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 US provisional applications 63/412166 and 63/340637 filed on 09/30/2022 and 05/11/2022, and non-provisional application 18/143912 filed on 05/05/2023.
Nonstatutory Double Patent Rejections
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. See In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent is shown to be commonly owned with this application. See 37 CFR 1.130(b).
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1, 4, 9-13, and 16-18 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2.
The limitations of claim 1 of US 12099808 B2 fully encompasses the limitations of claims 1, 9, and 10 in the instant application.
The limitations of claim 4 of US 12099808 B2 fully encompasses the limitations of claim 4 in the instant application.
The limitations of claim 5 of US 12099808 B2 fully encompasses the limitations of claim 17 in the instant application.
The limitations of claim 8 of US 12099808 B2 fully encompasses the limitations of claim 11 in the instant application.
The limitations of claim 9 of US 12099808 B2 fully encompasses the limitations of claim 12 in the instant application.
The limitations of claim 10 of US 12099808 B2 fully encompasses the limitations of claim 13 in the instant application.
The limitations of claim 11 of US 12099808 B2 fully encompasses the limitations of claim 18 in the instant application.
The limitations of claim 13 of US 12099808 B2 fully encompasses the limitations of claim 16 in the instant application.
Claim 2 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Chang et al. (US 2020/0152304 A1).
The limitations of claims in US 12099808 B2 do not disclose wherein the first content block is structured to provide a meditation exercise.
Chang discloses generating personalized and effective mental health therapies and recommendations (¶2) by structing content block to provide a meditation exercise (¶77).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to structure the first content block to provide a meditation exercise in order to generate personalized and effective mental health therapies (Chang, ¶2).
Claim 3 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Misrilall et al. (US 2022/0093253 A1).
The limitations of claims in US 12099808 B2 do not disclose wherein the first content block is structured to provide a breathing exercise.
Misrilall discloses performs natural language processing to analyze inputs from a target patient to generate a content block (Abstract) that is structured to provide a breathing exercise (¶80).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to structure the first content block to provide a breathing exercise in order to make specific type of content available to the user (Misrilall, ¶98).
Claim 5 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Bouyarmane (US 11797530 B1).
The limitations of claims in US 12099808 B2 do not disclose wherein a set of embeddings of the multi-lingual embedding model is language agnostic.
Bouyarmane discloses using language agnostic multi-lingual embedding architecture-based NLP model to generate embeddings for entity records expressed in different languages (Col 8, Rows 45-54, entity records expressed in multiple languages; Col 10, Rows 1-12, use hierarchical embedding models to generate embeddings at word level and entity record level; in view of Col 6, Rows 18-20 and Col 10, Rows 15-16, hierarchical embedding model comprises neural network based models at individual layers of the hierarchy such as bidirectional long short term memory units “BiLSTMs”).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to use a NLP model comprising language agnostic multi-lingual embedding architecture (compare Devesa, ¶48, using RNN-LSTM architecture for generating embeddings with Bouyarmane, Col 6, Rows 18-20, HEM comprising bidirectional LSTMs) to generate the set of user specific embeddings (Devesa, ¶62) in order to generate respective semantic similarity metrics between the user specific embeddings and set of content embeddings (i.e., entity records) expressed in different languages (Bouyarmane, Col 5, Rows 20-24).
Claim 6 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Bouyarmane (US 11797530 B1) and Aikawa et al. (US 2019/0354589 A1).
The limitations of claims in US 12099808 B2 do not disclose wherein the subset of the set of embeddings encode the shared semantic meaning and are characterized by the cosine similarity greater than 0.8.
Aikawa discloses collecting word embedding corresponding to word / phrase and to determine words / phrases of similar expression (Abstract) based on a cosine similarity greater than 0.8 (¶213).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to characterize embeddings encode shared semantic meaning to have a cosine similarity greater than 0.8 to ensure that similarity measure of two matching embedding is maximal (Devesa, ¶63).
Claims 7-8 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Devesa (US 2020/0211709 A1).
The limitations of claims in US 12099808 B2 do not disclose wherein the NLP model is trained on monolingual word embeddings and wherein the NLP model is trained on a pseudo-cross-lingual corpus including mixed contents of different languages.
Devesa discloses wherein the NLP model is trained on monolingual word embeddings (¶63, the recurrent neural network is trained in such a manner that similarity measure of two matching embedding is maximal where a single training input includes a question, its correct answer, and a randomly chosen incorrect answer; ¶69, the medical assistant 206 trained in any one of the one or more languages; i.e., the RNN is trained using training input comprising matching embeddings in at least one language) and wherein the NLP model is trained on a pseudo-cross-lingual corpus including mixed contents of different languages (¶63, the recurrent neural network is trained in such a manner that similarity measure of two matching embedding is maximal where a single training input includes a question, its correct answer, and a randomly chosen incorrect answer; ¶69, the medical assistant 206 trained in any one of the one or more languages; i.e., the RNN is trained using training input comprising matching embeddings in one or more languages).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to training the mono-lingual word embeddings and on a pseudo-cross-lingual corpus including mixed contents of different languages in order to enable dialogue with user in one or more languages (Devesa, ¶69).
