DETAILED ACTION
This action is in response to the amendment filed on 01/22/2026.
Response to Amendment
Applicant’s amendment filed on 01/22/2026 has been entered. Claims 1-20 have been amended. No claims have been canceled. No claims have been added. Claims 1 – 20 are still pending in this application, with claims 1, 8 and 15 being independent.
Allowable Subject Matter
Claim 15 – 20 are allowed.
The following is a statement of reasons for the indication of allowable subject matter: After search and consideration of the prior art, it has been determined that the prior art fails to teach or suggest in reasonable combination the limitations recited by independent claim 15. In particular, the prior art fails to teach or suggest in reasonable combination the following limitations: storing data associating the modified content with the performance metric and the database query; and applying the stored data to modify at least one subsequent system operation related to presentation, interpretation of generation of data presentations.
For example, Attallah et al. (US 12,287,954) discloses the following limitations recited in claim 15.
A system comprising: a processor configured to: cause a first data presentation to be displayed on a graphical user interface of a user device (Figure 6A; column 16 lines 44 – 51), the first data presentation comprising one or more editable fields (Figure 6A, 510 and Figure 6B, 606 ,607) associated with execution of a database query (The editable fields are associated with the execution of database queries to generate the dashboard show in Figure 6H.,column 4 lines 61-column 5 line 5; column 16 line 44-column 18 line 19), the data presentation being generated based at least in part on execution of a database query (The first presentation data is displayed after execution of database queries to generate the dashboard show in Figure 5J.,column 4 lines 61-column 5 line 5; column 14 lines 64-column 16 line 51); receive user input modifying content of at least one editable field of the one or more editable fields (column 16lines 51 – column 17 line 6); and store data comprising the modified content (column 17 lines 55 -61);and apply the stored data to modify at least one subsequent system operation relating to presentation, interpretation, or generation of data presentations (Figure 6G and Figure 6H, column 17 lines 62 -column 18 line 47).
Yet, Attallah fails to teach or suggest the following limitations recited by claim 15: each one of the one or more editable fields correspond to the a performance metric of a network device; and data associating the modified content with the performance metric and the database query is stored.
Additionally, Rose et al. (US 10,318,586) (“Rose”) discloses a system comprising a processor to: cause a first data presentation to be displayed on a graphical user interface of a user device (Fig.3, 322 and Fig.14, 1402, 1404, 1406; column 5 lines 59 – column 6 line 8, column 13 lines 5 – 42), the first data presentation comprising one or more editable fields (associated with execution of a database query (Fig.3, 322 and Fig.14, 1402, 1404, 1406; column 5 lines 59 – column 6 line 8, column 13 lines 5 – 42); and receive user input modifying content of a lest one editable filed of the one or more editable fields (Fig.4, 430, Fig.5, 522 and Fig.14, 1408; column 6 lines 15 – 59, column 13 lines 43- 58).
Lewis et al. (US 2025/0258851) discloses system comprising a processor configured to: receive user input to modify content associated with execution of a database query and telematics data, wherein the user input is received based at least in part on execution of a database query (An executable query is generated from a natural language request using a LLM. The executable query is executed. If execution is unsuccessful, a prompt for a modified language request is provided to a user. A user provides the modified language request to re-attempt to retrieve desired data., Fig.4, 430 and 440; [0101 – 0105] [0135] [0138 – 0145]); and store data associating the modified content with the telematics data and the database query (The modified language request and executable query are stored in a context database based on the user indicating that the executable query was responsive to the modified language request., [0145] [0149] [0150]).
Leung et al. (US 10,831,207) discloses a system (Abstract),wherein telematics data is associated with a performance metric (column 18 lines 14 – 51).
In combination Rose, Lewis and Leung teach the limitations of claim 15 except for the following: the performance metric being associated with a network device; and the stored data being applied to modify at least one subsequent operation relating to presentation, interpretation or generation of data presentations.
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.
Claim(s) 1 – 4, 7 -11 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis et al. (US 2025/0258851) (“Lewis” ) in view of Rose et al. (US 10,318,586) (“Rose”).
