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 .
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Response to Amendment
This communication is responsive to the applicant’s amendment dated 04/16/2026. The applicant(s) amended claims 1, 5, and 6, canceled claim 4, and added claim 21.
Response to Arguments
Applicant's arguments with respect to claim 1 have been considered but are moot in view of the new ground(s) of rejection because the arguments pertain to the newly amended limitations.
Claim Rejections - 35 USC § 103
Claim(s) 1-3 and 5-21 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenkranz et al. (US 20240296425 A1) in view of Schillace et al. (US 20240202582 A1).
Regarding claims 1 and 21, Rosenkranz teaches:
“receiving, from a user, a first natural language user input” (par. 0047; ‘In other implementations, for instance, an automated chatbot is used in place of a fill-in form, where the chatbot requests the user to input the requested information via a conversational, natural language dialog or message-based format using text and/or spoken-language digital audio received via a microphone embedded in a computing device.’);
“determining a profile associated with the user, user role, or context” (par. 0057; ‘The name of the user's company is obtained from the user's stored profile data, which can be extracted from the user connection network, in some implementations.’),
“generating the textual prompt to the language model based at least on the natural language user input and the one or more rules, wherein the textual prompt includes instructions to the language model to selectively initiate: access to the one or more knowledge base; performance of the one or more functions; or execution of the one or more plugins” (par. 0041; ‘Based on the explicit position-related data (e.g., job title) and inferred position-related data (e.g., skill keywords), the prompt generation subsystem is capable of generating the prompts, which are subsequently input to the generative language model to cause the GLM to output a job description based on the explicit and inferred position-related data.’ See also par. 0074); and
“providing the textual prompt to the language model” (par. 0074; ‘… inputs the prompt into the generative language model).
However, Rosenkranz does not expressly teach:
“the profile including indications of: one or more knowledge base; one or more functions; a templates including one or more rules usable in generation of a text prompt to the language model; and one or more plugins.”
Schillace teaches:
“the profile including indications of: one or more knowledge base; one or more functions; a templates including one or more rules usable in generation of a text prompt to the language model; and one or more plugins” (par. 0044; ‘To generate a skill chain, an orchestration prompt may be generated by chain orchestrator 203, which includes an indication of one or more skills from skill library 212 (which is also referred to herein as a “skill listing”) and at least a part of user input 202, such that the generative ML model generates a skill chain with which user input 202 is processed.’; par. 0045; ‘As another example, skill library 212 includes a database that stores a listing of skills. In some instances, a new skill may be registered (e.g., in the database or in an index), thereby indicating that the skill is available for use as part of a skill chain. For example, plug-in application 214 (e.g., aspects of which may be similar to application 116) may include one or more skills that are registered within skill library 212, such that processing using a skill of plug-in application 214 may be performed according to aspects described herein. It will therefore be appreciated that a set of skills in skill library 212 may be stored using any of a variety of techniques.’ The skill library reads on the profile.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Rosenkranz’s user profiles and prompt generation methods by incorporating Schillace’s skill library (database, files, plug-in application) such that the profile used for generating prompts includes knowledge base, functions, prompt generation templates, and plug-ins. The combination would accomplish tasks and/or generate model output that would otherwise not have been possible via a singular ML model evaluation. (Schillace: par. 0028)
Regarding claim 2 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the one or more knowledge base comprises a searchable corpus of data” (Schillace: par. 0045; ‘As another example, skill library 212 includes a database that stores a listing of skills. In some instances, a new skill may be registered (e.g., in the database or in an index), thereby indicating that the skill is available for use as part of a skill chain.’).
Regarding claim 3 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the one or more functions comprise one or more formulas usable to calculate and/or derive properties” (Rosenkranz: par. 0074; ‘Embodiments of the user profile module 107 may create a brand-new user profile for each user, select a user profile for each user from an existing profile template that most closely fits the interests, personality and behavior of the user from a pool of user profile templates and/or a hybrid user profile that customizes an existing user profile template using the gathered user data 130 and user-defined parameters.’).
Regarding claim 5 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the one or more rules are defined in natural language indicating business logic” (Rosenkranz: par. 0028; ‘The barriers to using job sites effectively to identify candidates and fill jobs can prevent businesses from achieving peak productivity and delay the achievement of other business objectives as well.’).
Regarding claim 6 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein at least one of the one or more rules is associated with a data object”
Regarding claim 7 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the one or more plugins comprise one or more data processing tools” (Schillace: par. 0050; ‘As an example, semantic memory store 218 stores semantic embeddings (also referred to herein as “semantic addresses”) associated with ML model 204 and/or ML model 206, each of which may correspond to one or more content objects.’).
Regarding claim 8 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein at least one of the plugins is activated prior to providing the prompt to the language model and is configured to provide a plugin output that is at least partially included in the prompt” (Schillace: par. 0045; ‘For example, plug-in application 214 (e.g., aspects of which may be similar to application 116) may include one or more skills that are registered within skill library 212, such that processing using a skill of plug-in application 214 may be performed according to aspects described herein.’).
Regarding claim 9 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“receiving an output from the language model, responsive to the natural language user input” (Schillace: par. 0020; ‘A generative model (also generally referred to herein as a type of ML model) used according to aspects described herein may generate any of a variety of output types (and may thus be a multimodal generative model, in some examples) and may be a generative transformer model and/or a large language model (LLM), a generative image model, in some examples.’).
Regarding claim 10 (dep. on claim 9), the combination of Rosenkranz in view of Schillace further teaches:
“wherein at least one of the plugins is activated after receiving the output and is configured to generate, based on at least a portion of the output, at least a portion of a result provided to the user” (Schillace: par. 0045; ‘For example, plug-in application 214 (e.g., aspects of which may be similar to application 116) may include one or more skills that are registered within skill library 212, such that processing using a skill of plug-in application 214 may be performed according to aspects described herein.’).
