DETAILED ACTION
The action is in response to the original filing on June 6, 2024 and the Remarks and Amendments filed on May 21, 2026. Claims 1-20 are pending and have been considered below. Claims 5 and 13 are amended accordingly.
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 .
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.
Claims 1, 3-4, 6-7, 9, 11-12, 14-15, 17, & 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Albert Jerry Cristoforo (U.S. Pat. Pub. No. 2015/0073961-A1 herein after “Cristoforo”) in view of Ran Jin (U.S. Pat. Pub. No. US-2024/0127038-A1 herein after “Jin”).
Regarding claims 1, 9, & 17 Cristoforo teaches [a] computer-implemented method, comprising “The embodiments or portions thereof of the system and method of the present invention may be implemented in computer hardware, firmware, and/or computer programs executing on programmable computers or servers that each includes a processor and a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements)” (Cristoforo, ¶ [0149]):
Normalizing (Fig. 3, item 302), by a processor set, “… the first step at 302 is to pre-process and transform the raw data to derive different representations for further exploration. During the process, pre-analysis and verification are used to guarantee the quality of data. Other typical pre-processing tasks include data reformatting, data interpolation, data cleaning, normalization, grouping, and integration” (Cristoforo, ¶ [0062]);
encoding, by the processor set, a data point (Sphere) of the “The spheres cannot distribute randomly on the surface since other characteristics of mutual fund data also need to be visualized. System users are familiar with the longitude and latitude when talking about spheres. The system and method of the present invention use this understanding in formulating new visual encodings.” (Cristoforo, ¶ [0088]) where to make the data presentable and scalable it is visually encoded;
converting, by the processor set, the encoded data point (Sphere) into an angle (Combination of latitude, longitude, and distance) on a spherical coordinate system (Universe) to determine a basis vector direction (Line from sphere to center of the universe) for data within the “The center is the center of the universe, and the volume of this special index sphere is also meaningful. It represents the average NAV of the entire mutual fund market so that the radius of this index has the same aspect ratio as the spheres. The index can be a “benchmark,” and the spheres outside the earth have higher NAV than the average value of market. Therefore, system users can compare the NAV of different mutual funds by checking their distance to the universe center and have a general idea of NAV size by checking the positional relation between the index and the center. The distance from a sphere to the universe center and the volume of the sphere both represent a mutual fund's NAV. This visualization method is called “Redundant Encoding,” which can help increase accuracy and enhance perception.” (Cristoforo, ¶ [0088]) where the distance from the center is considered a vector, which is used to compare with other data points, additionally, the latitude and longitude information can be interpreted as an angle, which when combined with the distance from the center of the universe (3D model sphere) can be used to locate the data point within the 3D model;
generating, by the processor set, a basis vector (Line from sphere to center of the universe) comprising the encoded data point (Sphere) based on the angle (Combination of latitude, longitude, and distance) and the normalizing “The system is designed with customizable visualization that can be adapted to different data models and applied to multiple domains. The system adapts to the different data models by using a virtual universe and a set of spheres to visualize the data, leveraging the longitude, latitude and distance from the center of sphere to define a data model.” (Cristoforo, ¶ [0046]) where the line from the center of the universe (3D model sphere) is comprised of a latitude, longitude, and distance from the center, which is interpreted as a vector. “FIG. 4A, generally at 400A, shows an exemplary visualization of a large data set according to an embodiment of the invention. As described above, the system and method of the present invention use spheres to present meta data sets, and latitude and longitude dimensions are used to distinguish points in the space. The circles that go through the two poles are lines of longitude. The circles that are parallel to the equator are lines of latitude. Every data point is presented on a big sphere (earth). For mutual fund data sets each sphere represents one fund. The radius of the sphere, color of the sphere, size of the sphere can all be used to represent mutual fund results data” (Cristoforo, ¶ [0089]); and
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Cristoforo, Fig. 4A, the sphere-based visualization, which uses distance from the center (chosen baseline index) to compare values of different data points located around the visualization.
generating, by the processor set, instructions to render a spherical model comprising the basis vector (Line from sphere to center of the universe), wherein the instructions are configured to cause a client device to render the spherical model “The visual analytic system and method of the present invention can access the Graphics Processing Unit (GPU) of a computer. For example, the system and method can access libraries of routines and functions of the GPU to efficiently handle the visualization of large data sets. The GPU enables a computer to render complex 3D computer animations” (Cristoforo, ¶ [0051]) when executed, instructions cause the device to render the associated graphics, additionally, “The descriptions are applicable in any computing or processing environment. The embodiments, or portions thereof, may be implemented in hardware, software, or a combination of the two. For example, the embodiments, or portions thereof, may be implemented using circuitry, such as one or more of programmable logic (e.g., an ASIC), logic gates, a processor, and a memory” (Cristoforo, ¶ [0152]).
