DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Non-Final Office Action is in reply to the communications filed on 18 February 2026.
Claims 2, 7, 12 and 17 have been canceled.
Claims 1, 3-6, 8-11, 13-16 and 18-24 are currently pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 18 February 2026 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-6, 8-11, 13-16 and 18-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more, and therefore directed to non-statutory subject matter.
Under Step 1, the claims fall within the statutory categories (namely, a system and a method).
Under Step 2A Prong 1, the claims are analyzed to determine whether the claims recite any judicial exceptions including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes).
Claims 1 and 11 recite in part, extract contextual data associated with a plurality of tasks from the plurality of data sets; determine at least a configurable task modifier as a function of the contextual data; determine at least a configurable task modifier as a function of the contextual data…determining the configurable task modifier…; generating…a task update instruction comprising at least one task identifier and at least one modification parameter associated with the configurable task modifier; query the plurality of tasks to identify at least one task of the plurality of tasks as a function of the at least a configurable task modifier.
The claims can be considered as to fall within the mental process groupings of abstract ideas because the steps of extracting, determining, querying mimic human thought processes of observation, evaluation, judgment, and opinion, perhaps with paper and pencil. The Courts generally treat collecting information as well as analyzing information by steps people go through in their minds and/or by pen & paper as essentially mental processes within the abstract-idea category. See 2106.04(a)(2) Part III.
The claims recite the steps, determine at least a configurable task modifier as a function of the contextual data using an analysis machine-learning model which comprises: converting at least communication datum in audio format into a form of machine-readable code using an automatic speech recognition process implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data. When given their broadest reasonable interpretation in light of the disclosure, the machine learning model and cepstral normalization are mathematical calculations (see paragraph [0032-0033], [0061] of Specification). The use of mathematical models to perform the training of the analysis machine learning model encompasses mathematical concepts.
Under Step 2A Prong 2 the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application.
This judicial exception is not integrated into a practical application. The claims recite additional elements: a server, a receiving module operating on the at least a server, receive a plurality of data sets from one or more data sources, wherein the plurality of data sets comprises a communication datum in text or audio format; and wherein receiving the plurality of data sets from the one or more data sources comprises instantiating a chatbot, wherein the chatbot is configured to respond to input using a decision tree, wherein the decision tree comprises at least: a root node, wherein the root node is configured to receive the input; and a terminal node corresponding to an exit indication; a context analyzing module operating on the at least a server; using an analysis machine-learning model which comprises: receiving analysis training data, wherein the analysis training data correlates a plurality of exemplary context data to a plurality of exemplary configurable task modifier data; training, iteratively, the analysis machine-learning model using the analysis training data, wherein training the analysis machine learning model includes retraining the analysis machine-learning model with feedback from previous iterations of the analysis machine-learning model; a task update module operating on the at least a server; and a task update database communicatively connected to the server; update the at least one task comprising a calendar event by executing the task update instruction to modify at least one calendar field corresponding to the at least one modification parameter.
The recitation of a receiving module, context analyzing module and task update module operating on a server that performs the claimed steps is recited at a high level of generality and merely automates the steps. The task update database is used in its ordinary capacity to store data and therefore does not integrate a judicial exception into a practical application.
The “receiving” steps amount to mere data gathering, which is a form of insignificant extra-solution activity. Furthermore, the limitations, wherein the chatbot is configured to respond to input using a decision tree, wherein the decision tree comprises at least: a root node, wherein the root node is configured to receive the input; and a terminal node corresponding to an exit indication merely describes the structure of the chatbot to receive input and therefore does not impose any meaningful limits on the claim.
The recitation, “update the at least one task comprising a calendar event by executing the task update instruction to modify at least one calendar field corresponding to the at least one modification parameter” adds insignificant extra solution to the judicial exception, i.e., (data gathering) as discussed in MPEP 2106.05(g).
The additional elements of “using an analysis machine-learning model which comprises: receiving analysis training data, wherein the analysis training data correlates a plurality of exemplary context data to a plurality of exemplary configurable task modifier data; training, iteratively, the analysis machine-learning model using the analysis training data, wherein training the analysis machine learning model includes retraining the analysis machine-learning model with feedback from previous iterations of the analysis machine-learning model” provide nothing more than mere instructions to implement the abstract idea on a generic computer. The limitations, “training, iteratively, the analysis machine-learning model using the analysis training data, wherein training the analysis machine learning model includes retraining the analysis machine-learning model with feedback from previous iterations of the analysis machine-learning model” only recite the outcome of ‘training” the analysis machine learning model and do not include details about how the “training” and retraining are accomplished. See MPEP 2106.05(f).
The limitations, “determining the configurable task modifier using the trained analysis machine-learning model” and “generating using the trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter associated with the configurable task modifier” are performed “using the trained analysis machine-learning model.” The trained analysis machine-learning model is used to generally apply the abstract idea without placing any limits on how the trained analysis machine learning model functions.
Each of the additional limitations is no more than mere instructions to apply the exception using a generic modules and server. The combination of these additional elements is no more than mere instructions to apply the exception using generic computing components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Under Step 2B of the 2019 PEG the claims are analyzed to determine whether the claims recite additional elements that amount to an inventive concept (aka “significantly more”) than the recited judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount mere instructions to apply the exception using generic modules, database and servers. The background does not provide any indication that the modules and server are anything other than generic, off-the-shelf computing components. For the receiving step that was considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The courts decisions in Symantec, OIP Techs., Inc., v. Amazon.com, Inc. and TLI (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For the updating step that was considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The court’s decision in Flook indicate that readjusting of data is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claims are not patent eligible.