Claim 14 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Kurowski et al. (US 2016/0071432 A1).
The limitations of claims in US 12099808 B2 do not disclose wherein the first conversational session comprises a first conversation between a coach and the user.
Kurowski teaches a health and wellness management system generating recommendations to a user (Abstract) in a conversational session comprising a conversation between a coach and the user (¶296, primary care provider being a health coach).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to provide a health coach in order to provide virtual primary care providers as medical practitioner (Kurowski, ¶296).
Claim 15 is rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Kurowski et al. (US 2016/0071432 A1).
The limitations of claims in US 12099808 B2 do not disclose wherein the first conversational session satisfies a minimum conversation length criterion.
Ladkat discloses classifying high quality conversational session based on a conversational session satisfying a minimum conversation length criterion (¶48).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to classify first conversational session satisfying a minimum conversation length criterion as a high quality conversation (Ladkat, ¶48).
Claims 19-20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over the claims of US 12099808 B2 in view of Bouyarmane (US 11797530 B1) and Chang et al. (US 2020/0152304 A1).
The limitations of claim 1 of US 12099808 B2 encompasses the limitations of claim 19 in the instant application except trained semantic embedding model does not include a language agnostic multilingual embedding architecture and wherein the first content block is structured to provide a meditation exercise.
Bouyarmane discloses using language agnostic multi-lingual embedding architecture-based NLP model to generate embeddings for entity records expressed in different languages (Col 8, Rows 45-54, entity records expressed in multiple languages; Col 10, Rows 1-12, use hierarchical embedding models to generate embeddings at word level and entity record level; in view of Col 6, Rows 18-20 and Col 10, Rows 15-16, hierarchical embedding model comprises neural network based models at individual layers of the hierarchy such as bidirectional long short term memory units “BiLSTMs”).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to use a semantic embedding NLP model comprising language agnostic multi-lingual embedding architecture (compare Devesa, ¶48, using RNN-LSTM architecture for generating embeddings with Bouyarmane, Col 6, Rows 18-20, HEM comprising bidirectional LSTMs) to generate the set of user specific embeddings (Devesa, ¶62) in order to generate respective semantic similarity metrics between the user specific embeddings and set of content embeddings (i.e., entity records) expressed in different languages (Bouyarmane, Col 5, Rows 20-24).
Further, Chang discloses generating personalized and effective mental health therapies and recommendations (¶2) by structing content block to provide a meditation exercise (¶77).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to structure the first content block to provide a meditation exercise in order to generate personalized and effective mental health therapies (Chang, ¶2).
The limitations of claim 12 of US 12099808 B2 fully encompasses the limitations of claim 20 in the instant application.
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.
Claims 1 and 19 recites a method (i.e., a process). Reply to Decision on Appeal of June 8, 2021
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.
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 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”).
Under Prong (1), Claim 1 recites a method comprising:
(1) receiving a plurality of user-specific text strings associated with a user;
(2) using a natural language processing (NLP) model, generating a set of user-specific embeddings, comprising, for each user-specific text string of the plurality: generating a respective user-specific embedding based on the user-specific text string, wherein the respective user-specific embedding preserves semantic language information from the user-specific text string;
(3) generating a first set of content embeddings, comprising, for each content block of a set of content blocks, generating a respective content embedding associated with the content block;
(4) based on a set of semantic similarity metrics determined between the set of user-specific embeddings and the set of content embeddings, determining a ranked list of content blocks selected from the set of content blocks; and
(5) based on the ranked list, providing at least one content block to the user.
Claim 19 recites a method comprising:
receiving a plurality of user-specific text strings associated with a user;
(1) using a natural language processing (NLP) model comprising language-agnostic multi-lingual embedding architecture,
(2) generating a set of user-specific embeddings, comprising, for each user-specific text string of the plurality: generating a respective user-specific embedding based on the user-specific text string, wherein the respective user-specific embedding preserves semantic language information from the user-specific text string;
(3) generating a first set of content embeddings, comprising, for each content block of a set of content blocks, generating a respective content embedding associated with the content block;
(4) based on a set of semantic similarity metrics determined between the set of user-specific embeddings and the set of content embeddings, determining a ranked list of content blocks selected from the set of content blocks; and
(5) based on the ranked list, providing a first content block to the user, wherein the first content block is structured to provide a meditation exercise.
With regard to claims 1 and 19, individually, step (1) corresponds to a step of collecting information, limited to a particular format (user-specific text strings associated with a user). This constitutes a mental process within the realm of abstract ideas. 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”). See also 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”).
