DETAILED ACTION
The applicant’s response, from 22 June 2026, has been fully considered. Amendments to the claims, from 22 June 2026, were received and entered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant 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 .
Claim Status
Claims 14-17 are newly added.
Claims 1-17 is/are currently pending and under examination herein.
Claims 1-17 is/are rejected.
Priority
The instant application claims priority as a CON of PCT/EP2020/081560 filed 10 November 2020 and US provisional application 62934576 filed 13 November 2019. Foreign priority is claimed to EP 20153112.6 filed 22 January 2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. In this action, claims 1-17 are examined as though they had an effective filing date of 13 November 2019. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further analysis of the disclosure(s) of the priority application(s).
Drawings
The drawings received on 12 May 2022 are accepted.
Claim Rejections - 35 USC § 101
The previously issued 35 USC 101 rejection is withdrawn in response to applicant’s arguments. Particularly, the examiner is convinced that the invention represents an improvement to technology in the field of diagnostics given the invention adjusts to the users own specific conditions and capabilities to improve diagnostics from test results (Page 8, Paragraph 1 of remarks). The examiner found the cited section from the specification (Paragraph 0048) particularly convincing - As an example, in case the user profile is a profile for users having a specific illness, such as an illness leading to a frequent tremor, the profile-specific measurement setup adjustments, as will be outlined in further detail below, may comprise increased tolerance for motion blur, such as motion blur of the images used for analysis, as compared to, e.g., users of profiles not indicating a frequent tremor.
Claim Rejections - 35 USC § 103
Arguments associated with the previously issued 35 USC 103 rejection are considered unpersuasive (see response to arguments below the rejection). The following rejection is reiterated and modified as has been necessitated by amendment.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-13 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al. (U.S. 20160104057 A1, Previous Office Action), in view of Tyrrell et al. (U.S. 20160077091 A1, Previous Office Action). The italicized text corresponds to the reference art.
Applicable claims include:
Claim 1. A method for adjusting a measurement setup used in an analytical method of determining a concentration of an analyte in a body fluid, the analytical method comprising using a camera of a mobile device to capture at least one image of at least a part of an optical test strip having a test field, and further comprising determining at least one analyte concentration value from color formation of the test field, wherein the adjustment method comprises: i) carrying out, by a plurality of users, a plurality of analyte measurements, wherein the analyte measurements at least partly comprise using the camera to capture images of at least a part of an optical test strip having a test field, thereby obtaining training data on the analyte measurements, wherein the training data include at least one of: color information derived from the images; information derived from at least one color reference card visible in the images; analyte measurement values derived from the images; sensor data obtained by using at least one sensor of the users' mobile devices selected from the group consisting of: an angle sensor, a light sensor, a motion sensor, an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a GPS sensor, a pressure sensor, a temperature sensor, and a biometric sensor; setup information relating to a setup of the users' mobile devices for carrying out the analyte measurements; and health information relating to the users; ii) analyzing the training data obtained in step i) to identify similarities in the training data and identifying a plurality of user profiles according to the similarities in the training data, wherein each set of training data comprises a variable, wherein the similarities of two sets of training data are defined by the values of the respective variables being in a specific range or being spaced apart by no more than a given threshold, wherein the similarities identified in step ii) at least partially refer to at least one of the following: lighting conditions when performing step i); a users' tremor when performing step i); and iii) providing profile-specific measurement setup adjustments for at least one of the user profiles, wherein the profile-specific measurement setup adjustments refer to at least one specific setting for carrying out the at least one analyte measurement by the user, the user belonging to a specific user profile, wherein the profile-specific measurement setup adjustments at least partially refer to at least one of: a handling procedure of the analyte measurement; a hardware setup of the mobile device; a software setup of the mobile device; instructions given by the mobile device to the user; a degree of reliability of measurement result of the analyte measurement; a tolerance range for admissible parameters when performing the analyte measurement; a timing sequence for performing the analyte measurement; a failsafe algorithm for performing the analyte measurement; and an enhanced analyte measurement accuracy.
Claim 2. The adjustment method according to claim 1, wherein step ii) comprises using at least one self-learning algorithm.
Claim 3. The adjustment method according to claim 1, further comprising transmitting the training data obtained in step i) from the users' mobile devices to at least one evaluation server device, wherein at least step ii) is performed by the evaluation server device.
Claim 4. The adjustment method according to claim 1, wherein the profile-specific measurement setup adjustments at least partially refer to camera adjustments when carrying out step i).