Dependent claims 3-6, 8-10 and 13-16, 18-24 do not add “significantly more” to the abstract idea. The dependent claims further recite a mental process because they recite limitations further narrowing the judicial exception.
Claims 5, 6, 15, 16, 21, 22 further narrow the plurality of data sets and data sources.
Claims 3, and 13 recite converting, the communication datum in an audio format into a textual format; and extracting the contextual data as a function of the communication datum in the textual format which can be considered as a mental process because the limitations encompass a person either mentally or with pen and paper, observing and translating audio to text and evaluating the context from the text.
Claims 4 and 14 recite, extracting, at least a communication task datum from the contextual data; and determining the at least a configurable task modifier as a function of the at least a communication task datum. Claims 8 and 18 recite, receiving user data pertaining to a plurality of users; identifying, at least one user identifier associated with at least a user of the plurality of users as a function of the user data and the at least a configurable task modifier; and linking the at least one task to the at least one user identifier. The steps of extracting, determining, identifying, linking, mimic human thought processes of observation, evaluation, judgment, and opinion, perhaps with paper and pencil. The Courts generally treat collecting information as well as analyzing information by steps people go through in their minds and/or by pen & paper as essentially mental processes within the abstract-idea category. See 2106.04(a)(2) Part III.
Claims 9 and 19 recite, wherein updating the at least a task comprises: receiving a user response datum related to the at least a configurable task modifier, wherein the user response datum comprises an action modification and updating the at least a task as a function of the user response datum which is mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). Claims 10 and 20 recite generate a notification datum as a function of the update of the at least a task; and transmit the notification datum to at least a downstream device which is mere data output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g). Further evaluation of the receiving and transmitting steps under Step 2B indicates the steps to be well-understood, routine, conventional activity in the field. The courts decisions in Symantec, OIP Techs., Inc., v. Amazon.com, Inc. and TLI (MPEP 2106.05(d)(II)) indicate that mere collection or receipt or transmission of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Re-evaluation of the updating step under Step 2B indicates that the step is well-understood, routine, conventional activity in the field. The court’s decision in Flook specify that readjusting of data is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Claim 23 and 24 recite, wherein training the analysis machine-learning model further includes: applying elements from the analysis training data to an input set of nodes of a neural network; and adjusting, using a training algorithm the connections and weights of nodes in adjacent layers of the neural network to produce desired values at an output set of nodes. The limitations are directed to the structure of the neural network and merely recites the outcome of “applying” and “adjusting” without reciting details of how the outcome is accomplished. The limitations provide nothing more than mere instructions to implement an abstract idea on a generic computer.
The additional elements of the dependent claims amount to mere instructions to apply the exception using generic processors. Even when viewed as an ordered combination, these additional elements do not integrate the abstract idea into a practical application nor include additional elements that are sufficient to amount to significantly more than the judicial exception. The dependent claims are held to be ineligible under Steps 2A1/2A2/2B at least similar to the rationale as discussed above regarding claims 1 and 11. The claims are not patent eligible.
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.
Claim(s) 1, 3-6, 8-11, 13-16, 18-20 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over Guha et al (US 2024/0104467 A1) in view of Smith et al (US 2024/0370793 A1) in view of Watkins (US Patent #11,983,494 B1) and in view of Matsuoka et al (US 2023/0077130 A1).
Claims 1 and 11: Guha discloses a system for automatic task control, the system comprising (see [0032]: the system 100 can include a task management component(s) (TMC) 128 that can manage (e.g., automatically and dynamically manage, in real time or near real time) performance of tasks by or associated with users (e.g., 108, 110, and/or 112) to facilitate desirable (e.g., favorable, efficient, improved, or optimal) workflow management, in accordance with defined task management criteria:
at least a server (see [0163]: server);
a receiving module operating on the at least a server, wherein the receiving module is designed and configured to (see [0047]: The TMC 128 can continue to monitor the performance of tasks and/or other activities by the user 108 and/or the other users (e.g., users 110 and/or 112, and/or other users) and feedback relating to the users and/or tasks, a):
receive a plurality of data sets from one or more data sources, wherein the plurality of data sets comprises a communication datum in text or audio format; and extract contextual data associated with a plurality of tasks from the plurality of data sets (see [0048]: In an example scenario, the additional task-related information can indicate that the user 108 has been performing assigned tasks in accordance with the task schedule and in accordance with the respective priority levels of the respective tasks, and the feedback information can indicate that the user 108 is experiencing a higher than normal amount of stress or fatigue during a particular work afternoon. For instance, the TMC 128 can perform emotion, stress, or fatigue detection to facilitate determining whether the user 108 is experiencing an undesirably high amount of stress or fatigue, wherein analysis of visual images captured by a camera of a communication device (e.g., 102) or other biometric information captured by another sensor (e.g., heart rate or blood pressure sensor, or electronic or smart watch) associated with the user 108 can indicate to the TMC 128 that the user 108 is experiencing a higher than normal amount of stress or fatigue during a particular work afternoon. [0108]: facilitate managing a conversation with a user (or another VA or device) based at least in part on sentiment and personality attributes of the user, the context of the user or context of the interaction with the user (or other VA or device), including the verbal words spoken, and the characteristics of the verbal words spoken, by the user (or other VA or device) during the conversation, the environmental conditions associated with the user,)
