DETAILED ACTION
This action is in response to the Amendment dated 13 March 2026. Claims 1, 6, 13-15, 17 and 20 are amended. Claims 7, 8 and 16 have been cancelled. Claims 22-24 have been added. Claims 1-6, 9, 10, 12-15 and 17-24 remain pending and have been considered below.
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 .
Claims Interpreted as Invoking 35 U.S.C. 112(f)/Sixth Paragraph
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a data ingestion subsystem to ingest,” “a domain modeling subsystem to apply,” “an episode configuration subsystem to define,” “an analytic engine subsystem to generate,” “an episode production subsystem to generate,” “an episode player subsystem to generate,” “an episode interaction subsystem to facilitate,” and “an episode customization subsystem to enable” in claims 1-6, 9, 10, 12 and 22.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following is a quotation 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 of this title, 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 1-6, 9, 10, 12-15 and 17-21 are rejected under 35 U.S.C. 103 as being unpatentable over Man et al. (US 2025/0077563 A1) in view of Panuganty et al. (US 2021/0248136 A1) and further in view of Bojanic et al. (US 2011/0276603 A1) and further in view of Hawes et al. (US 2024/0403194 A1, provisional: 63/519789).
As for independent claim 1, Man teaches a system comprising:
a data ingestion subsystem to ingest data from external databases, the data from the external databases comprising financial data, insurance data, retail sales data, or real estate data [(e.g. see Man paragraphs 0012, 0017, 0059) ”For implementing automatic construction activity summary generation within a computing platform, various systems, software, and/or databases utilizing machine learning and/or artificial intelligence (AI) can be leveraged for intelligent ingestion and processing of construction project information. For example, machine learning models can be utilized to ingest new data that is generated throughout the planning, design, and implementation phases … ingesting and compiling data for use in the preparation of construction activity summaries. Such applications include … financial applications (e.g., budget applications, invoicing applications, payment processors, etc.) … the back-end computing platform 102 may also be configured to receive data from one or more external data sources that may be used to facilitate functions related to the processes disclosed herein”].
an episode configuration subsystem to define and manage episode configurations [(e.g. see Man paragraph 0087) ”FIG. 7A illustrates an example interface 700A, which may be utilized for automatically generating construction activity summaries. As illustrated in FIG. 7A, the interface 700A is configured to accept a user input or prompt to automatically generate a user summary for the project, titled “Project Name.” For example, the interface 700A may ask the end user to input a requested timeframe or date for the requested summary, as illustrated by the input prompt 710 of the interface 700A. Then, the user may click or otherwise interface with a confirmation button, 712, via the client device, to send the request to generate the summary for the timeframe input in the input prompt 710. While illustrated as requiring user input, the illustrated interface 700 and/or an application it is associated with (e.g., a “Summary Generator” application) may not require user input and may automatically populate the timeframe or may automatically generate the summary, via API request or by automatic generation at a given frequency”].
an analytic engine subsystem to generate results objects containing structured analytic values derived from the ingested data according to the episode configurations by leveraging the interface with the external databases [(e.g. see Man paragraphs 0079, 0085, 0088, 0091) ”FIG. 7B illustrates an example interface 700B, which illustrates a construction activity summary 720 for a given timeframe 725 … Based on an evaluation of the context-based prompt 532, in view of, at least, the trained construction-based data set 505A and the ongoing construction project data 450, the LLM 500A may utilize a language evaluator and/or generator 540 to output a contextual response to the context-based prompt … The method 600 includes determining what data categories and/or parameters are to be utilized in generating the contextual response, based on the context-based prompt, as illustrated in block 610. Thus, when the context-based prompt includes instructions to generate a construction activity summary for a given time frame, determining categories and parameters may include determining what are the most important parameters and/or categories for a response, based on ranking of importance that is determined during the training of the trained construction-based data set 505A. Thus, with categories/parameters for the construction activity summary determined, the method 600 may include utilizing the ongoing construction project data 450, in view of an evaluation with respect to the trained construction based data set, to determine and/or parse the ongoing construction project data 450 to determine the subjects and associated data for use in the construction activity summary, as illustrated in block 620 … the interface 800A may result from execution of the actions of block 416 of the method 400. The interface 800A may take the form of a “Solutions Engine” application, which utilizes output of the LLM 500A to automatically generate solutions to issues, which are predicted, based on the results of the automatic summary generation. As illustrated, the solutions engine of the interface 800A may output/present to a user a plurality of suggested actions 831, 832, 833, 834, 835, 836, where each of the suggested actions are associated with a respective update 731, 732, 733, 734, 735, 736 of the construction activity summary 720”].