Claim 5. An analytical method of determining a concentration of an analyte in a body fluid, comprising: a) using a camera of a mobile device to capture at least one image of at least a part of an optical test strip having a test field and determining at least one analyte concentration value from color formation of the test field; b) performing the adjustment method according to claim 1; c) carrying out, by at least one individual user, a plurality of analyte measurements, wherein the analyte measurements at least partly comprise using the camera to capture images of at least a part of an optical test strip having a test field and thereby obtaining user-specific training data on the analyte measurements; d) analyzing the user-specific training data obtained in step c) and assigning the individual user to at least one individual user profile of the user profiles; and providing user profile-specific measurement setup adjustments for the individual user profile.
Claim 6. The analytical method according to claim 5, wherein the method further comprises, before step c): assigning the individual user to a predefined starting profile.
Claim 7. The analytical method according to claim 5, further comprising transmitting the user-specific training data obtained in step c) from the individual user's mobile device to at least one evaluation server device.
Claim 8. The analytical method according to claim 7, wherein the user profile-specific measurement setup adjustments for the individual user profile are transmitted from the evaluation server device to the individual user's mobile device.
Claim 9. The analytical method according to claim 8, further comprising: i. the individual performing, using the individual user's mobile device, at least one analyte measurement, wherein the analyte measurement at least partly comprises using the camera to capture at least one image of at least a part of an optical test strip having a test field, and ii. evaluating the at least one image for deriving at least one measurement value of the concentration of the analyte in the body fluid, wherein the analyte measurement is performed by using the user profile-specific measurement setup adjustments for the individual user.
Claim 10. The analytical method according to claim 5, wherein step d) comprises using at least one self-learning algorithm.
Claim 11. An adjustment system for performing the method according to claim 1, comprising: I) a receiving device configured for receiving training data on analytical measurements, the training data being obtained by a plurality of users carrying out a plurality of analyte measurements, wherein the analyte measurements at least partly comprise using the camera to capture images of at least a part of an optical test strip having a test field, thereby obtaining training data on the analyte measurements; II) an evaluation server configured for performing step ii) of the adjustment method according to claim 1; and III) a transmitter configured for transmitting profile-specific measurement setup adjustments provided in step ii).
Claim 12. A mobile device having at least one camera and being configured for performing the following steps: step c) of the analytical method according to claim 5; receiving the user profile-specific measurement setup adjustments for the individual user profile.
Claim 13. A non-transitory computer readable medium having stored thereon computer- executable instructions which, when the program is executed by a mobile device having a camera, cause the mobile device to carry out the method of claim 9.
Claim 17. The analytical method according to claim 6, wherein the predefined starting profile includes predefined settings and the method further comprises: replacing at least one of the predefined settings of the predefined starting profile with user profile-specific measurement setup adjustments.
Regarding Claim 1.ii, Shen et al. teaches analyzing training data to identify similarities and identifying user profiles according to the similarities (Page 4, Paragraph 0038: the machine learning engine can identify contextual similarities) based on comparing numerical variables against a threshold (Page 4, Paragraph 0039: a dimension reduction analysis). Shen et al. also teaches that the similarities identified can refer to lighting conditions or a users' tremor (Page 1, paragraph 00014: the machine learning engine can then compute preference profiles with contextual conditions (e.g., camera orientation or lighting condition).
Regarding Claim 1.iii, Shen et al. teaches providing profile-specific measurement setup adjustments for user profiles (Page 1, Paragraph 0014: The adjustment parameters can be used to tune an image to virtually any adjustment that can be made to an image during capture or post-processing).
Regarding Claim 2, Shen et al. teaches using at least one self-learning algorithm for Claim 1.ii. (Page 4, Paragraph 0038: the machine learning engine can identify contextual similarities).
Regarding Claim 3, Shen et al. teaches transmitting training data (raw photos and contextual information) from the users' mobile devices to an evaluation server device (Page 1, Paragraph 0012: The camera-enabled device can send the selection to a machine learning engine (e.g., implemented on the camera-enabled device, a cloud computer server, or the same device as the user interface).
Regarding Claim 4, Shen et al. teaches the profile-specific measurement setup adjustments at least partially refer to camera adjustments (Page 1, paragraph 0011: tune a digital image according to various parameters such as color saturation, white balance, exposure, lens shading, and focus location).
Regarding Claim 5.b and 5.d, Shen et al. teaches analyzing training data to identify similarities and identifying user profiles according to the similarities (Page 4, Paragraph 0038: the machine learning engine can identify contextual similarities) based on comparing numerical variables within a certain range (Page 4, Paragraph 0039: a dimension reduction analysis). Shen et al. also teaches the similarities identified can refer to lighting conditions or a users' tremor (Page 1, paragraph 00014: the machine learning engine can then compute preference profiles with contextual conditions (e.g., camera orientation or lighting condition). Shen et al. also teaches providing profile-specific measurement setup adjustments for user profiles (Page 1, Paragraph 0014: The adjustment parameters can be used to tune an image to virtually any adjustment that can be made to an image during capture or post-processing).