a context analyzing module operating on the at least a server, wherein the context analyzing module is designed and configured to: determine at least a configurable task modifier as a function of the contextual data (see [0047]: The task organizer component 204 and/or the AI component 206 can perform an analysis (e.g., AI-based analysis) on the additional task-related information, feedback information, and/or other desired information, and/or the previous (e.g., historical) task-related information, feedback information, and/or other desired information relating to the users (e.g., users 108, 110, and/or 112, and/or other users) and/or tasks. [0049]: Based at least in part on such determinations, the task organizer component 204 can adaptively adjust attributes associated with the work tasks of the work and task schedule of the user 108, or recommend that the attributes associated with the work tasks of the user 108 be adaptively adjusted, to allocate the last hour of this work day as relaxation (e.g., leisure) or rest time for the user 108 and reorganize the remaining tasks assigned to the user 108) using an analysis machine-learning model (see [0079]: AI and/or ML techniques and algorithms) which comprises:
receiving analysis training data from a task update database communicatively connected to the server, wherein the analysis training data correlates a plurality of exemplary context data to a plurality of exemplary configurable task modifier data (see [0079]: the AI component 206 can employ, build (e.g., construct or create), and/or import, AI and/or ML techniques and algorithms, AI and/or ML models (e.g. trained models), neural networks (e.g. trained neural networks), Markov chains (e.g., trained Markov chains) for determining or learning a correlation, relationship, or causation between an event and another event (e.g., occurrence of another event), determining or learning about attributes associated with tasks (e.g., type of task, amount of time to perform a task, level of expertise desired to perform a task, specifications associated with a task, or other characteristic associated with a task), determining or learning adaptive adjustments that can be made to attributes associated with tasks to enhance performance of tasks by users, determining or learning desirable assignments of tasks to users, determining or learning an allocation of time to perform a task. [0080]: Based at least in part on the results of the analysis, the AI component 206 can determine, train, and generate a model that can relate to tasks and users, wherein the model can model or be representative of historical performance of tasks by users, characteristics (e.g., education, work experience or skill relating to tasks, age, personality, employment role or position, demographic, or other characteristics) associated with users, attributes associated with tasks, levels of user expertise associated with tasks, environments associated with users or tasks, and/or other features relating to the users (e.g., users 108, 110, and/or 112) and tasks to be performed by users, such as described herein. [0085]: The data store 220 that can store data structures (e.g., user data, metadata));
training, iteratively, the analysis machine-learning model using the analysis training data, wherein training the analysis machine learning model includes retraining the analysis machine-learning model with feedback from previous iterations of the analysis machine-learning model (See [0080]: The AI component 206 can update (e.g., modify, adjust, or change), and further train and enhance, the model as additional data (e.g., task-related information, user feedback information, sensor information, biometric information, environmental information, or other information) associated with users or tasks is received and analyzed by the AI component 206. In some embodiments, as part of the data analysis, and the determining and training of the model, the AI component 206 can employ (and/or train) Markov chains, a neural network(s), or other AI-based or ML-based modeling, techniques, functions, or algorithms);
determining the configurable task modifier using the trained analysis machine-learning model (see [0079]: the AI component 206 can employ, build (e.g., construct or create), and/or import, AI and/or ML techniques and algorithms, AI and/or ML models (e.g. trained models) neural networks (e.g., trained neural networks), Markov chains (e.g., trained Markov chains) for determining or learning a correlation, relationship, or causation between an event and another event (e.g., occurrence of another event), determining or learning about attributes associated with tasks (e.g., type of task, amount of time to perform a task, level of expertise desired to perform a task, specifications associated with a task, or other characteristic associated with a task), determining or learning adaptive adjustments that can be made to attributes associated with tasks to enhance performance of tasks by users, determining or learning desirable assignments of tasks to users, determining or learning an allocation of time to perform a task);
a task update module operating on the at least a server, wherein the task update module is designed and configured to: query the plurality of tasks to identify at least one task of the plurality of tasks as a function of the at least a configurable task modifier (See [0049]: the task attribute adjustment may involve a re-allocation of respective amounts of time to perform the respective remaining tasks (e.g., the task organizer component 204 may shorten the amounts of time to perform some of the remaining tasks, in accordance with the experience or expertise of the user 108 and/or historical performance times of the user 108 performing those work tasks or similar work tasks), or adjustment of the work schedule of the user 108 to add part or all of that hour of work to another work day or an off day. See [0051]: the task organizer component 204 can receive such acceptance indication (e.g., a verbal or written acceptance message, or a selection of a control or button indicating acceptable) from the user 108 via an interface(s) of the TMC 128, communication device 102, or VA 116, and the task organizer component 204 can perform the adjustment of the attributes associated with the tasks of the work and task schedule of the user 108, in accordance with the recommended task and schedule adjustment. See also [138]);
and update the at least one task comprising a calendar event (See [0043]: based at least in part on the results of the analysis by the task organizer component 204 and/or AI component 206, the task organizer component 204 can adaptively adjust calendar information relating to a task in the electronic calendar).
