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 .
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 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.
Priority
Acknowledgment is made of applicant's claim for domestic priority based on US non-provisional application 17/351120 filed on 06/17/2021.
Claim Rejections - 35 USC § 101
35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 USC 101 as directing toward non-statutory subject matter.
Claim 1 recites a method (“process”). Claim 20 recites a computer system comprising a processor, display, and memory (“machine”).
To distinguish ineligible claims that merely recite a judicial exception from eligible claims that require an implementation of judicial exception, the Supreme Court uses a two-step framework: Step One (Step 2A), determine whether the claims at issue are directed to one of those patent-ineligible concepts; and Step Two (Step 2B), if so, ask “what else is there in the claims?” to determine whether the additional elements transform the nature of the claim into a patent eligible application. Alice Corp. Pty. Ltd. v. CLS Bank Int’l., 134 S. Ct. 2347, 2355 (2014).
Step One (Step 2A) is a two prong test that requires the determination of whether the claims at issue are directed to an enumerated patent ineligible concept. See MPEP 2106.04.
Specifically, Step 2A Prong (1) requires the determination of the specific limitations in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea and determining whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04(a).
The enumerated patent ineligible concepts comprising:
(a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
(b) Certain methods of organizing human activity – fundamental economic principles / practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules / instructions) and
(c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a).
If the claim recites an enumerated patent ineligible concept, then Prong (2) of Step One (Step 2A) requires the determination of whether the claim integrates the patent ineligible concept into a practical application. Individually and in combination, identifying whether there are any additional elements recited in the claim beyond the judicial exceptions and evaluating those additional elements to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. See MPEP 2106.04(d).
Under Step Two (Step 2B), if the claim does not integrate the ineligible concept into a practical application and therefore directed to a judicial exception, evaluate whether the claim provides an inventive concept by determining whether there are additional elements, individually and in ordered combination, amount to significantly more than the exception itself. See MPEP 2106.04.
Step 2A Prong (1)
The “directed to” inquiry does not ask whether the claims involve a patent ineligible concept but, considered in light of the specification, whether the claim as a whole is directed to excluded subject matter or directed to an improvement to computer functionality. Enfish L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1335 (Fed. Cir. 2016).
Therefore, Prong (1) of Step 2A requires identifying specific limitations in the claims that recites (“describes” or “set forth”) an abstract idea and determine whether the identified limitations falls within the subject matter groupings of abstract ideas enumerated. See MPEP 2106.04 (“Thus, it is sufficient for this analysis for the examiner to identify that the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) aligns with at least one judicial exception”).
In particular, MPEP 2106.04(a)(2) states “a claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation”.
Under Prong (1), claim 1 recites a method for generating real-time and diagnostic omnichannel interaction insights, the method comprising:
(1) obtaining one or more transcripts corresponding to a plurality of digital service channels;
(2) generating one or more channel-specific prompts for the one or more transcripts based on each digital service channel corresponding to a respective transcript and metadata extracted from the one or more transcripts;
(3) inputting the one or more channel-specific prompts to one or more machine learning models to obtain channel-specific insights; and
(4) generating and displaying analytical insights by integrating the channel-specific insights with member-specific healthcare data, using sentiment analysis and data filtering.
Claim 11 recites a non-transitory computer-readable medium configured to store computer code comprising instructions, wherein the instructions, when executed by a processor, cause the processor to implement the method of claim 1.
Claim 20 recites a computer system comprising:
one or more processors;
a display; and
memory;
wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprising instructions for:
(1) obtaining one or more transcripts corresponding to a plurality of digital service channels;
(2) generating one or more channel-specific prompts for the one or more transcripts based on each digital service channel corresponding to a respective transcript and metadata extracted from the one or more transcripts;
(3) inputting the one or more channel-specific prompts to one or more machine learning models to obtain channel-specific insights; and
(4) generating and displaying analytical insights by integrating the channel-specific insights with member-specific healthcare data, using sentiment analysis and data filtering.
Individually, under the broadest reasonable interpretation, step (1) corresponds to collecting information, including when limited to particular content (obtaining transcripts of phone conversation, email messages, instant messaging, web portal, or social media per claim 6) that does not change its character as information, are within the realm of abstract idea. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1353 (Fed. Cir. 2016) (“we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract idea”).
Individually and under the broadest reasonable interpretation, step (2) corresponds to generating prompts (i.e., questions or queries) for the collected transcripts based on channel (e.g., claim 2, referencing a library when mentally creating the question or query, or claim 3, perform mental analysis, classification, data recognition when mentally creating the question or query) and metadata (e.g., referencing a time of the transcript when mentally creating the question or query). Analyzing information by steps people go through in their minds are essentially mental processes within the abstract idea category. Id. at 1354 (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”).
Individually and under the broadest reasonable interpretation, steps (3)-(4) corresponds to applying the prompt to machine learning model to generate analytical insights for displaying, using sentiment analysis and data filtering (i.e., data analysis). Merely presenting the results of abstract processes of collecting and analyzing information, without more, is abstract as an ancillary part of such collection and analysis. Id.
As an ordered combination, steps (1)-(4) described a series of mental analysis of collecting text transcripts, mentally generating questions or queries of the text transcripts, apply the questions / queries to machine learning models to generate analytical insights for display. Such ordered combination at such high level of generality could practically be performed in human mind. MPEP 2106.04(a)(2)IIIA (“a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind”).
