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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on July 29, 2026 has been entered.
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, 5-8, and 12-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the claimed invention fall inside one of the four statutory categories (process, machine, manufacture, or composition of matter)? Yes for claims 1, 5-8, and 12-15.
Claims 1 and 5-7 are drawn to a method for processing text data to check the mental health status of a user (i.e., process). Claims 8 and 12-15 are drawn to an apparatus for processing text data to check the mental health status of a user (i.e., a manufacture).
Step 2A - Prong One: Do the claims recite a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon)? Yes, for claims 1, 5-8, and 12-15.
Claim 1 recites:
A method comprising: collecting text data …; (Mental Process: data collection)
… removes obsolete text from the text data to produce extracted meaningful text and converts the extracted meaningful text to lowercase letters to produce a … text; (Mental Process: evaluation)
performing … labeling of the … meaningful text to produce a labeled meaningful text; (Mental Process: evaluation)
performing … word embedding for the labeled meaningful text to produce a word embedding result; (Mental Process: evaluation)
and checking a mental health status of a user … the text data by applying the word embedding result …, … dividing a paragraph into a plurality of sentences in response to the text data being a paragraph, removing the obsolete text from the text data, the obsolete text comprising hash tags, special characters, numbers, and spaces: tokenizing by classifying at least one text that is included in the text data into a plurality of words, and converting a meaningful text into lowercase letters, wherein tokenizing comprises: removing meaningless text including pronouns, prepositions, conjunctions, articles, and URLs from the text data, checking a headword or morpheme based on at least one text classified as a word among the plurality of words, and converting slang and emoticons that are included in the text data into words having a same meaning, wherein converting the slang and the emoticons comprises matching the slang and the emoticons with an emotion-based text corpus that is classified … (Mental Process: evaluation)
Claim 5 recites:
further comprising: … parts of speech including nouns, adjectives, adverbs, determiners, and conjunctions in the meaningful text. (Mental Process: evaluation)
Claim 6 recites:
wherein performing labelling of the meaningful text comprises: generating a text corpus based on the meaningful text; (Mental Process: evaluation)
labeling the text corpus…; (Mental Process: evaluation)
performing keyword-based labeling based on a circumplex model of emotions; (Mental Process: evaluation)
and classifying the text corpus according to emotion based on the labeling. (Mental Process: evaluation)
Claim 7 recites:
wherein performing word embedding comprises applying the labeled meaningful text .... (Mental Process: evaluation)
Claim 8 recites:
convert meaningful text extracted by removing obsolete text from the text data into lowercase letters to produce a … meaningful text, labelling the … meaningful text to produce a labeled meaningful text, and checking a mental health status of a user, by applying a word embedding result for the labeled meaningful text …, … dividing a paragraph into a plurality of sentences in response to the text data being a paragraph, removing the obsolete text from the text data, the obsolete text comprising hash tags, special characters, numbers, and spaces, and tokenizing by classifying at least one text that is included in the text data into a plurality of words, … removing meaningless text including pronouns, prepositions, conjunctions, articles and URLs from the text data, checking a headword or morpheme based on at least one text classified as a word among the plurality of words, and converting slang and emoticons that are included in the text data into words having a same meaning, converts the slang and the emoticons by matching the slang and the emoticons with an emotion-based text corpus … (Mental Process: evaluation)
Claim 12 recites:
… parts of speech including nouns, adjectives, adverbs, determiners, and conjunctions in the meaningful text. (Mental Process: evaluation)
Claim 13 recites:
… converts the meaningful text into lowercase letters. (Mental Process: evaluation)
Claim 14 recites:
… a text corpus based on the meaningful text, performs labeling of the text corpus to produce a labeled text corpus …, performs keyword-based labeling based on a circumplex model of emotions, and classifies the labeled text corpus according to emotion based on the labeling. (Mental Process: evaluation)
Claim 15 recites:
… the labeled meaningful text … (Mental Process: evaluation)
But for the generic recitation of one service server, a social network service, an electronic apparatus, a memory, a BERT deep learning algorithm, a communication unit, and a control unit, the identified steps are practically capable of being performed by human analog, including mental processes and certain methods of organizing human activity. These identified steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because humans can collect text data, remove obsolete text, obtain meaningful text, convert meaningful text to lowercase letters, label text, check mental health status of a user, divide a paragraph into sentences, classify text, and convert slang.
