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 Remarks
This communication is considered fully responsive to the Amendment filed on 2/18/26.
Response to Arguments
Applicant’s 2/18/26 arguments with respect to claims have been considered but are moot in view of new ground(s) of rejection.
Claim Interpretation
Per applicant’s IFW [0028] claimed “unstructured data” may be data that refers to information that lacks a predefined format or structure and includes a variety of formats and types and example given include natural language text, videos, images and audio. Per application’s IFW [0002;16-17], claimed “unstructured data” may be spoken and/or natural language such as a user asking a question using natural language (i.e. English) in written or spoken form and such “unstructured data” processed using strategies of natural language processing (NLP).
Allowable Subject Matter
Claim 10 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.
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 1-9 and 11-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2019/0042988 to Brown et al. (“Brown”) in view of U.S. Patent No. 2024/0370477 to Taylert et al. (“Taylert”) and further in view of U.S. Patent No. 2025/0063083 to Grinberg et al. (“Grinberg”).
As to claim 1, Brown discloses a method (Brown: fig 1-34), comprising:
receiving, at a software as a service (SaaS) platform, a first indication identifying an initialization, at a user device, of a virtual artificial intelligence (Al) assistant for message delivery management at the SaaS platform (Brown: fig 1-34, [0003-251]: fig 1 ... one or more users 20 may access the system computer device 26 and AI agent system 10 directly through network 22A ... the AI agent system 10 may include one or more user interfaces such as browsers, and textual or graphical user interfaces through which users access the AI agent system 10 (see with fig 17-21 - receiving ... a first indication identifying an initialization, at a user device, of a virtual artificial intelligence (Al) assistant) [0064] ... system computer device 26 may function as a cloud system that includes e.g. software-as-a-service (SaaS) (see with [0064] & fig 17-21 - receiving, at a software as a service (SaaS) platform, a first indication identifying an initialization, at a user device, of a virtual artificial intelligence (Al) assistant for message delivery management at the SaaS platform) [0066] fig 17-21 ... show screen displays of user interface(s) implementing the AI agent system 10 with client system 12 ... that may include a generic image of an AI agent 302 and an agent text readout 304 e.g. agent speaks to user by text “Hello user X how can I help you today?” (see with [006;664] & see fig 17-21 - receiving, at a software as a service (SaaS) platform, a first indication identifying an initialization, at a user device, of a virtual artificial intelligence (Al) assistant for message delivery management at the SaaS platform) [0158-159]);
receiving, via the virtual AI assistant and at the SaaS platform, a user input identifying a query with first unstructured data (Brown: fig 1-34, [0003-251]: fig 17-21 displays user interface(s) implementing the AI agent system 10 (see with [0064;66] - receiving, via the virtual AI assistant and at the SaaS platform ...) ... interface includes a user input text button 306 e.g. enabling user to input text (a user input identifying a query with first unstructured data) and a user voice input button 308 e.g. user inputs request by voice (a user input identifying a query with first unstructured data) [0159]).
Brown did not explicitly disclose sending, via a first application programming interface (API) call to a generative AI model, a first prompt identifying the first unstructured data of the query and an identifier of a function related to the message delivery management at the SaaS platform.
Taylert discloses sending, via a first application programming interface (API) call to a generative AI model (Taylert: fig 1-15, [0008-120]: see fig 8-12 user 800 utterance (first unstructured data of a query) and block 804 & 806 API that requests suggested replies to generative-chat-app (sending, via a first application programming interface (API) call to a generative AI model)),
a first prompt identifying the first unstructured data of the query and an identifier of a function related to the message delivery management at the SaaS platform (Taylert: fig 1-15, [0008-120]: see fig 8-12 ... see fig 9 ... and the user has asked the following prompt “how can Drift help me create revenue?” (a first prompt identifying the first unstructured data of the query ...) as depicted in payload 902 of the request includes several fields including, among others, an identification of the machine learning or other “model” that is currently in use to support the conversation (see with [0104] - and an identifier of a function related to the message delivery management at the SaaS platform) [0079] ... one or more functions implemented in cloud-based architecture ... available service models leveraged in whole or in part include: software as a service (SaaS) ... (PaaS) ... (IaaS) [0104]).
Brown and Taylert are analogous art because they are from the same field of endeavor with respect to generative AI.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Taylert into the method by Brown. The suggestion/motivation would have been to provide an API as a tool that allows service providers and its customers to generate more accurate and relevant answers to outside inquires in live-chat mode using the power of semantics and generative AI (Taylert: [0076]).