an episode production subsystem to generate episode data packages for the episodes based on the structured analytic values of the results objects, the episode data packages comprising language for the episodes [(e.g. see Man paragraph 0086 and Fig. 7B) ”the parsed data can be used to generate natural language for a plurality of updates for use in the construction activity summary and/or contextual response … The output of blocks 631, 632, 633, 634, 635, 636 may then be intelligently ordered and/or organized by the LLM 500A, thereby generating a natural language, automated construction activity summary, output as summary data”].
Man does not specifically teach visualizations specifications for the episodes or an episode player subsystem to play the episodes based on the episode data packages on a computing device via a user interface. However, in the same field of invention, Panuganty teaches:
and visualizations specifications for the episodes [(e.g. see Panuganty paragraphs 0121, 0207, 0342, 0343 and Fig. 31) ”base the narrated analytics playlist on predefined design themes, branding themes, etc … output from the insight engine module 116, and determines how to describe and/or articulate the output. As one example, in response to receiving an insight from the insight engine that corresponds to chartable data, story narrator module 118 determines to include a chart and a descriptive narrative of the chart within the narrated analytics playlist … story narrator module 118 identifies how to augment the insights identified by the insight engine module with additional information, such as visual information (e.g., charts, graphs, etc.) … a plurality of scenes to include in a narrated analytics playlist is identified. For example, story narrator module 1116 of FIG. 11 identifies scene 3106, scene 3108, scene 3110, and scene 3112 … a type of chart included in the scene”].
and an episode player subsystem to play the episodes based on the episode data packages on a computing device via a user interface [(e.g. see Panuganty paragraphs 0127, 0207, 0471) ”Playback module 132 receives a narrated analytics playlist, and outputs the content for consumption. This can include playing out audio, rendering video and/or images, displaying text-based content, and so forth … Animator module 1118 receives the bundled information, and uses the bundled information to generate audio and/or video outputs that are consumable by a playback engine … this includes generating and/or obtaining result content 5506 from content sources 5508 for inclusion in the query result 5502 as specified in the scripts 5504, such as visuals (text strings, images, charts, videos, animations, and so forth), audio (e.g., audio files generated based on the scripts 5504), and so on. Thus, the query result 5502 includes the result content 5506 for output, as well as instructions for outputting the content, such as content ordering and timing”].
Therefore, considering the teachings of Man and Panuganty, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add visualizations specifications for the episodes and an episode player subsystem to play the episodes based on the episode data packages on a computing device via a user interface, as taught by Panuganty, to the teachings of Man because it efficiently presents relevant data of interest which saves the user’s time and system resources (e.g. see Panuganty paragraph 0002).
Man and Panuganty do not specifically teach a domain modeling subsystem to apply semantics to the data and provide an interface with the external databases. However, in the same field of invention or solving similar problems, Bojanic teaches:
a domain modeling subsystem to apply semantics to the data and provide an interface with the external databases [(e.g. see Bojanic paragraphs 0032-0035) ”a dependency graph extracted from a particular domain (242, 252, or 262) can be parsed by the corresponding provider module (240, 250, or 260) to identify references to external objects that do not belong to the domain for that provider module (240, 250, or 260) … If such external object references are found, they can be used to generate addition extraction operation representations, which can be executed to extract additional dependency graphs, which may reveal new dependencies of those objects in their native domains. For example, a first database table in one database domain may depend on a second database table in a second database domain, and that second database table in the second database domain may in turn depend on a third database table, also in the second database domain … A provider that locates a reference to an external object can generate an object that identifies the external object and includes properties to assist a provider in the object's native domain in extracting dependencies of that object”].
Therefore, considering the teachings of Man, Panuganty and Bojanic, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add a domain modeling subsystem to apply semantics to the data and provide an interface with the external databases, as taught by Bojanic, to the teachings of Man and Panuganty because it provides permission benefits by limiting access to external databases based on domain origin (e.g. see Bojanic paragraph 0014).