Independent claim 8 describes parallel steps as claim 1 (and therefore recite limitations that fall within this subject matter of grouping abstract ideas), and these claims are therefore determined to recite an abstract idea under the same analysis. Dependent claims 5-7 and 12-15 are directed to steps that are practically performed in the human mind, including mental evaluation (analyzing parts of speech, labelling meaningful text, performing word embedding (i.e. associating words with means based on context, experiences, and relationships), etc.) for a method of processing text data to check the mental health status of a user.
Each claim amounts to a form of collecting, generating, and analyzing information, and therefore falls within the scope of a method for organizing human activity, (i.e., an abstract idea). The Court considers a claim a mental process when the claim contains limitations that can practically be performed in the human mind. A claim that recites mental processes include “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, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) As such, the Examiner concludes that claims 5-7 and 12-15 recite an abstract idea.
MPEP 2106.04(a)(2)(II) discusses certain methods of organizing human activity.
The Supreme Court has identified a number of concepts falling within the “certain
methods of organizing human activity” grouping as abstract ideas. Sub-groupings of
organizing human activity encompass both activity of a single person and activity that
involves multiple people, and thus, certain activity between a person and a computer or an electronic apparatus (as is the case in the Applicant’s claimed invention). These sub-groupings fall within the “certain methods of organizing human activity”.
Step 2A – Prong Two: Do the claims recite additional elements that integrate the exception into a practical application of the exception? No
Examiner interprets the following limitations as additional elements.
content uploaded to at least one service server providing a social network service, by an electronic apparatus
by which performing preprocessing the electronic apparatus
to a deep learning algorithm by the electronic apparatus
wherein performing preprocessing comprises:
and stored in a memory of the electronic apparatus.
displaying
a BERT algorithm, which is the deep learning algorithm
An apparatus comprising: a memory; a communication unit for collecting text data, which is content uploaded to a service server through communication with at least one service server providing social network service; and a control unit for performing: preprocessing to
uploaded the text data,
wherein the control unit performs preprocessing
and wherein the control unit performs tokenizing
classified and stored in the memory of the apparatus.
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. The requirement to execute the claimed steps/functions using one service server, a social network service, an electronic apparatus, a BERT deep learning algorithm, a memory, a communication unit, and a control unit is recited at a high level of generality and amount to no more than instructions to apply the exception (independent claims 1 and 8 and dependent claims 5-7 and 12-15) and thus is equivalent to adding the words “apply it” on a computer and/or mere instructions to implement the abstract idea. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve to link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined “an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 5-7 and 12-15 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective independent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? i.e., Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea? No
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1 and 8 and dependent claims 5-7 and 12-15 are equivalent to adding the words “apply it” on a computer, and/or link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a computer and/or instructions to implement the abstract idea on a computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., a memory; a communication unit for collecting text data, which is content uploaded to a service server through communication with at least one service server providing social network service; and a control unit for performing: preprocessing, etc.) and/or appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Applicant of claimed invention discloses ([0035], “The user terminal … is a terminal which is capable of using social network services provided by the service server”), ([0035], “it may be an electronic apparatus such as a smart phone, a tablet PC or the like”), ([0074], “The control unit … may perform a machine learning or deep learning algorithm”), ([0043], “The memory … may store various programs”), ([0042], “The display unit … outputs output data”), and ([0040], “the communication unit … may perform wireless communication”).
The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, claims 1, 5-8, and 12-15 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 6-8, and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable under US 11631401 B1 (“Nudd”) in view of KR 102403463 B1 (“Youngho”) and in further view of US 20180375807 A1 (“Krans”).
