DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/22/2026 has been entered.
Response to Amendment
1 This action is in response to the amendment filed on 06/22/2026. Claims 1, 7-8, 14-15, 20 have been amended. Claims 1-20 remain rejected.
Response to Arguments
2 Applicant’s arguments with respect to claims 1, 8, and 15 filed on 06/22/2026, with respect to the rejection under 35 U.S.C. § 103 regarding that the prior art does not teach the following but not limited to the amended parts “…the ML model assigning a respective probability to each of the one or more candidate next elements…” and “…to provide one or more aptness scores that are independent of the respective probability assigned to each of the one or more candidate next elements by the ML model…”. This argument has been considered, but are moot due to new grounds of rejection under similar prior art.
3 In addition, the applicant has also argued that the current prior art, specifically prior art from Scanlon, does not teach “predict, based on the input data and the at least one mood driver, one or more candidate next elements of the sequence using the ML model”. This argument has been fully considered, but are not persuasive. Although it may be true that Scanlon teaches an AI state module in order to have a prediction/analysis type method that may differ from machine learning models themselves, but as mentioned previously, the execution from those AI state modules relies on the data that is taught from a ML model/algorithm ([0029] reciting “The AI emotional state module 242c may, for example, utilize the transcribed audio data 244b, the physical data 244a, and/or the computed human intent to compute a state of the conversation and/or of an emotional parameter thereof. In some embodiments, the AI emotional state module 242c may comprise a set of encoded AI instructions that are configured to classify the input 216 and/or the human intent based on execution of a ML algorithm that has been trained to classify conversational and/or emotional states based on one or more training data sets…”). Therefore, the rejection for specifically this part will be retained.
4 Another specific part of the independent claims state that the prior art, specifically prior art from Flax, does not explicitly teach “…to obtain, from a knowledge base, expertise data relating to the sequence; evaluate the one or more candidate next elements, using the expertise data…”. This argument has been fully considered, but are not persuasive. For more clarifications, the specific knowledge base according to Flax does contain data that is specifically from “experts” suggesting the data in the knowledge bases contains “expertise” data ([0012] reciting “accessing a knowledge base comprising data obtained via expert evaluation of a training set of subjects and embodying a computational model of a relationship between mental state and heart rate characteristics;”), the data is accessed thanks to certain functions of the invention ([0065] reciting “The assessment method 300, which may be implemented via suitable program instructions executed by the processor 114 of the assessment server 104, then proceeds to further analyze the heartbeat record in order to perform an assessment of the subject's stress levels. In order to do this, information in the knowledge base is accessed 306.”), as well as the knowledge bases is linked to a decision module, which the module evaluates certain topics according to specific rules given ([0082] reciting “The decision module 610 classifies the stress level of the subject 106 according to the rules associated with the particular computational model. For example, in the template model, and the multi-parametric/distance model, the decision module 610 classifies the stress level of the subject 106 by comparing a computed distance metric with the segmentation values 506, 514.”; [0084] reciting “Typically, the decision module 610 requires access to the knowledge base 124, in order to retrieve the decision criteria. An output stress level indication 612 is produced from the decision module 610.”). Therefore, the rejection for specifically this part will be retained.
5 Regarding claims 2-7, 9-14, 16-20, they directly/indirectly depend on independent claims 1, 8, and 15 respectively. Applicant does not argue anything other than independent claims 1, 8, and 15. The limitations in those claims, in conjunction with combination, was previously established as explained.
Claim Rejections - 35 USC § 103
6 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.
7 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.
8 Claim(s) 1-2, 5-6, 8-9, 12-13, 15-16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scanlon et al. (US 20230401976 A1) in view of Flax et al. (US 20170156657 A1) and Alexandar et al. (US 20210150146 A1).
