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 .
Response to Amendment in Response to Non-Final Office Action
under 37 C.F.R. § 1.111
This is a Final Office action in response to communications filed on February
17, 2026. Applicant amended claims 1, 5-8, 12, and 14-15 and cancelled claims 2-4 and 9-11. Examiner withdraws objections for various informalities to claims 1, 3-4, 7-8, 11, and 14-15. Claims 1, 5-8, and 12-15 remain pending in this 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, 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, which is content uploaded to at least one service server providing a social network service, by an electronic apparatus;
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;
performing, by the electronic apparatus, labeling of the preprocessed meaningful text to produce a labeled meaningful text;
performing, by the electronic apparatus, word embedding for the labeled meaningful text to produce a word embedding result;
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, wherein performing preprocessing comprises: 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.
These steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can collect text data, extract meaningful text from the collected data, label the meaningful text, and based on the meaningful text, check the mental health status of a person. Applicant of claimed invention discloses “there is a need of a convenient, highly efficient, and smart healthcare management platform that motivates, supports, and facilitates social interactions among seniors/users via networked computing devices” [column 1, lines 44-47].
Independent claim 8 describes nearly identical 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 towards mini-tasks (displaying parts of speech, labelling meaningful text, and performing word embedding, 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). As such, the Examiner concludes that claims 5-7 and 12-15 recite an abstract idea.
Step 2A – Prong Two: Do the claims recite additional elements that integrate the exception into a practical application of the exception? No
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 a computer processor (independent claims 1 and 8 and dependent claims 5-7 and 12-15) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of a computer processor (independent claims 1 and 8 and dependent claims 5-7 and 12-15) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. 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 merely to generally 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 generic computer, and/or generally 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 generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Dependent claims 5-7 and 12-15 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). 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.
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”).
In regards to claim 1, Nudd discloses the following limitations with the exception of the underlined limitations.
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” Examiner notes that the conversion software can convert text to lowercase letters.);
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.”), wherein performing preprocessing comprises: 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 (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.): 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” Examiner notes that data can include meaningless text.), 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 (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.).
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”)
Nudd and Youngho are considered analogous to the claimed invention because they are in the field of medical information and the detection of medical conditions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: collecting text data, which is content uploaded to at least one service server providing a social network service, by an electronic apparatus; 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; performing, by the electronic apparatus, labeling of the preprocessed meaningful text to produce a labeled meaningful text; performing, by the electronic apparatus, word embedding for the labeled meaningful text to produce a word embedding result; 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, 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, and converting slang and emoticons that are included in the text data into words having a same meaning, as disclosed by Nudd, 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, as disclosed by Youngho, to provide medical text data and sentence elements for a method and system that recognizes personal health information of medical data using a machine-learning model. One skilled in the art would understand and recognize the value of the addition of medical text data and sentence elements to improve a method and system that recognizes personal health information of medical data using a machine-learning model.
In regards 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 regards 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.
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”).
Nudd and Youngho are considered analogous to the claimed invention because they are in the field of medical information and the detection of medical conditions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: collecting text data, which is content uploaded to at least one service server providing a social network service, by an electronic apparatus; 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; performing, by the electronic apparatus, labeling of the preprocessed meaningful text to produce a labeled meaningful text; performing, by the electronic apparatus, word embedding for the labeled meaningful text to produce a word embedding result; 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, 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, and converting slang and emoticons that are included in the text data into words having a same meaning, as disclosed by Nudd, wherein the performing the word embedding is a performing the word embedding by applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm, as disclosed by Youngho, to provide a BERT model for a method and system for recognizing personal health information of medical data using a machine-learning model. One skilled in the art would understand and recognize the value of the addition of medical text data and sentence elements a BERT model to improve a method and system for recognizing personal health information of medical data using a machine-learning model.
In regards to claim 8, Nudd discloses
An apparatus comprising: 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” Examiner notes that the conversion software can convert text to lowercase letters.), 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”) wherein the control unit performs preprocessing by: 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 (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.), 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 (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.).
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”)
Nudd and Youngho are considered analogous to the claimed invention because they are in the field of medical information and the detection of medical conditions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for an apparatus comprising: 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 convert meaningful text extracted by removing obsolete text from the text data into lowercase letters to produce a preprocessed meaningful text, labelling the preprocessed meaningful text to produce a labeled meaningful text, and checking a mental health status of a user, who has uploaded the text data, by applying a word embedding result for the labeled meaningful text to a deep learning algorithm, 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, and wherein the control unit performs tokenizing by: removing meaningless text including pronouns, prepositions, conjunctions, articles and URLs from the text data, and converting slang and emoticons that are included in the text data into words having a same meaning, as disclosed by Nudd, 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, as disclosed by Youngho, to provide medical text data and sentence elements for a method and system that recognizes personal health information of medical data using a machine-learning model. One skilled in the art would understand and recognize the value of the addition of medical text data and sentence elements to improve a method and system that recognizes personal health information of medical data using a machine-learning model.
