Notice of Pre-AIA or AIA Status
1. 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
2. Applicant's arguments filed 04/29/2026 have been fully considered but they are not persuasive.
First Applicant argues the rejection does not identify any disclosure in McCourt of outputting “the extracted plurality of topics" that were extracted by applying an LDA topic modeling algorithm to
extracted words, as recited in claim 1. The Office cites McCourt paragraphs [0094]-[0096] for "receiving user/administrator input that names/labels topics," but claim 1 is not directed merely to naming or labeling topics. Claim 1 requires outputting "the extracted plurality of topics through a display device." The rejection does not show that McCourt discloses outputting the plurality of topics extracted from the extracted words in the claimed LDA-based processing sequence.
Examiner Response:
The Examiner respectfully disagrees. Applicant’s argument attacks McCourt individually rather than addressing the combined rejection. McCourt is not relied upon to teach the claimed use of Latent Dirichlet Algorithm (LDA) to extract the plurality of topics from the extracted words. Rather, as set forth in the rejection, Mehta is relied upon for applying LDA topic modeling to extracted words to extract topics. McCourt is relied upon to show that once topics have been generated by a topic model, those topics and their corresponding words may be provided to a client device and displayed through a graphical user interface. In particular McCourt teaches identifying words corresponding to each topic and using those identified words to generate a word bubble display in which each bubble corresponds to a different topic and the words inside the bubble correspond to that topic. See McCourt ¶[0094]-[0095]. McCourt further teaches providing a graphical user interface with topic information and options for interacting with the displayed topics (see McCourt ¶[0096]). Therefore, McCourt is not cited merely for naming or labeling topics. McCourt teaches outputting topic information, including displayed topic groupings and corresponding words through a display device. Accordingly, when McCourt’s topic display teachings are applied to the LDA based topic extraction taught by Mehta in the personality analysis system of MacKay and Mehta, the resulting combination outputs the extracted plurality of topics through a display device as claimed. Applicant’s argument is therefore not persuasive because is require McCourt alone to disclose the entire LDA based processing sequence, whereas the rejection relies upon the combined teachings of the reference
Applicant further argues that the rejection does not identify any disclosure in Preuss of receiving "from an administrator, selection information indicating which of the plurality of topics are associated with the personality," and that Preuss merely discloses selecting questions to customize the personality traits being sought.
Examiner Response:
The Examiner respectfully disagrees. Applicant’s argument again attacks Preuss individually rather than addressing the combined rejection. Preuss is not relied upon to teach the plurality of topics or the LDA based extraction of topics. Mehta is relied upon for LDA based topic extraction, McCourt is relied upon for outputting the extracted plurality of topics through a display and excluding words corresponding to topics. Preuss is relied upon for teaching the administrator/personality based selection information. The claim recites “receive, from an administrator, selection information indicating which of the plurality of topics are associated with the personality.” Preuss discloses that an interviewer may select and de select questions to customize the personality traits they are looking for in candidates (see Preuss ¶54). Therefore, Preuss teaches receiving selection information from an administrator indicating what information is associated with desired personality traits. Applicant states that “selection of questions is not the same as selection information indicating which of the plurality of topics are associated with the personality.” The Examiner disagrees in view of the combination. MacKay and Mehta disclose using natural language processing and deep learning techniques for detecting emotional state and personality related responses of users. Mehta further discloses LDA topic modeling for extracting topics from words. McCourt discloses outputting the extracted plurality of topics through a display device and removing words corresponding to topics when updating the topic model output. Preuss supplies the missing teaching that the selection information is received from an administrator and is based on desired personality traits. Therefore, when Preuss’ administrator based selection information is incorporated into the topic display and topic removal system of McCourt, as applied to the personality analysis system of MacKay and Mehta, the resulting combination receives, from an administrator, selection information indicating which of the plurality of topics are associated with the personality. It would have been obvious to one of ordinary skill in the art to incorporate Preuss’ ability to select information based on desired personality traits into MacKay, Mehta and McCourt because Preuss teaches that customized interview question mappings improve processing efficiency of candidate score calculations in ¶[0036]. Applying Preuss’ personality based administrator selection to McCourt’s displayed topics would have predictably allowed the administrator to indicate which topics are associated with desired personality traits and McCourt’s topic output updating, removal technique would then exclude words corresponding to topics not indicated by that selection information.
