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 Arguments
Applicant's arguments filed 4/6/2026 have been fully considered but they are not persuasive. As per applicants arguments toward the means plus function format, examiner notes that the claim interpretation paragraphs are presented, so as to clearly set the claim scope boundaries. The form paragraph will be maintained in the office action. Furthermore, examiner notes that the claimed ‘units’, with descriptors such as ‘text data acquisition’, ‘text data adverb extraction’, ‘unregistered adverb degree value assignment’ are placeholders in conjunction with the word unit, with the function described afterward. As to applicants arguments on pp9-10 of the response, these arguments are toward the newly amended claim language; examiner notes the further recitations to the Kim reference addressing these claim limitations. On pp 10 of the response, the arguments toward claims 9-18, are newly presented claim limitations – examiner notes the introduction of the Yoshikawa reference to address some of these claims, and further recitation to the Kim reference for the other newly presented claim limitations.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a text data acquisition unit”, “text set storage unit”, “a text data adverb extraction unit”, “adverb set storage unit”, “an unregistered adverb degree value assignment unit”, in claim 1;
“a distributed representation model update unit” in claim 4;
“a text data emotion-degree calculation unit”, “emotion-degree-value set storage unit” in claim 7.
The above cited sections of the claims, use a generic placeholder, various version of “unit”, for performing the claimed function (first prong of the 3-prong analysis (see MPEP 2181, I); the placeholder “unit” is followed by functional language (second prong of the 3-prong analysis (see MPEP2181 I); the generic placeholder is not modified by sufficient structure, material, or acts performing the claimed function (third prong of the 3-prong analysis (see MPEP 2181 I).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8, 11-14, 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kim et al (20160210279).
As per claim 1, Kim et al (20160210279) teaches an adverb dictionary update apparatus that updates an adverb dictionary used for emotion degree estimation, the apparatus:
comprising: a text data acquisition unit that acquires text data including an object of the emotion degree estimation and stores the text data in a text set storage unit (as acquiring the text via an utterance of a user – paragraph 0064, to perform an emotional analysis – see para 0057);
a text data adverb extraction unit that extracts adverbs related to each piece of text data from the text set storage unit and stores the extracted adverbs in an adverb set storage unit (as reading the text converted from the user utterance – para 0054, the information is sent to the server, and hence stored – fig. 2 and para 0058; see also, para 0170-0171 by using machine learning and using previously store utterances, Kim et al (20160210279) teaches the storage and retrieval of the extracted features; and the features are adverbs – see paragraph 179-180 collecting and storing the adverbs in a dictionary);
and an unregistered adverb degree value assignment unit that reads a list of the extracted adverbs from the adverb set storage unit, that reads, from an adverb dictionary storage unit (as comparison and recommending, when there is a match or not – see para 0182, 0183),
an adverb dictionary including, as a table, registered adverbs and degree values, that updates the adverb dictionary by assigning a degree value to an unregistered adverb among the extracted adverbs while referring to the degree values of the registered adverbs (see, the analysis of the utterances determines the strength of the emotion – para 0019; the value of the strength of emotion by adding up emotion-adverb correlation values with a weight – para 0022; see further in para 0189, 0192), by calculating a degree of similarity between the unregistered adverb and the registered adverbs using a distributed representation and assigning, to the unregistered adverb, a degree value based on the degree of similarity (as, comparing the entered adverb with a stored adverb, para 0182, and if it matches, the strength of the emotion is determined as strong/normal/weak – para 0183), and
that stores the updated adverb dictionary in the adverb dictionary recording medium (as updating the dictionary – para 0179-0180 – constructing an emotion degree adverb dictionary within a dictionary, and then adding adverbs, in para 0180).
As per claim 2, Kim et al (20160210279) the adverb dictionary update apparatus according to claim 1, wherein the unregistered adverb degree value assignment unit calculates a degree of similarity between the unregistered adverb and all the registered adverbs (as, comparing the entered adverb with a stored adverb, para 0182),
and assigns, to the degree value of the unregistered adverb, a degree value of the registered adverb having a highest degree of similarity (and if it matches, the strength of the emotion is determined as strong/normal/weak – para 0183) .