In regards 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 regards 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 regards 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.
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”).
Nudd and Youngho are considered analogous to the claimed invention because they are in the field of medical information and the detection of medical conditions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for an apparatus for checking mental health using contents, comprising: 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 convert meaningful text extracted by removing obsolete text from the text data into lowercase letters, labelling the preprocessed meaningful text, and checking the mental health status of a user, who has uploaded the text data, by applying a word embedding result for the labeled meaningful text to a deep learning algorithm, wherein the control unit removes obsolete text including hash tags, special characters, numbers and spaces from the text data, and tokenizes by classifying at least one text included in the text data into words, wherein the control unit removes meaningless text including pronouns, prepositions, conjunctions, articles and URLs from the text data, and converts slang and emoticons included in the text data into words having the same meaning to perform the tokenizing, wherein the control unit displays, wherein the control unit converts the meaningful text into lowercase letters, wherein the control unit generates a text corpus based on the meaningful text, performs labeling of the text corpus for each social network service, performs keyword-based labeling based on a circumplex model of emotions, and classifies the text corpus according to emotion based on the labeling, as disclosed by Nudd, wherein when the text data is a paragraph, the control unit divides the paragraph into a plurality of sentences, checks a headword or morpheme based on at least one text classified as the word, wherein the control unit performs the word embedding by applying the labeled meaningful text to a BERT algorithm, which is the deep learning algorithm, as disclosed by Youngho, to provide a BERT model for a method and system for recognizing personal health information of medical data using a machine-learning model.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable under Nudd in view of Youngho and CN 109509556 A (“Liu”).
In regards to claim 5, Nudd discloses the following limitation with the exception of the underlined limitation.
further comprising: displaying (column 10, lines 19-21, “the … output device is a display which may display … data output”) parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text.
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”).
Nudd and Liu are considered analogous to the claimed invention because they are in the field of medical information and the detection of medical conditions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method comprising: collecting text data, which is content uploaded to at least one service server providing a social network service, by an electronic apparatus; 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; performing, by the electronic apparatus, labeling of the preprocessed meaningful text to produce a labeled meaningful text; performing, by the electronic apparatus, word embedding for the labeled meaningful text to produce a word embedding result; 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, 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, and converting slang and emoticons that are included in the text data into words having a same meaning, as disclosed by Nudd, parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text, as disclosed by Liu, to provide text data and a word set for a medical knowledge map generating method, device, electronic device and computer readable medium. One skilled in the art would understand and recognize the value of the addition of text data and a word set to improve a medical knowledge map generating method, device, electronic device and computer readable medium.
In regards to claim 12, Nudd discloses the following limitation with the exception of the underlined limitation.
wherein the control unit displays (column 10, lines 19-21, “the … output device is a display which may display … data output”) parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text.
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”).
Nudd and Liu are considered analogous to the claimed invention because they are in the field of medical information and the detection of medical conditions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for an apparatus comprising: 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 convert meaningful text extracted by removing obsolete text from the text data into lowercase letters to produce a preprocessed meaningful text, labelling the preprocessed meaningful text to produce a labeled meaningful text, and checking a mental health status of a user, who has uploaded the text data, by applying a word embedding result for the labeled meaningful text to a deep learning algorithm, 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, and wherein the control unit performs tokenizing by: removing meaningless text including pronouns, prepositions, conjunctions, articles and URLs from the text data, and converting slang and emoticons that are included in the text data into words having a same meaning, as disclosed by Nudd, parts of speech including nouns, adjectives, adverbs, determiners and conjunctions in the meaningful text, as disclosed by Liu, to provide the part of speech for a medical knowledge map generating method, device, electronic device and computer readable medium.
Response to Remarks
Applicant's arguments filed February 17, 2026 have been fully considered but they are not persuasive. Claims 1, 5-8, and 12-15 remain pending in this application. Applicant acknowledges the claim for foreign priority. With respect to rejections under 35 U.S.C. § 101, Applicant argues that “amended independent claims 1 and 8 are patent eligible under Prong Two of the Revised Step 2A Guidance and the July 2024 Examples” (See Amendment in Response to Non-Final Office Action under 37 C.F.R. § 1.111, Remarks, Rejection under 35 U.S.C. § 101, page 11, paragraph 2) and “amended independent claims 1 and 8 are not directed to abstract ideas according to the Revised Step 2A Guidance, and the July 2024 Examples and also recite significantly more than an abstract idea according to Step 2B of the USPTO Subject Matter Eligibility Test” (See Amendment in Response to Non-Final Office Action under 37 C.F.R. § 1.111, Remarks, Rejection under 35 U.S.C. § 101, page 13, paragraph 1). Examiner acknowledges Applicant’s remarks. Claim 1 recites a method comprising: collecting text data, which is content uploaded to at least one service server providing a social network service, by an electronic apparatus;
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;
performing, by the electronic apparatus, labeling of the preprocessed meaningful text to produce a labeled meaningful text; performing, by the electronic apparatus, word embedding for the labeled meaningful text to produce a word embedding result;
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, wherein performing preprocessing comprises: 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. These steps amount to a form of mental process and organizing human activity (i.e., an abstract idea) because a human can collect text data, extract meaningful text from the collected data, label the meaningful text, and based on the meaningful text, check the mental health status of a person. Applicant of claimed invention discloses “there is a need of a convenient, highly efficient, and smart healthcare management platform that motivates, supports, and facilitates social interactions among seniors/users via networked computing devices” [column 1, lines 44-47].