Cristoforo does not teach where the input data set is an artificial intelligence model input data set.
Jin teaches where the input data set is an artificial intelligence model input data set “… the visualization framework can be used to generate visual representations that visualize the distance and similarity among different datasets and different AI methods. As noted above, constructing an AI Map is challenging. This is partly because constructing an AI Map involves strong reliance on the unique numerical representations of datasets, AI methods, as well as accurate predictions of their interactions (e.g., an AI method's performance on a dataset) before an AI method is tested on a particular dataset” (Jin, ¶ [0025]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of three-dimensional visualization of data taught by Cristoforo with the method of using an artificial intelligence input data set taught by Jin to produce a method of three-dimensional visualization of artificial intelligence input data sets. The motivation to do so would be to track and easily monitor the AI data and processing “Existing AutoML methods mainly aim to improve the modeling accuracy (i.e., dataset-AI method interaction) for a given dataset but they discover no knowledge about the reproducibility of AI methods, and they provide no validation of data quality” (Jin, ¶ [0015]).
In regards to claim 9, claim 1 is substantially similar to claim 9, hence the rejection analysis for claim 1 is also applied to claim 9. Cristoforo in view of Jin teach the additional limitations of [a] computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: perform the above functions. “The embodiments or portions thereof of the system and method of the present invention may be implemented in computer hardware, firmware, and/or computer programs executing on programmable computers or servers that each includes a processor and a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements). Any computer program may be implemented in a high-level procedural or object-oriented programming language to communicate within and outside of computer-based systems” (Cristoforo, ¶ [0149]).
In regards to claim 17, claim 1 is substantially similar to claim 17, hence the rejection analysis for claim 1 is also applied to claim 17. Cristoforo in view of Jin teach the additional limitations of [a] system comprising: a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: perform the above functions. “Although the embodiments, or portions thereof, are not limited in this respect, the embodiments, or portions thereof, may be implemented with memory devices in microcontrollers, general purpose microprocessors, digital signal processors (DSPs), reduced instruction-set computing (RISC), and complex instruction-set computing (CISC), among other electronic components. Moreover, the embodiments, or portions thereof, described above may also be implemented using integrated circuit blocks referred to as main memory, cache memory, or other types of memory that store electronic instructions to be executed by a microprocessor or store data that may be used in arithmetic operations” (Cristoforo, ¶ [0151]).
Regarding claims 3, 11, & 19 Cristoforo in view of Jin teach [t]he computer-implemented method of claim 1, further comprising:
determining data markers (Cristoforo, Fig. 13, items 1301-1304) based on a type of data point within the artificial intelligence model (Jin, ¶ [0025]) input data set “The system user can further select a particular fund from the eighteen funds. For example, the user can select the fund with fund id “FSE,” and the sphere that corresponds to the fund can be visualized in a different color, for example, blue and can be displayed in an enlarged sphere compared to the spheres that correspond to the unselected funds” (Cristoforo, ¶ [0100]) where each fund receives a corresponding, discernible data marker; and
applying data markers (Cristoforo, Fig. 13, items 1301-1304) to the artificial intelligence model (Jin, ¶ [0025]) input data set “when dragging the time axis to future time, many new spheres of QDII will be born in the universe, as shown in FIGS. 9 and 10. By auto-playing the market change from year 2012 to year 2018, the future trend of QDII is evident” (Cristoforo, ¶ [0114]) where adding data causes more spheres or data markers to appear/change.
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Cristoforo, Fig. 13, spherical model with color-coded data points.
In regards to claim 11, claim 3 is substantially similar to claim 11, hence the rejection analysis for claim 3 is also applied to claim 11. Cristoforo in view of Jin teach the additional limitations of [t]he computer program product of claim 9, wherein the program instructions are executable to: perform the above functions.
In regards to claim 19, claim 3 is substantially similar to claim 19, hence the rejection analysis for claim 3 is also applied to claim 19. Cristoforo in view of Jin teach the additional limitations of [t]he system of claim 17, wherein the program instructions are executable to: perform the above functions.