Man, Panuganty and Bojanic do not specifically teach and to generate diagnostics associated with the episodes … the diagnostics to provide traceability as to how the episode data packages are generated based on the structured analytic values of the results objects by identifying data sources, analytic operations, and generations steps associated with the language for the episodes and the visualization specifications for the episodes. However, in the same field of invention or solving similar problems, Hawes teaches:
and to generate diagnostics associated with the episodes … the diagnostics to provide traceability as to how the episode data packages are generated based on the structured analytic values of the results objects by identifying data sources, analytic operations, and generations steps associated with the language for the episodes and the visualization specifications for the episodes [(e.g. see Hawes paragraphs 0009, 0075, 0125: prov ‘789 paragraphs 0009, 0080, 0124) ”generates a final response that is to provide to the user 150. The final response may include some or all of the subsequent LLM output and/or other information. The final response may be formatted according to a user selection, such as a string of text or data object (or link to data object stored in an ontology). A data object may be identified with a unique identifier associated with an object. The final response may include text, images, maps, interactive graphical user interfaces, datasets, database items, audio, actions, or other types or formats of information … an “LLM thought” 421 in the example of FIG. 4 (e.g., first LLM thought 421a and/or raw first LLM thought 421b). A user may select to view first LLM thought 421a or raw first LLM thought 421b by selecting show raw button 422. First LLM thought 421 can include a step-by-step outline for accomplishing the task requested by the user. The LLM may generate the information of first LLM thought 421 based on a prompt provided to the LLM. Advantageously, causing the LLM to generate step-by-step thought process for accomplishing a task, such as by providing a prompt with to the LLM with instructions to do so, may facilitate understanding operations performed by the LLM in accomplishing the user's requested task, which can help a user understand how an LLM output or response was generated which may improve debugging and/or prompt engineering … The system can display a debug panel that allows the user to view various portions of information associated with interactions between the user, the LLM, and/or external data services. For example, the debug panel can show the prompt provided to the LLM. The prompt can include the user input and the additional information provided by the system to augment the user's input. The system can display to the user one or more outputs of the LLM while performing the user-requested task. The system can display to the user one or more inputs to the LLM while performing the user requested task. The system can display to the user one or more operations performed by the system while performing the user requested task”].
Therefore, considering the teachings of Man, Panuganty, Bojanic and Hawes, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add and to generate diagnostics associated with the episodes … the diagnostics to provide traceability as to how the episode data packages are generated based on the structured analytic values of the results objects by identifying data sources, analytic operations, and generations steps associated with the language for the episodes and the visualization specifications for the episodes, as taught by Hawes, to the teachings of Man, Panuganty and Bojanic because it improves the performance and accuracy of an artificial intelligence system enabling users to debug interactions (e.g. see Hawes paragraph 0017, prov ‘789 paragraph 0005).
As for dependent claim 2, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 1, but Man does not specifically teach the following limitation. However, Panuganty teaches:
further comprising an episode interaction subsystem to facilitate interactivity with the episodes based on user inputs provided on the computing device via the user interface [(e.g. see Panuganty paragraphs 0127, 0185) ”Playback module 132 receives a narrated analytics playlist, and outputs the content for consumption. This can include playing out audio, rendering video and/or images, displaying text-based content, and so forth. As one example, a user can interact with a particular narrated analytics playlist via controls displayed by playback module 132, such as pausing playback, skipping content in the playlist, requesting drill-up content and/or drill-down content, inputting a search query during playback of content, etc. In various implementations, the playback module includes feedback controls, such as controls corresponding to giving explicit positive feedback and/or explicit negative feedback of the content being played out at a particular point in time … includes playback controls 914 that interface with a playback module to allow input that modifies the rendering and/or playback of playlist content 908, such as pausing the content, rewinding the content, skipping the content, etc.”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 3, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 1 and Man further teaches:
further comprising an episode customization subsystem to enable a user to manage automated creation and distribution frequency of the episodes on the computing device via the user interface [(e.g. see Man paragraphs 0087, 0088) ”The construction activity summary 720 may be generated in response to, for example, user input via the interface 700A, may be generated automatically for a given timeframe via an API, and/or may be automatically populated based on a scheduled frequency for generating summaries … may automatically generate the summary … by automatic generation at a given frequency”].