Further, individually and considered in light of the specification, at ¶100: “In a preferred set of variations, for instance, representing the inputs in a quantitative format includes generating a set of embeddings for any or all of the set of inputs, wherein the embeddings function to represent each of the set of inputs (e.g., each message) with a numerical array (e.g., vector, matrix, etc.) which quantifies the message or other input (e.g., questionnaire response). The embedding pre-processing can be performed by the embedding model and/or by any other suitable model(s). In examples, the embedding pre-processing can be performed with a natural language model (e.g., for multiple languages), preferably with a machine learning (e.g., deep learning) natural language embeddings model, but can additionally or alternatively be performed with any other suitable models, algorithms, and/or tools”.
Therefore, steps (2)-(3) in claim 1 correspond to a mathematical process of generating numerical array or vector representations for user-specific text and content blocks. The Court of Appeals for the Federal Circuit (“CAFC”) held that analyzing information by mathematical algorithms as essentially mental processes within the abstract idea category. Electric Power Grp., 830 F.3d at 1353 (“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”). Accordingly, steps (2)-(3) are essentially a mental process.
Further, individually and considered in light of the specification, at ¶65 “The content selection model is preferably an algorithmic and/or mathematical model. For example, the content selection model can be configured to: determine a similarity matrix (e.g., cosine similarity matrix) between user-specific embeddings and content embeddings; determine a score for each content embedding based on the matrix (e.g., taking the maximum similarity value for each content embedding, preferably given all instances of user embedding and content embedding similarities; taking an average similarity value for each content embedding, preferably given all instances of user embedding and content embedding similarities; etc.); and sort the content embeddings into a ranked list based on the scores (e.g., wherein the content embedding with the highest score, corresponding to the greatest similarity, is placed at the start of the ranked list)”.
Therefore, step (4) corresponds to a mathematical process of sorting embedded numerical representations that corresponds to analyzing information using mathematical algorithms., which are essentially mental processes.
Finally, step (5) provides at least one content block to the user based on the ranked list, which corresponds to displaying certain results of collection and analysis. At such high level of generality, this step focuses on an abstract idea. 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”).
In ordered combination, steps (1)-(5) amounted to collecting user specific text strings, using mathematical models to generate numerical vector representations for user specific text strings and content blocks, mathematically sort the content blocks, and provide at least one content block to the user. Accordingly, claim 1 described analyzing information by mathematical algorithms and are therefore essentially mental processes within the abstract idea category. Electric Power Grp., 830 F.3d at 1353 (“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”).
Further regarding claim 19, based on the ranked list, providing a first content block to the user, wherein the first content block is structured to provide a meditation exercise. In other words, the ordered combination of limitations corresponds to certain methods of organizing human activity such as managing personal behavior including teaching and following rules or instructions.
As such, claims 1 and 19 described a process that employs mathematical models to analyze user inputs in order to select a corresponding content block through mathematical sorting. Thus, claims 1 and 19 described patent ineligible subject matter enumerated under category (a) Mathematical Concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations, (b) Certain methods of organizing human activity –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).
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 held that when a claim containing an abstract idea (e.g., mathematical formula) implements or applies that abstract idea (e.g., math formula) in a structure or process which, when considered as a whole, is performing a function which the patent laws were designed to protect (e. g., transforming or reducing an article to a different state or thing), then the claim satisfies the requirements of §101. Diamond v. Diehr, 450 U.S. 175, 192 (1981); MPEP 2106.04(d)I (“Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP 2106.05(b)”). See also Gottschalk v. Benson, 409 U.S. 63, 70 (1972) (“Transformation and reduction of an article "to a different state or thing" is the clue to the patentability of a process claim that does not include particular machines”).
In particular, the Supreme Court and the CAFC distinguished between computer-functionality improvements from the 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 Diehr, the claims involved a method for curing rubber by using Arrhenius equation to constantly measure actual temperature inside a mold and feeding the temperature measurements into a computer to repeatedly recalculate the cure time to open the press. Diehr, 450 U.S. at 178-79. Since the Supreme Court viewed the claims not as an attempt to patent a mathematical formula, but to an industrial process for molding of rubber products, the claims were statutory. Id. at 192-93.
The key here, as noted by the CAFC, is that the Supreme Court in Diehr looked to how the claims "used that equation in a process designed to solve a technological problem in `conventional industry practice.'" McRO, Inc. v. Bandai Namco Games America, Inc., 837 F.3d 1299, 1312 (Fed. Cir. 2016).When looked at as a whole, "the claims in Diehr were patent eligible because they improved an existing technological process, not because they were implemented on a computer." Id. at 1312-13.
On the other hand, in 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. Electric Power Grp., 830 F.3d at 1355. There, claims specified what information in the power-grid field it is desirable to gather, analyze, and display in “real time” but they do 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. Id. at 1356.