Applicant further argues “Claim 1 requires excluding a word included in a topic that is not indicated by the selection information. The rejection does not identify any disclosure in the cited references of excluding words from a topic based on selection information that indicates which of the plurality of topics are associated with the personality.”
Examiner Response:
The Examiner respectfully disagrees. McCourt is relied upon for teaching excluding words corresponding to a topic. Preuss is relied upon for teaching that the selection information is based on desired chose personality traits. The claim recites exclude a word included in a topic that is not indicated by the selection information. McCourt discloses removing topic identifiers and removing words corresponding to a topic when updating the topic model output (see McCourt ¶[0120]-[0121]). Therefore, McCourt teaches the portion of the limitation requiring excluding a word included in a topic. Applicant argues that McCourt does not disclose excluding words based on selection information indicating which topics are associated with the personality. However, the rejection does not rely on McCourt alone for the personality based selection information. As discussed above, Preuss discloses that an interviewer may select and de select questions to customize the personality traits they are looking for. Therefore, Preuss discloses the personality based selection information, while McCourt teaches removal of words corresponding to topics when the topic model output is updated. Therefore, in the combined system, the administrator selection information of Preuss indicates which topics are associated with the desired personality traits and McCourt’s topic output updating technique excludes words corresponding to topics that are not indicated by that selection information. Applicant’s argument is not persuasive because it requires a single reference to disclose both the personality based selection information and the topic word exclusion, whereas the rejection relies on the combined teachings of Preuss and McCourt.
Applicant further argues the rejection does not establish the claimed relationship among the recited limitations.
Examiner Response:
The Examiner respectfully disagrees. Applicant’s argument again attacks the references individually rather than the combination as a while. As explained above, Mehta is relied upon for applying LDA topic modeling to extracted words to extract topics, McCourt is relied upon for outputting the extracted topics and removing words corresponding to topics and Preuss is relied upon for the administrator selection information. When the teachings are combined the resulting system applies LDA to extract topics, outputs the extracted topics, receives administrator selection information indicating which topics are associated with the personality and excludes words corresponding to topics not indicated by the selection information. Therefore, the claimed relationship is established by the combined teachings of the references.
Applicant further argues that the stated rationale for combination does not cure the alleged deficiencies
Examiner Response:
The Examiner respectfully disagrees. The rationale for combination is not merely a general statement that topic modeling is useful. Mehta teaches using topic modeling in personality detection because language use and word frequency correlate with personality. McCourt teaches that topic modeling is useful for grouping written and verbal communications for review analysis or intervention. Preuss further teaches that customized interview question mappings improve processing efficiency of candidate score calculations. Therefore, one of ordinary skill in the art would have been motivated to combine the teachings to improve the relevance and efficiency of personality related topic selection by allowing administrator selection of personality associated topics and excluding words from topics not selected.
Applicant further argues that MacKay does not disclose a directive sentence that requests a response to a plurality of adjectives that are thought to describe the user well and a directive sentence that requests a response to a strength and a weakness of the personality of the user.
Examiner Response:
The Examiner respectfully disagrees. MacKay discloses the same concept as the claimed directive sentences. MacKay ¶[0111] discloses that the input may be “prompted” and it may include interview data or survey/questionnaire responses. MacKay ¶[0251] further discloses that a user may input, such as by survey or questionnaire, information about personality traits, values, cognitive traits, emotional traits, values and decision points that a candidate would exhibit. Therefore, MacKay teaches prompting a user to provide responses regarding personality descriptors, which corresponds to requesting adjectives or descriptive traits that describe the user.
MacKay ¶[0095] also discloses positive and negative personality related dimensions. For example, MacKay discloses dimensions including positive, negative traits, beliefs, opinions, perspectives, motivations, biases, states, emotional approaches, manners, reactions and interpersonal dynamics. ¶[0103] of MacKay further discloses that a dimension such as ego may be positive or negative and that another dimension, curiosity, may receive a positive rating. These positive and negative personality related dimensions correspond to strengths and weaknesses of the personality. Applicant’s argument is not persuasive because it requires MacKay to use the exact words “adjectives,” “strength” and “weakness,” while MacKay teaches prompted survey/questionnaire response directed to personality descriptors and positive/negative personality characteristics.