Regarding claims 6 & 14, Cristoforo in view of Jin teach [t]he computer-implemented method of claim 1, further comprising:
determining a direction of the artificial intelligence model (Jin, ¶ [0025]) input data set based on the basis vector and a normalized data set “If in year 2012, the fund's NAV shrinks below the average value, then it will move inside the virtual earth. So system users can have a general idea of growth rate and volatility by checking the sphere's movement speed and range” (Cristoforo, ¶ [0105]) where the input data correlates to a fund selected to view by the user, and the basis vector is the distance from the origin of the three-dimensional sphere, and as the input data changes, the distance, size, color, etc. changes to match; and
redirecting the direction of the artificial intelligence model (Jin, ¶ [0025]) input data set based on user input “As explained above, the virtual earth represents any index that a system user may choose” (Cristoforo, ¶ [0105]) where the user may select the input data and source from the user interface and modify the three-dimensional model accordingly, additionally, “The system user can further select a particular fund from the eighteen funds. For example, the user can select the fund with fund id “FSE,” and the sphere that corresponds to the fund can be visualized in a different color, for example, blue and can be displayed in an enlarged sphere compared to the spheres that correspond to the unselected funds” (Cristoforo, ¶ [0100]).
In regards to claim 14, claim 6 is substantially similar to claim 14, hence the rejection analysis for claim 6 is also applied to claim 14. Cristoforo in view of Jin teach the additional limitations of [t]he computer program product of claim 9, wherein the program instructions are executable to: perform the above functions.
Regarding claims 7 & 15, Cristoforo in view of Jin teach [t]he computer-implemented method of claim 1, further comprising scaling the spherical model based on user input “The radius, and therefore size, of each sphere representing a mutual fund can be customizable by the system user and may be selected such that it provides good visualization results, e.g., does not hide other spheres. Under an alternative embodiment, the radius of each sphere can be mapped to a particular business meaningful parameter or another dimension of data” (Cristoforo, ¶ [0087]) “The system user can further select a particular fund from the eighteen funds. For example, the user can select the fund with fund id “FSE,” and the sphere that corresponds to the fund can be visualized in a different color, for example, blue and can be displayed in an enlarged sphere compared to the spheres that correspond to the unselected funds” (Cristoforo, ¶ [0100]).
In regards to claim 15, claim 7 is substantially similar to claim 15, hence the rejection analysis for claim 7 is also applied to claim 15. Cristoforo in view of Jin teach the additional limitations of [t]he computer program product of claim 9, wherein the program instructions are executable to: perform the above functions.
Claims 2, 5, 8, 10, 13, 16, & 18 are rejected under 35 U.S.C. 103 as being unpatentable over Cristoforo in view of Jin, and further in view of Natalie Bucklin Et. Al. (Pat. Pub. No. WO-2022/240860-A1 herein after “Bucklin”).
Regarding claims 2, 10, & 18 Cristoforo in view of Jin teaches [t]he computer-implemented method of claim 1, further comprising:
Cristoforo in view of Jin fail to teach plotting data distributions and bias cases comprising distortion models, false positive data, false negative data, and missing data within the artificial intelligence model input data set on the spherical model.
Bucklin teaches plotting data distributions and bias cases comprising distortion models, false positive data, false negative data, and missing data within the artificial intelligence model input data set on the spherical model “Each point on the graph represents a single feature. The placement of the points along the X-axis measures the impact or importance of the feature, and the Y-axis measures the disparity of that feature's data distribution between the two protected classes” (Bucklin, ¶ [0091]) additionally, “The models may be tested in accordance with suitable testing techniques and scored according to a suitable scoring metric (e.g., an objective function). Different scoring metrics may place different weights on different aspects of a predictive model’s performance, including, without limitation, the model’s accuracy (e.g., the rate at which the model correctly predicts the outcome of the prediction problem), false positive rate (e.g., the rate at which the model incorrectly predicts a “positive” outcome), false negative rate (e.g., the rate at which the model incorrectly predicts a “negative” outcome), positive prediction value, negative prediction value, sensitivity, specificity, etc. The user may select a standard scoring metric (e.g., goodness-of-fit, R-square, etc.) from a set of options presented via user interface 620, or specific a custom scoring metric (e.g., a custom objective function) via user interface 620. Exploration engine 610 may use the user-selected or user-specified scoring metric to score the performance of the predictive models” (Bucklin, ¶ [0195]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of three-dimensional AI input data visualization taught by Cristoforo and Jin, with the method of plotting data distributions and bias cases taught by Bucklin to provide a more comprehensive display. The motivation to do so would be to easily visualize different types/sources of input data. “The system may also provide a bias vs accuracy visualization that provides additional insights into how the model is performing with regard to bias and fairness. The bias vs accuracy chart may illustrate the tradeoff between predictive accuracy and fairness, removing the need to manually note each model's accuracy score and fairness score for the protected features” (Bucklin, ¶ [0098]).