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.
Further, the fact that web site returns pre-designed ad more quickly than a newspaper could send the user is not an inventive concept because merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea. Id. at 1370.
Finally, the CAFC further determined that a database, a user profile keyed to a user identity, and a communication medium are all generic computer elements such that instructing one to apply an abstract idea and reciting no more than generic computer elements performing generic computer tasks that does not make an abstract idea patent eligible. Id. at 1368.
As an ordered combination, claims 1 and 19 to collect information (user specific text strings associated with a user) and for displaying analyzed information (based on semantic similarity / mathematical analysis of vector / embedding representations of user specific text strings and content block) required no more than entirely conventional and generic technologies employed for selecting information for collection, analysis and display by content or source in Electric Power Grp. Therefore, claims 1 and 19 no more differentiate a process from ordinary mental processes as the claims in Electric Power Grp.
Further, unlike the industrial process for curing rubber in Diehr, the claimed steps in claims 1, 10, and 19 for mathematical analysis of text strings and content into numerical vector representations and mathematically ranking / sorting content blocks are not a structure or process which, when considered as a whole, is performing a function which the patent laws were designed to protect.
Further, the claims 1, 10, and 19 do not solve a technological problem or to improve an existing technological process. Specifically, instead of being applied in a process designed to solve a technological problem like the conventional industry practice of curing rubber in Diehr or to a specific asserted improvement in database search and retrieval capabilities in Enfish, the method of claims 1, 10, and 19 focused on providing content tailored to user specific embeddings; i.e., tailoring content as a function of the user’s specific embeddings / text strings that is a fundamental practice and therefore abstract idea much like the software brain tasked with tailoring information as a function of the user’s personal characteristics and providing the tailored information to the user in Intellectual Ventures I.
In other words, neither the mathematical analysis of the collected text strings nor content blocks nor displaying the selected content blocks were specifically asserted for improving any technology or to solve a technological problem. Even if the displayed content blocks correspond to desirable information, the ordered combination amounts to collection and analysis of information similar to Electric Power Grp.
Therefore, as an ordered combination of computer components, claims 1, 10, and 19-20 do not integral abstract mathematical calculations and mental processes into a practical application and the claims are instead directed toward patent ineligible mathematical calculation, analysis by mathematical algorithm, and tailoring content as a function of user specific embeddings / personal characteristics.
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”)).
For example, in Intellectual Ventures I, the claims generally relates to customizing web page content as a function of navigation history and information known about the user via an interactive interface or a selectively tailored medium by which a web site user communicates with a web site information provider. Intellectual Ventures I., 792, F.3d at 1369. The CAFC held that the claim relates to an abstract concept of customizing information based on information known about the user and navigation history. Id.
Further, the claim provided no inventive concept to support patent eligibility because the interactive interface simply describes a generic web server with attendant software, tasked with providing web pages to and communicating with the user’s computer. Id. at 1370. Such required 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, the method of claims 1, 10, and 19 focused on mathematical calculation / analysis by mathematical algorithm and tailoring content as a function of user specific embeddings / personal characteristics. The ordered combination of limitations did not offer a meaningful limitation beyond generally linking the use of an abstract idea (collecting of user text inputs and contents, performing mathematical analysis thereof, and providing / displaying the results of such mathematical analysis) to a conventional computer environment.
In other words, unlike BASCOM that described an unconventional combination to provide both the benefits of a filter on a conventional local computer and the benefits of a filter on the conventional ISP server, the claims did not set forth any combination of components to improve a specifically asserted technology or implement a technological process.
That is, the mathematical calculations and analysis of collected user text inputs and tailoring of contents based on such mathematical calculations and analysis are like the software brain tasked with tailoring information and providing it to the user in Intellectual Ventures I, which provided no additional limitation beyond applying an abstract idea on a generic computer.
Therefore, claims 1, 10, and 19 do not supply an inventive concept.
Other dependent claims failed to integrate the abstract idea into a practical application or provide an inventive concept.
In particular, dependent claims 2-3 structured the first content lock to provide meditation exercise and breathing exercise respectively, which correspond to managing personal behavior or interactions between people including social activities, teaching, and following rules or instructions.
Dependent claims 4-8 and 11-12 focused on the NLP model for generating the mathematical embeddings in multi-lingual setting and how the mathematical embedding encodes shared semantic meaning with a cosine similarity greater than 0.8 (claim 6). Therefore, claims 4-8 and 11-12 focused on mathematical calculations / analysis by mathematical algorithm.
Dependent claim 9 focused on selecting first content block from a database. However, such content extractions are drawn to an abstract idea; e.g., collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory are drawn to an abstract idea. Content Extraction and Trans. v. Wells Fargo Bank, 776 F.3d 1343, 1347 (Fed. Cir. 2014).