Applicant further argues that Mehta does not disclose selecting some words from among the extracted words based on correlation between each of the plurality of topics and personality traits.
Examiner Response:
The Examiner respectfully disagrees. Mehta is relied upon for teaching LDA topic modeling in the context of personality detection from text. Mehta discloses that reliable correlations exist between writing style, including frequency of word use and personality. Mehta further discloses open vocabulary personality analysis in which single words, multi-word phrases and semantically related word clusters/topics are extracted from text, with LDA typically used to form clusters of semantically related words. Altogether this means Mehta teaches that words/topics extracted from text are evaluated based on their relationship and correlations to personality traits. Therefore, selecting words from extracted word based on correlation between topics and personality traits would have been obvious in view of Mehta’s teaching that word use and topic based language features are used to detect personality.
Applicant further argues that Mehta’s entropy based weights assigned to features extracted from text do not disclose the claimed stored word list that includes a plurality of weighted words nor does it identify disclosure of excluding a word included in a topic having the correlation output of that trained model less than a predetermined threshold..
Examiner Response:
The Examiner respectfully disagrees. Applicant’s argument characterizes Mehta too narrowly because it does not use the exact phrase “stored word list.” Mehta discloses the same concept because Mehta teaches that reliable correlations exist between writing styles including frequency of word us and personality. Mehta further teaches personality detection using psychologically relevant word buckets, such as affective process words and social process words, where the frequency of words in those buckets is counted and used to predict personality. Mehta also teaches assigning weights to extracted linguistic and emotional features using information entropy theory. Therefore, Mehta teaches using word based textual features, including psychologically relevant words, with assigned weights for personality detection. A stored word lists that includes weighted words is the routine implementation of Mehta’s weighted word based set because the system must store the word and their weights in order to apply them to later text for personality prediction.
Mehta further teaches threshold based personality mapping because only feature values above average are mapped to personality traits. Therefore, Mehta teaches comparing feature/personality relevance to a threshold. McCourt further teaches removing words corresponding to a topic when updating and rerunning the topic model output. Therefore, it would have been obvious to exclude words included in topics whose personality correlation is at or below a predetermined threshold, because Mehta teaches retaining/mapping personality relevant features above a threshold and McCourt teaches removing words corresponding to topics when updating the topic model output.
Applicant further argues that Leonardi and MacKay do not disclose the claimed output of prediction points for top five adaptive factors and top give maladaptive factors.
Examiner Response:
Leonardi is relied upon for teaching a transformer based pretrained language model that outputs numerical personality prediction scores. MacKay is relied upon for teaching personality/emotion-cognition factors that are assigned values or ratings. In particular, MacKay ¶[0103] discloses that each linguistic rule has one or more dimensions and a value for each dimension. MacKay further discloses that the dimensions include personality traits, beliefs, opinions, perspectives, motivations, biases, states, emotional approaches, manners, reactions and interpersonal dynamics as well as positive and negative dimensions and that curiosity receives a +1 rating. Therefore, MacKay discloses scoring personality related factors as positive/negative dimensions. The claimed adaptive factors correspond to positive personality and emotion cognition dimensions and the maladaptive factors correspond to the negative ones. The claimed predictions points correspond to the numerical values or ratings assigned to those dimensions. The fact that MacKay does not use the exact terms adaptive and maladaptive does not distinguish the claim because MacKay discloses the same positive and negative scored personality dimension concept. Further, MacKay does not merely disclose isolated traits. MacKay discloses generating a profile with personality traits, expression traits, cognitive traits, emotional traits, values and biases and further discloses that the rules include dimensions with assigned values and ratings. MacKay lists far more than five personality and emotion cognition dimensions, including positive and negative dimensions. Therefore, when MacKay outputs the score personality profile, the system outputs at least five positive adaptive scored factors and at least five negative maladaptive scored factors. Selecting the top five of each is simply the presentation of the highest valued scored dimensions already generated by MacKay. Therefore, the fact that MacKay does not use the exact words “top five adaptive” and “top five maladaptive” does not distinguish the claim, because MacKay discloses the same personality factor output.
