DETAILED ACTION
This non-final office action is responsive to application 16/985,518 with applicant’s amendments and request for reconsideration as submitted 05 Sept 2025.
Claim status is currently pending and under examination for claims 1-20 of which independent claims are 1, 9 and 13; amended claims are 1-2, 9 and 13; no claims are currently in condition for allowance.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/05/2025 has been entered.
Response to Remarks
Applicant’s responsive remarks filed 09/05/25 are considered together with amendments in light of each of the remaining issues as follows:
Rejection of claims 1-20 under 35 U.S.C. 101 eligibility as being directed to an abstract idea without significantly more is hereby withdrawn in light of amendments and remarks which are at least partly persuasive. While the claim is seen to encompass some abstract idea, the specific interface screen and server memory for generated dialogue based on edits are found in support of non-conventional arrangements to satisfy practical application and/or significantly more similar to McRO and BasCOM. Accordingly, the rejection is withdrawn.
Applicant’s remarks the prior art have been considered, but they are moot in view of the new grounds of rejection as necessitated by applicant’s amendments. Updated search and consideration identifies additional prior art to meet the scope as amended, newly applied references include primary reference Ferrari as well as Gaur, Hakkani and Zhong. Significant teachings from Shriberg remain of pertinence and Poulin (instant applicant) picks up lesser limitations from earlier work. An updated rejection under 35 U.S.C. 103 as obvious over a combination of prior art is detailed below. Additional findings of further consideration are noted in conclusion.
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:
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.
Claims 1-3, 6, 9-10, 13-15, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over: Ferrari, Lauren Marcoccio, US PG Pub No 2013/0325491A1 hereinafter Ferrari, in view of Poulin, Christian D., US Patent No 9,817,949B2 hereinafter Poulin (instant applicant), in view of Shriberg et al., US PG Pub No 2019/0385711A1 hereinafter Shriberg, in view of Gaur et al., “Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention” hereinafter Gaur, in view of Hakkani-Tur et al., US PG Pub No 2021/0217408A1 hereinafter Hakkani as evidenced by Provisional 62,727,833 and further in view of Zhong et al., “E3: Entailment-driven Extracting and Editing for Conversational Machine Reading” hereinafter Zhong (arXiv: 1906.05373v1).
With respect to claim 1, Ferrari teaches:
A computer-implemented method {Ferrari [0007] “The computer implemented method and system disclosed herein address… psychological methods” with [0030] “group therapy sessions on the GUI” similar at [0090] goal-based, see claim 1} comprising:
configuring, by one or more networked computer systems, an assessment change module {Ferrari Fig 11:1104,03 networked computer with modules 1101d-m, [0031] “therapist assesses one or more deficient areas of the client”} by:
causing rendering on a client device of one or more user interface screens with a portion for entering text specifying a goal and another portion for specifying contact information of a buddy system {Ferrari [0079] “Figs. 8K-8L …text fields provided on the goal” screenshots illustrate, similar again [0032] GUI with text field. Also, [0060,59] “interface displays the clients’ basic information, important dates, and contact information” Figs 5E-H, 6R. The device can be an iphone/ipad as web based connected device [0027-29], Fig 11:1101a,f,1103};
executing the assessment change module {Ferrari [0086] “executing modules” Fig 11}, comprising
receiving, through one or more of the rendered user interface screens of the assessment change module from the client device over a network, a user-set goal {Ferrari [0009] “defined goal measurements via the GUI” similar at [0090] and illustrated e.g. Fig 8K set up goal of custom goal, received via network Fig 11:1104,1101f,a};
However, Ferrari does not expressly disclose the following limitations which were previously disclosed by Poulin:
instantiating analysis objects that are persistent programming objects stored in a database on a computer hard drive of a server {Poulin see [Col3 Lines34-40] “analysis objects 20 are instantiated” where “analysis objects 20 that are persistent objects, i.e., stored on a computer hard drive 17a of the server in a database” Fig 1} by:
initializing parameters by a processor {Poulin [Col3 Lines38-39] “initialized with parameters… of the server” coupled to a processor [Col2 Line20]}; and
placing the analysis objects with initialized parameters into memory, where they are executed {Poulin [Col3 Lines37-40] “placed into main memory (not shown) of the server 17, where they are executed” begins “the analysis objects are instantiated, i.e., initialized with parameters and placed into main memory (not shown) of the server 17, where they are executed”};
executing an assistance processing module that comprises analysis objects that are persistent programming objects stored in a database on a computer hard drive of a server {Poulin [Col3 Lines22-40] “executed through the data analysis software” with “analysis objects that are persistent objects, i.e., stored on a computer hard drive 17a of the server in a database”}, comprising:
instantiating the analysis objects by initializing parameters and placing the analysis objects into memory of the server where they are executed {Poulin [Col3 Lines37-40] “analysis objects 20 are instantiated, i.e., initialized with parameters and placed into main memory (not shown) of the server 17, where they are executed”};
Poulin is directed to computer methods & systems of trained ML models for predicting risk factors such as health risk thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to instantiate analysis objects placed into server memory and initialize parameters per Poulin as applying known techniques to a known device ready for improvement to yield predictable results thus reasonably allowing a user to load data objects for execution such as using “industry standard file format for a feature vector” [Col3 Line46-47].