9 Regarding claim 1, Scanlon teaches a system comprising: a computing platform having a hardware processor and a system memory storing a software code ([0043] reciting “Any of the processes and methods described herein may be performed and/or facilitated by hardware, software (including microcode), firmware, or any combination thereof. For example, a storage medium (e.g., a hard disk, Random Access Memory (RAM) device, cache memory device, Universal Serial Bus (USB) mass storage device, and/or Digital Video Disk (DVD)”) and a machine learning (ML) model ([0028] reciting “In some embodiments, the AI intent module 242b may comprise a set of encoded AI instructions that are configured to classify the input 216 based on execution of a Machine Learning (ML) algorithm that has been trained to classify human conversational intents based on one or more training data sets”) trained to predict a next element of a sequence ([0045] reciting “According to some embodiments, the input (i.e., the spoken sentence of the user) may be analyzed to determine which path the simulated conversation should take and/or which subsequent conversational node should be activated and/or progressed to…may be utilized to select, identify, and/or otherwise determine the path and/or next node.”);
the hardware processor configured to execute the software code to ([0122] reciting “In some embodiments, hard-wired circuitry or custom hardware may be used in place of, or in combination with, software instructions for implementation of the processes of various embodiments. Thus, embodiments are not limited to any specific combination of hardware and software”):
receive input data identifying an element of the sequence ([0068] reciting “As depicted, this ideal path 510 may comprise strong or “high confidence” input at the first set “I” of nodes 502a-c with a direct progression to the third node 506. In some embodiments, any actual path that deviates from the ideal path 510 may reduce (or increase, depending upon whether low or high scores are designated as desirable) the user's points/score.”);
determine, using the input data, at least one mood driver of the sequence ([0029] reciting “In some embodiments, the AI emotional state module 242c may comprise a set of encoded AI instructions that are configured to classify the input 216 and/or the human intent based on execution of a ML algorithm that has been trained to classify conversational and/or emotional states based on one or more training data sets”);
predict, based on the input data and the at least one mood driver, one or more candidate next elements of the sequence using the ML model ([0029] reciting “The AI emotional state module 242c may, for example, utilize the transcribed audio data 244b, the physical data 244a, and/or the computed human intent to compute a state of the conversation and/or of an emotional parameter thereof. In some embodiments, the AI emotional state module 242c may comprise a set of encoded AI instructions that are configured to classify the input 216 and/or the human intent based on execution of a ML algorithm that has been trained to classify conversational and/or emotional states based on one or more training data sets…”), ;
evaluate the one or more candidate next elements, using , the input data, and the at least one mood driver (see similar rejections previously in the claim), to provide one or more aptness scores each of the one or more aptness scores in relation to the element identified by the input data. ([0067] reciting “An “average” evaluation of the user input may result, for example, in an average conversational outcome and/or score.”; [0031] reciting “According to some embodiments, one or more scores, ranks, standings, and/or results (e.g., derived from the one or more outcomes) may be utilized to define scoring data 244d that comprises the output 218 transmitted to one or more of the user devices 202a-c. In some embodiments, scoring data 244d may be computed (e.g., by the scoring module 242e) for each of the user devices 202a-c, and a user device 202a-c (and associated user) with the highest (or lowest) score may be ranked first and may be indicated as a winner.”); and
determine, using the one or more aptness scores assigned to each of the one or more candidate next elements by the ML model, the next element of the sequence ([0051] reciting “In some embodiments, and with reference back to the first node 404, in the case that it is determined that the user communicated with high confidence at 404-3, the method 400 may proceed (e.g., in accordance with the identified path, direction, and/or next node) to “B”.)”).
10 Scanlon does not explicitly teach … the ML model assigning a respective probability to each of the one or more candidate next elements; obtain, from a knowledge base, expertise data relating to the sequence; evaluate the one or more candidate next elements, using the expertise data… the input data, and the at least one mood driver, to provide one or more aptness scores each that are independent of the respective probability assigned to each of the one or more candidate next elements by the ML model providing a measure of consistency and coherence of a respective one of the one or more candidate next elements in relation to the element identified by the input data; and determine, using the one or more aptness scores and the respective probability assigned to each of the one or more candidate next elements by the ML model, the next element of the sequence.
11 Flax teaches to obtain, from a knowledge base, expertise data relating to the sequence; evaluate the one or more candidate next elements, using the expertise data… ([0012] reciting “accessing a knowledge base comprising data obtained via expert evaluation of a training set of subjects and embodying a computational model of a relationship between mental state and heart rate characteristics;”; [0065] reciting “The assessment method 300, which may be implemented via suitable program instructions executed by the processor 114 of the assessment server 104, then proceeds to further analyze the heartbeat record in order to perform an assessment of the subject's stress levels. In order to do this, information in the knowledge base is accessed 306.”; [0082] reciting “The decision module 610 classifies the stress level of the subject 106 according to the rules associated with the particular computational model. For example, in the template model, and the multi-parametric/distance model, the decision module 610 classifies the stress level of the subject 106 by comparing a computed distance metric with the segmentation values 506, 514.”; [0084] reciting “Typically, the decision module 610 requires access to the knowledge base 124, in order to retrieve the decision criteria. An output stress level indication 612 is produced from the decision module 610.”).
12 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Scanlon) to incorporate the teachings of Flax to provide a method that can obtain a type of expertise data provided by experts from a specific knowledge base to use for evaluating elements alongside with the input data and the type of mood driver(s) taught by Scanlon. Doing so would assess the mental state of a subject as stated by Flax ([0009] recited).