In regards to claim 10, claim 2 is substantially similar to claim 10, hence the rejection analysis for claim 2 is also applied to claim 10. Cristoforo in view of Jin and Bucklin teach the additional limitations of [t]he computer program product of claim 9, wherein the program instructions are executable to: perform the above functions.
In regards to claim 18, claim 1 is substantially similar to claim 18, hence the rejection analysis for claim 2 is also applied to claim 18. Cristoforo in view of Jin and Bucklin teach the additional limitations of [t]he system of claim 17, wherein the program instructions are executable to: perform the above functions.
Regarding claims 5 & 13, Cristoforo in view of Jin teach [t]he computer-implemented method of claim 3,
Cristoforo in view of Jin do not teach wherein the data markers are indicative of data distributions and bias cases comprising distortion model, false positive data, false negative data, or missing data.
Bucklin teaches wherein the data markers are indicative of data distributions and bias cases comprising distortion models, false positive data, false negative data, or missing data “When the system determines that the user has hovered over (or otherwise interacted with) the bars, the system may display additional details, including both absolute and relative fairness scores, the number of values for the class, and/or a summary of the fairness test results, as depicted in page 345. Specifically, the chart 344 may include the pop-up window 346 that displays additional information about how the model has treated males over females. That is, the system may display how different protected classes compare with regard to the bias and fairness of the model” (Bucklin, ¶ [0083]) additionally, “For example, key characteristics of variables may be computed and displayed, the prevalence of missing data may be displayed and a treatment strategy may be recommended, outliers in numerical variables may be detected and, if found, a treatment strategy may be recommended, and/or other data anomalies may be detected automatically (e.g., inkers, non-informative variables whose values never change) and recommended treatments may be made available to the user” (Bucklin, ¶ [0299]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of three-dimensional AI input data visualization taught by Cristoforo and Jin, with the method of plotting data markers for data distributions and bias cases taught by Bucklin to provide a more comprehensive display. The motivation to do so would be to easily visualize different types/sources of input data. “The system may also provide a bias vs accuracy visualization that provides additional insights into how the model is performing with regard to bias and fairness. The bias vs accuracy chart may illustrate the tradeoff between predictive accuracy and fairness, removing the need to manually note each model's accuracy score and fairness score for the protected features” (Bucklin, ¶ [0098])
In regards to claim 13, claim 5 is substantially similar to claim 13, hence the rejection analysis for claim 5 is also applied to claim 13. Cristoforo in view of Jin and Bucklin teach the additional limitations of [t]he computer program product of claim 9, wherein the program instructions are executable to: perform the above functions.
Regarding claim 8 & 16, Cristoforo in view of Jin teach [t]he computer-implemented method of claim 1, further comprising displaying“As described above, the system and method of the present invention use spheres to present meta data sets, and latitude and longitude dimensions are used to distinguish points in the space. The circles that go through the two poles are lines of longitude. The circles that are parallel to the equator are lines of latitude. Every data point is presented on a big sphere (earth). For mutual fund data sets each sphere represents one fund. The radius of the sphere, color of the sphere, size of the sphere can all be used to represent mutual fund results data” (Cristoforo, ¶ [0089]) where a spherical model is used to display data points.
Cristoforo in view of Jin fail to explicitly teach displaying a bias.
Bucklin teaches displaying a bias “Using the input elements depicted within the graphical element 354, the user may select a protected feature and two class values of that feature to measure for data disparities. For instance, the user may select “gender,” “male,” and “female.” The system may then present the chart 356 that depicts data disparity vs feature importance. The chart 356 can be used to perform root-cause analysis of the model's bias for the selected classes (e.g., the data disparity vs feature importance chart can be used to identify which features in the dataset impact bias the most). The chart 356 may also detail where the bias exists within the feature” (Bucklin, ¶ [0090]) where an input data bias may be output to a display.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of spherical AI input data visualization taught by Cristoforo and Jin, with the method of plotting bias cases taught by Bucklin to provide a more comprehensive display. The motivation to do so would be to easily visualize different types/sources of input data. “The system may also provide a bias vs accuracy visualization that provides additional insights into how the model is performing with regard to bias and fairness. The bias vs accuracy chart may illustrate the tradeoff between predictive accuracy and fairness, removing the need to manually note each model's accuracy score and fairness score for the protected features” (Bucklin, ¶ [0098])
In regards to claim 16, claim 8 is substantially similar to claim 16, hence the rejection analysis for claim 8 is also applied to claim 16. Cristoforo in view of Jin and Bucklin teach the additional limitations of [t]he computer program product of claim 9, wherein the program instructions are executable to: perform the above functions.