Although Guha teaches [0108]: facilitate managing a conversation with a user (or another VA or device),Guha does not expressly disclose wherein receiving the plurality of data sets from the one or more data sources comprises instantiating a chatbot and converting at least communication datum in audio format into a form of machine-readable code using an automatic speech recognition process implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data but Smith which also discloses a system of adjusting tasks teaches, wherein receiving the plurality of data sets from the one or more data sources comprises instantiating a chatbot (see [0076], [0112] Now referring to FIG. 7, method 700, at step 705, includes identifying, by processor 104, plurality of tasks 116 associated with first resource. method 700 may include receiving first resource 112 as a function of an interaction between a user and a chatbot.), converting at least communication datum in audio format into a form of machine-readable code using an automatic speech recognition process implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data (See [0031]: in automatic speech recognition process may use cepstral normalization to normalize for different speakers and recording conditions).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in Guha instantiating a chatbot and converting at least communication datum in audio format into a form of machine-readable code using an automatic speech recognition process implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data as taught by Smith for receiving submissions from a user (Smith, [0112] and to improve results and optimize some classification-related measure of training data (Smith, [0031]).
Guha and Smith do not expressly disclose wherein the chatbot is configured to respond to input using a decision tree, wherein the decision tree comprises at least: a root node, wherein the root node is configured to receive the input; and a terminal node corresponding to an exit indication but Watkins which also discloses a conversational system for receiving user submissions teaches, wherein the chatbot is configured to respond to input using a decision tree, wherein the decision tree comprises at least: a root node, wherein the root node is configured to receive the input; and a terminal node corresponding to an exit indication (see col. 44 lines 14-30: configured to respond to a chatbot input using a decision tree. A “decision tree,” as used in this disclosure, is a data structure that represents and combines one or more determinations or other computations based on and/or concerning data provided thereto, as well as earlier such determinations or calculations, as nodes of a tree data structure where inputs of some nodes are connected to outputs of others. Decision tree may have at least a root node, or node that receives data input to the decision tree, corresponding to at least a candidate input into a chatbot. Decision tree has at least a terminal node, which may alternatively or additionally be referred to herein as a “leaf node,” corresponding to at least an exit indication).
Therefore, it would have been obvious to one of ordinary skill in the art to combine the chatbot of Guha and Smith with Watkins’ chatbot which is configured to respond to input using a decision tree, wherein the decision tree comprises at least: a root node, wherein the root node is configured to receive the input; and a terminal node corresponding to an exit indication to provide a structure way of responding to a chatbot input (Watkins, col. 44 lines 14-17)
Guha, Smith and Watkins do not explicitly disclose generating, using the trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter associated with the configurable task modifier; and update the at least one task comprising a calendar event by executing the task update instruction to modify at least one calendar field corresponding to the at least one modification parameter but Matsuoka in the same field of endeavor teaches generating, using the trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter associated with the configurable task modifier; and update the at least one task comprising a calendar event by executing the task update instruction to modify at least one calendar field corresponding to the at least one modification parameter (see [0012] In yet another implementation, the computer-implemented method includes transmitting a calendar item modification recommendation. [0046] In general, the process of generating or recommending a task includes obtaining data associated with the member, who is generally a user registered with the task facilitation service. The obtained data may correspond to information provided by the member and stored in association with the user model, sensor data from devices associated with the member, information provided by third-party services associated with the member, and the like. [0055]: Example clustering algorithms that may trained using sample member datasets (e.g., historical member data, hypothetical member data, etc.) to classify a member in order to identify categories of tasks that may be of relevance to the member. [0197]: The training data 432 may be used to train the machine-learning model of the task generator 428 and/or the representative to generate task recommendations that may be of interest to the member. [0305] The foregoing discussion described an instance in which a calendar application includes a calendar item and corresponding calendar item data that is transmitted to task facilitation service 102. Task facilitation service 102 then creates a corresponding task within task facilitation service 102 and transmits an update to application data for the calendar application to indicate creation of the task at task facilitation service 102. [307]: FIG. 13, for example, illustrates user interface 1100 (initially presented in FIG. 11) with a recommended calendar item 1118, namely, a house cleaning. In the specific example of FIG. 13, the recommendation is indicated visually by a dashed border. [0310]: For example, acceptance or rejection of a recommendation may be considered an indication of a member's preferences and used to update the member model associated with the member. As another example, the acceptance or rejection and parameters of the recommendation may be used as training data for one or more models or algorithms used by task facilitation service 102 to generate task recommendations. Stated differently, task facilitation service 102 may use the response of member 118 to a recommended calendar item as a data point/feedback to further improve and enhance various functions of task facilitation service 102.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in Guha as modified by Smith, and Watkins, a system and method to generating, using the trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter associated with the configurable task modifier; and update the at least one task comprising a calendar event by executing the task update instruction to modify at least one calendar field corresponding to the at least one modification parameter as taught by Matsuoka because it would “eliminate[s] the time, computing resources, and risk of error associated with manual synchronization of data. (Matsuoka, [0254]).
Claims 3 and 13: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 2 and 12 above. Matsuoka further teaches, the one or more data sources comprises a communication channel (see [0129] User recordings 206 may further include audio and/or video recordings within the member area 202 corresponding to possible issues for which tasks may be generated. For instance, the member may utilize their smartphone or other recording device to generate an audio and/or video recording. [0133]: task recommendation system 112 may utilize NLP or other artificial intelligence to identify a possible task related to painting of the identified room), wherein extracting the contextual data comprises: converting, using an automatic speech recognition, the communication datum in an audio format into a textual format; and extracting the contextual data as a function of the communication datum in the textual format (see [0182]: one or more machine-learning models configured to parse natural language input from the member to identify data corresponding to a possible task for the member. The one or more machine-learning models may include one or more natural language processors that can convert audio and/or video to text, parse text to derive a semantic meaning such as an interest or intent.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the context determination of Guha as modified by Smith, Watkins and Matsuoka, the one or more data sources comprises a communication channel, converting, using an automatic speech recognition, the communication datum in an audio format into a textual format; and extracting the contextual data as a function of the communication datum in the textual format as taught by Matsuoka because it “may be used to identify tasks that can be performed for the benefit of the member” (Matsuoka, [0128]) and would help identify an interest or intent of the user (Matsuoka, [0182]).