In regard to claim 1, Nudd discloses
A method comprising: collecting text data (column 3, lines 6-7, “the conversation … contains … text data”), which is content uploaded to at least one service server providing a social network service (column 5, lines 60-67 – column 6, lines 1-2, “server … hosts a network-based software application … to … receive data … the … server … may be … a social network server”), by an electronic apparatus (column 6, lines 28-33, “user device … can be … any … electronic device capable of accessing the network”);
performing preprocessing by which the electronic apparatus removes obsolete text from the text data to produce extracted meaningful text and converts the extracted meaningful text to lowercase letters to produce a preprocessed meaningful text (column 24, lines 22-26, “the question responder … may remove … text…, apply the text to speech conversion (e.g., using a conversion software) to the text”);
performing, by the electronic apparatus, labeling of the preprocessed meaningful text to produce a labeled meaningful text (column 21, lines 50-51, “The … module … may label the keyword … returned by the … training procedure”);
performing, by the electronic apparatus, word embedding for the labeled meaningful text to produce a word embedding result (column 19, lines 60-61, “The word embeddings are from a learning algorithm”);
and checking a mental health status of a user who has uploaded the text data by applying the word embedding result to a deep learning algorithm by the electronic apparatus (column 2, lines 38-40, “A machine learning system may be used to analyze whether a senior has a … mental … condition.”), removing the obsolete text from the text data, the obsolete text comprising hash tags, special characters, numbers, and spaces (column 9, lines 56-64, “a database management system … may … delete … data using programmatic operations”): tokenizing by classifying at least one text that is included in the text data into a plurality of words (column 22, lines 20-21, “the … module … may preprocess each document by splitting it up into tokens (one per word)”), and converting a meaningful text into lowercase letters (column 22, lines 20-22, “the … module … may preprocess each document by splitting it up into tokens (one per word) that are all in lower case”), wherein tokenizing comprises: removing meaningless text including pronouns, prepositions, conjunctions, articles and URLs from the text data (column 9, lines 56-64, “a database management system … may … delete … data using programmatic operations”), and converting the text data into words having a same meaning (column 24, lines 22-26, “the question responder … may … apply the text to speech conversion (e.g., using a conversion software) to the text”), with an emotion-based text corpus that is classified (column 20, lines 35-36, “the … module … may classify each word in the text of a conversation”) and stored in a memory of the electronic apparatus (column 5, lines 6-8, “The computing subsystem … includes … components, such as a processor, memory, storage”).
Nudd does not disclose
wherein performing preprocessing comprises: dividing a paragraph into a plurality of sentences in response to the text data being a paragraph
checking a headword or morpheme based on at least one text classified as a word among the plurality of words
slang and emoticons that are included in
wherein converting the slang and the emoticons comprises matching the slang and the emoticons
However, in an analogous art of health information machine learning models, Youngho discloses
wherein performing preprocessing comprises: dividing a paragraph into a plurality of sentences in response to the text data being a paragraph (page 4, paragraph 2, “Medical text data … of the present invention may include a plurality of sentences as text data” Examiner notes that a paragraph can be a group of sentences.)
checking a headword or morpheme based on at least one text classified as a word among the plurality of words (page 4, paragraph 2, “each sentence may be divided into sentence detail elements in units of morphemes”)
Therefore, it would have been obvious to one of ordinary skill in the art
(PHOSITA) before the effective filing date to have added the missing claimed
limitations, the claim limitations, wherein performing preprocessing comprises: dividing a paragraph into a plurality of sentences in response to the text data being a paragraph
checking a headword or morpheme based on at least one text classified as a word among the plurality of words, of Youngho into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health ; rather it would enhance Nudd by adding a plurality of sentences and morphemes.