For claim 1, Lewis discloses a system (Abstract) comprising: a processor ([0006]) configured to: receive a natural language query from a user (Fig.4, 420; [0097 – 0100]); and receive a user input that modifies the natural language query based on executing of database query derived from the natural language query (An executable query is generated from a natural language request using a LLM. The executable query is executed. If execution is unsuccessful, a prompt for a modified language request is provided to a user. A user provides the modified language request to re-attempt to retrieve desired data., Fig.4, 430 and 440; [0101 – 0105] [0135] [0138 – 0145]); and store data that associates the modified natural language query and the database query (The modified language request and executable query are stored in a context database based on the user indicating that the executable query was responsive to the modified language request., [0145] [0149] [0150]), wherein the data is accessible for use in processing subsequent natural language queries (The modified language request and associated executable query are used as contextual information to process subsequent natural language queries., [0145] [0149] [0150]).
Yet, Lewis fails to teach that receiving a user input that modifies the natural language query based on executing of database query derived from the natural language query comprises the following: displaying one or more editable fields based at least in part on execution of a database query derived from the natural language query; and receiving user input that modifies content of at least one data field of the one or more data editable fields of a user interface.
However, Rose discloses a system and method for processing a natural language query (Abstract), wherein a natural language query is modified according to the following: displaying one or more editable fields (Fig.3, 322 and Fig.14, 1402, 1404, 1406; column 5 lines 59 – column 6 line 8, column 13 lines 5 – 42); and receiving user input that modifies content of at least one data field of the one or more editable fields of a user interface (Fig.4, 430, Fig.5, 522 and Fig.14, 1408; column 6 lines 15 – 59, column 13 lines 43- 58).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Lewis’s invention in the same way that Rose’s invention has been improved to achieve the following, predictable results for the purpose of increasing user satisfaction by providing an easy and efficient means to enable successful interpretation and processing of natural language requests (Rose, column 1 lines 12 -18; column 3 lines 28 – 35), wherein receiving a user input that modifies the natural language query based on executing of database query derived from the natural language query further comprises the following: displaying one or more editable fields; and receiving user input that modifies content of at least one data field of the one or more data editable fields of a user interface.
For claim 8, Lewis discloses a method (Abstract) comprising: receiving, by an operations management system (fleet management system, Fig.1, 110; [0063] [0067] [0068] [0089]), a natural language query from a user (Fig.4, 420; [0068] [0089] [0097 – 0100]); and receiving a user input that modifies the natural language query based on executing of database query derived from the natural language query (An executable query is generated from a natural language request using a LLM. The executable query is executed. If execution is unsuccessful, a prompt for a modified language request is provided to a user. A user provides the modified language request to re-attempt to retrieve desired data.,Fig.4, 430 and 440; [0101 – 0105] [0135] [0138 – 0145]); and storing, by the operations management system ([0063] [0067] [0068][0089]), data that associates the modified natural language query and the database query (The modified language request and executable query are stored in a context database based on the user indicating that the executable query was responsive to the modified language request., [0149] [0150]), wherein the data is accessible for use in processing subsequent natural language queries (The modified language request and associated executable query are used as contextual information to process subsequent natural language queries., [0145] [0149] [0150]).
Yet, Lewis fails to teach that receiving a user input that modifies the natural language query based on executing of database query derived from the natural language query comprises the following: displaying one or more editable fields based at least in part on execution of a database query derived from the natural language query; and receiving user input that modifies content of at least one data field of the one or more data editable fields of a user interface.
However, Rose discloses a system and method for processing a natural language query (Abstract), wherein a natural language query is modified according to the following: displaying one or more editable fields (Fig.3, 322 and Fig.14, 1402, 1404, 1406; column 5 lines 59 – column 6 line 8, column 13 lines 5 – 42); and receiving user input that modifies content of at least one data field of the one or more editable fields of a user interface (Fig.4, 430, Fig.5, 522 and Fig.14, 1408; column 6 lines 15 – 59, column 13 lines 43- 58).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve Lewis’s invention in the same way that Rose’s invention has been improved to achieve the following, predictable results for the purpose of increasing user satisfaction by providing an easy and efficient means to enable successful interpretation and processing of natural language requests (Rose, column 1 lines 12 -18; column 3 lines 28 – 35), wherein receiving a user input that modifies the natural language query based on executing of database query derived from the natural language query further comprises the following: displaying one or more editable fields; and receiving user input that modifies content of at least one data field of the one or more data editable fields of a user interface.