As for dependent claim 4, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 3, but Man does not specifically teach the following limitation. However, Panuganty teaches:
wherein the episode customization subsystem is to enable the user to personalize the episodes in accordance with theming elements [(e.g. see Panuganty paragraphs 0173, 0207) ”base the narrated analytics playlist on predefined design themes, branding themes, etc … various implementations provide the ability to customize themes that control multiple facets of what is displayed (e.g., a font type, a font size, a color pallet, cursor types, etc.), such as through the use of selectable user interface controls.”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 5, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 1 and Man further teaches:
wherein, to generate the language for the episodes, the episode production system is to interface with a large language model [(e.g. see Man paragraphs 0043, 0061) ”FIG. 5 depicts an example diagram illustrating aspects of a large language model (LLM) utilized in conjunction with the process … configured to automatically generate summaries of construction projects, by utilizing Large Language Models (LLMs)”].
As for dependent claim 6, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 5 and Man further teaches:
wherein, to interface with the large language model, the episode production subsystem is to provide prompts that constrain the large language model to a context window associated with the structured analytic values of the results objects [(e.g. see Man paragraphs 0079, 0085) ”The LLM 500A is further configured to receive, as input, a context-based prompt 532. Based on an evaluation of the context-based prompt 532, in view of, at least, the trained construction-based data set 505A and the ongoing construction project data 450, the LLM 500A may utilize a language evaluator and/or generator 540 to output a contextual response to the context-based prompt … The method 600 includes determining what data categories and/or parameters are to be utilized in generating the contextual response, based on the context-based prompt, as illustrated in block 610. Thus, when the context-based prompt includes instructions to generate a construction activity summary for a given time frame, determining categories and parameters may include determining what are the most important parameters and/or categories for a response, based on ranking of importance that is determined during the training of the trained construction-based data set 505A. Thus, with categories/parameters for the construction activity summary determined, the method 600 may include utilizing the ongoing construction project data 450, in view of an evaluation with respect to the trained construction based data set, to determine and/or parse the ongoing construction project data 450 to determine the subjects and associated data for use in the construction activity summary, as illustrated in block 620”].
and fact check outputs provided by the large language model by comparing the outputs provided by the large language model to the structured analytic values of the results object [(e.g. see Man paragraph 0026) ”the LLM will have been trained to understand the form and contextual meaning of various forms of data on the construction management platform, it may have capabilities for determining discrepancies in data or actions performed in the construction project, based on its learnings from the ingestion of historical construction data. For example, an invoice sent out during the timeframe for the generated construction activity summary may be flagged, in the construction activity summary by the LLM, as improper. In this example, the LLM may predict that the invoice is improper by comparing the invoice amount (e.g., $10,000) versus historical, similar invoices (e.g., generally in the range of $1,000) and determining the invoiced cost is far too high. In the construction activity summary, or as a separate notification, the LLM may indicate that someone should review the invoice and may even indicate the likely cause of the discrepancy”].
As for dependent claim 9, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 1, but Man and Panuganty do not specifically teach the following limitation. However, Bojanic teaches:
wherein the domain modeling subsystem is to provide the interface with the external databases using a domain graph and a mapping between the domain graph and the external databases, the mapping between the domain graph and the external databases specifying tables to utilize for entities in the domain graph [(e.g. see Bojanic paragraphs 0001, 0033) ”a dependency graph that represents dependencies between different database objects, such as different databases, database tables, columns in database tables, etc … If such external object references are found, they can be used to generate addition extraction operation representations, which can be executed to extract additional dependency graphs, which may reveal new dependencies of those objects in their native domains. For example, a first database table in one database domain may depend on a second database table in a second database domain, and that second database table in the second database domain may in turn depend on a third database table, also in the second database domain”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 10, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 1, but Man does not specifically teach the following limitation. However, Panuganty teaches:
wherein the data ingestion subsystem is to ingest unstructured data from the external databases [(e.g. see Panuganty paragraphs 0094, 0124, 0213) ”having multiple sources of data oftentimes corresponds to the data being acquired in multiple formats, such as each source providing the respective data in a respective format that is from data originating from other sources … a second data source may correspond to unstructured text data … communicate with external devices … indications of whether the data is … external to an organization”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 12, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 1, but Man does not specifically teach the following limitation. However, Panuganty teaches:
wherein the episode data packages comprises text files and audio files, and wherein the episode player subsystem comprises a library to interpret the text files and the audio files [(e.g. see Panuganty paragraphs 0127, 0207, 0471) ”Playback module 132 receives a narrated analytics playlist, and outputs the content for consumption. This can include playing out audio, rendering video and/or images, displaying text-based content, and so forth… Animator module 1118 receives the bundled information, and uses the bundled information to generate audio and/or video outputs that are consumable by a playback engine … this includes generating and/or obtaining result content 5506 from content sources 5508 for inclusion in the query result 5502 as specified in the scripts 5504, such as visuals (text strings, images, charts, videos, animations, and so forth), audio (e.g., audio files generated based on the scripts 5504), and so on. Thus, the query result 5502 includes the result content 5506 for output, as well as instructions for outputting the content, such as content ordering and timing”].