Dependent claims 13-15, 18, and 20 focused on collecting user specific text strings. Collecting information, including when limited to particular content (which does not change its character as information), is within the realm of abstract ideas. Electric Power Grp., 830 F. 3d at 1353.
Dependent claim 16, identifies important subset of messages / user specific text strings. Collecting data, recognizing certain data within the collected data set, and storing that recognized data in a memory are drawn to an abstract idea. Content Extraction and Trans., 776 F.3d at 1347.
Claim 17 focused on tailoring content as a function of user characteristics much like Intellectual Ventures I.
For the above reasons, Claims 1-20 are patent ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
(a) NOVELTY; PRIOR ART.—A person shall be entitled to a patent unless—
(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention; or
(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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(b) EXCEPTIONS.—
(1) DISCLOSURES MADE 1 YEAR OR LESS BEFORE THE EFFECTIVE FILING DATE OF THE CLAIMED INVENTION.—A disclosure made 1 year or less before the effective filing date of a claimed invention shall not be prior art to the claimed invention under subsection (a)(1) if—
(A) the disclosure was made by the inventor or joint inventor or by another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor; or
(B) the subject matter disclosed had, before such disclosure, been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor.
(2) DISCLOSURES APPEARING IN APPLICATIONS AND PATENTS.—A disclosure shall not be prior art to a claimed invention under subsection (a)(2) if—
(A) the subject matter disclosed was obtained directly or indirectly from the inventor or a joint inventor;
(B) the subject matter disclosed had, before such subject matter was effectively filed under subsection (a)(2), been publicly disclosed by the inventor or a joint inventor or another who obtained the subject matter disclosed directly or indirectly from the inventor or a joint inventor; or
(C) the subject matter disclosed and the claimed invention, not later than the effective filing date of the claimed invention, were owned by the same person or subject to an obligation of assignment to the same person.
Claims 1, 4, 7-13 and 17 are rejected under 35 USC 102(a)(2) as being anticipated by Devesa (US 2020/0211709 A1).
Regarding Claim 1, Devesa discloses a method comprising:
receiving a plurality of user-specific text strings associated with a user (¶45 and ¶47, receiving user enquiry comprising text strings and converting text strings into tokens);
using a natural language processing (NLP) model, generating a set of user-specific embeddings (¶47, convert text string of user enquiry into tokens; ¶48, converting tokens into word embedding using RNN-LSTM based model; ¶62, using recurrent neural networks to generate sentence embedding), comprising, for each user-specific text string of the plurality: generating a respective user-specific embedding based on the user-specific text string, wherein the respective user-specific embedding preserves semantic language information from the user-specific text string (¶73, RNN reads input sequence (user enquiry or the user symptoms) one word at a time, encodes the input sequence into a state vector that compresses semantic information);
generating a first set of content embeddings, comprising, for each content block of a set of content blocks, generating a respective content embedding associated with the content block (¶60, creating word embedding of words presented in a health profile of the user / corpus of medical triage conversation);
based on a set of semantic similarity metrics determined between the set of user-specific embeddings and the set of content embeddings, determining a ranked list of content blocks selected from the set of content blocks (¶63 in view of ¶59, using cosine-similarity / rank loss function to match word embedding of user enquiry with answers in user health profile to select one or more relevant answers (i.e., relevant answers with respective cosine-similarity measure)); and
based on the ranked list, providing a first content block to the user (¶64 in view of ¶59, display one or more relevant answers for the user enquiry based on the selection of the one or more relevant answers).
Regarding Claim 4, Devesa discloses wherein the NLP model comprises a multi-lingual embedding model trained using user-specific text data in multiple languages (¶62, medical assistant system 206 creates sentence embedding of entire sentences in the plurality of dialogue conversations and use the sentence embedding to produce end to end trainable neural translation models with decoder that uses global embedding to generate natural language from different language embedding; ¶69, medical assistant system 206 trained in one or more languages of the dialogue with the user).
Regarding Claim 7, Devesa discloses wherein the NLP model is trained on monolingual word embeddings (¶63, the recurrent neural network is trained in such a manner that similarity measure of two matching embedding is maximal where a single training input includes a question, its correct answer, and a randomly chosen incorrect answer; ¶69, the medical assistant 206 trained in any one of the one or more languages; i.e., the RNN is trained using training input comprising matching embeddings in at least one language).
Regarding Claim 8, Devesa discloses wherein the NLP model is trained on a pseudo-cross-lingual corpus including mixed contents of different languages (¶63, the recurrent neural network is trained in such a manner that similarity measure of two matching embedding is maximal where a single training input includes a question, its correct answer, and a randomly chosen incorrect answer; ¶69, the medical assistant 206 trained in any one of the one or more languages; i.e., the RNN is trained using training input comprising matching embeddings in one or more languages).