Further, Krans is also analogous art because it is in the field of virtual assistant systems. Krans discloses
slang (page 14, Table 1, column 2, row 6, slang) and emoticons that are included in ([0055], “text analysis includes emoticon analysis”).
wherein converting the slang (page 14, Table 1, column 2, row 6, slang) and the emoticons comprises matching the slang and the emoticons ([0055], “text analysis includes emoticon analysis”)
Substituting slang and emoticons that are included in wherein converting the slang and the emoticons comprises matching the slang and the emoticons of Krans into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health; rather it would enhance Nudd by adding slang, text analysis, and emoticon analysis.
In regard to claim 6, Nudd discloses
wherein performing labelling of the meaningful text comprises (column 21, lines 50-52, “The … module … may label the keyword vectors … with … topic names”): generating a text corpus based on the meaningful text (column 26, lines 41-45, “generation of the natural language passage of text … is also based on … Ubuntu dialog corpus” Examiner notes that Ubuntu dialog corpus is a text corpus.);
labeling (column 21, lines 50-52, “The … module … may label the keyword vectors … with … topic names”) the text corpus (column 26, lines 41-45, “generation of the natural language passage of text … is also based on … Ubuntu dialog corpus” Examiner notes that Ubuntu dialog corpus is a text corpus.) for each social network service (column 5, lines 60-67-column 6, lines 1-2, “server … hosts a network-based software application … to … receive data … the … server … may be … a social network server”);
performing keyword-based labeling based on a circumplex model of emotions (column 33, lines 16-22, “The sentiments are all negative, indicating … negative mood … These are all discussed on a positive way, as indicated by the positive sentiment” Examiner notes that a circumplex model of emotions can relate to the degree of pleasantness (positive sentiment) or unpleasantness (negative sentiment).);
and classifying the text corpus according to emotion (column 26, lines 41-45, “generation of the natural language passage of text … is also based on … Ubuntu dialog corpus” Examiner notes that Ubuntu dialog corpus is a text corpus.) based on the labeling (column 21, lines 50-52, “The … module … may label the keyword vectors … with … topic names”).
In regard to claim 7, Nudd does not disclose
wherein performing word embedding comprises applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm.
However, in an analogous art of health information machine learning models, Youngho discloses
wherein performing word embedding comprises applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm (page 4, paragraph 5, “The machine-learned model … may be implemented by a BERT (Bidirectional Encoder Representations from Transformers) model”).
Therefore, it would have been obvious to one of ordinary skill in the art
(PHOSITA) before the effective filing date to have added the missing claimed
limitations, the claim limitations, wherein performing word embedding comprises applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm, of Youngho into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health ; rather it would enhance Nudd by adding a BERT algorithm.
In regard to claim 8, Nudd discloses
An apparatus comprising: a memory (column 5, lines 6-8, “The computing subsystem … includes … components, such as … memory”);
a communication unit for (column 6, lines 25-28, “user device … is a computing device including … communication capabilities”) collecting text data (column 3, lines 6-7, “the conversation … contains … text data”), which is content uploaded to a service server through communication with at least one service server providing social network service (column 5, lines 60-67-column 6, lines 1-2, “server … hosts a network-based software application … to … receive data … the … server … may be … a social network server”);
and a control unit for performing: preprocessing to (column 9, lines 22-23, “The processor … comprises … a general purpose controller”), convert meaningful text extracted by removing obsolete text from the text data into lowercase letters to produce a preprocessed meaningful text (column 24, lines 22-26, “the question responder … may remove … text…, apply the text to speech conversion (e.g., using a conversion software) to the text”), labelling the preprocessed meaningful text to produce a labeled meaningful text (column 21, lines 50-51, “The … module … may label the keyword … returned by the … training procedure”), and checking a mental health status of a user, who has uploaded the text data (column 2, lines 38-40, “A machine learning system may be used to analyze whether a senior has a … mental … condition.”), by applying a word embedding result for the labeled meaningful text to a deep learning algorithm (column 19, lines 60-61, “The word embeddings are from a learning algorithm”), removing the obsolete text from the text data, the obsolete text comprising hash tags, special characters, numbers, and spaces (column 9, lines 56-64, “a database management system … may … delete … data using programmatic operations” Examiner notes that hash tags, special characters, numbers, and spaces are text characters or can be treated as text characters.), and tokenizing by classifying at least one text that is included in the text data into a plurality of words (column 22, lines 20-21, “the … module … may preprocess each document by splitting it up into tokens (one per word)”), and wherein the control unit performs tokenizing by: removing meaningless text including pronouns, prepositions, conjunctions, articles and URLs from the text data (column 9, lines 56-64, “a database management system … may … delete … data using programmatic operations” Examiner notes that data can include meaningless text.), and converting the text data into words having a same meaning (column 24, lines 22-26, “the question responder … may remove … text…, apply the text to speech conversion (e.g., using a conversion software) to the text”), wherein the control unit converts (column 9, lines 22-23, “The processor … comprises … a … controller”) with an emotion-based text corpus that is classified (column 20, lines 35-36, “the … module … may classify each word in the text of a conversation”) and stored in the memory of the apparatus (column 5, lines 6-8, “The computing subsystem … includes … memory, storage”).