As per claim 3, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 2,
wherein the unregistered adverb degree value assignment unit compares a degree of similarity between the unregistered adverb and the registered adverb having the highest degree of similarity (as comparison and recommending, when there is a match or not – see para 0182, 0183; also, the analysis of the utterances determines the strength of the emotion – para 0019; the value of the strength of emotion by adding up emotion-adverb correlation values with a weight – para 0022) with a predetermined threshold (as, comparing/measuring the strength of emotion value to the frequency count of the adverb in the dictionary – end of para 0022),
when the degree of similarity is equal to or greater than the predetermined threshold, the unregistered adverb degree value assignment unit assigns, to the degree value of the unregistered adverb, the degree value of the registered adverb having the highest degree of similarity, and when the degree of similarity is less than the threshold value, the unregistered adverb degree value assignment unit assigns, to the degree value of the unregistered adverb, a predetermined reference value (as, discussed above, the appearance frequency is the threshold to determine, the degree of emotion, ie –“strong”, “normal”, “weak”…when the unregistered adverb appears, in the dictionary, then it is assigned a score, as to how often that adverb appears; when the unregistered adverb does NOT appear (ie, less than the threshold value of ‘not appearing’), then the adverb is assigned “weak” status – see para 0180 – 0184; see further details, in para 0187-0194, explaining the construction of the emotion dependence strength of the adverb correlation dictionary).
As per claim 4, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 1, further comprising:
a distributed representation model that realizes a distributed representation of an adverb (as, the adverb is represented by emotion strength, frequency of occurrence, and other attributes – e, g, colloquial style, morpheme analysis, etc. – fig. 12, see also figure 14, for other features); and a distributed representation model update unit that updates the distributed representation model (as, updating the models, of the features, as they change – para 0058).
As per claim 5, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 1, further comprising:
a correction interface with which adverbs and the degree values in the adverb dictionary are corrected by a user's operation (as allowing the user to change the sticker groups when provided to the user on the terminal – para 0020, -- the stickers are derived from the utterance interpretation, including the “degree of emotion” tied to the adverbs -- , wherein the correction interface includes:
an adverb display area in which an object adverb is displayed, a degree value input area to which a degree value corresponding to the object adverb is input, and a determination button (as, showing the recommended stickers as well as graphic data – para 0208, see Figure 10, showing the sticker ID with “sticker: sadness four” and “sticker: consolation two” .
As per claim 6, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 5, wherein the correction interface further includes
an area in which a plurality of adverbs and a plurality of degree values are disposed in accordance with the plurality of degree values (as, shown in figure 4, determining the ranking of the relation/preference, which is then displayed – see Fig. 10 for the detailed information displayed).
As per claim 7, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 1, further comprising
a text data emotion-degree calculation unit that reads each piece of text data from the text set storage unit (as reading the text converted from the user utterance – para 0054, the information is sent to the server, and hence stored – fig. 2 and para 0058),
reads the extracted adverbs from the adverb set storage unit, reads the adverb dictionary from the adverb dictionary storage unit, calculates a degree value of emotion of the each piece of text data (as reading the extracted adverbs from the utterance, reading the adverb dictionary, and comparing – starting in para 0089, the utterances are collected, the surface factor from the utterance are extracted – para 0091, analyzing the dialogue between the 2 users to determine intention/emotion/relation – para 0095, and the emotion analysis uses previous utterances, and analyzing using machine learning – para 0170-0171 – examiner notes, by using machine learning and using previously stored utterances, Kim et al (20160210279) teaches the storage, retrieval, and comparison of the stored features; the stored features include type and strength of emotion – para 0173, 0174),
and stores the each piece of text data, the extracted adverbs, and the degree values of emotion in an emotion-degree-value set storage unit (as storing/updating the emotion features .
Claim 8 is a method claim whose steps are performed throughout the apparatus claims 1-7 above and as such, claim 8 is similar in scope and content to these claim features found throughout claims 1-7 above; therefore, claim 8 is rejected under similar rationale as presented against claims 1-7 above.
As per claim 11, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 1, wherein the unregistered adverb degree value assignment unit initializes a degree value of the unregistered adverb to a reference value before calculating the degree of similarity (as starting with a reference value comparison, the stored value of accumulated scores – para 0020, 0022).
As per claim 12, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 11, wherein the reference value is a value indicating a medium level of the degree value (as associating a medium level – para 0129).
As per claim 13, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 1, wherein the adverb dictionary further includes a history of check item indicating whether a user has checked the degree value assigned to each adverb (as, using stickers to the adverb as marked by the user – para 0207, and tracking the number of times until a ‘critical number of times’ is met – para 0207).