However, the combination Ferrari and Poulin does not appear to disclose the following limitations which are met by Shriberg:
querying a database storing a plurality of machine learning models that each predict a psychological risk value to determine whether one of the plurality of machine learning models predicts the psychological risk value associated with the received goal {Shriberg Figs 20A-21: 2050 “model repository” retrieves models from model server [0386], again 4:416, 54:5416. The models are [0285,87] “machine learning models… neural networks” and [0313] “select the most appropriate model” where [0390] “each model is then combined (fused) by weighting” e.g. [0410] “Each of models 4102-4108 includes deep learning… that assesses the patient’s depression…from collected patient data 2206 (Fig 22)” illustrates composite model builder aggregated from plurality of models, e.g. ensemble or random forest [0427,87]. Psychological is described as mental health state or behavioral disorders [0025,53], [0167] for therapy [0481], [0561]. Further, risk value is score [0071,77] “risk can be quantified in a form of a normalized score… risk of having the mental condition” [0171] “quantification of the risk” such that [0167] “score indicating a mental state prediction” e.g. [0421] “score acts to fine-tune a prediction of depression” score/value is also probability [0330], [0424]. Goals are disclosed including target mental state [0079], [0384]};
in response to determining that one of the plurality of machine learning models predicts the psychological risk value associated with the received goal, obtaining two or more psychological risk values outputted by that machine learning model {Shriberg [0422-24] “scoring iterations” Figs 29 & 33 illustrate iterating overs models after receiving models 3310, the models providing the score(s) replete. [0014] “the score can be continuously updated” [0173], second value being an updated/iterated score may be output from model types [0286] “LSTM…seq2seq” hence [0308] “model output weights are temporally based”, e.g. subject to an iterative error calculation [0335-36]};
assessing, by the assessment change module based on the two or more outputted psychological risk values, temporal changes in a real-time psychological risk value associated with the received goal {Shriberg [0014] “assessment can comprise a score that indicates… target mental states at a future point in time” hence Title assessment of mental health, more particularly as per [0173,72] “change in normalized score” i.e. “scores over time” so as it [0293] “analyzes trends over time” Figs 23B & 25 show temporal modeling such as lstm/seq2seq [0286-87], [0308] “model output weights are temporally based”};
based on contact information specified in the portion of the one or more user interface screens, generating by one or more networked computer systems a dialog between the client device and the buddy system, with the dialog associated with the assessed temporal changes in the psychological real-time risk value associated with the goal {Shriberg Figs 17 & 7-9 generated dialogue [0191] “Interaction control logic generator 702 customizes the dialogue” by [0187] “backend automated dialog system” via user interfaces shown Figs 59-61 described [0171,0196] and further associated with [0273] conversation queue/deque subject to the score reaching a confidence threshold. For effect [0513] “prompt healthcare providers to pursue a change in treatment, or to pursue the current course of treatment more aggressively”};
Shriberg is directed to psychological/mental health state assessment with dialogue modeling thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ the teachings of Shriberg in combination for the motivation of a “backend automated dialog system destined for patients” [0187] where “model’s predictions over time may be recorded by mental health professionals. These results may be used to show a patient’s progress out of a depressive state” and “help direct the patient to a proper level of therapy” [0523,564].