Nudd does not disclose
wherein the control unit performs preprocessing by: dividing a paragraph into a plurality of sentences in response to the text data being a paragraph
checking a headword or morpheme based on at least one text classified as a word among the plurality of words
slang and emoticons that are included in
the slang and the emoticons by matching the slang and the emoticons
However, in an analogous art of health information machine learning models, Youngho discloses
wherein the control unit performs preprocessing by: dividing a paragraph into a plurality of sentences in response to the text data being a paragraph (page 4, paragraph 2, “Medical text data … of the present invention may include a plurality of sentences as text data” Examiner notes that a paragraph can be a group of sentences.)
checking a headword or morpheme based on at least one text classified as a word among the plurality of words (page 4, paragraph 2, “each sentence may be divided into sentence detail elements in units of morphemes”)
Therefore, it would have been obvious to one of ordinary skill in the art
(PHOSITA) before the effective filing date to have added the missing claimed
limitations, the claim limitations, wherein the control unit performs preprocessing by: dividing a paragraph into a plurality of sentences in response to the text data being a paragraph, checking a headword or morpheme based on at least one text classified as a word among the plurality of words, of Youngho into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health; rather it would enhance Nudd by adding a plurality of sentences and morphemes.
Further, Krans is also analogous art because it is in the field of virtual assistant systems. Krans discloses
slang (page 14, Table 1, column 2, row 6, slang) and emoticons that are included in ([0055], “text analysis includes emoticon analysis”).
the slang (page 14, Table 1, column 2, row 6, slang) and the emoticons by matching the slang and the emoticons ([0055], “text analysis includes emoticon analysis”)
Substituting slang and emoticons that are included in, the slang and the emoticons by matching the slang and the emoticons of Krans into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health; rather it would enhance Nudd by adding slang, text analysis, and emoticon analysis.
In regard to claim 13, Nudd discloses
wherein the control unit converts the meaningful text into lowercase letters (column 22, lines 20-22, “the … module … may preprocess each document by splitting it up into tokens (one per word) that are all in lower case”).
In regard to claim 14, Nudd discloses
wherein the control unit generates a text corpus based on the meaningful text (column 26, lines 41-45, “generation of the natural language passage of text … is also based on … Ubuntu dialog corpus” Examiner notes that Ubuntu dialog corpus is a text corpus.), performs labeling (column 21, lines 50-52, “The … module … may label the keyword vectors … with … topic names”) of the text corpus to produce a labeled text corpus (column 26, lines 41-45, “generation of the natural language passage of text … is also based on … Ubuntu dialog corpus” Examiner notes that Ubuntu dialog corpus is a text corpus.) for each social network service (column 5, lines 60-67-column 6, lines 1-2, “server … hosts a network-based software application … to … receive data … the … server … may be … a social network server”), performs keyword-based labeling based on a circumplex model of emotions (column 33, lines 16-22, “The sentiments are all negative, indicating … negative mood … These are all discussed on a positive way, as indicated by the positive sentiment” Examiner notes that a circumplex model of emotions relates to the degree of pleasantness (positive sentiment) or unpleasantness (negative sentiment).), and classifies the labeled text corpus according to emotion (column 26, lines 41-45, “generation of the natural language passage of text … is also based on … Ubuntu dialog corpus” Examiner notes that Ubuntu dialog corpus is a text.) based on the labeling (column 21, lines 50-52, “The … module … may label the keyword vectors … with … topic names”).
