DETAILED ACTION
This communication is in response to the application filed 12/18/2026. Claims 1-6, 8-17, 19-20 are pending in the application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claim(s) 12/18/2025 have been considered. The rejection of claims 1-20 under 35 U.S.C 101 has been withdrawn.
Regarding the arguments in relating to the amended limitations in the independent claims 1, 12, 20 and subsequent claims on pages 10-12, please see the new combination of references with columns and lines cited below.
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) 1-2, 5-6, 12-13, 16-17, 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akmal et al. (US 20230087244) in view of Wei et al. (US 20230394328) and further in view of Imani et al. (US 20240362416).
As per claims 1, 12, 20, Akmal et al. teaches
a method of interpreting natural language comparisons during visual analysis, comprising: at a computing system having one or more processors and memory storing one or more programs configured for execution by the one or more processors: (fig. 6B: memory, processors; fig. 7B: natural language processing module; para. 52: the digital assistant provides a visualized or animated response to a user's query in a user's preferred format; shows visualized comparison between comparable contents to provide better representation of the information requested by the user. This can be desirable for improving user experience by decreasing the amount of time spent browsing for searching for better representation of information);
obtaining metadata for a dataset, wherein the metadata defines dataset fields for the dataset and the metadata and dataset fields are stored at the computer system, wherein the dataset fields comprise dimensions and measures (para. 293: the metadata from the server module may inform the visualization engine about
a specific module to use based on a type (e.g., speed, volume, density, height, timeline, /dimensions etc.) of query from the user. The metadata provided by the server module may refer to data in the visualization that needs to be visualized; para. 272: the server module may provide metadata for visual representation of the information; fig. 13A: dimensions: speed, the Ferrari 488, producer, manufacturer; measures: 329.9 KPH, a mind-engine sports car, the Italian, Ferrari);
obtaining from a user interface of the computing system a natural language utterance that includes a comparison query, wherein the comparison query includes a comparison target and a comparison term associated with the comparison target, at least one of which are different from the dataset fields (para. 233: using the processing modules, data, and models implemented in digital assistant module, the digital assistant can perform at least some of the following: converting speech input into text; identifying a user's intent expressed in a natural language input received from the user; para. 288: the user may request to show comparison between different persons or animals or objects. For example, the user request may be "show height comparison between Lebron James and Michael Jordan"; para. 299: the user may request to show speed comparison between different
persons or animals or objects. For example, the user request may be "show speed comparison between Mako Shark and Usain bolt"; figs. 10A, 10D);
interpreting the natural language utterance based on the dataset (para. 56: information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent; and generating output responses to the user in an audible (e.g., speech) and/or visual form; para. 250, 263: clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user's intent),
generating and displaying on the user interface a visualization based on the transformed queries to generate a plurality of transformed queries for querying the dataset, wherein each of the plurality of transformed queries includes a transformed comparison target and a transformed query term based on the comparison query and the dataset fields (para. 232-235: convert speech/utterance to text, the front-end speech pre-processor performs a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors; para. 274: the server may return a template that contains attribute information and resources to create three-dimensional images, audio, text, etc. for constructing visualization of snippet information. The one or more templates may include one or more virtual objects to present height scale and other information helpful for graphical presentation or animation for showing height information. Further, the metadata or information from the server may override default information that the client module may have to generate the visualization; para. 288: the user may request to show comparison between different persons or animals or objects. For example, the user request may be "show height comparison between Lebron James and Michael Jordan"; para. 299: the user may request to show speed comparison between different persons or animals or objects. For example, the user request may be "show speed comparison between Mako Shark and Usain bolt"; figs. 10A, 10D);
configuring the user interface to allow a user to make a selection of a transformed query of the plurality of transformed queries: and in response to the selection of the transformed query, issuing the transformed query to the dataset and displaying on the user interface a visualization of results from execution at the dataset of the transformed query (fig. 10D-I; para. 106: digital assistant client module 229 also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by DA server 106. Digital assistant client module 229 passes the additional input to DA server 106 to help DA server 106 in intent deduction and/or fulfillment of the user's intent expressed in the user request; para. 174: a user is enabled to select one or more of the graphics by making a gesture on the graphics; para. 394: he electronic device may prompt a user to select a format from the list of the formats for a visual representation).
Akma does not teach wherein each of the transformed queries is associated with a probability measure determined by the multi-step chain-of-thought reasoning prompting that characterizes likely correctness of the transformed query.