Claims 4 and 14: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Guha discloses wherein determining the at least a configurable task modifier comprises: extracting, using a language processing module of the context analyzing module, at least a communication task datum from the contextual data; and determining the at least a configurable task modifier as a function of the at least a communication task datum (see [0109] The conversation manager component 508 can comprise a modulator component 510 that can be utilized to modulate or adjust the voice, including adjusting the characteristics of the voice, produced by the voice generator component 506. For example, based at least in part on the sentiment and personality attributes of the user, the context, the environmental conditions, and/or the VA personality attributes, the modulator component 510 can adjust (e.g., increase or decrease) the speed and/or cadence of the verbal words emitted by the voice generator component 506, the inflection and/or tone of the voice and/or verbal words emitted by the voice generator component 506, the language and/or dialect of the verbal words emitted by the voice generator component 506).
Claims 5 and 15: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Guha further discloses the plurality of data sets comprises an outcome datum; and the one or more data sources comprises an outcome machine-learning module (see [0124]: task-related information relating to a group of tasks associated with a user includes previous performance outcomes of performance of the task or a similar task by the user and/or other user(s)). [0018]: TMC can perform an artificial intelligence (AI)-based analysis on the task-related information, the assessment information, the biometric information, the feedback information, and/or the other information. The AI-based analysis can comprise utilizing or applying AI, machine learning (ML), models, neural networks, functions, and/or other AI-based techniques and algorithms on such information. See also [0058]).
Claims 6 and 16: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Guha discloses wherein: the plurality of data sets comprises user activity data, wherein the user activity data comprises interactions of the user with a graphical user interface element related to the plurality of tasks; and the one or more data sources comprises at least a downstream device (see [0091]: As indicated at reference numeral 402 of the task management flow 400, the TMC 128 can monitor and observe users (e.g., users 108, 110, and/or 112) and tasks, including information relating to tasks, can collect information from users, information relating to users and/or tasks (e.g., from other sources, such as communication devices (e.g., 102, 104, and/or 106), VAs (e.g., 116, 118, and/or 120), sensors (e.g., 122, 124, and/or 126), applications (e.g., applications installed on or accessed by communication devices or VAs), data source device(s) 130, and/or other data sources or devices), ).
Claims 8 and 18: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Guha discloses wherein updating the at least a task comprises: receiving user data pertaining to a plurality of users; identifying, using the task update module, at least one user identifier associated with at least a user of the plurality of users as a function of the user data and the at least a configurable task modifier; and linking the at least one task to the at least one user identifier (see [0056] As an alternative example scenario relating to expertise, if the user 108 is unavailable or unable to accommodate having to perform the particular task, or if it is otherwise desirable (e.g., desirable to give another user more experience in performing such task) to assign the particular task to another user (e.g., user 110), the task organizer component 204 can assign the particular task to the other user 110, and can allocate a desirable (e.g., suitable or sufficient) amount of time for the user 110 to perform the particular task (e.g., an amount of time that can be longer than the amount of time that would have been allocated to user 108, since user 110 has a relatively lower expertise level than the user 108 with respect to that particular task). [0085] The data store 220 that can store data structures (e.g., user data, metadata), code structure(s) (e.g., modules, objects, hashes, classes, procedures) or instructions, information relating to users, VAs, communication devices or other devices, interactions, events, contexts associated with users, tasks, or interactions, status or progress of tasks, status or progress of interactions associated with users, demographic data, data privacy, activities relating to interactions, environmental conditions associated with users, tasks, or interactions, identifiers or authentication credentials associated with users, entities, devices, or components, updates to user profiles of users, parameters, traffic flows, policies, defined task management criteria, algorithms (e.g., task management algorithm(s)).
Claims 9 and 19: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Guha discloses wherein updating the at least a task comprises: receiving a user response datum related to the at least a configurable task modifier, wherein the user response datum comprises an action modification; and updating the at least a task as a function of the user response datum (see [0051]: If, instead, the user 108 has decided to override the recommended task and schedule adjustment to have the attributes associated with the tasks changed in a different way, as desired by the user 108, the task organizer component 204 can receive such override indication from the user 108 via an interface(s) of the TMC 128, communication device 102, or VA 116, and the task organizer component 204 can adjust the attributes associated with the tasks of the work and task schedule of the user 108, in accordance with the change desired by the user).
Claims 10 and 20: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Guha discloses a communication module operating on the at least a server, wherein the communication module is designed and configured to: generate a notification datum as a function of the update of the at least a task; and transmit the notification datum to at least a downstream device (see [0070]: In response to receiving the task update information, the schedule component 214 can update (e.g., modify) information in the task schedule of the user 108 to indicate the lack of progress being made towards completion of the task by the user 108 (e.g., can update information to highlight, emphasize, or increase the priority level of the task), and/or the notification component 208 can present notification information, to the user 108 (e.g., via an interface(s) of the TMC 128, communication device 102, or VA 116), and/or to another entity (e.g., a supervisor, manager, or other entity associated with the user 108 or the enterprise)).