Thus, Claims 1-3 and 20 described patent ineligible subject matter enumerated under category (c) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).
Step 2A Prong (2).
Under Prong (2) of Step 2A, the goal is to determine whether the claim is directed to the recited exception by evaluating whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. See MPEP 2106.04II(A).
In particular, evaluating integration into a practical application requires identifying whether there are any additional elements recited in the claim beyond the judicial exception and evaluating those additional elements, individually and in combination, to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit (“CAFC”). See MPEP 2106.04(d).
The Supreme Court and the CAFC distinguished between (1) computer-functionality improvements from the (2) uses of existing computers as tools in aid of processes focused on abstract ideas. Electric Power Grp., L.L.C. v. Alstom SA, 830 F.3d 1350, 1354 (Fed. Cir. 2016) (“…we relied on the distinction made in Alice between, on one hand, computer-functionality improvement and, on the other, uses of existing computers as tools in aid of processes focused on “abstract ideas”…”).
In one example, the CAFC applied Alice inquiry to ask whether the focus of the claims is on the specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database) or instead, on a process that qualifies as an abstract idea for which computers are invoked merely as a tool. Enfish L.L.C. v. Microsoft Corp., 822 F.3d 1327, 1335-36 (Fed. Cir. 2016).
In Enfish, the claims were specifically directed to a self-referential table for a computer database. Id. at 1337. In particular, the claim language required a four step algorithm specifically directed to a self-referential table for a computer database that improves upon prior art information search and retrieval systems by employing a flexible, self-referential table to store data. Id. at 1336-37. CAFC determined that the plain focus of the claims was on an improvement to computer functionality itself (i.e., the self-referential table for a computer database), not on economic or other tasks for which a computer is used in its ordinary capacity. Id at 1335-36.
Therefore, the focus of the claims is on a specific asserted improvement in computer capabilities (i.e., the self-referential table for a computer database), not on economic or other tasks for which a computer is used in its ordinary capacity. Id. at 1336. See also MPEP 2106.04(d)I (“an improvement in the functioning of a computer or an improvement to other technology or technical field, as discussed in MPEP 2106.04(d)(1) and 2106.05(a)”).
In another example, in Core Wireless, asserted claims were directed to an improved user interface for computing devices: a computing device comprising a display screen, the computing device being configured to display on the screen a main menu listing at least a first application, and additionally being configured to display on the screen an application summary window that can be reached directly from the main menu, wherein the application summary window displays a limited list of at least one function offered within the first application, each function in the list being selectable to launch the first application and initiate the selected function, and wherein the application summary window is displayed while the application is in an unlaunched state. Core Wireless Licensing SARL v. LG Electronics, 880 F.3d 1356, 1360 (Fed. Cir. 2018).
According to the CAFC, (1) the requirement "an application summary that can be reached directly from the menu" specified a particular manner by which the summary window must be accessed such that the claims were directed to a particular manner of summarizing and presenting information in electronic devices. Id. at 1362. Further (2), the claims required the application summary window list a limited set of data that restrained the type of data that can be displayed in the summary window. Id. at 1363. Finally (3), the claims required a specific manner of displaying a limited set of information to the user rather than a conventional user interface displaying a generic index on a computer such that the claims recited an improved user interface for electronic devices. Id.
On the other hand, with respect to a claim for detecting and automatically analyzing events on an interconnected electric power grid in real time over a wide area, the CAFC held that such claim clearly focused on a combination of abstract ideas comprising collecting information limited to particular content and analyzing information by mental steps or by mathematical algorithms. Electric Power Grp., 830 F.3d 1350 at 1354.
Specifically, the claims specified what information in the power-grid field it is desirable to gather, analyze, and display in “real time” but they did not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology such that the claims failed to state an inventive concept. Id. at 1356.
Further, a process for gathering and analyzing information of a specified content, then displaying the results is not a particular assertedly inventive technology for performing those functions. Id. Even though the claims required “displaying concurrent visualization” of two or more types of information that corresponded to time-synchronized display, the displays were anything but readily available. Id at 1355.
In other words, it is a case where selecting information for collection, analysis, and display by content or source that did nothing significant to differentiate a process from ordinary mental processes. Id. at 1355. The claims did not require an arguably inventive set of components or methods, did not invoke any assertedly inventive programming, and merely required the selection and manipulation of information to provide a “humanly comprehensible” amount of information useful for users that did not transform an otherwise abstract processes of information collection and analysis. Id.
In another example, in Intellectual Ventures I, the CAFC held that tailoring content as a function of the user’s personal characteristics is a fundamental practice long prevalent in our system and therefore an abstract idea. Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1369-70 (Fed. Cir. 2015).
The CAFC determined that while the claims recited interactive interface / web page manager which tailor webpage to specific individual based on profile, the interactive interface simply describes a generic web server with attendant software tasked with providing web pages to and communicating with user’s computer that amounts to “apply it on a computer”. Id. at 1370-71. That is, requiring the use of a software brain tasked with tailoring information and providing it to the user provides no additional limitation beyond applying an abstract idea, restricted to the internet, on a generic computer. Id. at 1371.
In the instant application, Claims 1 and 20 recited steps (1)-(4) described a series of mental analysis of collecting text transcripts, mentally generating questions or queries of the text transcripts, apply the questions / queries to machine learning models to generate analytical insights for display.