Brown and Taylert further disclose receiving a first output of the generative AI model identifying first structured data related to the message delivery management based on the function (Taylert: fig 1-15, [0008-120]: see fig 4-12 and see fig 6 block 610 cluster: offer_demo (based on the function) and block 612 utterance: “would you like to schedule a demo of drift?” (output of the generative AI model identifying first structured data ...) and block 614 utterance: “would you like to see how drift works on a website?” (output of the generative AI model identifying first structured data ...) ... in fig 6 the two different utterances are indicated as members of an utterance cluster ... the system defines utterance clusters by training a machine learning (ML) classifier (see with fig 6 - output of the generative AI model identifying first structured data related to the message delivery management) [0061]).
Same motivation applies as mentioned above to make the proposed modification.
Brown did not explicitly disclose the identifier of the function comprising a function definition that describes a structure of an API call executable at the SaaS platform.
Grinberg discloses the identifier of the function comprising a function definition that describes a structure of an API call executable at the SaaS platform.
Specifically, Grinberg discloses sending, via a first application programming interface (API) call to a generative AI model (Grinberg: fig 1-14, [0011-167]: fig 1-3 & 6 ... prompts (i.e. sending to generative AI model) may be in the form of free language input (unstructured) requests for provision to an AI agent UI, as a coded message directed to an AI agent API, or a combination of both (also see with [0062] below- sending, via a first application programming interface (API) call to a generative AI model) [0120] ... different categories of AI functionality exist and exact definitions of these categories remain an open question as categories of Artificial intelligence are not mutually exclusive and AI systems may possess multiple functionalities simultaneously, and their capabilities may span across different areas of AI and for instance, an AI system may have both generative AI functionality to produce new content, such as text or images, and analytical AI functionality to analyze existing data, detect patterns, make decisions, or identify anomalies like a fraud and these functionalities may be intertwined within a single AI system, allowing it to generate new content while also performing analysis on that content or other data (see with [0120] above - sending, via a first application programming interface (API) call to a generative AI model) [0062]),
a first prompt identifying the first unstructured data of the query and an identifier of a function related to the message delivery management at the SaaS platform (Grinberg: fig 1-14, [0011-167]: fig 1-3 & 6 ... prompts (i.e. sending to generative AI model) may be in the form of free language input (unstructured) requests for provision to an AI agent UI, as a coded message directed to an AI agent API, or a combination of both (also see with [0062] above - a first prompt identifying the first unstructured data of the query ...) [0120] ... fig 6 illustrates example of a user interface for generating or editing formulas using AI functionality ... an AI agent be used to assess a user board on a UI to then identify to be added to the board and tasks may be generated and added automatically to the board following the AI agent's identification or may be provided as suggestions or options to a user (see with [0062; 120] above - a first prompt identifying the first unstructured data of the query and an identifier of a function related to the message delivery management at the SaaS platform) [0107-108] ... embodiments involve configuring a transfer of at least one of structured data or unstructured data from the at least one of the plurality of linked SaaS platform elements (see with [0062;120] above - a first prompt identifying the first unstructured data of the query and an identifier of a function related to the message delivery management at the SaaS platform) [0101] ... ),
the identifier of the function comprising a function definition that describes a structure of an API call executable at the SaaS platform (Grinberg: fig 1-14, [0011-167]: fig 1-3 & 6 ... flexibility in constructing a product that suits desired needs accomplished through the use of boards and a board may be a table configured to contain items e.g., individual items presented in horizontal rows defining objects or entities that are managed in the platform task, project, client, deal (see with fig 6, [0062;120;107-108;101] above - identifier(s) of function(s) comprising function definition(s) that describes a structure of an API call executable at the SaaS platform) ... a board may contain information beyond what is displayed in a table, for example, a board may further contain cell comments, hidden rows and columns, formulas, data validation rules, filters, specific formatting, audits logs, version history, cross-referencing with different boards, external linking with data sources, permissions of access or a combination thereof (see with fig 6, [0062;120;107-108;101] above - identifier(s) of function(s) comprising function definition(s) that describes a structure of an API call executable at the SaaS platform) [0031] ... comparison of information (function) associated with