However, the combination Ferrari, Poulin and Shriberg does not appear to disclose the following limitation which is met by Gaur:
posting by the assistance processing module to the buddy system, the generated dialog
{Gaur [P.521] Eq.6 “posts” of user are concatenated using subReddits (buddy system) Fig 3 and conversational dialogue Table 4. Gaur also considers “prominent” seed terms Fig 2 frequency is the y-axis, similar Table 4 again},
Gaur is directed to mental health modeling with social media thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify posting per Gaur in combination for the motivation “Our study aims to develop mapping and learning approaches for estimating the suicide risk severity level of an individual, based on his/her posted content… with the anticipated inclusion of individual’s social data and the rapidly growing patient-generated health data, MHPs will be better informed about the patient’s conditions including their suicidality to enable timely intervention” [P.515 Last2¶].
However, the combination Ferrari, Poulin, Shriberg and Gaur does not appear to disclose the following limitations which are met by Hakkani:
tracking by the assistance processing module edits to the generated dialog made on the buddy system {Hakkani [0047] “dialogue state tracking… updated dialogue state” where updates are edits, e.g. [0124] “change of intent during the dialogue can be detected” system shown Fig 1. Corresponding provisional support at [0042], [0119], Fig 1}; and
at a time subsequent to the tracking of the changes, outputting, by the assistance processing module to the buddy system, another generated dialog with the suggested text {Hakkani at [0076] “timestep corresponding to each dialogue turn” over [0047] “dialogue sequence… sequence of T turns” with [0023] “Outputs from the dialogue state tracker” thus [0044] “generates a response 214 to provide to the client device” e.g. [0034] “generate multiple token-level outputs for each dialogue turn”. Provisional support corresponds [0071], [0042,39], [0028,23]}
Hakkani is directed to dialogue models thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to employ the dialogue state tracker over timesteps per Hakkani in combination for the stated motivation “motivation for dialogue state tracking came from uncertainty in speech recognition, as well as to provide a comprehensive input to a downstream dialogue policy… DST to improve belief tracking performance” [0065-67].
However, the combination Ferrari, Poulin, Shriberg, Gaur and Hakkani does not appear to disclose the following limitation which is met by Zhong:
text based on counts of edits, with edits having a higher frequency number of edits being given more prominence in modifying suggested text than counts of lower frequency values {Zhong see Figs 1,4 text of dialogue, and Figs 2-3 E3 edit network based on BERT transformer, introduced [P.2 ¶1] “E3 is the first extract-and-edit method for conversational dialogue”. The edits are detailed [P.5 ¶4-9] where count is summation ∑Uedit Eq.20 attentive decoder with LSTM (known model for sequence data) and prominence is weighting parameterized model [P.4-5 Sect3.3]. Further, [P.7 Sect4.3] “relevance” is described in terms of high and low BLEU scores which is a benchmark for text tasks, Table 2}.
Zhong is directed to dialog sequence text with generative modeling techniques thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify edits according to the teachings of Zhong in combination to arrive at the invention as claimed for the motivation “E3 both answers the user’s original question more accurately, and generates more coherent and relevant follow-up questions” [P.6 Last¶].
With respect to claim 2, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the computer-implemented method of claim 1. Poulin teaches wherein the one or more user interface screens are:
one or more user interface screens of one or more HTML pages {Poulin discloses [Col3 Line43] “output as an HTML or equivalent web page”).
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify Ferrari’s [0028-29] “web-based therapy management… website” using HTML per Poulin as obvious to try in choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success where websites implementation via HTML is a known standard markup language for web pages of ubiquitous internet.
With respect to claim 3, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the computer-implemented method of claim 1 wherein
the one of the plurality of machine learning models is one or more of a mental health model, a suicidality classifier model, a suicide ideation classifier model {Shriberg Fig 29:2950,2920 illustrates “Train Models” to “Classify a Mental Health State” [0344] “model 2553 identifies types of topics… such as death, suicide” and “models for processing the answer for mental health state”}.