Claim Rejections - 35 USC § 103
3. 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:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
4. Claims 1-6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over MacKay (US 2022/0245354) in view of Mehta et al “Recent Trends in deep learning based personality detection” herein Mehta, further in view of McCourt (US 2022/0383863), further in view of Preuss (US 2021/0233030) and further in view of Leonardi et al. “Multilingual Transformer-Based Personality Traits Estimation” herein Leonardi.
Regarding Claim 1:
MacKay discloses a personality assessment device comprising (Figs. 2 and 3):
at least one processor comprising processing circuitry (MacKay: p[0057]-[0059] discloses processors)
and a memory comprising one or more storage media storing instructions, which when executed individually or collectively by the at least one processor (MacKay: p[0057]-[0059] discloses memory), causes the device to:
transmit, to a terminal, a message that includes a plurality of interview questions and at least one directive sentence and to receive, from the terminal, a first response to each of the plurality of interview questions and a second response to the at least one directive sentence (MacKay: p[0006] creates a personality quiz including a plurality of questions; p[0036] discloses an instruction based question format i.e., directive; p[0035] discloses that the system has an unending number of questions for the user to answer, the user is essentially directed to interact with these prompts);
automatically tokenize the first response and the second response into words using natural language processing (MacKay: p[0056] tokenizes words using NLP and pre-processing steps), extract words having predetermined parts of speech including adjectives, verbs, and nouns, (MacKay: p[0052], p[0086] and p[0010] altogether disclose the extraction of parts of speech including verbs, nouns, adjectives and adverbs) and select at least some of the extracted words based on a computed correlation with personality traits using a trained correlation model stored in the memory (MacKay: p[0161] explicitly mentions selecting individual words, these words are matched to emotion/cognition/sentiment-based categories (i.e., this data can be used to infer personality traits) this is done by the emotion-cognitive sensor; p[0067] discloses that machine learning models can be trained on the rules themselves, the trained models can locate similar co-occurrence vectors, and their output directly effects the emotion-cognitive sensor additionally this emotion-cognitive sensor can update/generate new rules)
;
MacKay does not explicitly disclose:
apply Latent Dirichlet Allocation (LDA) topic modeling algorithm to the extracted words to extract a plurality of topics.
However, Mehta discloses apply Latent Dirichlet Allocation (LDA) topic modeling algorithm to the extracted words to extract a plurality of topics (Mehta: teaches personality detection from language use, extracting a collection of language features from text and producing topic clusters of semantically related words using Latent Dirichlet Allocation (LDA). The result of the LDA step is multiple topic clusters, each cluster being of semantically related words for personality detection).
MacKay and Mehta are combinable because they are both from the same field of endeavor, deep learning based personality and emotion-cognition detection, e.g., both disclose systems for using multi-layered neural networks for detecting the emotional state and responses of users. It would have been obvious to one of ordinary skill in the art to incorporate LDA-based topic modeling of Mehta into the emotion-cognitive natural language processing of MacKay to enhance personality prediction accuracy by extracting semantically meaningful groups of words (topics) and selecting only those with demonstrated personality relevance. The motivation for doing so would be “Reliable correlations of writing style (e.g., frequency of word use) with personality were found by some of the earliest works” as disclosed in Section 4.1 of Mehta and LDA is a method of finding reliable correlations between words and writing styles to detect personality.
MacKay and Mehta do not explicitly disclose output the extracted plurality of topics through a display device, receive, from an administrator, selection, information indicating which of the plurality of topics are associated with the personality, and exclude a word included in a topic that is not indicated by the selection information;
However, McCourt discloses:
output the extracted plurality of topics through a display device (McCourt: ¶[0094]-[0096] discloses receiving input that names/labels topics), …, and exclude a word included in a topic (McCourt: ¶[0120]-[0121] discloses that topic identifiers are removed/updated and removes words corresponding to a topic when updating the topic model output);
MacKay and Mehta in view of McCourt are combinable because they are all from pertinent fields of endeavor. MacKay and Mehta disclose systems for using multi-layered neural networks for detecting the emotional state and responses of users, and McCourt discloses a topic-display and topic-output updating/removal technique. It would have been obvious to one of ordinary skill in the art to incorporate this feature into MacKay and Mehta’s personality trait/topic clustering techniques. The motivation for doing so is “Topic modeling in written and verbal communications can be extremely useful for grouping a large number of communications for review, analysis, or intervention.” as disclosed in ¶[0004] of McCourt.