each of the received responses is performed by a predetermined AI agent and a predetermined AI agent refers to one that had previously been selected, identified (see with fig 6, [0062;120;107-108;101;31] above - the identifier of the function comprising a function definition that describes a structure of an API call executable at the SaaS platform) or used in an application and, for example, a predetermined AI agent may refer to one which was selected by a developer prior to the current operation, one which had been selected independent of developer input, one whose utility has been pre-characterized (see with fig 6, [0062;120;107-108;101;31] above - the identifier of the function comprising a function definition that describes a structure of an API call executable at the SaaS platform) or one that was selected by a trained AI agent (see with fig 6, [0062;120;107-108;101;31] above - the identifier of the function comprising a function definition that describes a structure of an API call executable at the SaaS platform) and non-limiting examples of a predetermined AI agent include a simple reflex agent, a model-based reflex agent, a goal-based agent, a utility-based agent, or a learning agent and an AI agent in this document could also be a multi-agent system involving coordination of other or lower-level agents (see with fig 6, [0062;120;107-108;101;31] above - the identifier(s) of function(s) comprising a function definition(s) that describes a structure of an API call executable at the SaaS platform) [0127] ... analyzing the query for determining a context includes at least one of: analyzing and breaking down a language used in the query; identifying keywords; classify the query; inferring an intent; analyzing details associated with the user, or a combination (see with fig 6, [0062;120;107-108;101;31;127] above - the identifier(s) of function(s) comprising a function definition(s) that describes a structure of an API call executable at the SaaS platform) [0162]).
Brown, Taylert and Grinberg are analogous art because they are from the same field of endeavor with respect to AI agents.
Before the effective filing date, for AIA , it would have been obvious to a person of ordinary skill in the art to incorporate the strategies by Grinberg into the method by Brown and Taylert. The suggestion/motivation would have been to provide AI systems possessing multiple functionalities simultaneously, and their capabilities may span across different areas of AI and for instance, an AI system may have both generative AI functionality to produce new content and analytical AI functionality (Grinberg: [0062]).
As to claim 2, Brown, Taylert and Grinberg disclose wherein the first structured data identifies an API call for a database query related to retrieving message data stored at the SaaS platform (Brown: fig 1-34, [0003-251]: ... the system comprises a set of APIs that facilitate interfacing the artificial intelligence agent system with client device(s) ... the set of APIs include a query API that facilitates coordination of the world model and enterprise resources to prepare and obtain information for responding to queries (wherein the first structured data identifies an API call for a database query related to retrieving message data stored ...) ... the set of APIs includes a cloud-based call initiating API (see with [0064;66] - ... stored at the SaaS platform) that facilitates real-time, event-based notification (message data) of users of an enterprise system coupled to the artificial intelligence agent system (wherein the first structured data identifies an API call for a database query related to retrieving message data stored at the SaaS platform) [0004]).
For motivation, see rejection of claim 1.
As to claim 3, Brown, Taylert and Grinberg disclose wherein the message data pertains to messages of one or more of a short messaging service (SMS) channel, a multimedia messaging service (MMS) channel, or an instant messaging service channel (Brown: fig 1-34, [0003-251]: ... client system 12 has one or more interfaces methods 60 also referred to as communication channels that an AI agent system 10 may use for communicating with users ... include, but not limited to, phone interfaces 62 e.g. VOIP or PSTN, custom application interfaces 64 e.g. mobile, web0 and/or enterprise application interfaces, text message interfaces 66 e.g. MMS/SMS interfaces, chat interfaces 70 e.g. chatbot, email interfaces 72, video interfaces 74, social media interfaces etc [0075]).
For motivation, see rejection of claim 1.
As to claim 4, Brown, Taylert and Grinberg disclose converting message data into a visual format based on the first structured data; and providing the message data in the visual format for presentation at the virtual AI assistant at the user device (Taylert: fig 1-15, [0008-120]: see fig 3 example utterance: “would you like to schedule a meeting with one of our account executives to learn more?” (based on the first structured data) and see fig 4 example of converting message data into a visual format based on the first structured data “utterance” from fig 3 and providing the message data in the visual format for presentation at the virtual AI assistant at the user device response of Driftbot “Perfect! We can help with that.” and/or “How many people work for your company”).
For motivation, see rejection of claim 1.
As to claim 5, see similar rejection to claim 1 where the method is taught by the method.