In contrast to the four step algorithm that described a specifically asserted self-referential table that improved database search and retrieval functions in Enfish, the claims do not describe limitations that optimize any specifically asserted computer technology because the claims do not describe any improvement to any specifically asserted structured machine learning model but merely using the machine learning model as a tool to generate analytical insights.
Unlike the specifically asserted improved user interface specifying a particular manner to access data such that the claims were directed to a particular manner of summarizing and presenting information in electronic devices, steps (1)-(4) merely require displaying the analytical insights, with member-specific healthcare data, based on mental processes like sentiment analysis and data filtering (i.e., picking and choosing data to be displayed or not displayed).
Therefore, steps (1)-(4) are akin to the steps of gathering and analyzing information of a specified content and then displaying the results in Electric Power Grp. or software brain tasked with tailoring information and providing it to the user provides no additional limitation beyond applying an abstract idea, restricted to the internet, on a generic computer in Intellectual Ventures I.
Because claims 1 and 20 do not specifically assert any structured improvement to a machine learning model but merely used the model as a tool, do not improve a user interface in a particular manner but merely tasked a display with tailoring information and providing to the user, claims 1 and 20 are directed to the abstract idea / mental process of collecting text transcripts, mentally generating questions or queries of the text transcripts, apply the questions / queries to machine learning models to generate analytical insights for display.
Step 2B Inventive Concept.
The Guideline stated that if the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception, and requires further analysis under Step 2B where it may still be eligible if it amounts to an “inventive concept”. See MPEP 2106.04IIA and MPEP 2106.05.
Further, an inventive concept can be found in the non-conventional and non-generic arrangement of known conventional pieces. BASCOM Global Internet Servs. v. AT&T Mobility, 827, F3d 1341, 1350 (Fed. Cir. 2016).
In BASCOM, the CAFC held that filtering content is an abstract idea because it is a longstanding, well-known method of organizing human behavior similar to concepts previously found to be abstract. BASCOM, 827 F.3d at 1348. However, the CAFC determined that the claims did not merely recite filtering content along with the requirement to perform it on the internet or on a set of generic computer components, nor did the claims preempt all ways of filtering content on the internet. Id. at 1350.
Rather, the inventive concept described and claimed was the installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user that gives the filtering tool both the benefits of a filter on a local computer and the benefits of a filter on an internet service provider “ISP” server. Id. By taking a prior art filter solution (one size fits all filter at internet service provider “ISP” server) and making it more dynamic and efficient (providing individualized filtering at the ISP server), the claimed invention improves the performance of the computer system itself. Id. at 1351.
On the other hand, implementation via computers does not offer a meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment. Alice, 134 S. Ct. at 2360 (“Nearly every computer will include a “communications controller” and “data storage unit” capable of performing the basic calculation, storage, and transmission functions required by the method claims”). Intellectual Ventures I L.L.C. v. Capital One Bank, 792 F.3d 1363, 1370-71 (Fed. Cir. 2015) (“Steps that do nothing more than spell out what it means to “apply it on a computer” cannot confer patent-eligibility).
Similarly, limiting an abstract idea to one field of use do not convert otherwise ineligible concept into an inventive concept. Intellectual Ventures I L.L.C. v. Erie Indem. Co., 850 F.3d 1315, 1328 (Fed. Cir. 2017). Neither does adding computer functionality to increase the speed or efficiency of the process confer patent eligibility on an otherwise abstract idea. Intellectual Ventures I, 792 F.3d at 1367 (citing Bancorp Servs., LLC v. Sun Life Insurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“The fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter”)).
Individually, in the instant application, claim 1 recites generic computer components such as one or more machine learning models. Claim 20 recites computer system comprising processor, display, memory storing program for execution by the processor.
Such individual recitation of generic computer components (processor, computer program, memory, display) are purely functional and generic because nearly every computer will include such processor and data storage unit capable of performing basic calculation necessary for data calculation / analysis, storage, and transmission.
As an ordered combination, unlike BASCOM that describes an unconventional combination of a conventional ISP server with a customized filter specific to each user that is remote from end-users to provide both the benefits of a filter on a conventional local computer and the benefits of a filter on the conventional ISP server, implementing steps (1) –(4) collecting text transcripts, mentally generating questions or queries of the text transcripts, apply the questions / queries to machine learning models to generate analytical insights for display do not involve a unconventional combination of conventional pieces because the combination amounts to “apply it on a computer” or limiting the application of (1)-(4) to the field of computers.
To the extent that implementing steps (1)-(4) on a processor / computer in the field of computers results in reduction in memory requirement and computational requirement, merely adding computer functionality to increase the speed or efficiency of approving a pending business transaction between user and merchant does not confer patent eligibility on an otherwise abstract idea.
Dependent claims 4-9 and 13-19 also failed to integrated claim 1 into a practical application under step 2A or an inventive step under step 2B for the following reasons:
Claim 4 requires machine learning models to perform analysis of transcript, which is mental process that can be performed in a human mind at this level of generality.
Claim 5 generally requires that the machine learning models correspond to healthcare domain, which a human doctor / nurse practitioner / health insurance agent can also provide corresponding mental analysis at this level of generality.
Claims 6-8 described the particular contents to collect the transcript, which is akin to Electric Power Grp. where collecting data were limited to certain types.
