semantic relationships from the natural language input using the language model (fig. 6, pars. 64 and 66, relationships are extracted from social graph relevant to query).
8. The method of claim 1, Baldua discloses further comprising: displaying the updated mapping in an interactive graphical user interface; and adjusting an adaptive layout of the strategy map responsive to the updated mapping (fig. 5A and 6, interface 612 and query planning 680 using entity graphs 632, see pars. 119-120, view and refine system generated modified version of search and mapping information, par. 144 where different data are represented by different mappings).
9. Baldua discloses wherein the strategy map comprises a pillar object and a project object, and wherein the pillar object is stored at a first location in data storage and references the project object stored at a second location in the data storage (par. 199, retrieve data related to the query taxonomy using an entity graph by traversing different locations and join data from two or more data resources, wherein pillar objects are data object related to the query in different locations).
10. The method of claim 1, Baldua discloses further comprising: processing the user input received as voice input through a user interface to update the mapping (fig. 1, item 116, par. 67-69, language models are trained on and used by dynamic query planning systems using audio, by the use of GPT models like BERT).
System claims 12-19 comprise substantially the same subject matter as method claims 2-10 and are therefore rejected on the merits.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the field of searching using plan optimization:
USPN. 2025/0258819: fig. 3, par. 65, large language models
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCIN R FILIPCZYK whose telephone number is (571)272-4019. The examiner can normally be reached M-F 7-4 EST.
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February 2, 2026
/MARCIN R FILIPCZYK/Primary Examiner, Art Unit 2153