With respect to claim 6, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the computer-implemented method of claim 1 wherein generating the dialog, further comprises:
generating the dialog correlated to wording appropriate to the assessed temporal changes in psychological real-time risk value. {Shriberg [0215] “weighted word scores of the responses” where “models correlate individual words and phrases to specific health states” similar at [0211]}
With respect to claim 9, the rejection of claim 1 is incorporated. The difference in scope being a computer program product stored on non-transitory computer readable storage device comprising instructions for performing limitations of claim 1. Ferrari discloses [0105] “computer program product comprising a non-transitory computer readable storage medium that stores computer program codes comprising instructions executable by at least one processor” which can be a [0108] “combination of hardware and software” similar at [0086], Fig 11-12. The remainder of this claim is rejected for the same rationale as claim 1.
With respect to Claim 10, the product of claim 9 is taught by the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong and whom further disclose performing the limitation of claim 6. Therefore, rejection of claim 6 is applied to claim 10.
With respect to claim 13, the rejection of claim 1 is incorporated. The difference in scope being an apparatus comprising processor coupled to memory and computer readable storage device storing computer program product for performing limitations similar to claim 1. Ferrari discloses [0110] “computer that is in communication with one or more devices” and shown e.g. Figs 11-12 computer implementation with processor and memory coupled via bus, similar [0086], [0105-08]. The remainder of this claim is rejected for the same rationale as claim 1.
With respect to Claim 14, the apparatus of claim 13 is taught by the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong and whom further discloses performing the limitation of claim 6. Therefore, rejection claim 6 is applied to claim 14.
With respect to Claim 15, the apparatus of claim 14 is taught by the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong and whom further discloses performing the limitation of claim 1. Therefore, rejection claim 1 is applied to claim 15.
With respect to Claim 17, the product of claim 9 is taught by the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong and whom further discloses performing the limitation of claim 3. Therefore, rejection claim 3 is applied to claim 17.
With respect to Claim 19, the apparatus of claim 13 is taught by the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong and whom further discloses performing the limitation of claim 3. Therefore, rejection claim 3 is applied to claim 19.
Claims 4, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong in view of Spenciner et al., US PG Pub No 20200381117A1 hereinafter Spenciner as evidenced by US Provisional 62/854,179.
With respect to claim 4, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the computer-implemented method of claim 1. Spenciner teaches wherein the method comprises
generating a newly-generated machine learning model that predicts the psychological risk value associated with the received goal in response to determining there is no machine learning model in the plurality of machine learning models that predicts the psychological risk value associated with the received goal {Spenciner [0026-27] “generates a new training model 145… newly trained prediction model (e.g., trained models 150 & 160)” illustrated Fig 1 models comprise emotional state prediction model such that emotion is psychological and risk is disclosed [0016]. Further, the determining may be a mapping and goal is a target, see [0028] “map the machine learning algorithm to the output data (e.g., the emotional state” The values are scores [0079-80], and models are stored in memory for access [0039]}.
Spenciner is directed to machine learning models with emotional state thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to generate new models per Spenciner in combination for the motivation of deploying models and enabling accurate learning from models (Spenciner [0028-29], [0003]).
With respect to Claim 18, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the product of claim 9 and further perform limitation of claim 4 in combination with Spenciner. Therefore, rejection claim 4 is applied to claim 18.
With respect to Claim 20, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the apparatus of claim 13 and performs limitation of claim 4 in combination with Spenciner. Therefore, rejection claim 4 is applied to claim 20.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong in view of Cheong et al., “An intelligent platform with automatic assessment and engagement features for active online discussions” hereinafter Cheong.
With respect to claim 4, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the computer-implemented method of claim 1. Cheong teaches wherein the method comprises
loading a leaderboard that is indicative of a psychological risk associated with the received goal in response to determining there is no machine learning model in the plurality of machine learning models that predicts the psychological risk associated with the received goal {Cheong Fig 1 top middle-left “User can… view, check Leaderboard” described [P.3 Sect2.1 ¶4] “leaderboard is used… display the real-time ranking of the avatars on a leaderboard based on their cumulative thoughtfulness score” thoughtfulness is psychological, determining model is model selection with cross-validation per [P.7 Sect3.3]}.