Claim 9 is directed toward storing collected transcript in a cloud object storage. However, collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory are drawn to an abstract idea. In re TLI Comm. L.L.C. v. AV Automotive, L.L.C., 823 F.3d 607, 613 (Fed. Cir. 2016)(citing Content Extraction and Trans. v. Wells Fargo Bank, 776 F.3d 1343, 1347 (Fed. Cir. 2014)). Claim 9 described no particular steps for replication, autoscaling, encryption at rest, and on-demand backup, only a statement of intent for doing so that amounted to apply it, however developed.
Claim 10 further describe analysis of transcript segment that amounted to mental steps.
Claim 11 requires storing insights as encrypted files using conventional, well-known, routine 256 AES encryption. Claim 12 corresponds to optimizing transcript using conventional, well-known, routine Amazon Web Services to store the transcript into a database. Claim 15 requires indexing (i.e., storing) analytical insights and/or transcripts chronologically for real-time querying.
As previously noted, recognizing certain data within the collected data set, and storing that recognized data in a memory are drawn to an abstract idea. TLI, 823 F.3d at 613. See also See MPEP 2106.04(a)(2)IIIA (“a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind”).
Claim 13 further described the filtering step of step (4) for picking and choosing data for display. Analyzing information by mathematical algorithms, without more, are essentially mental processes within the abstract-idea category. Electric Power Grp., 830 F.3d at 1354.
Claim 14 described displaying analytical insights by generating data visualization, which is akin to gather, analyze, and display power-grid field information for display in real time by use of anything but entirely conventional, generic technology that failed to state an inventive concept. Id. at 1356.
Claim 16 of requiring API to perform functions are akin to requiring a generic computer to process analytical insights that are essentially mental processes at this level of generality.
Claim 17 further described the transcript combining text and speech data and claim 18 require transcript text is anonymized and HIPAA compliant, which merely limited data collection to particular content, which is abstract. Electric Power Grp., 830 F.3d 1350 at 1354.
Claim 19 requires mentally generating insights across multiple channels for a particular person, like tailoring web content for a particular user in Intellectual Ventures I.
In conclusion, the process of claims 1-20 focused on mental processes: collecting text transcripts, mentally generating questions or queries of the text transcripts, apply the questions / queries to machine learning models to generate analytical insights for display.
Therefore, Claims 1-20 are not eligible for a patent.
Claim Rejections - 35 USC § 103
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 103 that form the basis for the rejections under this section made 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, 4-8, 13, 16-17, and 19-20 are rejected under 35 USC 103 for being unpatentable over Thondikulam et al. (US 12586682 B1) in view of Hariprasad (US 2026/0179775 A1).
Regarding Claims 1 and 20, Thondikulam discloses a computer system (Figs. 1-2 and Col 10, Rows 1-10, computing device 200 implementing client device 115, server 120) comprising:
one or more processors (Col 10, Rows 25-27, processor 235);
a display (Col 11, Rows 33-37); and
memory (Col 10, Rows 63-67, memory 237);
wherein the memory stores one or more programs configured for execution by the one or more processors (Col 10, Rows 25-27, processor 235 executes software instructions; Col 11, Rows 1-6, memory 237 stores instructions / computer programs / software), and the one or more programs comprising instructions for:
obtaining one or more transcripts corresponding to a plurality of digital service channels (Col 19, Rows 21-28, conversational interface engine 206 connects patient members on one or more engagement channels (e.g., Col 19, Rows 58-67, patient member requested prescription refill via phone call a day before and engages digital care via instant messaging chat the next day); Col 20, Rows 15-21, receiving a stream of conversational text and speech for stream processing comprising real time speech to text translation using one or more language models, transcription models, and voice recognition models; see further Col 22, Rows 19-22, receiving user input through website, instant messaging chat, phone call, or through IVR);
generating one or more channel-specific prompts for the one or more transcripts based on each digital service channel corresponding to a respective transcript (Col 22, Rows 20-28, when receiving user input from website, instant messaging chat, phone call, or through IVR (i.e., channels), convert speech to text if user input is an audio stream or speech, determine the context of the user input (e.g., Col 19, Row 62 – Col 20, Row 1, contextually aware that a patient member requested a prescription refill the day before via phone call with a call center when the patient engages digital care via instant messaging chat the next day), and forward the text and context to NLP pipeline for natural language understanding of user intent (i.e., generating prompts comprising converted text and patient context for NLU machine learning models));
inputting the one or more channel-specific prompts to one or more machine learning models to obtain channel-specific insights (Col 16, Rows 4-12, NLU machine learning model to classify user input utterance (e.g., text or speech) and user interaction history on different channels; Col 20, Rows 29-34 and Col 20, Row 63 – Col 21, Row 1, using machine learning models 226 / NLU models to process user input to recognize intent / meaning of the user’s input by identifying user’s sentiment, determining their objective, identify entities in the user input, and extract important information about those entities); and
generating and displaying analytical insights by integrating the channel-specific insights with member-specific healthcare data (Col 21, Rows 25-39, generate questionnaire (per Col 17, Rows 21-29, screening questionnaire in association with an identified intent of patient member during a patient care and coordination workflow) based on the identified intent to direct or route the patient member to the appropriate healthcare services and make all the context of the patient member seeking care readily available to the appropriate healthcare services such as live nurse), using sentiment analysis (Col 20, Rows 4-65, identifying the user’s sentiment in the user input) and data filtering (Col 20, Row 67 – Col 21, Row 24, modify / adjust text classification and intent recognition by weighing patient’s demographic data, past medical history, recent and current healthcare appoints, physician encounters, visit data, location, language, time of medical event, etc. ).