Independent claim 8 describes nearly identical 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 towards mini-tasks (displaying parts of speech, labelling meaningful text, and performing word embedding, 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). As such, the Examiner concludes that claims 5-7 and 12-15 recite an abstract idea.
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 a computer processor (independent claims 1 and 8 and dependent claims 5-7 and 12-15) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer.
Similarly, the limitations of a computer processor (independent claims 1 and 8 and dependent claims 5-7 and 12-15) are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. 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 merely to generally 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.
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 generic computer, and/or generally 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 generic computer and/or mere instructions to implement the abstract idea on a generic computer or/and append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. Dependent claims 5-7 and 12-15 fail to include any additional elements. In other words, each of the limitations/elements recited in respective independent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in each respective claim). 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.
With respect to rejections under 35 U.S.C. § 102 (a)(1), Applicant argues that “Nudd fails to cure what Youngho lacks with respect to amended independent claim 1. For similar reasons, Nudd and Youngho also fail to disclose or render obvious reach and every limitation of amended independent claim 1” (See Amendment in Response to Non-Final Office Action under 37 C.F.R. § 1.111, Remarks, Rejection under 35 U.S.C. § 102(a)(1), page 18, paragraph 1) and “as the Nudd and Youngho references fail to disclose each and every limitation as required by amended independent claims 1 and 8, neither an anticipation rejection nor an obviousness rejection can be supported” (See Amendment in Response to Non-Final Office Action under 37 C.F.R. § 1.111, Remarks, Rejection under 35 U.S.C. § 102(a)(1), page 18, paragraph 2). Examiner acknowledges Applicant’s remarks. Regarding 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” Examiner notes that the conversion software can convert text to lowercase letters.); 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” Examiner notes that hash tags, special characters, numbers, and spaces are text characters or can be treated as text characters.): 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” Examiner notes that data can include meaningless text.), 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 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.) and 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”).
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, and it is improper to import claim limitations from the specification into the claim. 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, are maintained. Independent claim 8 is almost identical to independent claim 1. Therefore, the rejections of independent claim 8 and dependent claims 13-15, as obvious by Nudd in view of Youngho, are maintained.
With respect to rejections under 35 U.S.C. § 103, Applicant argues that “Nudd and Youngho, alone or in combination, fail to disclose or suggest each and every claim limitation recited in amended independent claims 1 and 8” (See Amendment in Response to Non-Final Office Action under 37 C.F.R. § 1.111, Remarks, Rejection under 35 U.S.C. § 103, page 19, paragraph 2). Examiner acknowledges Applicant’s remarks. Regarding 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” Examiner notes that the conversion software can convert text to lowercase letters.); 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” Examiner notes that hash tags, special characters, numbers, and spaces are text characters or can be treated as text characters.): 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” Examiner notes that data can include meaningless text.), 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 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.) and 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”).
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, and it is improper to import claim limitations from the specification into the claim. 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, are maintained. Independent claim 8 is almost identical to independent claim 1. Therefore, the rejections of independent claim 8 and dependent claims 13-15, as obvious by Nudd in view of Youngho, are maintained.
With respect to rejections under 35 U.S.C. § 103, Applicant argues that “Nudd fails to disclose or suggest each and every claim limitation recited in amended independent claims 1 and 8, respectively. For similar reasons as discussed with respect to amended independent claims 1 and 8, Liu also fails to disclose or suggest each and every claim limitation recited in amended independent claims 1 and 8” (See Amendment in Response to Non-Final Office Action under 37 C.F.R. § 1.111, Remarks, Rejection under 35 U.S.C. § 103, page 19, paragraph 4). Examiner acknowledges Applicant’s remarks. Regarding 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” Examiner notes that the conversion software can convert text to lowercase letters.); 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” Examiner notes that hash tags, special characters, numbers, and spaces are text characters or can be treated as text characters.): 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” Examiner notes that data can include meaningless text.), 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 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.) and 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”). Regarding claim 5, 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, and it is improper to import claim limitations from the specification into the claim. 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, are maintained. Independent claim 8 is almost identical to independent claim 1. Therefore, the rejections of independent claim 8 and dependent claims 13-15, as obvious by Nudd in view of Youngho, are maintained. Furthermore, the rejections of claim 5 (which is dependent on claim 1) and claim 12 (which is almost identical to claim 5 and dependent on claim 8), as obvious by Nudd in view of Youngho and Liu, are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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LISA H ANTOINE
Examiner
Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715