Claims 23 and 24: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Smith further discloses, wherein training the analysis machine-learning model further includes: applying elements from the analysis training data to an input set of nodes of a neural network; and adjusting, using a training algorithm the connections and weights of nodes in adjacent layers of the neural network to produce desired values at an output set of nodes (see [0094]: an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data 304 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the training of the machine learning model of Guha as modified by Smith, Watkins and Matsuoka, wherein training the analysis machine-learning model further includes: applying elements from the analysis training data to an input set of nodes of a neural network; and adjusting, using a training algorithm the connections and weights of nodes in adjacent layers of the neural network to produce desired values at an output set of nodes as taught by Smith for optimal task reallocation (Smith, [0005]).
Claim(s) 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Guha, Smith, Watkins and Matsuoka as applied to claims 1 and 11 above, and further in view of Nikolopoulos et al (US 2017/0236445 A1).
Claims 21 and 22: The combination of Guha, Smith, Watkins and Matsuoka discloses the claimed invention as applied to claims 1 and 11 above. Guha discloses wherein the plurality of data sets further comprises user activity data, wherein the user activity data comprises session durations (see [0018]: analyze user activity [0045]: play games online (e.g., online electronic gaming) for a short time (e.g., 15 to 30 minutes) , and actions performed per session (see [0058]: the actions the user(s) is performing or techniques the user(s) is using to perform the particular task). The combination of Guha, Smith, Watkins and Matsuoka does not expressly disclose a number of logins but Nikolopoulos teaches user activity data comprises, a number of logins (see [0011]: In some examples, the at least one KPI may be an index corresponding to the amount of login accesses of the user at the electronic user access device in a given period of time. The login accesses of the user at the electronic user access device by the user may be an indication of a user's engagement or participation metrics related to the activity. Therefore measuring the login accesses may provide an indication about the user's behavior with respect to the aforementioned metrics.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the user activity data of Guha as modified by Smith, Watkins and Matsuoka, a number of logins as taught by Nikolopoulos because it would “provide an indication about the user's behavior” with respect to a user’s engagement or participation (Nikolopoulos, [0011]).
Response to Arguments
Applicant's arguments filed 18 February 2026 have been fully considered but they are not persuasive.
Applicant argues,
“The Office Action characterizes the steps of extracting contextual data, determining a configurable task modifier, and updating a task as, “mimicking human thought processes such as observation, evaluation, judgment, and opinion, perhaps with paper and pencil.” (Office Action p. 3). However, this characterization improperly abstracts the claim and fails to account for the specific technical limitations now expressly recited. As amended, claim 1 requires automatic speech recognition implementing cepstral normalization to normalize for different speakers and recording conditions, iterative training and retraining of an analysis machine-learning model using correlated training data and feedback from previous iterations, generation, using the trained machine-learning model, of a task update instruction comprising at least one task identifier and at least one modification parameter, and execution of that instruction to modify at least one calendar field of a task. These operations are not mental steps and cannot be performed by a human using pen and paper. MPEP § 2106.04(a)(2), subsection III, makes clear that a claim does not recite a mental process when it includes limitations that cannot practically be performed in the human mind, even if the claim involves data analysis. Here, the claimed combination of cepstral-normalized automatic speech recognition, iterative machine-learning training using correlated multi-dimensional training data, and generation and execution of a machine-readable task update instruction that programmatically mutates a calendar data structure is well beyond the capabilities of the human mind. As recognized by the Federal Circuit, claims involving a “several-step manipulation of data that, except in its most simplistic form, could not conceivably be performed in the human mind or with pencil and paper” do not recite a mental process. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1148 (Fed. Cir. 2016).”
The examiner respectfully disagrees with the Applicant. The Examiner asserts that there is no indication in the disclosure of how any of the mentioned analysis model are actually being trained. Applicant's reference to these machine learning terms is clearly lacking the inner-workings of the intelligence aspect of what constitutes “training” of the machine learning model since there is no disclosure that provides how the machine learning model is implemented from each subsequent iteration utilizing the outputs from previous iterations. Applicant's claim language clearly demonstrates that the machine learning terms are merely being used as a tool implemented by the computer environment without expressly providing any detail of what constitutes the specific details of the learning algorithms. Examiner asserts that the limitations of claim 1, “converting at least communication datum in audio format into a form of machine-readable code using an automatic speech recognition process implementing cepstral normalization to normalize for different speakers and recording conditions and extracting contextual data” requires the implementation of the mathematical concept, (i.e. cepstral normalization. See paragraph [0032-0033], [0061] of Specification). Therefore, the claim recites an abstract idea that falls under the mathematical concepts grouping.
Applicant argues,
“The amended claim is also consistent with USPTO Subject Matter Eligibility Example 39, which confirms that claims directed to training and using a neural network do not recite a mental process where the steps are not practically performable in the human mind. Like Example 39, claim 1 recites receiving correlated training data, iteratively training a machine-learning model with feedback from prior iterations, and using the trained model to produce an output that controls downstream system behavior. In the present claim, output is not merely informational, but instead comprises a task update instruction that is executed to modify a calendar event, further distinguishing the claim from any abstract mental activity. Additionally, while the claim may involve mathematical techniques internally within the machine-learning model or speech recognition process, no mathematical relationship, formula, or calculation is recited in the claim itself. Nor is the claim directed to a fundamental economic practice or a method of organizing human activity. Rather, the claim is directed to a machine-implemented system that automatically controls task state by generating and executing structured task update instructions using a trained analysis machine-learning model.”