Thondikulam does not disclose generating the channel-specific prompt for the transcripts based on metadata extracted from the one or more transcripts.
Hariprasad discloses a patient specific clinical assessment system (Figs. 1A-1B) obtaining one or more transcripts corresponding to a plurality of digital service channels (¶55, patient interface 110 enables patient to complete structured intake forms (i.e., filling web based forms in ¶124) and engage interactively with inputs (¶73, Sarah logs her fatigue severity and dietary intake via input 142) via voice enabled system (¶124) to provide patient health data (¶72); ¶74, using NLP and data standardization technique (¶61, text analytics, semantic data processing, medical transcription processes) to normalize patient health data into a structured format per ¶80; see further Fig. 2, steps 204-206),
generating one or more channel-specific prompts (Fig. 2, steps 208 and 210) for the one or more transcripts based on each digital service channel corresponding to a respective transcript (¶80, map the structured data to standardized medical coding system per Fig. 2, step 208) and metadata extracted from the one or more transcripts (¶81, augment patient health data with metadata (¶74, append normalized patient health data with metadata) per Fig. 2, step 210);
inputting the one or more channel-specific prompts to one or more machine learning models to obtain channel-specific insights (¶81, combine the coded data from step 208 and the metadata from step 210 to form a processed data set 212 as input for AI LLM; per ¶75, AI LLM 102 analyzes patient health data to identify clinical insights); and
generating and displaying analytical insights by integrating the channel-specific insights with member-specific healthcare data (¶59, LLM 102 dynamically retrieves relevant data from historical data repositories / historical patient data and then combined with patient data to generate insights that are both comprehensive and individualized (¶72, processing patient health data to generate personalized clinical insights and care recommendation); ¶76, display refined clinical insights on clinician interface 112)
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to generate the one or more channel-specific prompts for the one or more transcripts based on each digital service channel corresponding to a respective transcript and metadata extracted from the one or more transcripts in order to support context aware analysis by the machine learning models (Dougherty, ¶57; compare Thondikulam, Col 21, Rows 5-15, adjusting machine learning model / NLU model classification based on patient member’s visit data based on contextual awareness of patient’s prescription refill via phone call a day before per Col 19, Rows 62-64) to generate personalized clinical insights and care recommendations (Hariprasad, ¶72).
Regarding Claim 4, Thondikulam discloses wherein applying one or more machine learning models comprises performing in-memory analysis of transcript segments of the one or more transcripts (Col 22, Rows 24-28, forward the converted text and context to NLP pipelines 607 for natural language understanding of user intent; per Col 20, Rows 22-45, using machine learning models / NLU models to identify intent in the user input).
Regarding Claim 5, Thondikulam discloses wherein the one or more machine learning models are trained on healthcare terminology, medications and treatments to output healthcare domain-specific data (Col 17, Rows 1-9, NLU models trained on patient member clinical context (e.g., medications, treatments) and medical terms and dictionary; Col 20, Rows 30-39, using machine learning models to understand user input textual words or phrases to identify potential health / disease condition and to process clinical data (past medical history, EHR), pharmacy data (medication adherence)).
Regarding Claim 6, Thondikulam discloses wherein the plurality of digital service channels includes two or more channels selected from the group consisting of:
(i) a phone channel for interaction with agents trained to respond about benefits, claims and providers (Col 19, Rows 21-28, engagement channels to appropriate healthcare services including a phone call; Col 21, Rows 64 – 67, phone call regarding answers to a billing or insurance question);
(ii) an email or secure messaging channel for written inquiries about benefits, claims, healthcare documents, including attachments for evidence of claim, explanation of benefits statements (Col 19, Rows 21-28, engagement channels to appropriate healthcare services including email; Col 21, Rows 64-67, email regarding answers to a billing or insurance question);
(iii) a chat or instant messaging channel for real-time interaction to obtain healthcare related information (Col 19, Rows 21-28, engagement channels to appropriate healthcare services including instant messaging chat);
(iv) a web portal channel (Fig. 8C) for secure online accounts to view benefits (Fig. 8D), check claim status (Fig. 8F, Member Services), order identifier cards (Fig. 8F, Member Services), updating contact information (Fig. 8B, “Your Information”), uploading claims and documents (Fig. 8F, Member Services; see further Col 19, Rows 21-28, engagement channels to appropriate healthcare services including website associated with a health insurance organization per Col 8, Rows 28-32; see further Col 23, Rows 9-37) and
(v) a social media channel for responding to public inquiries, providing updates during events impacting members.
Regarding Claim 7, Thondikulam discloses wherein the one or more transcripts include:
(i) text related to healthcare topics (Col 22, Rows 22-24, convert user input into text) including claims (Col 21, Rows 65-67, patient member wanting to known answer to insurance question), benefits (Col 21, Rows 65-67, patient member wanting to known answer to insurance question), insurance plans (Col 21, Rows 65-67, patient member wanting to known answer to insurance question), coverage (Col 21, Rows 65-67, “I want to know my co-pay”), medical terminology (Col 20, Rows 30-39, using machine learning models to understand user input textual words or phrases to identify potential health / disease condition; e.g., Col 21, Rows 1-4, identify “throwing up blood at home” as “vomiting”), and regulations (Col 21, Rows 5-8 and Rows 20-24, classify text and recognize intent based on rules / regulatory requirements provided by physicians and clinical staff);
(ii) at least some data with protected health information (Col 21, Rows 60-63, “when is my next appointment”);
(iii) communication between patients, insurance companies and healthcare providers (Col 19, Rows 21-25, providing conversational interface connecting patient members on one or more engagement channels to appropriate healthcare services; e.g., Col 21, Rows 34-39, phone call to consult with a live nurse for patient care; Col 21, Row 65 – Col 22, Row 3, connecting patient member to call center agent regarding billing or insurance question); and
(iv) speech and text data, including telephonic operations and call recordings (Col 22, Rows 19-24, receiving user input via website, instant messaging chat, phone call, or IVR).