Wei teaches
wherein the transformed comparison target and query term are dataset fields and wherein each of the transformed queries is associated with a probability measure determined by the multi-step chain-of-thought reasoning prompting that characterizes likely correctness of the transformed query (pages 11-12, tables 11-12: accuracies (%) of different prompting methods: naïve prompting, chain of thought, query recursion; para. 28-31: decompose a posed query or problem into intermediate steps that are solved individually. Resolve the intermediate steps/multi-step in concert with resolution of the desired output value, leveraging the richer context of the reasoning trace to guide and refine the desired output value, generate such chains of thought as intermediate traces, improve the robustness of model output and improve accuracy of the ultimate answers; para. 94, 134: the machine-learned model comprises a transformer architecture into which the input data structure according to the present disclosure can be input);
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma to include chain-of-thought reasoning prompting of Wei in order to effectively improve reliability through manageable steps of problem solving.
Akma and Wei do not explicitly teach a text summary describing the multi-step chain-of-thought reasoning for the comparison query.
Imani et al. teaches
a text summary describing the multi-step chain-of-thought reasoning for the comparison query (para. 14: LLMs can also perform various NLP tasks, such as classification, summarization, translation, generation, and dialogue; para. 23: chain of thought (COT) method to provide reasoning outputs for each question in the dataset; fig. 1);
Imani also teaches
configuring the user interface to allow a user to make a selection of a transformed query of the plurality of transformed queries: and in response to the selection of the transformed query, issuing the transformed query to the dataset and displaying on the user interface a visualization of results from execution at the dataset of the transformed query (para. 14: LLMs use deep neural networks, such as transformers, to learn from billions or trillions of words, and to produce text on any topic or domain. LLMs can also perform various NLP tasks, such as classification, summarization, translation, generation, and dialogue; para. 19, 23: input prompts are the inputs or queries that a user or a program gives to the LLM, in order to elicit a specific response from the LLM. Prompts can be natural language sentences or questions, or code snippets or commands, or any combination of text or code, depending on the domain and the task. The COT provides a way of thinking as an input prompt to the LLM to break the question into a series of intermediate steps that lead to a final answer for the question; para. 78: A display controller may also be provided, for converting data stored in the memory into text, graphics, and/or moving images (as appropriate) shown on the display device).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma, Wei to include text summary describing chain-of-thought reasoning prompting of Imani in order to effectively enhance the reliability and accuracy of the outputs to the users in various natural language processing tasks.
As per claims 2, 13, Akmal et al. does not explicitly teach said claims.
Wei teaches
wherein the multi-step chain-of-thought reasoning prompting comprises identifying relevant attributes and values by inputting a prompt containing the comparison query and a representation of the dataset to a trained large language model (para. 41: intermediate states can include intermediate values associated with component subtasks, declarations of knowns determined (explicitly or implicitly) from the instructive
query, logical steps to progress from a problem to a solution, a log of subtasks performed to generate the instructive response, etc.; para. 47-49: attributes and values; fig. 10A: model trainer; para. 25: a machine-learned model can be trained to learn relationships between terms in an input query. Prompting a machine-learned model can include providing an instructive input query and an instructive output response before an operative query of interest).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma to include chain-of-thought reasoning prompting of Wei in order to effectively improve reliability through manageable steps of problem solving.
As per claims 5, 16, Akmal and Wei et al. do not teach said claims.
wherein generating the text summary (fig. 203: content summary; para. 1769: content, summary, or URL related to the selection is fetched from caches and returned to the user)
Akmal et al. does not explicitly teach describing the multi-step chain-of-though reasoning comprises inputting prompts used for the multi-step chain-of-though reasoning and any output obtained therefrom to a trained large language model to obtain a text output summarizing process, input and intermediate output.
Wei teaches said limitations at para. 41: intermediate states can include intermediate values associated with component subtasks, declarations of knowns determined (explicitly or implicitly) from the instructive query, logical steps to progress from a problem to a solution, a log of subtasks performed to generate the instructive response, etc.; para. 47-49: attributes and values; fig. 10A: model trainer; para. 25: a machine-learned model can be trained to learn relationships between terms in an input query. Prompting a machine-learned model can include providing an instructive input query and an instructive output response before an operative query of interest).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma to include chain-of-thought reasoning prompting of Wei in order to effectively improve reliability through manageable steps of problem solving.