In regard to claim 15, Nudd does not disclose
wherein the control unit performs the word embedding by applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm.
However, in an analogous art of health information machine learning models, Youngho discloses
wherein the control unit performs the word embedding by applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm (page 4, paragraph 5, “The machine-learned model … may be implemented by a BERT (Bidirectional Encoder Representations from Transformers) model”).
Therefore, it would have been obvious to one of ordinary skill in the art
(PHOSITA) before the effective filing date to have added the missing claimed
limitations, the claim limitations, wherein the control unit performs the word embedding by applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm, of Youngho into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health ; rather it would enhance Nudd by adding a BERT algorithm.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable under Nudd in view of Youngho and in further view of Krans and CN 109509556 A (“Liu”).
In regard to claim 5, Nudd discloses
further comprising: displaying (column 10, lines 19-21, “the … output device is a display which may display … data output”)
Nudd does not disclose
parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text.
However, in the analogous art of medical knowledge mapping, Liu discloses
parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text (page 2, paragraph 9, “text data to the … word extraction processing to generate the … word set comprises: … marking the part of
speech of each vocabulary in the … data”).
Therefore, it would have been obvious to one of ordinary skill in the art
(PHOSITA) before the effective filing date to have added the missing claimed
limitations, the claim limitations, parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text, of Liu into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health; rather it would enhance Nudd by adding word extraction processing.
In regard to claim 12, Nudd discloses
wherein the control unit displays (column 10, lines 19-21, “the … output device is a display which may display … data output”)
Nudd does not disclose
parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text.
However, in the analogous art of medical knowledge mapping, Liu discloses
parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text (page 2, paragraph 9, “text data to the … word extraction processing to generate the … word set comprises: … marking the part of
speech of each vocabulary in the … data”).
Therefore, it would have been obvious to one of ordinary skill in the art
(PHOSITA) before the effective filing date to have added the missing claimed
limitations, the claim limitations, parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text, of Liu into systems and methods that facilitate conversations among seniors to improve senior health of Nudd would not change the basic principle operation of Nudd, nor would it render Nudd unsuitable for its intended purpose of facilitating conversations among seniors to improve senior health; rather it would enhance Nudd by adding word extraction processing.
Response to Remarks
Applicant's submissions filed July 29, 2026 have been fully considered but they are not persuasive. Claims 1, 5-8, and 12-15 remain pending in this application. Pertaining to rejections under 35 U.S.C. § 101, Applicant submits that “independent claim 1 recites several limitations that are clearly not practically performable in the human mind in view of the August 101 Memo” (See Amendment in Response to Final Office Action under 37 C.F.R. § 1.116, Remarks, Rejection under 35 U.S.C. § 101, page 9, paragraph 1). Examiner acknowledges Applicant’s remarks. The claim limitations define “collecting text data … removing obsolete text from the text data to produce extracted meaningful text … converting the extracted meaningful text to lowercase letters to produce a … text … performing … labeling of the … meaningful text to produce a labeled meaningful text … performing … word embedding for the labeled meaningful text to produce a word embedding result … checking a mental health status of a user … dividing a paragraph into a plurality of sentences … classifying at least one text that is included in the text data into a plurality of words … and converting slang” and are interpreted as the abstract idea itself, as it is practically capable of being performed in the human mind, through mental processes and certain methods of organizing human activity, including processing data to assess mental health status of a user. The recited data processing using an electronic apparatus, the BERT deep learning algorithm, a control unit, and a communication unit do not integrate the abstract idea into a practical application or significantly more because the claims do not specify any particular details that provide a technical improvement for analyzing data to check the mental health of a user. Therefore, claims 1, 5-8, and 12-15 are not eligible subject matter under 35 USC 101.