Regarding Claim 8, Thondikulam discloses wherein obtaining the one or more transcripts comprises interfacing with one or more third-party provider computers to receive text and/or speech data (Fig. 6 and Col 22, Rows 17-22, portal 615; in view of Col 3, Rows 32-58, user interaction engine 202 sends and receives data, via communication unit 241, to and from client devices 115, medical devices 145, healthcare management server 120, data sources 135, and third-party servers 140).
Regarding Claim 13, Thondikulam discloses wherein applying data filtering comprises: filtering the channel-specific insights and the member-specific data by a plurality of parameters, including time (Col 21, Rows 5-8 and Rows 17-18, modify / adjust text classification and intent recognition based on time of medical events), topic (Col 21, Rows 5-8 and Rows 12-14, modify / adjust text classification and intent recognition based on known medical conditions, medications), and sentiment (Col 20, Rows 63-65, recognize meaning of the user input by identifying user’s sentiment).
Regarding Claim 16, Thondikulam discloses providing one or more application programming interfaces (APIs) for (i) retrieving and/or (ii) interpreting user interface filters to query, the one or more call transcripts, the channel-specific insights, and/or the analytical insights (Col 8, Rows 56-60, facilitate an API that allows conversational interface application to access data and information for perform functionality of adjusting text classification and intent recognition of transcribed user input per Col 21, Rows 1-24).
Regarding Claim 17, Thondikulam discloses wherein the one or more transcripts combines text or speech data obtained from the plurality of digital service channels (Col 20, Rows 7-17, user input comprises one or more of text, speech, voice to text, image, and video where conversational interface engine 206 receives conversational text and speech for stream processing: receiving a batch of conversational text and speech for batch processing), wherein at least two of the plurality of digital service channels generate text or speech in distinct format or structure (Col 19, Rows 25-28, Col 20, Rows 7-17 and Col 22, Rows 20-22, engagement channels including website, mobile application, phone call, interactive voice response, instant messaging chat, email to input text, speech voice to text, image, and video).
Regarding Claim 19, Thondikulam discloses wherein generating the analytical insights comprises integrating interactions across multiple health service channels for a same member over time (Col 19, Rows 58-67, identify patient member request prescription refill via phone call a day before and engages digital care via instant messaging chat the next day, being contextually aware that patient member requested a recent prescription refill; Col 21, Rows 5-8 and 12-14, adjust text classification and intent recognition of current input based on past medical history; see also Hariprasad, ¶75, utilize Sarah’s historical medical records and real-time updates from her wearable device to generate insights).
Claim 2 is rejected under 35 USC 103 for being unpatentable over Thondikulam et al. (US 12586682 B1) and Hariprasad (US 2026/0179775 A1) as applied to Claim 1, in view of Madisetti et al. (US 2025/0355907 A1).
Regarding Claim 2, Thondikulam does not disclose wherein generating the one or more channel-specific prompts is based on a prompt library for different digital service channels, wherein the prompt library includes domain-specific prompt engineering resources and libraries to devise prompts relevant for domain-specific dialogues.
Madisetti discloses generating a prompt library for different digital service channels, the prompt library includes domain-specific prompt engineering resources and libraries to devise prompts relevant for domain-specific dialogues (¶62, AI Broker Database 820 with an index of “derived requests” that may be used in future to select which set of derived request an incoming request fall into, the “derived requests” are derived prompts generated for different categories corresponding to h-LLMs specialized for specific tasks or categories per ¶¶51-56: multi-modal / multi-channel LLM such as GPT-4) and generating one or more channel-specific prompts based on the prompt library for different digital service channels (¶63, for a user entered prompt, generate multiple derived prompts for different categories, convert the derived prompts into embeddings, use the prompt embeddings to search a vector database to return a list of similar embeddings corresponding to user prompt to create the derived prompts for multiple h-LLMs).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to generating the one or more channel-specific prompts is based on a prompt library for different digital service channels in order to create an index of derived requests / prompts for use in the future to process incoming requests / prompts (Madisetti, ¶64).
Claim 3 is rejected under 35 USC 103 for being unpatentable over Thondikulam et al. (US 12586682 B1) and Hariprasad (US 2026/0179775 A1) as applied to Claim 1, in view of Siracusano et al. (US 2024/0411994 A1).