As per claims 6, 17, Akmal et al. teaches
wherein generating the visualization (fig. 8B: visualization engine) comprises generating a default visualization (para. 273: the metadata from the server module may be supplemental information or information to override a default visualization rendered by the client module 810. For example, in response to query about a basketball player's height, the server module provides metadata that includes three-dimensional geometry and animation required to present visualization of the basketball player's height).
Akmal does not teach based on a most common canonical visualization for a cardinality obtained via the multi-step chain-of-thought reasoning for the comparison query.
Wei teaches said limitation on page 9, table 6, e.g., Q: Yes or no: Is it common to see frost during some college commencements? A: College commencement ceremonies can happen in December, May, and June. December is in the winter, so there can be frost. Thus, there could be frost at some commencements. So, the answer is yes; para. 25: prompting a machine-learned model using a "chain of thought" that traces the reasoning used to generate an output responsive to a given input; para. 31-32, 65: providing a prompt comprising a few instructive traces according to the present disclosure can dramatically improve performance on difficult math word problems for large language models. When scaled to 540B parameters, chain of thought prompting can perform comparably with task-specific finetuned models on a variety of tasks; para. 110-111: the machine-learned model(s) can process the statistical data to generate a visualization output.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma to include chain-of-thought reasoning prompting of Wei in order to effectively improve reliability through manageable steps of problem solving.
Claim(s) 3-4, 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akmal et al. (US 20230087244) in view of Wei et al. (US 20230394328) and further in view of Imani et al. (US 20240362416) and Zadeh et al. (20180204111).
As per claims 3, 14, Akmal et al. teaches
the representation of the dataset and the relevant attributes and values to the trained large language model (para. 27-28: resolve the intermediate steps in concert with resolution of the desired output value, leveraging the richer context of the reasoning trace to guide and refine the desired output value; para. 65: providing a prompt comprising a few instructive traces according to the present disclosure can dramatically improve performance on difficult math word problems for large language models. When scaled to 540B parameters, chain of thought prompting can perform comparably with task-specific finetuned models on a variety of tasks; para. 83: date understanding, which involves inferring a date from a given context).
Akmal does not explicitly teach wherein the multi-step chain-of-thought reasoning prompting.
Wei further teaches said limitation at para. 25: prompting a machine-learned model using a "chain of thought" that traces the reasoning used to generate an output responsive to a given input; para. 31-32, 65: providing a prompt comprising a few instructive traces according to the present disclosure can dramatically improve performance on difficult math word problems for large language models. When scaled to 540B parameters, chain of thought prompting can perform comparably with task-specific finetuned models on a variety of tasks.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma to include chain-of-thought reasoning prompting of Wei in order to effectively improve reliability through manageable steps of problem solving.
Akmal, Wei, Imani et al. do not teach: further comprises inferring cardinality and concreteness of the comparison query by inputting another prompt containing the comparison query.
Zadeh teaches said limitation at para. 2334: for learning machines, the VC
dimension (Vapnik - Chervonenkis dimension) is a measure of the capacity of a statistical classification algorithm (e.g. the cardinality of the largest set of points that the algorithm can shatter (e.g. with the model making no errors, when evaluating that set of data points); para. 874: For concreteness, it is convenient to define a relevance function, R (q/p), as a function in which the first argument, q, is a question or a topic; the second argument, p, is a proposition, topic, document, web page or a collection of such objects; and R is the degree to which p is relevant to q. When q is a question, computation of R(q/p) involves an assessment of the degree of relevance of p to q, with p playing the role of question-relevant information. For example, if q: What is the number of cars in California, and p: Population of California is 37 million, then p is question relevant to q in the sense that p constrains, albeit imprecisely, the number of cars in California. The constraint is a function of world knowledge; para. 2498: selecting the object prompts a user interface for entering/updating/correcting data or annotation regarding the object).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma, Wei, Imani to include inferring cardinality and concreteness of the comparison query by inputting another prompt of Zadeh in order to effectively refine user intent and provide more relevant and accurate query results.
As per claims 4, 15, Wei teaches
wherein the multi-step chain-of-thought reasoning prompting further comprises: inferring a comparative analysis response by inputting yet another prompt containing the comparison query, the representation of the dataset, the relevant attributes and values (para. 25: prompting a machine-learned model using a "chain of thought" that traces the reasoning used to generate an output responsive to a given input, providing an instructive trace explaining the sequence of reasoning steps or logical states between the instructive input query and the instructive output response, example prompts, better leverage the network of learned associations to communicate more instructive context with a given prompt; para. 99: Prompts for Chain of Thought here were generated by merging Query Recursion prompts for subproblems, and
prompts for Naive Prompting were generated from the Chain of Thought prompts by removing reasoning chains),
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma to include chain-of-thought reasoning prompting of Wei in order to effectively improve reliability through manageable steps of problem solving.