However, the examiner is not persuaded. As discussed in MPEP 2106.07 while it would be acceptable for applicants to cite training materials or examples in support of an argument for finding eligibility in an appropriate factual situation, applicants should not be required to model their claims or responses after the training materials or examples to attain eligibility. The evaluation of whether the claimed invention qualifies as patent-eligible subject matter should be made on a claim-by-claim basis, because claims do not automatically rise or fall with similar claims in an application. Here, it is important for Applicant to note that representative claim 1 does not parallel the factual patterns discussed in Training Example 39. For example, claim 1 does not recite training a neural network as recited in Training Example 39. Also, in Example 39 recites no abstract idea. Thus, the training example cannot be relied upon to attain eligibility.
Applicant argues,
“Here, claim 1 does not merely analyze information, identify patterns, or provide advisory output. Instead, the claim integrates the analysis machine-learning model into a machine-controlled task execution pipeline that automatically modifies the operational state of a computerized task management system. As amended, claim 1 expressly requires generating, using a trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter, and further requires executing that instruction to modify at least one calendar field of a task. This execution step meaningfully limits any alleged abstract idea by tying the claimed analysis directly to a concrete, system-level action that changes the state of a computer-implemented calendar system.”
However, the examiner disagrees with the Applicant’s arguments. The limitations, “generating, using a trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter, and further requires executing that instruction to modify at least one calendar field of a task” is not sufficient impose meaningful limitations. As claimed the limitations are merely directed to changing information associated with a calendar, such as changing a due date associated with a task (See Applicant’s specification [0056]) but does not change the “state of computer-implemented calendar system.”
Applicant further argues that,
“The USPTO guidance recognizes integration into a practical application where a claim applies an abstract idea to effect a transformation or improvement in the operation of a computer or other technology. MPEP § 2106.04(d). The amended claim does precisely that by transforming a calendar data structure from a first state to a second state through execution of a machine-generated instruction derived from trained model output. The claim therefore goes beyond merely collecting, analyzing, or displaying information and instead improves the functioning of a computer-based task management system by enabling automated, model-driven control of task attributes. Additionally, claim 1 recites specific technological mechanisms that constrain how any alleged judicial exception is applied. These include automatic speech recognition implementing cepstral normalization, iterative training and retraining of a machine-learning model using correlated training data and feedback from previous iterations, and generation of a structured task update instruction that is consumed by a task update module to modify a calendar event. These limitations ensure that the claim is not directed to an abstract result or outcome, but rather to a specific technological solution for automatically controlling task state based on multimodal contextual inputs. Finally, the claim does not attempt to monopolize task management, scheduling, or machine learning in the abstract. Instead, it is narrowly directed to a particular system architecture in which a trained analysis machine-learning model generates executable task update instructions that are applied to calendar events. Alternative task management systems, recommendation-based analytics, and non-executing decision-support tools remain entirely outside the scope of the claim.”
However, the examiner is not persuaded. Examiner asserts the amended claim does not indicate an improvement in the functioning of computer-based task management system. The claims are directed to mere automation of a task management system. The claimed automatic speech recognition implementing cepstral normalization, iterative training and retraining of a machine-learning model using correlated training data and feedback from previous iterations are invoked as tools to analyze information associated with the claimed tasks. Furthermore, updating a field event (such as a date) in a calendar does not transform the calendar from one state to another state. The calendar still contains dates. The Applicant’s specification does not provide any indication of how the calendar transforms from one state to another.
Applicant further argues,
“Here, the Office Action does not establish, under any of the permissible MPEP criteria, that the additional elements of claim 1 are well-understood, routine, or conventional. In particular, the Examiner has not shown that it was conventional to generate, using a trained analysis machine-learning model, a task update instruction comprising a task identifier and a modification parameter, and to execute that instruction to automatically modify a calendar field of a task. Nor has the Examiner shown that it was conventional to combine such execution logic with automatic speech recognition implementing cepstral normalization, iterative retraining with feedback, and structured task state mutation within a task management system. Importantly, these additional elements are not merely generic computer components performing generic functions. Instead, they define a specific, nonconventional arrangement of components in which a trained machine-learning model produces an executable control instruction that is consumed by a task update module to directly modify a calendar data structure. As the Federal Circuit explained in BASCOM Global Internet Services, Inc. v. AT&T Mobility LLC, 827 F.3d 1341, 1350 (Fed. Cir. 2016), an inventive concept may be found in the “non-conventional and non-generic arrangement of known, conventional pieces.” Even if individual components were known in isolation, their ordered combination may still constitute an inventive concept.
That is precisely the case here. The claim does not simply recite generic data analysis followed by generic output. Rather, it recites a particular technical configuration in which: (i) contextual data is extracted using speech recognition with cepstral normalization; (ii) a machine-learning model is iteratively trained using correlated training data and feedback from prior iterations; (iii) the trained model generates a structured task update instruction; and (iv) that instruction is executed to modify a calendar event. The Office Action does not cite prior art demonstrating this arrangement, nor does it explain why such a configuration would have been routine or conventional at the time of the invention. Further, the additional elements cannot be dismissed as “insignificant extra-solution activity.” The execution of a task update instruction to modify a calendar field is not a mere post-solution display or token application of results. Instead, it is the core operational mechanism by which the claimed system controls task state. Without these elements, the claim would not function as claimed. As such, they impose meaningful limitations on any alleged abstract idea and are integral to the claimed technological solution. Accordingly, because the Examiner has not met the evidentiary burden required to establish that the additional claim elements are well-understood, routine, and conventional, and because the ordered combination of those elements reflects a nonconventional arrangement that improves the operation of a computer-based task management system, claim 1 recites an inventive concept under Step 2B. The § 101 rejection should therefore be withdrawn.