13 Scanlon in view of Flax does not explicitly teach … the ML model assigning a respective probability to each of the one or more candidate next elements; … the input data, and the at least one mood driver, to provide one or more aptness scores each that are independent of the respective probability assigned to each of the one or more candidate next elements by the ML model providing a measure of consistency and coherence of a respective one of the one or more candidate next elements in relation to the element identified by the input data; and determine, using the one or more aptness scores and the respective probability assigned to each of the one or more candidate next elements by the ML model, the next element of the sequence.
14 Alexander teaches … the ML model assigning a respective probability to each of the one or more candidate next elements ([0015] reciting “Implementations described herein provide a system that is configured to analyze a corpus of historical chat data to identify a list of “best” responses.”; [0017] reciting “The testing may include determining a probability or likelihood that the response corresponding to the particular response vector is associated with a context corresponding to a context vector, where association means that the response followed a sequence of inputs comprising the context in a conversation session.”; [0044] reciting “A plurality of context-response pairs and labels may be generated for the training data 440. Some of the context-response pairs include true pairs that are selected from the same conversation session, and some of the context-response pairs are false pairs that are not selected from the same conversation session…The administrator may further configure the probability of choosing, by the training data generator 415, a positive example from later in a conversation session.”);
… the input data, and the at least one mood driver, to provide one or more aptness scores each that are independent of the respective probability assigned to each of the one or more candidate next elements by the ML model providing a measure of consistency and coherence of a respective one of the one or more candidate next elements in relation to the element identified by the input data ([0017] reciting “The scores (referred to hereinafter as a coherence score) for each of the response vectors may be based on a set of similarity metrics and a set of fitness metrics for a particular response vector. In some cases, a particular response vector is compared to a set of other response vectors (e.g., every other response vector) to determine the set of similarity scores... The testing may include determining a probability or likelihood that the response corresponding to the particular response vector is associated with a context corresponding to a context vector, where association means that the response followed a sequence of inputs comprising the context in a conversation session...Further, the coherence score may be generated based on a mutual information function using the set of similarity metrics and the set of fitness metrics. The coherence score may represent the intuition that a good candidate response is given a high probability by the model (e.g., fitness score) whenever it is also very similar to a true response for a context (e.g., similarity score). Using the coherence score, the system may identify the list of best candidate response.”; [0035] reciting “That is, each row of each matrix may correspond to a context-response pair identified from the same conversation session from a corpus of conversation data.”; [0044] reciting “The administrator may configure the probability of choosing a negative example from later in the same conversation session, which promotes temporal acuity.”; [0100] reciting “At 920, the analytical data store may for each response vector, determining a coherence score, where each coherence score is based on the set of similarity metrics and the set of fitness metrics for said response vector.”);
and determine, using the one or more aptness scores and the respective probability assigned to each of the one or more candidate next elements by the ML model, the next element of the sequence ([0017] reciting “The testing may include determining a probability or likelihood that the response corresponding to the particular response vector is associated with a context corresponding to a context vector, where association means that the response followed a sequence of inputs comprising the context in a conversation session.”).
15 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Scanlon in view of Flax) to incorporate the teachings of Alexander to provide a method that can provide probabilities based on a type of machine learning that can measure to a type of consistency as well as coherence for the candidate elements, utilizing the aptness like scores that are provided by the teachings of Scanlon in view of Flax. Doing so would allow the system to identify the list of best candidate response as stated by Alexander ([0017] recited).
16 Regarding claim 2, Scanlon in view of Flax and Alexandar teaches the system of claim 1 (see claim 1 rejection above), wherein the sequence comprises a performance and the next element of the sequence is one of a continuation of the performance (Scanlon; [0048] reciting “In some embodiments, the third intent and/or emotive state may continue to direct the path of the conversation in the same “direction” to a tenth node 422.”) or a conclusion to the performance (Scanlon; [0047] reciting “The seventh node 416 may comprise, for example, a spoken statement, such as “I understand your concerns. I will send you my manager's contact information.” In some embodiments, the seventh node 416 may comprise an end of a current conversational path (e.g., there are no additional downstream nodes) and may accordingly comprise a conversational outcome (e.g., associated with and/or defining a final and/or total score for the user's virtual emotive conversation training session).”).