Regarding Claim 3, Thondikulam does not disclose wherein generating the one or more channel-specific prompts is based on:
analyzing common queries or intents in domain-specific omnichannel interaction data to identify frequently occurring query patterns, intents, and topics that domain users express;
using intent classification on domain-specific omnichannel interaction data to categorize utterances into distinct buckets to use intent categories to generate prompts;
using named entity recognition (NER) to extract entities from healthcare omnichannel interaction data to frame prompts incorporating the entities;
analyzing and/or reverse engineering prompt-response pairs from prior domain-specific omnichannel interaction data to discern patterns and templates for new prompts;
using input from domain experts to use domain knowledge to suggest prompts spanning different healthcare scenarios and contexts;
performing A/B tests with candidate prompts with a large language model to assess response quality, clarity, specificity and adherence to healthcare compliance, to iteratively refine prompts based on test results.
Siracusano discloses generating channel-specific prompts in the medical domain (¶48, LLM agent that generates queries to an LLM by using prompt templates; ¶126, LLM agent tailoring / adapting prompts to desired tasks and medical reports comprising text and images) using input from domain experts to use domain knowledge to suggest prompts spanning different healthcare scenarios and contexts (¶65, user is an expert interested in a particular topic (i.e., domain); ¶78, modify and add to prompt templates after receiving feedback of the user).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to generate at least one channel-specific prompt based on input from domain experts to use domain knowledge to suggest prompts spanning different healthcare scenarios and contexts in order to tailor / adapt prompts to a desired task (Siracusano, ¶126).
Claim 9 is rejected under 35 USC 103 for being unpatentable over Thondikulam et al. (US 12586682 B1) and Hariprasad (US 2026/0179775 A1) as applied to Claim 1, in view of Perry (AWS NoSQL: Choosing the best option for you).
Regarding Claim 9, Thondikulam discloses storing the one or more transcripts in a cloud object storage (Col 6, Rows 20-23 and Col 19, Rows 58-62, history of prior interactions across different engagement channels store in cloud based healthcare management server) and cataloging and storing the metadata (Fig. 6, meta data cataloged and stored according to call start timestamp, enterprise track ID, call end timestamp; Col 14, Rows 1-21, user interaction engine determines a history of user interaction data of patient members on different engagement channels including time context and store the data in data storage 243) in a NoSQL database (Col 13, Rows 8-10, database management system includes a NoSQL DBMS).
Thondikulam does not disclose the NoSQL database or persistent key-value datastore for replication, autoscaling, encryption at test, and on-demand backup.
Perry discloses an exemplary NoSQL database or persistent key-value datastore for replication (Amazon Neptune: “Neptune includes features for point-in-time recovery, multi-zone data replication, continuous backups, and read replicas”), autoscaling (Amazon Keyspaces: “It includes features for autoscaling and enables you to select between on-demand or provisioned resources”), encryption at test (Amazon Neptune: “Neptune includes features for point-in-time recovery, multi-zone data replication, continuous backups, and read replicas. It supports ACID transactions and provides encryption in-transit and at-rest”), and on-demand backup (Amazon DynamoDB: “Amazon DynamoDB is a document and key-value database. It is a fully managed service that includes features for backup and restore”).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to cataloging and storing the metadata (Fig. 6, meta data) in a NoSQL database or persistent key-value datastore for replication, autoscaling, encryption at test, and on-demand backup in order to store data with flexible schema, in large volume, and providing low latency (Perry, “What is ASW NoSQL?”).
Claims 10-11 are rejected under 35 USC 103 for being unpatentable over Thondikulam et al. (US 12586682 B1) and Hariprasad (US 2026/0179775 A1) and Perry (AWS NoSQL: Choosing the best option for you) as applied to Claim 9, in view of Dagli (Kubeflow Pipelines: Orchestrating Machine Learning Workflows - Part 3).
Regarding Claim 10, Thondikulam discloses wherein applying one or more machine learning models comprises performing in-memory analysis of transcript segments of the one or more transcripts, including, upon availability of the metadata, analyzing transcript segments based on metadata from a document database and the one or more transcripts (Fig. 6, meta data cataloged and stored according to call start timestamp, enterprise track ID, call end timestamp; Col 14, Rows 1-21, user interaction engine determines a history of user interaction data of patient members on different engagement channels including time context and store the data in data storage 243; Col 22, Rows 23-28, forward converted text and context (i.e., time context metadata) to the NLP pipelines for natural language understanding of user intent).
Thondikulam does not disclose initiating a Kubeflow-based job, deploying a plurality of pods, each pod processing transcript segments.
Dagli teaches initiating a Kubeflow-based job deploying a plurality of pods for portable machine learning workloads (Passing data between steps: “Under the hood, when Kubeflow Pipelines runs a component, a container image is started in a Kubernetes Pod and your component’s inputs are passed in as command-line arguments. When your component has finished, the component’s outputs are returned as files”).
Thondikulam discloses NLU processing as a machine learning model (Col 20, Rows 29-34).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to initiate a Kubeflow-based job, deploying a plurality of pods, each pod processing transcript segments based on the metadata and the transcripts (per established function of Thondikulam Col 22, Rows 25-28 requiring NLU model / machine learning model to process converted text and context (i.e., time context metadata)) in order to simplify the orchestration of machine learning pipeline (Dagli, “The main goals of Kubeflow Pipelines are: End-to-end orchestration: enabling and simplifying the orchestration of machine learning pipelines”).
Regarding Claim 11, Thondikulam discloses storing, by the plurality of pods (implementing Dagli’s Kubeflow pods to process transcript and context / metadata to simplify machine learning NLU processing), the channel-specific insights in a cloud object storage (Col 6, Rows 20-23 and Col 19, Rows 58-62, history of prior interactions across different engagement channels store in cloud based healthcare management server; Col 14, Rows 1-21, user interaction engine determines a history of user interaction data of patient members on different engagement channels including time context (Fig. 6, meta data cataloged and stored according to call start timestamp, enterprise track ID, call end timestamp) and store the data in data storage 243 in order to provide the context to live nurse per Col 21, Rows 37-39).