Examiner respectfully disagrees and maintains that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The recitation of a receiving module, context analyzing module and task update module operating on a server that performs the claimed steps is recited at a high level of generality and merely automates the steps. The task update database is used in its ordinary capacity to store data and therefore does not integrate a judicial exception into a practical application.
The “receiving” steps amount to mere data gathering, which is a form of insignificant extra-solution activity. Furthermore, the limitations, wherein the chatbot is configured to respond to input using a decision tree, wherein the decision tree comprises at least: a root node, wherein the root node is configured to receive the input; and a terminal node corresponding to an exit indication merely describes the structure of the chatbot to receive input and therefore does not impose any meaningful limits on the claim.
The additional elements of “using an analysis machine-learning model which comprises: receiving analysis training data, wherein the analysis training data correlates a plurality of exemplary context data to a plurality of exemplary configurable task modifier data; training, iteratively, the analysis machine-learning model using the analysis training data, wherein training the analysis machine learning model includes retraining the analysis machine-learning model with feedback from previous iterations of the analysis machine-learning model” provide nothing more than mere instructions to implement the abstract idea on a generic computer. The limitations, “training, iteratively, the analysis machine-learning model using the analysis training data, wherein training the analysis machine learning model includes retraining the analysis machine-learning model with feedback from previous iterations of the analysis machine-learning model” only recite the outcome of ‘training” the analysis machine learning model and do not include details about how the “training” and retraining are accomplished. See MPEP 2106.05(f).
The limitations, “determining the configurable task modifier using the trained analysis machine-learning model” and “generating, using the trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter associated with the configurable task modifier” are performed “using the trained analysis machine-learning model.” The trained analysis machine-learning model is used to generally apply the abstract idea without placing any limits on how the trained analysis machine learning model functions.
As indicated in the rejection above, the examiner has cited court cases to provide support that the steps that were considered as extra-solution activity are determined to be well-understood, routine, conventional activity in the field: For the receiving steps that was considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The courts decisions in Symantec, OIP Techs., Inc., v. Amazon.com, Inc. and TLI (MPEP 2106.05(d)(II)) indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For the updating step that was considered extra-solution activity in Step 2A, this has been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field. The court’s decision in Flook indicate that readjusting of data is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). The claims are not patent eligible.
Applicant's arguments with respect to the prior art rejections have been fully considered but they are not persuasive.
Applicant argues that “Matsuoka does not teach, suggest or motivate “generating, using the trained analysis machine-learning model, a task update instruction comprising at least one task identifier and at least one modification parameter associated with the configurable task modifier,”. However, the examiner disagrees with the Applicant arguments. Matsouka discloses at paragraph [0012] In yet another implementation, the computer-implemented method includes transmitting a calendar item modification recommendation. When the calendar item modification recommendation is received by a computing device, the computing device is enabled to approve the calendar item modification recommendation to modify a calendar item of the calendar. The method may further include receiving approval of the calendar item modification recommendation, in response, transmitting an update for application data of the calendar application to modify the calendar item according to the calendar item modification recommendation. [0161] In some examples, the data fields presented in a task template used by the member 118 to manually define a new task can be selected based on a determination generated using a machine learning algorithm of artificial intelligence. For example, the task creation sub-system 302 can use, as input to the machine learning algorithm or artificial intelligence, a member profile from the user datastore 108 and the selected task template from the task datastore 110 to identify which data fields may be omitted from the task template when presented to the member 118 for definition of a new task or project. See also paragraph [0345]: provide recommendations to modify existing calendar items. For example, task facilitation service 102 may receive calendar data and determine an event or task in the calendar conflicts with another event or task of member 118 but that may not be included in the calendar. As another example, task facilitation service 102 may determine that a particular even or task may result in member 118 being overbooked for a given time period and, as a result, may recommend moving or deleting a calendar event. Regardless of the basis for the modification, task facilitation service 102 may propose the modification for acceptance or rejection by member 118 or representative 106 and, if approved, initiate the corresponding modifications to any relevant external application data and internal data maintained in task facilitation service 102.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
WO 2013003007 A2: [0032] Figure 3 illustrates a computer-generated screen shot of a task/calendar item verification/assignment user interface for allowing user verification of a recommended task or calendar item. The user interface 300 is illustrative of a user interface component in which text strings classified as task items or calendar items may be presented to a user for acceptance, correction, or replacement. The task/calendar item classification interface 305 includes a statement 310 such as "The following task or calendar item is recommended:" for identifying to a receiving user the nature of the user interface component. A text box/field 315 is provided for presenting the candidate task or calendar item. For example, an example task of "Add additional building wing" is illustrated in the text box 315, followed by an identification of a recommended project workspace or group to which the task would be added.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAAME BALLOU whose telephone number is (571)270-1359. The examiner can normally be reached Monday-Friday 9am-5pm.
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, Lynda Jasmin can be reached at 571-272-6782. 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.
MAAME BALLOU
Examiner
Art Unit 3629
/MAAME BALLOU/Examiner, Art Unit 3629
/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
Prosecution Insights