As per claim 14, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 13, wherein the history of check item is set to a first state indicating that the user has not yet checked the degree value when the degree value is automatically assigned to the unregistered adverb (as tracking if the sticker is used, which affects the score – para 0051-0053; examiner notes, that a score of “0” would correspond to a recommended sticker not used, and hence the user has not approved of the recommended results).
As per claim 16, Yoshikawa (20190266182) teaches the adverb dictionary update apparatus according to claim 1, wherein the text data acquisition unit transmits a query to a text medium provided outside the adverb dictionary update apparatus and acquires the text data corresponding to the query from the text medium (as, processing the word/adverb data taken from user devices – para 0020, which is outside of the processing itself – figure 2).
As per claim 17, Yoshikawa (20190266182) teaches the adverb dictionary update apparatus according to claim 1, wherein the text data includes social networking service data or business data (as using the scoring on an interaction between two users – para 0018 – social network service).
As per claim 18, Yoshikawa (20190266182) teaches the adverb dictionary update apparatus according to claim 1, further comprising an emotion determination unit that determines an emotion of each piece of text data and stores a combination of each piece of text data and an obtained emotion determination result in a text data emotion determination result recoding medium (as storing the emotion score/evaluation with the text data – para 0148-0150).
Claim Rejections - 35 USC § 103
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.
Claim(s) 9-10, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al (20160210279) in view of Yoshikawa (20190266182).
As per claims 9-10, Kim et al (20160210279) teaches a closeness measurement between adverbs (as mapped above in claims 1-8), but does not detail the specifics of the types of measurement; Yoshikawa (20190266182) teaches a feature word generator, operating on adverbs/nouns/adjectives/verbs (para 0109), that attempts to find a similar word to the based/input word, determining a similarity by comparing feature vectors of the 2 words, and as an example, using a cosine similarity (para 0106). Therefore, it would have been obvious to one of ordinary skill in the art of word similarity deduction to further detail the process disclosed in Kim et al (20160210279) with a similarity calculation comparing feature vectors, with a cosine similarity function, as disclosed by Yoshikawa (20190266182), because it would advantageously improve the classification accuracy of the feature words (see, Yoshikawa (20190266182), para 0192).
In further detail, as per claim 9, the combination of Kim et al (20160210279) in view of Yoshikawa (20190266182) teaches the adverb dictionary update apparatus according to claim 1, wherein the unregistered adverb degree value assignment unit calculates the degree of similarity using cosine similarity between distributed representation vectors of the unregistered adverb and the registered adverbs (see Yoshikawa (20190266182), para 0106).
As per claim 10, the combination of Kim et al (20160210279) in view of Yoshikawa (20190266182) teaches the adverb dictionary update apparatus according to claim 1, wherein the distributed representation converts each adverb into a vector having a plurality of dimensions, and the degree of similarity is calculated based on a distance between the vectors (as calculating a similarity between the feature vectors of the words – see Yoshikawa (20190266182), para 0106).
As per claim 15, Kim et al (20160210279) teaches the adverb dictionary update apparatus according to claim 1 (as applied to claim 1 above), further comprising a user interface screen (see Kim, as mapped above, displaying the text and recommended suggestions, including stickers for particular recommendations); however, Kim et al (20160210279) does not explicitly teach displaying value/degree values (although Kim’s system tracks how many times a sticker is used); Yoshikawa (20190266182) teaches displaying the information tied to the word feature mapping (see para 0033, displaying to the user), and displaying the results – para 0157, 0158. Therefore, it would have been obvious to one of ordinary skill in the art of word recognition to improve the displayed information as taught by Kim et al (20160210279) with further displaying matching information and status, as taught by Yoshikawa (20190266182) because it would advantageously provide additional feedback to assist in the desired result of the user (para 0077, 0108).
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Please see related art listed on the PTO-892 form.
Furthermore, the following references were found that teach some features in applicants specification and/or claims:
Moitra et al (20140181125) teaches displaying the changing sentiment influence.
Lu et al (20240143922) teaches a part of speech tagger to determine types of adverbs – para 0021 (and hence, a degree/level of distinguishment between adverbs)
Wang et al (20170308523) teaches determining a degree of emphasis – para 0044, using adverbs (see tables between para 0047, 0048), using a SentiMo analyzer – para 0052).
Chang et al (20150286710) perform sentimental analysis of review showing a degree of negativity/positivity – para 0066 ,based on adverbs – para 0069-0070.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 06/13/2026
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