As to claim 5, Brown, Taylert and Grinberg disclose performing one or more database queries at the SaaS platform based on the first structured data ; and
obtaining second structured data identifying the message data based on the one or more database queries (Taylert: fig 1-15, [0008-120]: for example, see fig 2A MeetingAccepted, In Sales Chat with observation speech_act: yes (performing one or more database queries at the SaaS platform based on the first structured data) and speech_act: prompt_time linked to message data “Perfect, choose a time that works for you” (obtaining second structured data identifying the message data based on the one or more database queries)).
For motivation, see rejection of claim 1.
As to claim 6, see similar rejection to claims 1-5.
As to claim 6, Brown, Taylert and Grinberg disclose sending, via a second API call to the generative AI model, a second prompt identifying at least part of the second structured data identifying the message data (Taylert: fig 1-15, [0008-120]: see fig 8-12 user 800 utterance (1st 2nd ... n unstructured data of a query) and block 804 & 806 API that requests suggested replies to generative-chat-app (sending, via a 1st 2nd ... n application programming interface (API) call to the generative AI model) ... see fig 9 ... and the user has asked the following prompt “how can Drift help me create revenue?” (a 1st 2nd ... n prompt(s) ...) as depicted in payload 902 of the request (... identifying at least part of the 1st 2nd ... n structured data identifying the message data) includes several fields including, among others, an identification of the machine learning or other “model” that is currently in use to support the conversation (an identifier of a function related to the message delivery management) [0079] ... one or more functions implemented in cloud-based architecture ... available service models leveraged in whole or in part include: software as a service (SaaS) ... (PaaS) ... (IaaS) [0104]) ; and
receiving second output of the generative AI model identifying second unstructured data reflecting at least part of the message data (Taylert: fig 1-15, [0008-120]: for example, see fig 2B MeetingDeclined, In Sales Chat with observation speech_act: no and speech_act: come_back_soon identifying unstructured data reflecting at least part of the message data “Alright, you know where to find me when you are ready” (receiving 1st 2nd ... n output(s) of the generative AI model identifying 1st 2nd ... n unstructured data reflecting at least part of the message data)),
wherein the second unstructured data is provided for presentation at the virtual AI assistant at the user device (Taylert: fig 1-15, [0008-120]: for example, see fig 4 wherein the second unstructured data is provided for presentation at the virtual AI assistant at the user device “Perfect! We can help with that” (substituted with fig 2B example Alright, you know where to find me when you are ready” (wherein the 1st 2nd ... n unstructured data is provided for presentation at the virtual AI assistant at the user device)).
For motivation, see rejection of claim 1.
As to claim 7, see similar rejection to claim 1 where the method is taught by the method.
As to claim 7, Brown, Taylert and Grinberg further disclose wherein the identifier of the function comprises a function definition including one or more parameters for the function, the one or more parameters defined by the SaaS platform
the function definition including identifies one or more parameters for the function, the one or more parameters defined by the SaaS platform (Taylert: fig 1-15, [0008-120]: fig 9 ... as depicted, the payload 902 of the request includes several fields (parameters) including, among, other, an identification of the machine learning or other “model” that is currently in use to support the conversation (function definition including identifies one or more parameters for the function), a “temperature” ... a string indicating particular PII ... string identifying particular context associated with prompt [0079] ... the platform supports machine learning ... ML tasks are typically classified into various categories ... namely supervised learning, unsupervised learning and reinforcement learning (see with [0079] - function definition including identifies one or more parameters for the function) [0099] ... supervised learning is a task of inferring a function from labeled training data consisting of a set of training examples ... typically, each example is a pair consisting of an input object, typically a vector, and a desired output called a supervisory signal (function definition including identifies one or more parameters for the function...) ... one or more of functions of the computing platform may be cloud-based architecture ... available service models leveraged in whole or in part include SaaS ... PaaS ... IaaS (... the one or more parameters defined by the SaaS platform) [0103-104]).
For motivation, see rejection of claim 1.
As to claim 8, see similar rejection to claims 1-7.
As to claim 8, Brown, Taylert and Grinberg disclose wherein the first structured data of the first output of the generative AI model comprises an API call related to the message delivery management, the API call comprising the one or more parameters and corresponding one or more values of the one or more parameters (Taylert: fig 1-15, [0008-120]: ... a query to the event-level metadata (... comprises an API call related to the message delivery management) seeks some conversational moment of interest to the use and, based on event recognition, conversational moment of interest typically is embodied in one of: a speech act label output from the speech act classifier and an event label derived from a table of events ... a key moment may refer to a grouping of speech act labels or one or more event labels such that one-to-many approach is realized [0042] and see fig 2-12 for examples of 1st 2nd ... n structured data of 1st 2nd ... n outputs of the generative AI model ... the API call comprising the one or more parameters and corresponding one or more values of the one or more parameters detailed in rejection of claims 1-7).