Regarding Claim 9, Devesa discloses receiving a content request from the user (¶65, user replies to one or more relevant answers with a new set of user enquiry), and in response to receiving the content request, retrieving the ranked list (¶51, map word bedding of user enquiry with word embedding of question words in the medical triage conversation corpus; per ¶63, each question-answer pair was prepared using cosine similarity ranking loss function) and selecting the first content block from a database (¶103, database 210 provides storage location to health profile, corpus of medical triage conversation; in view of ¶55, medical triage conversation corpus includes a plurality of question-answer pairs), based on the ranked list (¶59, select one or more relevant answers for user enquiry based on mapping and health profile of the user with confidence level / probability of one or more relevant answers being the correct answer to the user enquiry; per ¶75, medical assistant system 206 ranks answers by associating confidence level with answers and ¶115, similarity function maps embedding of question with embedding of correct answer and gives confidence score for correct question-answer pair).
Regarding Claim 10, Devesa discloses after providing the first content block to the user:
receiving a second plurality of user-specific text strings associated with the user (¶65, updates user enquiry after receiving updated user enquiry from the user based on selection from the one or more relevant answers displayed to the user 102; i.e., a new set of user enquiry);
using the NLP model, generating a second set of user-specific embeddings, comprising, for each user-specific text string of the second plurality: generating a respective user-specific embedding based on the user-specific text string (¶47, convert text string of user enquiry into tokens; ¶48, converting tokens into word embedding using RNN-LSTM based model; ¶62, using recurrent neural networks to generate sentence embedding);
based on the second set of user-specific embeddings and a second set of content embeddings ((¶60, creating word embedding of words presented in a health profile of the user / corpus of medical triage conversation)), determining a second ranked list of content blocks selected from the set of content blocks (¶51, map word bedding of user enquiry with word embedding of question words in the medical triage conversation corpus; per ¶63, each question-answer pair was prepared using cosine similarity ranking loss function); and
selecting a second content block based on the second ranked list (¶59, select one or more relevant answers for user enquiry based on mapping and health profile of the user with confidence level / probability of one or more relevant answers being the correct answer to the user enquiry); and
providing the second content block to the user (¶64 in view of ¶59, display one or more relevant answers for the user enquiry based on the selection of the one or more relevant answers).
Regarding Claim 11, Devesa discloses generating a second set of content embeddings, comprising, for each content block of a second set of content blocks, generating a respective content embedding associated with the content block (¶51, identify graph with same structure of the user enquiry in the corpus of medical training dataset by mapping word embedding of the user enquiry with word embedding of question words present in the corpus of medical triage conversation).
Regarding Claim 12, Devesa discloses wherein the second set of content embeddings is equivalent to the first set of content embeddings (¶59, select one or more relevant answers to the user based on one or more relevant answers being the correct answer to the user enquiry; i.e., a first answer and a second answer are equivalent to the extent that they are correct answers to the user enquiry; see e.g., ¶¶88-89 and ¶91, answer rank 1.0 and answer rank 3.0 corresponding to amoxicillin are equivalent / correct answers to the enquiry “What are the symptoms of the flu?”).
Regarding Claim 13, Devesa discloses wherein receiving the plurality of user-specific text strings comprises receiving a set of messages of a first conversational session with the user (¶41, user 102 interacts with medical assistant system 206 in a bi-directional conversation; e.g., medical assistant 206 enquires the user “How are you feeling today”? and user initially enquires a question from the medical assistant system 206), and selecting the plurality of user-specific text strings from the set of messages (¶47, insert delineation tokens between the context and utterances to distinguish between responses of the medical assistant system 206 and the user 102; i.e., distinguish and select tokens of the user enquiry for conversion into word embedding per ¶48).
Regarding Claim 17, Devesa discloses wherein the first content block is provided to the user upon generating similarity metrics from the set of messages, and identifying the first content block based upon the similarity metrics corresponding to the set of messages (¶51, map word bedding of user enquiry with word embedding of question words in the medical triage conversation corpus; ¶59, select one or more relevant answers for user enquiry based on mapping and health profile of the user with confidence level / probability of one or more relevant answers being the correct answer to the user enquiry; per ¶63, each question-answer pair was prepared using cosine similarity ranking loss function).
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.
Claim 2 is rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Chang et al. (US 2020/0152304 A1).
Regarding Claim 2, Devesa does not disclose wherein the first content block is structured to provide a meditation exercise.
Chang discloses generating personalized and effective mental health therapies and recommendations (¶2) by structing content block to provide a meditation exercise (¶77).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to structure the first content block to provide a meditation exercise in order to generate personalized and effective mental health therapies (Chang, ¶2).
Claim 3 is rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Misrilall et al. (US 2022/0093253 A1).
Regarding Claim 3, Devesa does not disclose wherein the first content block is structured to provide a breathing exercise.