Akmal, Wei, Imani do not teach and the cardinality and concreteness, to the trained large language model; executing a query to the dataset based on the comparative analysis response to retrieve the response to the comparison query.
Zadeh teaches said limitation at para. 2334: for learning machines, the VC
dimension (Vapnik - Chervonenkis dimension) is a measure of the capacity of a statistical classification algorithm (e.g. the cardinality of the largest set of points that the algorithm can shatter (e.g. with the model making no errors, when evaluating that set of data points); para. 874: For concreteness, it is convenient to define a relevance function, R (q/p), as a function in which the first argument, q, is a question or a topic; the second argument, p, is a proposition, topic, document, web page or a collection of such objects; and R is the degree to which p is relevant to q. When q is a question, computation of R(q/p) involves an assessment of the degree of relevance of p to q, with p playing the role of question-relevant information. For example, if q: What is the number of cars in California, and p: Population of California is 37 million, then p is question relevant to q in the sense that p constrains, albeit imprecisely, the number of cars in California. The constraint is a function of world knowledge; para. 2498: selecting the object prompts a user interface for entering/updating/correcting data or annotation regarding the object).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma, Wei, Imani et al. to include inferring cardinality and concreteness of the comparison query by inputting another prompt of Zadeh in order to effectively refine user intent and provide more relevant and accurate query results.
As per claim 10, Akmal et al. teaches
showing a landing screen, in a graphical user interface used for displaying the visualization, the landing screen displaying a table containing metadata for the dataset in a data panel; and detecting the natural language utterance via the graphical user interface (para. 272-274: the server module 830 may provide metadata for visual representation of the information, in response to query about a basketball player's height, the server module 830 may provide metadata that includes three-dimensional geometry and animation required to present visualization of the basketball player's height. The server returns a template that contains attribute information and resources to create three-dimensional images, audio, text, etc. for constructing visualization of snippet information; para. 255: user utterance).
Akmal, Wei, Imani do not teach: in response to detecting a user input hovering over a data source thumbnail, allowing the user to view its corresponding metadata information.
Zadeh teaches said limitation at (para. 1982: when the mouse is over an object or hover over it, the whole process is initiated automatically, e.g. a picture in a web site or name in a text is selected (e.g. by mouse or pointer or user's finger on touch screen, or on monitor or display or pad or input pad or device, or hovered over by finger or mouse without touching or touching. Then, the relevant information is obtained from
about that text or image, and Qstore automatically shown or presented to the user, which is very convenient and useful for the user on Internet; para. 1765).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma, Wei, Imani to include hovering over a data source thumbnail of Zadeh in order for the users to quickly identify an object/data source for fast computation and/or manipulation of data.
Claim(s) 8, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akmal et al. (US 20230087244) in view of Wei et al. (US 20230394328) and further in view of Imani et al. (US 20240362416) and Galli (US 20240289554).
As per claims 8, 19, Akmal teaches at fig. 10I: interface for user to make selections; para. 52: the digital assistant, as discussed herein, intelligently provides a visualized or animated response to a user's query in a user's preferred format. The digital assistant additionally shows visualized comparison between comparable contents to provide better representation of the information requested by the user. This can be desirable for improving user experience by decreasing the amount of time spent browsing for searching for better representation of information.
Akmal, Wei, Imani et al. do not explicitly teach allowing the user to make a selection of a transformed query is sorted by the probability measure associated with each of the transformed queries.
Galli teaches
wherein the user interface allowing the user to make a selection of a transformed query is sorted by the probability measure associated with each of the
transformed queries (fig. 3B: displaying queries for user to select; para. 75: the system can propose a number of potential implied queries 370, such as “How to edit an email?” “How to save an email?” and “How to generate a universal block?”. These implied queries can be automatically displayed at query input 351 without the user typing in any questions. The order of these implied queries can be decided by a probability ranking based on multiple personal factors).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akmal, Wei, Imani to include sorted queries of Galli in order to effectively allow users to select and/or interact with the system, refine user intents and provide more relevant and accurate query results.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akmal et al. (US 20230087244) in view of Wei et al. (US 20230394328) and further in view of Imani et al. (US 20240362416), Zadeh et al. (20180204111) and Oattes et al. (US 20230185818).