MacKay, Mehta and McCourt do not explicitly disclose receive, from an administrator, selection, information indicating which of the plurality of topics are associated with the personality.
However, Preuss discloses receive, from an administrator, selection, information indicating which of the plurality of topics are associated with the personality (Preuss: ¶[0054] discloses an interviewer (administrator) may select questions to customize the personality traits they are looking for).
MacKay, Mehta and McCourt in view of Preuss are combinable because they all come from pertinent fields of endeavor, MacKay and Mehta disclose methods for disclose systems for using multi-layered neural networks for detecting the emotional state and responses of users, and McCourt discloses a topic-display and topic-output updating/removal technique. McCourt merely does not disclose that the topics chosen are related to personality. Preuss discloses administrator/interviewer selection of questions based on the personality traits being sought. McKay and Mehta provide the substantive personality-related analytics, McCourt provides topic display and topic output updating/removal mechanics and Preuss provides the missing administrator selection information being based on personality traits. It would have been obvious to one of ordinary skill in the art to incorporate Preuss’ personality based selection into the combined system. The motivation for doing so is “The customized data structure of the interview question mappings improves processing efficiency of the system candidate score calculations” as disclosed in ¶[0036] of Preuss.
MacKay, Mehta, McCourt and Preuss do not explicitly disclose and input the selected words into a transformer- based language model trained to output numerical scores for personality traits, and to predict personality of a user of the terminal based on outputs from the model, however, Leonardi discloses this limitation (Leonardi: Figure 4 discloses inputs of tokenized words into a transformer (BERT-based) encoder; Introduction the neural network “exploits” sentences embeddings to perform a regression on the objective personality trait which provides numerical output and scores; section 5.2 discloses regression predicts on of the five personality traits).
The combination of Mackay, Mehta, McCourt and Preuss in view of Leonardi are combinable because they are from the same field of endeavor of emotion and personality classification, e.g., both disclose system that perform natural language processing on textual input to identify emotional and cognitive information. It would have been obvious to one of ordinary skill in[ the art before the effective filing date of the claimed invention to disclose modify the combined system to input selected words/representation into a transformer based model. The motivation for doing so is “the wider adoption of deep learning techniques and the incremental availability of data and machine resources, novel studies emerged” as disclosed by Leonardi in Section 2.4. Additionally, MacKay states in paragraph [0068] that the machine learning models engine 320 can include any machine learning model or models from transformer models, such as BERT, RoBERTa etc.
Regarding Claim 2:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further discloses the personality assessment device of claim 1, wherein each of the plurality of interview questions is a question for predicting the personality of the user, requiring a subjective response (MacKay: p[0249] a profile is generated with personality traits, p[0111] says this is predicted from input which can include user data or transcribed interview data/surveys, p[0004]-[0005] describes subjective data), and the at least one directive sentence includes a directive sentence that requests a response to a plurality of adjectives that are thought to describe the user well (MacKay: p[0251] discloses the system solicits descriptors of personality, which often come inf the form of adjectives (e.g., honest, thoughtful, team-player etc.) to point to an ideal candidate, the word personality traits is reasonably broad enough to include adjectives considering they are a core component of all language and personality prediction) and a directive sentence that requests a response to a strength and a weakness of the personality of the user (MacKay: p[0095] discloses the system handles trait-related descriptors (ego, curiosity) and associates them with linguistic patterns and their emotional effects that can be deemed positive or negative such as self-esteem, empathy or patience, p[0103] discloses the user data can determine user ego that is positive negative and neutral based on their answers, responses and input (i.e., strength or weakness as it evaluates the user responses for their self-perception)).