17 Regarding claim 5, Scanlon in view of Flax and Alexandar teaches the system of claim 1 (see claim 1 rejection above), wherein the at least one mood driver of the sequence comprises an emotional state corresponding to the element identified by the input data (Scanlon; [0029] reciting “In some embodiments, the AI emotional state module 242c may comprise a set of encoded AI instructions that are configured to classify the input 216 and/or the human intent based on execution of a ML algorithm that has been trained to classify conversational and/or emotional states based on one or more training data sets”).
18 Regarding claim 6, Scanlon in view of Flax and Alexandar teaches the system of claim 1 (see claim 1 rejection above), wherein the at least one mood driver of the sequence comprises at least one of a physical state of a user, a location of the user, or a feature of an environment of the user (Scanlon; [0027] reciting “According to some embodiments, the input 216 may comprise physical data 244a and/or audio data 244b received from one or more of the user devices 202a-c. The physical data 244a may comprise, for example, data descriptive of various physical aspects and/or states of a user and/or their environment such as, but not limited to… (ii) body angle data (e.g., angle with respect to a camera and/or other location of interest, shoulder angle)… In some embodiments, the audio data 244b may comprise data descriptive of various noises (e.g., environmental), sounds (e.g., speech), etc., of the user and/or the user's environment.”).
19 Claims 8 and 15 has similar limitations as of claim 1, therefore they are rejected under the same rationale as claim 1.
20 Claims 9 and 16 has similar limitations as of claim 2, therefore they are rejected under the same rationale as claim 2.
21 Claim 12 has similar limitations as of claim 5, therefore it is rejected under the same rationale as claim 5.
22 Claims 13 and 19 has similar limitations as of claim 6, therefore they are rejected under the same rationale as claim 6.
23 Claim(s) 3, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scanlon et al. (US 20230401976 A1) in view of Flax et al. (US 20170156657 A1) and Alexandar et al. (US 20210150146 A1) as of claim 1, further in view of Medeot et al. (US 20210049990 A1).
24 Regarding claim 3, Scanlon in view of Flax and Alexandar teaches the system of claim 1 (see claim 1 rejection above), wherein the sequence and the next element of the sequence comprise at least one of or one (ii) or more of movements, postures, or facial expressions of a physical or virtual object (Scanlon; [0027] reciting “The physical data 244a may comprise, for example, data descriptive of various physical aspects and/or states of a user and/or their environment such as, but not limited to…(v) facial expression data (e.g., frowning, smiling, lip, mouth, eyebrow, eye, and/or cheek positioning data)”; [0045] reciting “…attributes of the spoken sentence (e.g., a tone, pitch, cadence, timing, volume) and/or other input (e.g., the user's position in the virtual environment, the bearing or direction of the user's actual or virtual gaze, arm movements (e.g., gesturing) of the user) may be utilized to select, identify, and/or otherwise determine the path and/or next node.”).
25 Scanlon in view of Flax and Alexandar does not explicitly teach wherein the sequence and the next element of the sequence comprise at least one of (i) musical chords…
26 Medeot teaches wherein the sequence and the next element of the sequence comprise at least one of (i) musical chords ([Abstract] reciting “…determining an initial sequence of notes for the piece of music; determining at least one probability distribution for selecting at least one subsequent note from a set of candidate notes; generating a biasing output based on data of the initial sequence of notes; and extending the initial sequence of notes with at least one subsequent note selected from the set of candidate notes according to the probability distribution and the biasing output…”; [0059] reciting “For example, the first note could always be the same, or it could be different in each case. Next, a second note is selected. The selection of the second note may be based on the first note.”)…
27 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Scanlon in view of Flax and Alexandar) to incorporate the teachings of Medeot to provide a method to incorporate the sequences that can be taught by Scanlon in view of Flax and Alexandar to be able to incorporate musical chords with similar sequence methods. Doing so would determine at least one probability distribution for selecting at least one subsequent note from a set of candidate notes as stated by Medeot ([Abstract] recited).
28 Claims 10 and 17 has similar limitations as of claim 3, therefore they are rejected under the same rationale as claim 3.
29 Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scanlon et al. (US 20230401976 A1) in view of Flax et al. (US 20170156657 A1) and Alexandar et al. (US 20210150146 A1) as of claim 1, further in view of Zhao et al. (US 20210012510 A1).
30 Regarding claim 4, Scanlon in view of Flax and Alexandar teaches the system of claim 1 (see claim 1 rejection above), but does not explicitly teach wherein the sequence comprises a video sequence, and the next element of the sequence comprises at least one video frame.