Pertaining to rejections under 35 U.S.C. § 101, Applicant submits that “any assumed judicial exception recited in amended independent claims 1 and 8 are integrated into a practical application” (See Amendment in Response to Final Office Action under 37 C.F.R. § 1.116, Remarks, Rejection under 35 U.S.C. § 101, page 10, paragraph 3), “the claims are not reciting generic computing components at a high level of generality to simply ‘apply’ an abstract concept” (See Amendment in Response to Final Office Action under 37 C.F.R. § 1.116, Remarks, Rejection under 35 U.S.C. § 101, page 13, paragraph 1), and “amended independent claim 1 clearly recites an integration of a practical application under the August 101 Memo and MPEP §§ 2106.04(d)(1) and 2106.05(a)” (See Amendment in Response to Final Office Action under 37 C.F.R. § 1.116, Remarks, Rejection under 35 U.S.C. § 101, page 14, paragraph 2). Examiner acknowledges Applicant’s remarks. The requirement to execute the claims using one service server, a social network service, an electronic apparatus, a BERT deep learning algorithm, a memory, a communication unit, and a control unit is recited at a high level of generality and amount to no more than instructions to apply the judicial exception and thus serve to limit the application of the abstract idea to a computerized environment. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)). Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)). Therefore, claims 1, 5-8, and 12-15 are not eligible subject matter under 35 USC 101.
Pertaining to the rejections under 35 U.S.C. § 103, Applicant submits that “Nudd and Youngho, alone or in combination fail to disclose or suggest, ‘converting slang and emoticons that are included in the text data into words having a same meaning’ and ‘wherein converting the slang and the emoticons comprises matching the slang and the emoticons with an emotion-based text corpus that is classified and stored in a memory of the electronic apparatus’ (emphasis added), as recited in amended independent claim 1” (See Amendment in Response to Final Office Action under 37 C.F.R. § 1.116, Remarks, Rejection under 35 U.S.C. § 103, page 19, paragraph 1). Examiner acknowledges Applicant’s remarks. Regarding claim 1, Nudd discloses and converting slang and emoticons that are included in the text data into words having a same meaning (column 24, lines 22-26, “the question responder … may remove … text…, apply the text to speech conversion (e.g., using a conversion software) to the text” Examiner notes that the conversion software can convert slang and emoticons into words.) and Krans discloses slang (page 14, Table 1, column 2, row 6, slang) and emoticons that are included in ([0055], “text analysis includes emoticon analysis”), wherein converting the slang (page 14, Table 1, column 2, row 6, slang) and the emoticons comprises matching the slang and the emoticons ([0055], “text analysis includes emoticon analysis”).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during examination. Under BRI, the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification. The BRI of conversion software (disclosed by Nudd) suggests that the conversion software of Nudd is capable of converting slang and emoticons to other words with similar meanings. In addition, the disclosure by Krans of slang and emoticon analysis indicates the inclusion of slang and emoticons in the application of Krans. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of independent claim 1 and dependent claims 6-7, as obvious by Nudd in view of Youngho and in further view of Krans, are maintained. Independent claim 8 cites limitations parallel to independent claim 1. Therefore, the rejections of independent claim 8 and dependent claims 13-15, as obvious by Nudd in view of Youngho and in further view of Krans, are maintained. Furthermore, the rejections of dependent claims 5 and 12 (which depend on claims 1 and 8, respectively), as obvious by Nudd in view of Youngho and in further view of Krans and Liu, are maintained.