As modified by Hariprasad, storing the channel-specific insights in a cloud object storage as encrypted files, using 256 AES encryption (Hariprasad, ¶121).
Claim 12 is rejected under 35 USC 103 for being unpatentable over Thondikulam et al. (US 12586682 B1) and Hariprasad (US 2026/0179775 A1) as applied to claim 1, in view of Ghosal (Building complex workflows with Amazon MWAA, AWS Step Functions, AWS Glue, and Amazon EMR).
Regarding Claim 12, Thondikulam discloses wherein obtaining the one or more transcripts comprises optimizing transcript processing using a read-optimized document database as an intermediary cache (Col 14, Rows 61-67, user interaction engine 202 instantiates data ingestion layer that transports data from assorted data sources 135 to data storage 243 where it can be stored, accessed, and analyzed (e.g., Col 22, Rows 24-28, NLP pipeline analysis of converted text and context from such data storage 243) by the conversational interface application 110).
Thondikulam does not disclose wherein an hourly ETL job, orchestrated via Airflow, activates either a transient EMR cluster or an AWS Glue job, thereby identifying and migrating new records into a database.
Ghosal discloses using Airflow to orchestrate hourly ETL job (“Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your extract, transform, and load (ETL) jobs and data pipelines”), activates either a transient EMR cluster or an AWS Glue job, thereby identifying and migrating new records into a database (“You can use AWS Step Functions as a serverless function orchestrator to build scalable big data pipelines using services such as Amazon EMR to run Apache Spark and other open-source applications on AWS in a cost-effective manner, and use AWS Glue for a serverless environment to prepare (extract and transform) and load large amounts of datasets from a variety of sources for analytics and data processing with Apache Spark ETL jobs”).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to use Airflow to orchestrate hourly ETL job, activate either a transient EMR cluster or an AWS Glue job, thereby identifying and migrating new records into a database in order to load large amounts of datasets from a variety of sources for analytics and data processing (Ghosal; e.g., Thondikulam, Col 14, Rows 61-65, use Airflow to orchestrate hourly ETL job, activating either a transient EMR cluster or AWS Glue job, to ingest / transport data from assorted data sources 135 into data storage).
Claims 14-15 and 18 are rejected under 35 USC 103 for being unpatentable over Thondikulam et al. (US 12586682 B1) and Hariprasad (US 2026/0179775 A1) as applied to claim 1, in view of Dougherty et al. (US 2024/0177730 A1).
Regarding Claims 14-15, Thondikulam does not disclose generating a data visualization based on the analytical insights, the data visualization subject to a predetermined latency.
Dougherty teaches generating a data visualization based on the analytical insights, the data visualization subject to a predetermined latency (¶33, provide analytics in a dashboard format to one or more users for review such as providing a therapist dynamic support on a display in real time or quasi-real time) and indexing the analytical insights and/or the one or more transcripts chronologically for real-time querying (Fig. 1; ¶43, interactive table display the time at which text was spoken, individual who spoke, content of speech, enabling user to pick from a list of a plurality of tags).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to generate a data visualization based on analytical insights and indexing the insights or the transcripts chronologically for real-time querying to make patient member context readily available to practitioners like nurses and therapists (Thondikulam, Col 21, Rows 37-39; Dougherty, ¶33, provide analytics to an authenticated user for review such as a therapist).
Regarding Claim 18, Thondikulam as modified by Hariprasad discloses wherein the one or more transcripts include text that is protected health information that is compliant with HIPAA and/or privacy regulations (Hariprasad, ¶104, data encryption complies with HIPAA and ¶111, perform regulatory checks to ensure compliance with HIPAA).
The combination does not disclose the transcripts include text that is anonymized.
Dougherty teaches applying anonymized tags to text transcripts (¶36).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to anonymized transcripts including the text in order to enable the user to apply anonymization to one or mor strings of text (Dougherty, ¶36).
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 12094458 B2 and US 2023/0252980 A1 disclose obtaining historical conversation data (Fig. 4, step 402) to identify communication channel type, participants, semantic content of conversation, metadata associated with the conversation (Fig. 4, step 404), generate training set including historical conversation data and classification label (Fig. 4, step 406), apply machine learning to the training set to train machine learning model to associate conversation data with label for extracting portion of conversation, receive feedback for supervised machine learning model, and update machine learning model based on feedback (Fig. 4, steps 408, 410, and 412).
US 11451666 B1 discloses using a first machine learning model to process audio data from an audio channel to generate first call insight information and using a second machine learning model to process image data to generate second call insight information to generate combined call insight information (Abstract).
US 11854540 B2 discloses using a generative pretrained transformer based LLM to process multi-modal (i.e., multi-channel input data comprising text, audio, image, video).
US 2023/0105969 A1 discloses responding to a healthcare inquiry from user comprising inputs from speech data channel, image/video channel, audio channel using corresponding machine learning models to generate medical recommendations to the user.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to examiner Richard Z. Zhu whose telephone number is 571-270-1587 or examiner’s supervisor Hai Phan whose telephone number is 571-272-6338. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 07/25/2026
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