Cheong is directed to dialogue with chat bots and machine learning thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to use the leaderboard of Cheong in combination for the motivation of “improving engagement” (Cheong [P.5 ¶1], [P.3 Sect2.1 ¶4]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong in view of Bates et al., US PG Pub No 20190286540A1 hereinafter Bates.
With respect to claim 7, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the computer-implemented method of claim 6. Bates teaches wherein posting to the buddy system further comprises:
posting to the buddy system, the real-time psychological risk value. {Bates Fig 12:122,126 GUI shows postings risk, [0043] “posts revealing risk factors” e.g. [0060] “post for indications of suicide risk, the level of risk might be coded as None, Low, Moderate, High and Immediate” immediacy conveys real-time as does the post having a timestamp illustrated Fig 12:126, [0040], [0058-63]}
Bates is directed to machine learning classifiers for social media risk thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to post risk per Bates in combination to arrive at the invention as claimed for the motivation being that “Posts in the triage queue are prioritized… perform triage tasks and actions based on relevancy and information received through the system from the social media content” (Bates [0007], [0029]).
Claims 8, 11-12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ferrari, Poulin, Shriberg, Gaur, Hakkani, Zhong and Bates in view of Shah et al., US PG Pub No 20190115027A1 hereinafter Shah.
With respect to claim 8, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani, Zhong and Bates teaches the method of claim 7. Shah teaches wherein tracking the changes to the generated dialog made on the buddy system comprises:
tracking a count of changes made to the generated dialog {Shah Fig 1:124 [0048] “tracks dialog state/responsive action pairs, and in particular tracks the number of positive and negative turn-level feedback values” where number is count, feedback is edit. Dialog State Tracker introduced [0042-45]}, and wherein the method further comprises:
adapting a subsequent generated dialog based on the count of changes made to the generated dialog {Shah [0066,65] “dialog management policy model (e.g., πF and πR) may be updated” as “updating the turn-level model (πF) based on the one or more turn-level feedback values” similar at [0050], [0048]}.
Shah is directed to dialog generation with machine learning thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify dialog state tracker according to Shah for the motivation “optimality of a dialog state/responsive action pair, (s,a) based on turn-level feedback” (Shah [0052]).
With respect to Claim 11, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches the product of claim 10 and further performs the limitation of claim 8 in combination with Bates and Shah. Therefore, rejection of claim 8 is applied to claim 10.
With respect to Claim 12, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani, Zhong, Bates and Shah teaches the product of claim 11 and further teaches performing the limitation of claim 8. Therefore, rejection of claim 8 is applied to claim 12.
With respect to Claim 16, the combination of Ferrari, Poulin, Shriberg, Gaur, Hakkani and Zhong teaches apparatus of claim 15 and further performs the limitation of claim 8 in combination with Bates and Shah. Therefore, rejection of claim 8 is applied to claim 16.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Budzianowski et Vulic, “Hello, It’s GPT-2 – How can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems” arXiv 1907.0577v2 Fig 1-3
Howard et al., “Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study” arXiv: 1907.02581v2 uses GPT for mental health and Reddit suicidality
Higgins et al., US PG Pub No 2021/0027648A1 smartphone app interface for social goals
Lee et al., US PG Pub No 2022/0382995A1 goal-oriented dialogue uses BERT
Chao et Lane, “BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer” arXiv: 1907.03040v1 per Title
CLaC at CLPsych 2019 conference for predicting suicide risk, top papers noted include Matero et al., “Suicide Risk Assessment with Multi-level Dual-Context Language and BERT” Fig 3; Mohammadi et al., “CLaC at CLPsych 2019: Fusion of Neural features and Predicted Class Probabilities for Suicide Risk Assessment Based on Online Posts” Fig 1; Bitew et al., “Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPSych 2019 Shared Task A” Fig 1 TF-IDF
Conclusion
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