Pertaining to the rejections under 35 U.S.C. § 103, Applicant submits that “neither Nudd nor Youngho provides any motivation to combine systems to achieve the claimed invention” (See Amendment in Response to Final Office Action under 37 C.F.R. § 1.116, Remarks, Rejection under 35 U.S.C. § 103, page 19, paragraph 2) and “Nudd, Youngho, and Liu, alone or in combination, fail to disclose or suggest the claimed invention of amended independent claims 1 and 8, respectively, to a person skilled in the art applying common sense” (See Amendment in Response to Final Office Action under 37 C.F.R. § 1.116, Remarks, Rejection under 35 U.S.C. § 103, page 20, paragraph 3) Examiner acknowledges Applicant’s remarks. Nudd discloses limitations that are in the field of conversation-facilitating health improvement systems; whereas, Youngho discloses limitations that are in the field of health information machine learning models. By definition, health information machine learning models can seamlessly be integrated into health improvement systems. Liu discloses limitations that are in the field of medical knowledge mapping and would also be effortlessly combined with Nudd and Youngho.
MPEP § 2143 discusses the establishment of obviousness by combining or modifying the disclosures of the prior art to produce the claimed invention. See In re Kahn, 441 F.3d 977, 986, 78 USPQ2d 1329, 1335 (Fed. Cir. 2006) (discussing rationale underlying the motivation-suggestion-teaching test as a guard against using hindsight in an obviousness analysis). A “motivation to combine may be found explicitly or implicitly in market forces; design incentives; the ‘interrelated teachings of multiple patents’; ‘any need or problem known in the field of endeavor at the time of invention and addressed by the patent’; and the background knowledge, creativity, and common sense of the person of ordinary skill.” Zup v. Nash Mfg., 896 F.3d 1365, 1371, 127 USPQ2d 1423, 1427 (Fed. Cir. 2018) (quoting Plantronics, Inc. v. Aliph, Inc., 724 F.3d 1343, 1354 [107 USPQ2d 1706] (Fed. Cir. 2013) (citing Perfect Web Techs., Inc. v. InfoUSA, Inc., 587 F.3d 1324, 1328 [92 USPQ2d 1849] (Fed. Cir. 2009) (quoting KSR, 550 U.S. at 418-21)). Applicant’s argument is not persuasive because the motivation to combine is both explicit and implicit in Nudd, which discloses a health improvement system, Youngho, which discloses a health information machine learning model, and Liu, which discloses medical knowledge mapping. Therefore, the rejections of independent claim 1 and dependent claims 6-7, as obvious by Nudd in view of Youngho and in further view of Krans, are maintained. Independent claim 8 cites limitations parallel to independent claim 1. Therefore, the rejections of independent claim 8 and dependent claims 13-15, as obvious by Nudd in view of Youngho and in further view of Krans, are maintained. Furthermore, the rejections of dependent claims 5 and 12 (which depend on claims 1 and 8, respectively), as obvious by Nudd in view of Youngho and in further view of Krans and Liu, are maintained.
Regarding claims 5 and 12, Nudd discloses further comprising: displaying (column 10, lines 19-21, “the … output device is a display which may display … data output”) and Liu discloses parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text (page 2, paragraph 9, “text data to the … word extraction processing to generate the … word set comprises: … marking the part of speech of each vocabulary in the … data”).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during examination. Under BRI, the words of a claim must be given their plain meaning unless such meaning is inconsistent with the specification. The BRI of an output display device is the most inclusive understanding of display devices allowing for a range of devices that could be used to display content. The BRI of word extraction processing suggests that parts of speech can be determined from meaningful text. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of independent claim 1 and dependent claims 6-7, as obvious by Nudd in view of Youngho and in further view of Krans, are maintained. Independent claim 8 cites limitations parallel to independent claim 1. Therefore, the rejections of independent claim 8 and dependent claims 13-15, as obvious by Nudd in view of Youngho and in further view of Krans, are maintained. Furthermore, the rejections of dependent claims 5 and 12 (which depend on claims 1 and 8, respectively), as obvious by Nudd in view of Youngho and in further view of Krans and Liu, are maintained.
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/LISA H ANTOINE/
Examiner, Art Unit 3715
/JAMES B HULL/Primary Examiner, Art Unit 3715
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