Misrilall discloses performs natural language processing to analyze inputs from a target patient to generate a content block (Abstract) that is structured to provide a breathing exercise (¶80).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to structure the first content block to provide a breathing exercise in order to make specific type of content available to the user (Misrilall, ¶98).
Claim 5 is rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Bouyarmane (US 11797530 B1).
Regarding Claim 5, Devesa does not disclose wherein a set of embeddings of the multi-lingual embedding model is language agnostic.
Bouyarmane discloses using language agnostic multi-lingual embedding architecture-based NLP model to generate embeddings for entity records expressed in different languages (Col 8, Rows 45-54, entity records expressed in multiple languages; Col 10, Rows 1-12, use hierarchical embedding models to generate embeddings at word level and entity record level; in view of Col 6, Rows 18-20 and Col 10, Rows 15-16, hierarchical embedding model comprises neural network based models at individual layers of the hierarchy such as bidirectional long short term memory units “BiLSTMs”).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to use a NLP model comprising language agnostic multi-lingual embedding architecture (compare Devesa, ¶48, using RNN-LSTM architecture for generating embeddings with Bouyarmane, Col 6, Rows 18-20, HEM comprising bidirectional LSTMs) to generate the set of user specific embeddings (Devesa, ¶62) in order to generate respective semantic similarity metrics between the user specific embeddings and set of content embeddings (i.e., entity records) expressed in different languages (Bouyarmane, Col 5, Rows 20-24).
Claim 6 is rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Bouyarmane (US 11797530 B1) and Aikawa et al. (US 2019/0354589 A1).
Regarding Claim 6, Devesa discloses wherein a subset of the set of embeddings encode a shared semantic meaning and are characterized by a cosine similarity (¶63, medical assistant system 206 utilizes embedding functions for embedding context and utterance pairs using ranking loss function including cosine similarity measure for preparing the one or more relevant answers where the RNN is trained to ensure similarity measure of two matching embedding is maximal)
Devesa does not disclose wherein the subset of the set of embeddings encode the shared semantic meaning and are characterized by the cosine similarity greater than 0.8.
Aikawa discloses collecting word embedding corresponding to word / phrase and to determine words / phrases of similar expression (Abstract) based on a cosine similarity greater than 0.8 (¶213).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to characterize embeddings encode shared semantic meaning to have a cosine similarity greater than 0.8 to ensure that similarity measure of two matching embedding is maximal (Devesa, ¶63).
Claim 15 is rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Ladkat et al. (US 2022/0172220 A1).
Regarding Claim 15, Devesa does not disclose wherein the first conversational session satisfies a minimum conversation length criterion.
Ladkat discloses classifying high quality conversational session based on a conversational session satisfying a minimum conversation length criterion (¶48).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to classify first conversational session satisfying a minimum conversation length criterion as a high quality conversation (Ladkat, ¶48).
Claims 16 and 18 are rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Cai et al. (US 2019/0349321 A1).
Regarding Claim 16, Devesa does not teach identifying an important subset of messages from the set of messages upon processing the set of messages with a classifier.
Cai discloses a system (Fig. 1), comprising: receiving, from a client device, a user query (¶49, processor 104 can receive a user query and parse the user query) and using a classifier to identify an important subset of user query to process the user query (¶123, processor 104 can remove stop words, count frequency of stemmed words, sort the frequencies, rank each sentence based on keywords it contains, and select sentences based on the ranked sentences).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to implement a classifier to process the set of messages to identify an important subset of the messages from the set of messages in order to token text data and remove stop words (Cai, ¶123).
Upon such processing, the established function of Devesa generates the set of user-specific embeddings from the important subset of messages (Devesa ¶47, convert text string of user enquiry into tokens; ¶48, converting tokens into word embedding using RNN-LSTM based model; ¶62, using recurrent neural networks to generate sentence embedding) without stop words.
Regarding Claim 18, Devesa does not teach wherein receiving the plurality of user-specific text strings comprises receiving a summary of a first conversational session with the user, and selecting the plurality of user-specific text strings from the summary.
Cai discloses a system (Fig. 1), comprising: receiving, from a client device, a user query / user specific text strings (¶49, processor 104 can receive a user query and parse the user query) and generating a summary of user query (¶49 and ¶51, processor 104 can receive a tuple or sequence of elements based on a parsed user query; ¶¶15-16, tokenizing data representation of user inputted text to output a summary of user inputted text).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to generate and transmit a summary of a first conversational session with a user and select the user specific text strings from the summary in order to limit the report summary to a number of sentences or words (Cai, ¶121).
Claim 14 is rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Kurowski et al. (US 2016/0071432 A1).
Regarding Claim 14, Devesa discloses wherein the first conversational session comprises a first conversation between a medical practitioner and the user (¶51 and ¶70, past interaction history of the user includes conversations between a user and a professional medical practitioner).
Devesa does not disclose the medical practitioner is a coach.