As to claim 9, see similar rejection to claims 1-8.
As to claim 9, Brown, Taylert and Grinberg further disclose determining whether the first unstructured data of the query pertains to permitted subject matter (Brown: fig 1-34, [0003-251]: world model 16 of knowledge of enterprise may be configured to enable access to ingested unstructured data or other related unstructured data so that it can be used to prepare a response to a query associated with the aligned business-specific topic (permitted subject matter) (determining whether the first unstructured data of the query pertains to permitted subject matter) [0184]), and
wherein sending the first prompt identifying the first unstructured data of the query and the identifier of the function is responsive to determining that the first unstructured data of the query pertains to the permitted subject matter related to the message delivery management (Taylert: fig 1-15, [0008-120]: see fig 8-12 ... see fig 9 ... and the user has asked the following prompt “how can Drift help me create revenue?” (wherein sending the first prompt identifying the first unstructured data of the query ... ) as depicted in payload 902 of the request includes several fields including, among others, an identification of the machine learning or other “model” that is currently in use to support the conversation (... and the identifier of the function is responsive to determining that the first unstructured data of the query ...) [0079] ... for example, see fig 4 wherein the first unstructured data is provided for presentation at the virtual AI assistant at the user device “Perfect! We can help with that” (... pertains to the permitted subject matter related to the message delivery management)).
For motivation, see rejection of claim 1.
. As to claim 11, Brown, Taylert and Grinberg disclose wherein the virtual AI assistant is configured to provide a personalized interactive guide through topics of message delivery management using client-specific data (Brown: fig 1-34, [0003-251]: fig 17-21 ... example computer screen displays of user interface implementing AI agent system 10 with client system 12 of client device ... fig 18 example showing a wind farm KPI application of AI agent system 10 shows a summary section in real time “live dashboard” and some topics covered in the summary section include (see with [0189] - each of the following - wherein the virtual AI assistant is configured to provide a personalized interactive guide through topics of message delivery management using client-specific data): number of installations, current energy output, expected energy output, energy total, energy today, operating hours and service this year and other tabs in this application include “production” “maintenance” or “operation and a voice query may be used in this section of the application (see with [0189] - wherein the virtual AI assistant is configured to provide a personalized interactive guide through topics of message delivery management using client-specific data) [0160] ... utilizing unstructured data, such as to enrich a knowledge base configured as a world model 16 of enterprise-related knowledge benefits from NPL processes ... include an algorithm and a base model facilitating detecting a topic, such as a business topic ... algorithm detects topic with NLP ... NLP-based algorithm may be used to generate topics ... may be used to recommend query topics, subject matter, domains and the like (see with [0160] - wherein the virtual AI assistant is configured to provide a personalized interactive guide through topics of message delivery management using client-specific data) [0189]).
For motivation, see rejection of claim 1.
As to claims 12-17, see similar rejection to claims 1-2,4-7, respectively, where the system is taught by the method.
As to claims 18-20, see similar rejection to claims 1-2 and 4, respectively, where the medium is taught by the method.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
A) US 20260037275 – Cyjon
Systems and methods for integrating generative artificial intelligence (AI) capabilities within Software as a Service (SaaS) platforms. One of the computer-implemented methods are for querying a generative AI model about structured data in a SaaS environment, enabling users to interact with and manipulate data through AI-assisted interfaces. One of the systems maintains a generative AI agent configured to interact with SaaS platform data as a virtual team member, capable of understanding context and nuances of project data. Also described are methods for color-context aware data analysis, generation of interactive elements in messaging sessions, and cross-application generative AI agent interactions triggered by user mentions. Also described is facilitating the creation of custom SaaS platform products by combining functionalities from existing products using generative AI. The systems and methods represent advancement in AI-driven SaaS customization and data analysis.
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 JUNE SISON whose telephone number is (571)270-5693. The examiner can normally be reached 9:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Emmanuel Moise can be reached at 571-272-3865. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUNE SISON/Primary Examiner, Art Unit 2455
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