Regarding Claim 3:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further discloses the personality assessment device of claim 1, wherein the device is further configured to transcribe, using an automatic speech recognition model executed by the at least one processor, at least one of the first response in the form of speech data and the second response in the form of speech data to the form of text data (MacKay: Fig. 8 S802 discloses receiving textual and or voice input, p[0074] discloses transcribed human speech input).
Regarding Claim 4:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further discloses the personality assessment device of claim 1, wherein the device is further configured to perform part-of-speech tagging on the tokenized responses and extract words corresponding to at least one of an adjective, a verb, and a noun from among words included in the responses, and to select a subset of the extracted words based on correlation with personality traits using the trained correlation model (MacKay: p[0052], p[0086] and p[0010] disclose the extracting parts of speech from user input including verbs, nouns, adjectives and adverbs recognized in the users response, these important parts of speech are grouped into emotions or cognition, each specific part of speech rule has its own category, these rule categories can include personality traits and emotional states).
Regarding Claim 5:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further disclose the personality assessment device of claim 4, except wherein the preprocessing unit is configured to extract a plurality of topics by applying a Latent Dirichlet Allocation (LDA) method to the extracted words and to select some words from among the extracted words based on correlation between each of the plurality of topics and the personality (Mehta: Section 4.1 paragraphs 11-12 discloses applying LDA to text responses to identify latent topics and using correlation between topics and personality traits to select important words/features for downstream personality classification).
It would have been obvious to one of ordinary skill in the art to incorporate LDA-based topic modeling of Mehta into the emotion-cognitive natural language processing of MacKay to enhance personality prediction accuracy by extracting semantically meaningful groups of words (topics) and selecting only those with demonstrated personality relevance. The motivation for doing so would be to use LDA as a complementary technique for categorization accuracy.
Regarding Claim 6:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further disclose the personality assessment device of claim 5, wherein the device is further configured to compute the correlation between each of the plurality of topics and the personality using a stored word list that includes a plurality of weighted words and to exclude a word included in a topic having the correlation of a predetermined threshold or less among the plurality of topics (Mehta: Section 4.1 discloses that reliable correlations exist between writing style, including frequency of word use and personality. Mehta further discloses psychologically relevant word buckets where word frequency is counted and used to predict personality, LDA topics formed from clusters of semantically related words and entropy based weights assigned to extracted linguistic emotional features. Therefore, Mehta teaches the same concept as a stored word list including weighted word because the system sues stored word based features and their weights to determine personality relevance. Mehta further teaches threshold based mapping because only feature values above average are mapped to personality traits which teaches excluding lower)
It would have been obvious to one of ordinary skill in the art to incorporate LDA-based topic modeling of Mehta into the emotion-cognitive natural language processing of MacKay to configure the preprocessing unit of Mackay’s personality assessment system to exclude words in topics having correlation below a predetermined threshold as taught by Mehta. The motivation for doing so would be to reflect trait relevance and filter out features with lower informative value.
Regarding Claim 8:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further disclose the personality assessment device of claim 5, wherein the preprocessing unit is configured to compute the correlation between each of the plurality of topics and the personality using a correlation decision model that outputs correlation with a topic and to exclude a word included in the topic having the correlation of the predetermined threshold or less among the plurality of topics (Mehta: Section 4.1 discloses trained machine learning models for personality detection using extracted textual features and disclose extracted language features being used to predict personality traits. Therefore, teaching the same concept as a trained correlation decision model because the trained model evaluates extracted text and topic features and outputs personality relevance and correlation for personality prediction).
It would have been obvious to one of ordinary skill in the art to incorporate LDA-based topic modeling of Mehta into the emotion-cognitive natural language processing of MacKay, to incorporate the ML-kNN classifier weighted with entropy theory. The motivation for doing so is that it enables the system to exclude topics that fall below relevance threshold, thereby streamlining the prediction efficiency.
Regarding Claim 9:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further discloses the personality assessment device of claim 1, wherein the device is further configured to embed the selected words into a numerical format (MacKay: p[0064] discloses the use of vector embeddings meaning the user input is being transformed into numerical data)and input the embedding into a trained personality prediction model implemented using a transformer-based language model (Leonardi: Figure 4 discloses inputs of tokenized words into a transformer (BERT-based) encoder; Introduction the neural network “exploits” sentences embeddings (numerical format) to perform a regression on the objective personality trait which provides numerical output and scores).