Regarding claim 11 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein at least one of the plugins is configured to access an object database comprising a plurality of objects stored according to an object ontology” (Schillace: par. 0050; ‘As an example, semantic memory store 218 stores semantic embeddings (also referred to herein as “semantic addresses”) associated with ML model 204 and/or ML model 206, each of which may correspond to one or more content objects.’).
Regarding claim 12 (dep. on claim 11), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the at least one of the plugins is configured to search the object database for an object associated with at least a portion of an output provided by the language model” (Rosenkranz: par. 0093; ‘In this case, the company description is considered inferred data generated by inferred data generator 514 querying the entity graph 510 for content items that mention the company name, formulating a prompt for the generative language model based on the query results, e.g., a prompt that instructs the generative model to create a summary of the query results, receiving output of the generative language model and using the output of the generative language model into a company description.’).
Regarding claim 13 (dep. on claim 11), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the at least one of the plugins is configured to search the object database for an object associated with at least a portion of the natural language user input” (Rosenkranz: par. 0080; ‘In this example, the job posting system incorporates input provided by a job poster via, e.g., a graphical user interface, into a query and uses the query to search the data store for a previously auto-generated description that matches or is similar to the query.’).
Regarding claim 14 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the language model is selected based on the determined profile” (Rosenkranz: par. 0074; ‘In response to a selection of GUI control element 308 or GUI control element 312, and a selection of GUI control element 316, the job posting system incorporates skill keywords, which are extracted from the user connection network that contains the user profile of the selected suggested user (e.g., user 306 or user 310), into the prompt for the generative language model and inputs the prompt into the generative language model to cause the generative language model to output a job description that the generative language model has machine-generated based on the skill keywords extracted from the user profile of the selected suggested user in the user connection network.’).
Regarding claim 15 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the one or more plugins are automatically selected according to one or more rules associated with the one or more templates associated with the profile” (Schillace: par. 0045; ‘For example, plug-in application 214 (e.g., aspects of which may be similar to application 116) may include one or more skills that are registered within skill library 212, such that processing using a skill of plug-in application 214 may be performed according to aspects described herein.’).
Regarding claim 16 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein the profile further comprises ontology data, wherein the ontology data comprises a semantic knowledge graph of data objects” (Schillace: par. 0048; ‘For example, a prompt template corresponding with a first model skill may be populated or otherwise processed to generate a prompt (e.g., including at least a part of user input 202 and/or context from semantic memory store 218) that is processed by ML model 204 accordingly.’).
Regarding claim 17 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein generating the prompt comprises: determining, receiving, or accessing a template associated with the profile” (Schillace: par. 0049; ‘ In other examples, a skill from skill library 212 may indicate (e.g., as part of an associated prompt template) that context should be obtained from semantic memory store 218, such that recall engine 210 is used to obtain such context accordingly.’) and
“generating the prompt based on at least the first natural language user input and the template” (Rosenkranz: par. 0040; ‘These additional, system-inferred parameter values are included in a prompt for a generative language model, to improve the likelihood that the prompt will cause a generative language model (GLM) to output a job description that both meets the requirements of the job poster and needs only minimal human review.’).
Regarding claim 18 (dep. on claim 17), the combination of Rosenkranz in view of Schillace further teaches:
“wherein generating the prompt based on at least the first natural language user input and the template further comprises: appending or replacing text in the template based on the first natural language input” (Rosenkranz: par. 0244; ‘In the example prompt template P0, brackets denote parameters that can be filled in or replaced with specific data values, e.g., at runtime.’; par. 0260; ‘In some implementations, the first position data is validated by searching at least one data store for a standardized job title that matches the first position data; retrieving, from the at least one data store, the standardized job title that matches the first position data; and replacing the first position data with the standardized job title.’).
Regarding claim 19 (dep. on claim 18), the combination of Rosenkranz in view of Schillace further teaches:
“accessing a first knowledge base of the one or more knowledge bases associated with the profile” (Rosenkranz: par. 0260; ‘In some implementations, the first position data is validated by searching at least one data store for a standardized job title that matches the first position data; retrieving, from the at least one data store, the standardized job title that matches the first position data; and replacing the first position data with the standardized job title.’);
“identifying information within the first knowledge base relevant to the first natural language user input” (Rosenkranz: par. 0260; ‘In some implementations, the first position data is validated by searching at least one data store for a standardized job title that matches the first position data; retrieving, from the at least one data store, the standardized job title that matches the first position data; and replacing the first position data with the standardized job title.’); and
“appending or replacing text in the template based on the identified information” (Rosenkranz: par. 0260; ‘In some implementations, the first position data is validated by searching at least one data store for a standardized job title that matches the first position data; retrieving, from the at least one data store, the standardized job title that matches the first position data; and replacing the first position data with the standardized job title.’).
Regarding claim 20 (dep. on claim 1), the combination of Rosenkranz in view of Schillace further teaches:
“wherein said determining the profile comprises: receiving user input identifying the profile” (Rosenkranz: par. 0059; ‘If a selection is received via GUI control element 116, the user's profile page is loaded into the user's display, thereby enabling the user to view their own profile page to, for example, research or recall information needed for the new job post.’).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK VILLENA whose telephone number is (571)270-3191. The examiner can normally be reached 10 am - 6pm EST Monday through Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571) 272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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MARK . VILLENA
Examiner
Art Unit 2658
/MARK VILLENA/Examiner, Art Unit 2658
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