Kurowski teaches a health and wellness management system generating recommendations to a user (Abstract) in a conversational session comprising a conversation between a coach and the user (¶296, primary care provider being a health coach).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to provide a health coach as medical practitioner in order to provide virtual primary care providers as medical practitioner (Kurowski, ¶296).
Claims 19-20 are rejected under 35 USC 103(a) as being unpatentable over Devesa (US 2020/0211709 A1) in view of Bouyarmane (US 11797530 B1) and Chang et al. (US 2020/0152304 A1).
Regarding Claim 19, Devesa discloses a method comprising:
receiving a plurality of user-specific text strings associated with a user (¶45 and ¶47, receiving user enquiry comprising text strings and converting text strings into tokens);
using a natural language processing (NLP) model comprising natural language embedding architecture (¶62, use recurrent neural networks to create sentence embeddings; ¶73, RNN architecture for modeling natural language), generating a set of user-specific embeddings (¶47, convert text string of user enquiry into tokens; ¶48, converting tokens into word embedding using RNN-LSTM based model), comprising, for each user-specific text string of the plurality: generating a respective user-specific embedding based on the user-specific text string, wherein the respective user-specific embedding preserves semantic language information from the user-specific text string (¶73, RNN reads input sequence (user enquiry or the user symptoms) one word at a time, encodes the input sequence into a state vector that compresses semantic information);
generating a first set of content embeddings, comprising, for each content block of a set of content blocks, generating a respective content embedding associated with the content block (¶60, creating word embedding of words presented in a health profile of the user / corpus of medical triage conversation);
based on a set of semantic similarity metrics determined between the set of user-specific embeddings and the set of content embeddings, determining a ranked list of content blocks selected from the set of content blocks (¶63 in view of ¶59, using cosine-similarity / rank loss function to match word embedding of user enquiry with answers in user health profile to select one or more relevant answers (i.e., relevant answers with respective cosine-similarity measure)); and
based on the ranked list, providing a first content block to the user (¶64 in view of ¶59, display one or more relevant answers for the user enquiry based on the selection of the one or more relevant answers).
Devesa does not disclose the natural language processing (NLP) model comprising language-agnostic multi-lingual embedding architecture.
Bouyarmane discloses using language agnostic multi-lingual embedding architecture-based NLP model to generate embeddings for entity records expressed in different languages (Col 8, Rows 45-54, entity records expressed in multiple languages; Col 10, Rows 1-12, use hierarchical embedding models to generate embeddings at word level and entity record level; in view of Col 6, Rows 18-20 and Col 10, Rows 15-16, hierarchical embedding model comprises neural network based models at individual layers of the hierarchy such as bidirectional long short term memory units “BiLSTMs”).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to use a NLP model comprising language agnostic multi-lingual embedding architecture (compare Devesa, ¶48, using RNN-LSTM architecture for generating embeddings with Bouyarmane, Col 6, Rows 18-20, HEM comprising bidirectional LSTMs) to generate the set of user specific embeddings (Devesa, ¶62) in order to generate respective semantic similarity metrics between the user specific embeddings and set of content embeddings (i.e., entity records) expressed in different languages (Bouyarmane, Col 5, Rows 20-24).
Devesa does not disclose wherein the first content block is structured to provide a meditation exercise.
Chang discloses generating personalized and effective mental health therapies and recommendations (¶2) by structing content block to provide a meditation exercise (¶77).
It would’ve been obvious to one ordinarily skilled in the art before the effective filing date of the invention to structure the first content block to provide a meditation exercise in order to generate personalized and effective mental health therapies (Chang, ¶2).
Regarding Claim 20, Devesa discloses initiating a first conversational session with the user using a mobile device of the user (¶41, user 102 with communication device 104 interacts with medical assistant system 206 in a bi-directional conversation; e.g., medical assistant 206 enquires the user “How are you feeling today”? and user initially enquires a question from the medical assistant system 206), wherein receiving the plurality of user-specific text strings comprises receiving a set of messages of the first conversational session with the user (¶47, tokenize user enquiry into tokens), and selecting the plurality of user-specific text strings from the set of messages (¶47, insert delineation tokens between the context and utterances to distinguish between responses of the medical assistant system 206 and the user 102; i.e., distinguish and select tokens of the user enquiry for conversion into word embedding per ¶48).
Conclusion
Prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 11100179 B1 discloses a query model computing semantical embedding of content object (e.g., a new post to be shown in a user’s newsfeed digest) (Col 22, Rows 18-24) and generate a set of ranked content suggestions for each retrieved content object (Col 22, Rows 64-67) based on semantic similarity (Col 24, Rows 1-7 and Rows 38-47).
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 King Poon whose telephone number is 571-272-7440. Examiner Richard Zhu can normally be reached on M-Th, 0730:1700.
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/RICHARD Z ZHU/Primary Examiner, Art Unit 2654 04/09/2026
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