For claims 2 and 9, Lewis and Rose further disclose, wherein the stored data (Lewis, context database) comprises one or more vector representations of the modified content (Lewis, The modified natural language request generates a successful/responsive execution of an associated executable query., [0128] [0145] [0149] [0150]) (Rose, column 13 lines 5- 58), and wherein the processor is configured to compare a vector representation of a subsequently received natural language query with the vector representations of the modified content (Lewis, [0129 – 0130]) (Rose, column 13 lines 5- 58).
For claims 3 and 10, Lewis and Rose further disclose, wherein the processor is configured to generate a database query corresponding to the subsequently received natural language query based at least in part on the stored data associating modified content and database queries (Lewis, [0123] [0125] [0129 – 0131]) (Rose, column 13 lines 5- 58).
For claims 4 and 11, Lewis further discloses, wherein the processor is configured to generate a second data presentation based at least in part on execution of the generated database query corresponding to the subsequent natural language query (Lewis, The response, including telematics data, is displayed to the user in response to a response natural language query., Fig.4, 450; [0083] [0087] [0146]).
For claims 7 and 14, Lewis further discloses wherein processing subsequent natural language queries is performed using a large language mode (Lewis, [0104] [0105] [0120])
Claim(s) 5, 6,12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lewis et al. (US 2025/02588510 (“Lewis” ) in view of Rose et al. (US 10,318,586) (“Rose”) and further in view of Nezami et al. (US 2023/0325599) (“Nezami”) and further in view of Carlson et al. (US 10,417,345) (“Carlson”).
For claims 5 and 12, the combination of Lewis and Rose fails to teach the following: augmenting the stored data by extrapolating additional natural language phrases from the modified content using entity-specific information associated with the user.
However, Nezami discloses system and method for augmenting training data (Abstract), comprising the following: augmenting stored data (training data comprising utterances provided by a client or customer) (Fig.4, 435 and 445; [0119] [0120]) by extrapolating additional natural language phrases from content using entity specific information (gazetteer) (The training data is augmented using information comprised in gazetteers., [0121] [0122] [0129 – 0134] [0137] [0141] [0142] [0153 – 0161).
Additionally, Carlson discloses a system and method for processing speech (Abstract), wherein entity specific information (information stored in a gazetteer, Fig.2, 286n) is user specific (Fig.2, 286n; column 8 lines 42 – column 9 lines 10).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Lewis and Rose in the same way that Nezami’s invention has been improved to achieve the following, predictable results for the purpose of generating training data to facilitate training of machine learning models to efficiently perform natural language processing (Lewis, [0146 – 0150]): augmenting the stored data (Lewis, training data, [0150]) by extrapolating additional natural language phrases from content, e.g. modified content (Lewis, The content comprises training data including modified natural language queries, [0145] [0149] [0150]) using entity specific information.
Moreover, it would have been obvious to one of ordinary skill in the art at the time of applicant’s filing to improve the invention disclosed by the combination of Lewis, Rose and Nezami in the same way that Carlson’s invention has been improved to achieve the following, predictable results for the purpose of generating training data to facilitate training of machine learning models to efficiently perform natural language processing (Lewis, [0146 – 0150]): the entity specific information further comprises entity specific information associated with the user.
For claim 6, Nezami further discloses, extracting, by the operations management system, entity-specific information from one or more datastores using a Named Entity Recognition (NER) named entity recognition model (Nezami, A pre-trained model is used to retrieve a set of named entities from knowledge bases., [0133] [0134] [0154- 0156]).
For claim 13, Nezami further discloses, extracting, by the operations management system, entity-specific information from one or more datastores using a Named Entity Recognition (NER) named entity recognition model (Nezami, A pre-trained model is used to retrieve a set of named entities from knowledge bases., [0133] [0134] [0154- 0156]).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-14 have been considered but are moot in view of the new ground(s)of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Attalah et al. (US 12,141,525) (similar to Attalah et al. cited above), Lee et al.(US 2023/0315856) (inventive concept of an user editing database queries generated from natural language queries) and Saxe et al. (US 2023/0315722) (inventive concept of an user editing database queries generated from natural language queries)
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SONIA L GAY whose telephone number is (571)270-1951. The examiner can normally be reached Monday-Friday 9-5 ET.
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/SONIA L GAY/Primary Examiner, Art Unit 2657