Regarding Claim 10:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further discloses the personality assessment device of claim 9, wherein the personality prediction model is a transformer-based pretrained language model stored in memory (Leonardi discloses using a BERT-based transformer model in Fig. 4 that is pretrained), and the personality prediction model is configured to output prediction points for top five adaptive factors and top five maladaptive personality factors (MacKay: p[0095] discloses the system handles trait-related descriptors (ego, curiosity) it associates these with emotional effects that can be deemed positive or negative such as self-esteem, empathy or patience, p[0103] discloses the user data can determine user ego that is positive negative and neutral based on their answers, responses and input (i.e., maladaptive and adaptive traits are found and output as it evaluates the user responses for their self-perception. MacKay further discloses that each linguistic rule has one or more dimensions and a value for each dimension and that a profile is generated with personality traits, expression traits, cognitive traits, emotional traits, values and biases. MacKay lists far more than five personality, emotion cognition dimensions including positive and negative dimensions. Therefore, when MacKay outputs the scored personality profile the system outputs at least five positive adaptive scored factors and at least five negative maladaptive scored factors).
The combination of Mackay, Mehta, McCourt and Preuss in view of Leonardi are combinable because they are from the same field of endeavor of emotion and personality classification, e.g., both disclose system that perform natural language processing on textual input to identify emotional and cognitive information. It would have been obvious to one of ordinary skill in[ the art before the effective filing date of the claimed invention to disclose a transformer based model. The motivation for doing so is “the wider adoption of deep learning techniques and the incremental availability of data and machine resources, novel studies emerged” as disclosed by Leonardi in Section 2.4. Additionally, MacKay states in paragraph [0068] that the machine learning models engine 320 can include any machine learning model or models from transformer models, such as BERT, RoBERTa etc.
5. Claims 11 rejected under 35 U.S.C. 103 as being unpatentable over MacKay in view of Mehta, further in view of McCourt, further in view of Preuss, further in view of Leonardi and further in view of Wrenn (US 2018/0103885).
Regarding Claim 11:
The combination of MacKay, Mehta, McCourt, Preuss and Leonardi further discloses the personality assessment device of claim 10, wherein the top five adaptive factors comprise extroversion, agreeableness, conscientiousness, openness, and emotional stability (Leonardi: Section 2.3 discloses the big five traits, i.e., extroversion, openness, agreeableness, conscientiousness and neuroticism (which is the inverse of emotional stability, meaning the measurement can also give you emotional stability or the lack thereof)), and the top five maladaptive factors comprise detachment, egocentrism, (MacKay: ¶[0103] discloses ego can be positive, negative or neutral (egocentrism). ¶[0095] discloses separateness, distance, attachment and empathy, these all support detachment. ¶[0095] also discloses negative affectivity such as positive, negative, anger disgust fear, sadness).
The proposed combination of MacKay, Mehta, McCourt, Preuss and Leonardi do not explicitly discloses disinhibition and psychoticism.
However, Wrenn discloses disinhibition and psychoticism (Wrenn: ¶[0081] discloses behavior data including coping impulses specifically impulsive reactions to anger, affection, fear, confusion and embarrassment and further discloses that the acquired data may be subjected to pattern analysis to identify repetitive patterns tendencies. Therefore, Wrenn teaches assessing impulsive behavioral tendencies which corresponds to disinhibition).
MacKay, Mehta, McCourt, Preuss and Leonardi in view of Wrenn are combinable because they are all from pertinent fields of endeavor involving computerized assessment of mental state, personality, behavior traits and emotion information. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wrenn’s assessment of coping impulses and impulsive behavioral tendencies into the personality assessment system of MacKay and Leonardi in order to identify disinhibition as a maladaptive personality factor. The motivation for doing so is that Wrenn teaches that behavioral health and social history information are often missed because they are time consuming and sensitive to collect and that collecting such information through a computer increases efficiency of health care delivery in ¶[0003]-[0004].
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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Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hai Phan can be reached at (571) 272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/IAN SCOTT MCLEAN/Examiner, Art Unit 2654
/HAI PHAN/Supervisory Patent Examiner, Art Unit 2654
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