As per claim 9, Akmal et al. teaches
providing, in a graphical user interface used for displaying the visualization (para. 52: the digital assistant, as discussed herein, intelligently provides a visualized or animated response to a user's query in a user's preferred format. The digital assistant
additionally shows visualized comparison between comparable contents to provide better representation of the information requested by the user. This can be desirable for
improving user experience by decreasing the amount of time spent browsing for searching for better representation of information; para. 102: output is provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above).
Akmal, Wei, Imani do not teach a drop-down menu of graph plot types; and in response to a user selecting a graph plot type, updating the visualization to use the graph plot type.
Zadeh teaches said limitation at para. 2408: the UI or drop-down menu is used for entry into database, for editing and entry, or for learning an image or object; para. 2498: selecting the object prompts a user interface for entering/updating/correcting data or annotation regarding the object; para. 1956: either analytically stored as curves or graphically stored as pixels, or based on thousands of faces stored from real people, tagged for expressions on their faces, for learning samples, as supervised learning); para. 2127, 2375: if the user wants to know "the population of US in 2000 according to US Census Bureau", in addition to the simple answer as an integer, the system presents all the available data for population, e.g. from 1900 to now, by a plot or graph in 2D on display for the user, as an extra information; para. 36: addition of reasoning and cognitive layers to the learning module (same as humans can do), continuous learning and updating the learning machine continuously (same as humans can do), simultaneous learning and recognition (same as humans can do), and conflict and contradiction resolution (same as humans can do), etc.)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma, Wei, Imani to include a drop-down menu of Zadeh in order to effectively allow users to select and/or interact with the system, refine user intent and provide more relevant and accurate query results.
Even if Akmal, Wei, Imani, Zadeh do not explicitly teach graph plot types.
Oattes teaches said limitations at para. 43, 59-60: the visualizations may be of different types and/or generated from different queries, as long as the collection is selected for the visualization; para. 3: changing the query visualization often would require sending new queries to a data source and updating each query individually, additionally using a greater amount of network resources, memory, and a higher number of interactions with the interface to achieve a desired result.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma, Wei, Imani, Zadeh to include graph plot types of Oattes in order to effectively allow options for specifying attributes of the query used to generate the visualizations and further provide more relevant and accurate query results to the users.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Akmal et al. (US 20230087244) in view of Wei et al. (US 20230394328) and further in view of Imani et al. (US 20240362416), Zadeh et al. (20180204111) and Platt et al. (US 11222184).
As per claim 11, Akmal, Wei, Imani, Zadeh do not teach said claims.
Platt teaches
wherein generating the visualization comprises: generating (i) unit charts for 1-1 comparisons between two items, (ii) bar charts for comparisons between one item and another set of multiple items (1-n comparisons), (iii) scatterplots for comparisons between multiple items (n comparisons), and (iv) dot plots support n-m comparisons between two sets (col. 1:32-39: (1) Visualizations such as charts and graphs are useful tools for communicating information about a data set. Examples of different types of visualizations that are widely used include line charts, bar charts, pie charts, scatterplot charts, etc. Visualization has been the predominant approach, both commercially and in terms of academic research, to the challenge of making data and data analytics meaningful to people; col. 6:1-12; col. 16:5-19).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Akma, Wei, Imani, Zadeh to include generating charts of Platt in order to effectively allow options for specifying attributes of the query used to generate the visualizations and further present different visualization types to better reflect relevant and accurate query results to the users.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Alkhalifa et al. teaches at para. 9: a summarizer unit may use one or more NLP models to process a relatively lengthy regulatory question (e.g., two or three paragraphs, possibly not framed as an explicit question), and output a more concise version of the question (e.g., one or two lines expressed as an explicit question); para. 33: the summarizer unit generally applies one or more of the NLP models to the textual data (or to pre-processed textual data) in order to generate a shorter summary of a particular regulatory question as represented by the textual data.
Lefkofsky (US 20200335187) teaches at para. 417: associate different intents with different thoughts and subsequently, when an oncologist voice utterance is received, associate the utterance with the intent, identify parameters related to the intent and then obtain the oncologist's prior impressions or thoughts and provide a response that is consistent with the prior thought or impression.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINH BLACK whose telephone number is (571)272-4106. The examiner can normally be reached 9AM-5PM EST M-F.
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/LINH BLACK/Examiner, Art Unit 2163 4/20/2026
/TONY MAHMOUDI/Supervisory Patent Examiner, Art Unit 2163
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