Response to Arguments
The Examiner appreciates the Applicant’s patience and cooperation in regard to the prosecution of this case. Applicant's arguments with respect to claims 1, 9, and 17, see Remarks, Pages 8-10, filed May 21st, 2026 have been fully considered but they are not persuasive.
On pages 8-9 of the Remarks, the Applicant argues that Cristoforo’s mapping of data to a position is direct and for visualization purposes as opposed to a converted point to an angle that determines a basis vector direction. The Applicant supports this reasoning by tethering the conversion to the encoded data and the spherical coordinates while asserting that Cristoforo lacks this connection. The Applicant asserts that Cristoforo merely assigns data to predefined positions for the purpose of visualization.
The Examiner considers these arguments, but would like emphasize that while Cristoforo serves to visualize data, it also analyses the input data “a visual analytics system is provided for determining the evolution of a data. The system can receive inputs from one or more data feeds and creating a virtual three-dimensional global sphere, segment the virtual three-dimensional global sphere according to longitudinal sections to define a first criterion for locating a data point in one of the longitudinal segments, and segment …” (Cristoforo, ¶ [0023]) which both visualizes the data based on its criteria for locating the data points and determines the location of the data points in the sphere. In essence, the data is not only visualized and mapped by Cristoforo, but is assigned a location on the sphere “the system can further determine a location of each data point with respect to the virtual three-dimensional global sphere from information about each data point received by the computer from the one or more data feeds” (Cristoforo, ¶ [0024]). This step of assigning the data to a location on the sphere is read as converting the data into an angle on the spherical coordinate system since the data is input and then displayed as an angle, or converted, to represent the input. Without a specific method of conversion such as the process mentioned in ¶ [0024] of the specification “converting an encoded data point into an angle on a spherical coordinate system involves mapping the high-dimensional data into a lower-dimensional representation suitable for data computation” the broadest reasonable interpretation of the converting step is taught by Cristoforo.
In response to Applicant's argument that the Examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Regarding the use of a latitude, longitude, and distance to produce an angle, spherical geometry and navigation in particular rely on coordinate points and distances to determine directions and trajectory. It is within reason to interpret that the inclusion of a latitude, longitude, and distance for graphing or visualization would be considered angles since a line from the center point of a sphere to the coordinates provided, in tandem with a distance, would produce an angled line. The Examiner believes the Applicant is within reason for pointing out this interpretation, however, only knowledge which was within the level of ordinary skill at the time the claimed invention was used.
On Page 9 of the Remarks, the Applicant argues that Cristoforo does not teach of a basis vector, the use of a basis vector, or that the data point is encoded and then the vector is determined based on the encoded data point to form a basis vector, as was the major topic of discussion during the interview. It is understood that the applicant claims a method of encoding a data point and a basis vector that comprises the encoded data point based on the angle.
The Examiner thanks the applicant for this clarification, however, given the broadest reasonable interpretation of the data point being encoded with no specific method of encoding, such as in the specification ¶ [0024] “conversion is achieved by first encoding the data point using a specified encoding scheme, such as amplitude encoding or basis encoding”, Cristoforo teaches that “Visual Analytics (VA) has been introduced to represent massive, multi-dimensional and temporal data with various visual encodings” (Cristoforo, ¶ [0006]), and further “Attributes of multi-dimensional data can be mapped to these visual encodings with optional data models, thus allowing a compact overview of the whole data set” (Cristoforo, ¶ [0053]) see “Visual Encodings” ¶ [0052]-[0054]. Given the broadest reasonable interpretation of a data point that is encoded, Cristoforo is able to teach the relevant limitations of claim 1.
Finally, in light of the specification ¶ [0025] “A basis vector of a data set may include a single data point or observation within the data set represented as a vector, wherein the single data point correlates to a plotted location on a three-dimensional coordinate system within a spherical model, and the data gradient of the data point correlates to direction of the vector. Input data may be mapped to spherical coordinates of a spherical model by generating a basis vector per input data …”. Given the claim language in light of the specification, a basis vector can be read as a point represented as a vector, which correlates to a point in three-dimensional coordinates within a spherical model. In order for Cristoforo to reasonably teach a vector that represents data, the data must have a magnitude and direction. Cristoforo states “The system adapts to the different data models by using a virtual universe and a set of spheres to visualize the data, leveraging the longitude, latitude and distance from the center of sphere to define a data model” (Cristoforo, ¶ [0046]) where longitude and latitude are direction of the vector, and distance from the center is magnitude. The location and direction of the visualization correlate to the data that is plotted, and therefore read upon the broadest reasonable interpretation of the claim language.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/C.A.U./Examiner, Art Unit 2611
/TAMMY GODDARD/Supervisory Patent Examiner, Art Unit 2611
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