The motivation to combine is the same as that used for claim 1.
As for independent claim 13, Man, Panuganty, Bojanic and Hawes teach a method. Claim 13 discloses substantially the same limitations as claims 1, 4 and 9. Therefore, it is rejected with the same rational as claims 1, 4 and 9. Further, Man teaches receiving a request to generate an episode that is associated with a dataset [(e.g. see Man paragraph 0087) ”the user may click or otherwise interface with a confirmation button, 712, via the client device, to send the request to generate the summary for the timeframe input in the input prompt 710”].
As for dependent claim 14, Man, Panuganty, Bojanic and Hawes teach the method as described in claim 13; further, claim 14 discloses substantially the same limitations as claim 5. Therefore, it is rejected with the same rational as claim 5.
As for dependent claim 15, Man, Panuganty, Bojanic and Hawes teach the method as described in claim 14; further, claim 15 discloses substantially the same limitations as claim 6. Therefore, it is rejected with the same rational as claim 6.
As for dependent claim 17, Man, Panuganty, Bojanic and Hawes teach the method as described in claim 13, but Man does not specifically teach the following limitation. However, Panuganty teaches:
wherein retrieving the dataset from the external database comprises retrieving an unstructured dataset, and wherein the method further comprises applying semantics to the unstructured dataset by leveraging the domain graph model [(e.g. see Panuganty paragraphs 0094, 0124, 0213, 0219) ” having multiple sources of data oftentimes corresponds to the data being acquired in multiple formats, such as each source providing the respective data in a respective format that is from data originating from other sources … a second data source may correspond to unstructured text data … communicate with external devices … indications of whether the data is … external to an organization … various natural language processing algorithms and/or models can be employed to identify similar wording, such as sematic matching algorithms … latent semantic analysis”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 18, Man, Panuganty, Bojanic and Hawes teach the method as described in claim 13; further, claim 18 discloses substantially the same limitations as claim 2. Therefore, it is rejected with the same rational as claim 2.
As for dependent claim 19, Man, Panuganty, Bojanic and Hawes teach the method as described in claim 13 and Man further teaches:
wherein receiving the request to generate the episode comprises receiving the request to generate the episode based on a user input [(e.g. see Man paragraph 0087) ”the user may click or otherwise interface with a confirmation button, 712, via the client device, to send the request to generate the summary for the timeframe input in the input prompt 710”].
As for independent claim 20, Man, Panuganty, Bojanic and Hawes teach a non-transitory computer-readable storage medium. Claim 20 discloses substantially the same limitations as claim 13. Therefore, it is rejected with the same rational as claim 13.
As for dependent claim 21, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 4, but Man does not specifically teach the following limitation. However, Panuganty teaches:
wherein the theming elements comprise at least one of voice settings for the episodes, background images for the episodes, a color palette for the episodes, or background music for the episodes [(e.g. see Panuganty paragraphs 0173, 0207) ”base the narrated analytics playlist on predefined design themes, branding themes, etc … various implementations provide the ability to customize themes that control multiple facets of what is displayed (e.g., a font type, a font size, a color pallet, cursor types, etc.), such as through the use of selectable user interface controls.”].
The motivation to combine is the same as that used for claim 1.