31 Zhao teaches wherein the sequence comprises a video sequence ([0043] reciting “The current image frame is an image frame being processed by the terminal. An image frame is the smallest unit image of a video frame sequence that constitutes a video image. The target candidate region is a candidate region that determines a target region.”; [0071] reciting “The target determination model is used for determining a machine learning model of a probability that the target is present in the target candidate image.”), and the next element of the sequence comprises at least one video frame ([0074] reciting “The terminal acquires the motion prediction data outputted by the motion prediction model, to obtain the motion prediction data of the next image frame relative to the current image frame by using the acquired motion prediction data.”).
32 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Scanlon in view of Flax and Alexandar) to incorporate the teachings of Zhao to provide a method to incorporate video sequences that contains a frame of the certain video, alongside with the sequence methods taught by Scanlon in view of Flax and Alexandar. Doing so would resolve the problem of a relatively high loss rate of target tracking in conventional methods as stated by Zhao ([0005] recited).
33 Claims 11 and 18 has similar limitations as of claim 4, therefore they are rejected under the same rationale as claim 4.
34 Claim(s) 7, 14, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Scanlon et al. (US 20230401976 A1) in view of Flax et al. (US 20170156657 A1) and Alexandar et al. (US 20210150146 A1) as of claim 1, further in view of Rajani et al. (US 20210374488 A1).
35 Regarding claim 7, Flax in view of Alexandar teaches the system of claim 1, wherein the hardware processor is further configured to execute the software code to (see claim 1 rejection above), but does not explicitly teach to identify a weighting factor for one of the probability assigned to each of the one or more candidate next elements by the ML model or the one or more aptness scores, relative to the other of the probability assigned to each of the one or more candidate next elements or the one or more aptness scores; and apply the weighting factor to provide one of a weighted probability for each of the one or more candidate next elements or weighted one or more aptness scores; wherein determining the next element of the sequence uses the one of the weighted probability for each of the one or more candidate next elements or one or more weighted aptness scores.
36 Rajani teaches to identify a weighting factor for one of the probability assigned to each of the one or more candidate next elements by the ML model or the one or more aptness scores, relative to the other of the probability assigned to each of the one or more candidate next elements or the one or more aptness scores ([0045] reciting “At process 425, the weighted kNN probabilities can be computed using the kNN. At process 428, probability distribution can then be generated over labels for the testing sequence based on the computed weighted kNN probability scores. At process 430, a classifier prediction is generated using the probability distribution over labels.”; [Claim 1] reciting “…a set of sequence indices that lead to a set of smallest distances between the respective normalized respective hidden state vector and the normalized test hidden state vector; computing a weighted probability score based on a set of distances corresponding to the set of sequence indices; and generating a probability distribution over the plurality of target labels for the test sequence based on the weighted probability score and one-hot encodings of each target label in the plurality of target labels.”); and
apply the weighting factor to provide one of a weighted probability for each of the one or more candidate next elements or weighted one or more aptness scores ([Claim 7] reciting “… generating, by the neural network, a prediction in response to the testing sequence and a confidence score associated with the predicted distribution; and in response to determining that the confidence score is lower than a threshold, computing the weighted probability score for fine-tuning the prediction of the testing sequence.”);
wherein determining the next element of the sequence uses the one of the weighted probability for each of the one or more candidate next elements or one or more weighted aptness scores ([Abstract] reciting “A set of indices for each sequence index that minimizes a distance between the respective hidden state vector and a test hidden state vector is then determined A weighted k-nearest neighbor probability score can then be computed from the set of indices to generate a probability distribution over labels for the test sequence.”).
37 It would have been obvious to one with ordinary skill before the effective filing date of the claimed invention, to have modified the method (taught by Flax in view of Alexandar) to incorporate the teachings of Rajani to provide a method that can determine the weighted probabilities of the various candidate elements, utilizing the probability methods and candidates provided by the teachings of Flax in view of Alexandar. Doing so would assess the mental state of a subject as stated by Flax ([0009] recited).
38 Claims 14 and 20 has similar limitations as of claim 7, therefore they are rejected under the same rationale as claim 7.
Conclusion
39 The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Rajani et al. (US 20210374488 A1) teaches a probability score based on test sequences, utilizing machine learning.
Liu et al. (US 20210374601 A1) teaches a prediction probability generated by machine learning, while also utilizing image classification.
40 Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNY TRAN LE whose telephone number is (571)272-5680. The examiner can normally be reached Mon-Thu: 7:30am-5pm; First Fridays Off; Second Fridays: 7:30am-4pm.
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, Kent Chang can be reached at (571) 272-7667. 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.
/JOHNNY T LE/Examiner, Art Unit 2614
/KENT W CHANG/Supervisory Patent Examiner, Art Unit 2614
Prosecution Insights