Claims 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Man et al. (US 2025/0077563 A1) in view of Panuganty et al. (US 2021/0248136 A1) and further in view of Bojanic et al. (US 2011/0276603 A1) and further in view of Hawes et al. (US 2024/0403194 A1, provisional: 63/519789), as applied to claim 1 above, and further in view of Miller et al. (US 2025/0139160 A1).
As for dependent claim 22, Man, Panuganty, Bojanic and Hawes teach the system as described in claim 1, but Man does not specifically teach to generate language for the episodes, the episode production is to generate audio for the episodes based on narration scripts. However, Panuganty teaches:
to generate language for the episodes, the episode production is to generate audio for the episodes based on narration scripts [(e.g. see Panuganty paragraphs 0275, 0290) ”the personalized analytics system applies a computational algorithm to the script to identify what components and/or visualizations to include in a playlist that help explain the various insights. One or more implementations augment the script with narrative description(s) using various types of machine-learning algorithms, such as grammar-based algorithms, language pattern algorithms, syntactic algorithms, etc. In turn, the textual description generated by these machine-learning algorithms can be converted into an audible output, such as through the use of various text-to-speech algorithms. The visualizations and audible output are then statically bundled to form a narrated analytics playlist … generate audible content for the newly generated images and/or scenes (e.g., images and/or scenes not inherently included in the narrated analytics playlist) to provide lip synching with the new content. Here, the phrase “lip synching” is used to denote audible output whose content is coordinated with a corresponding image. In other words, lip synching corresponds to outputting a synchronized audible output in which the audible description in the audible output corresponds to a currently rendered image and/or scene. Thus, if a user interacts with a narrated analytics playlist in such a way that new content is generated and/or rendered by a playback module, coordinated audio that describes the new content is output as well”].
The motivation to combine is the same as that used for claim 1.
Man, Panuganty, Bojanic and Hawes do not specifically teach to generate the visualization specifications for the episodes, the episode production subsystem is to generate avatars that are synchronized with the audio for the episodes. However, in the same field of invention, Miller teaches:
to generate the visualization specifications for the episodes, the episode production subsystem is to generate avatars that are synchronized with the audio for the episodes [(e.g. see Miller paragraphs 0019, 0048) ”large language models (LLMs), may be employed to generate sophisticated user-specific customized content in substantially real time. The output of the LLM may comprise text output. The LLM-generated text may then optionally be fed to a text-to-speech engine to generate audible speech content which may be streamed to a user device. Optionally, the audible speech content may be fed to a voice-to-video engine to generate a video content of an entity (e.g., a computer generated avatar (e.g., a two dimensional or three dimensional avatar) or a deepfake of a real person) “speaking”/“reading” the audio content in a text-to-video modeling process, where the speaker's mouth/lips and facial expressions may be synchronized with the audio (e.g., via lip and/or expression syncing) … The CPUs and/or AI processors may be utilized to execute the LLM, for text to speech applications, and/or to generate avatars (e.g., a two dimensional or three dimensional avatar) to be used in “speaking” text generated by the LLM”].
Therefore, considering the teachings of Man, Panuganty, Bojanic, Hawes and Miller, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add to generate the visualization specifications for the episodes, the episode production subsystem is to generate avatars that are synchronized with the audio for the episodes, as taught by Miller, to the teachings of Man, Panuganty, Bojanic and Hawes because it creates realistic looking but entirely fictional scenes (e.g. see Miller paragraph 0083).
As for dependent claim 23, Man, Panuganty, Bojanic and Hawes teach the method as described in claim 13; further, claim 23 discloses substantially the same limitations as claim 22. Therefore, it is rejected with the same rational as claim 22.
As for dependent claim 24, Man, Panuganty, Bojanic and Hawes teach the medium as described in claim 20; further, claim 24 discloses substantially the same limitations as claim 22. Therefore, it is rejected with the same rational as claim 22.
Response to Arguments
Applicant's arguments, filed 13 March 2026, have been fully considered but they are not persuasive.
Applicant argues that [“the cited references including Man, Panuganty, Bojanic and Kirk, either alone or in combination, fail to teach or suggest at least these features recited in amended claim 1.” (Page 8).].
The argument described above, in paragraph number 10, with respect to the newly added limitations to the independent claims has been considered, but is moot in view of the new grounds of rejection.
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.
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/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
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