Regarding Claim 6, Shen also teaches assigning the individual user to a predefined starting profile (Page 5, Paragraph 0044: The machine learning engine can generate a photo preference profile for a user).
Regarding Claim 7, Shen et al. also teaches transmitting training data (raw photos and contextual information) from the users' mobile devices to an evaluation server device (Page 1, Paragraph 0012: The camera-enabled device can send the selection to a machine learning engine, e.g., implemented on the camera-enabled device, a cloud computer server, or the same device as the user interface).
Regarding Claim 8, Shen et al. also teaches the user profile-specific measurement setup adjustments for the individual user profile are transmitted from the evaluation server device to the individual user's mobile device (Page 2, Paragraph 0019: the photo preference profile is computed externally by a computer server implementing a machine learning engine and the camera-enabled device can download the photo preference profile via a network interface).
Regarding Claim 9.ii, Shen et al. also teaches the analyte measurement is performed by using the user profile-specific measurement setup adjustments for the individual user (Page 6, paragraph 0058: the computing device can provide the photo preference profile to an image processor to adjust subsequently captured photographs provided to the image processor).
Regarding Claim 10, Shen et al. teaches using a self-learning algorithm to analyze training data to identify similarities and identifying user profiles according to the similarities (Page 4, Paragraph 0038: the machine learning engine can identify contextual similarities) based on comparing numerical variables within a certain range (Page 4, Paragraph 0039: a dimension reduction analysis). Shen et al. also teaches the similarities identified can refer to lighting conditions or a users' tremor (Page 1, paragraph 00014: the machine learning engine can then compute preference profiles with contextual conditions (e.g., camera orientation or lighting condition).
Regarding Claim 11, Shen et al. also teaches a receiving device configured for receiving training data on analytical measurements, the training data being obtained by multiple users carrying out multiple analyte measurements, wherein the analyte measurements use the camera to capture images an optical test strip with a test field and an evaluation server configured for performing step ii) of the adjustment method according to claim 1 (Page 2, Paragraph 0023: when uploading a training image to the machine learning engine, the image signal processor or the general processor can provide an image-context attribute associated with the training image to the machine learning engine). Shen et al. also teaches a transmitter configured for transmitting profile-specific measurement setup adjustments provided in step ii). (Page 2, Paragraph 0019: the photo preference profile is computed externally by a computer server implementing a machine learning engine and the camera-enabled device can download the photo preference profile via a network interface).
Regarding Claim 12, Shen et al. also teaches a mobile device with a camera that can perform step c) of the analytical method according to claim 5 and receiving the user profile-specific measurement setup adjustments for the individual user profile (Page 1, Paragraphs 0002-0003: A camera-enabled device (e.g., a digital camera or a camera-enabled phone) includes a camera module which is an image capturing component of the camera-enabled device. The camera module may be integrated with control electronics and an output interface to other logic component(s) of the camera-enabled device. The camera-enabled device can further include an image processor that transforms the output of the camera module into a digital image. The image processor may process and adjust the raw photographs based on default image processing settings and calibration parameters).
Regarding Claim 13, Shen et al. also teaches a machine-readable storage medium that when executed can carry out the method of claim 9 (Page 7, paragraph 0071: Software or firmware for use in implementing the techniques introduced here may be stored on a machine-readable storage medium and may be executed).
Regarding Claim 17, Shen et al. teach the predefined starting profile includes predefined settings and replacing at least one of the predefined settings of the predefined starting profile with user profile-specific measurement setup adjustments (Paragraph 0042: generate a regional, group-based, demographic-based photo preference profile. The machine learning engine can compute these photo preference profiles by averaging. For users who do not opt for the individualized preference learning process, their photographs can still be processed to align with the photo preference profile of an affiliated region, group or demographic). This is consistent with the published specification discussion of predefined starting profile (Paragraph 0084: the predefined starting profile, which may be used as a default starting profile, may comprise predefined settings which may represent an average user).
Shen et al. does not teach using the camera to capture images of at least a part of an optical test strip having a test field, thereby obtaining training data (Claim 1.i). Shen et al. also does not teach some of the profile specific measurement adjustments (Claim 1.iii). Shen et al. also does not teach using the camera to capture images of part of an optical test strip having a test field and data on the analyte measurements (Claims 5.a, 5.c, and 9.i).
Regarding Claim 1.i, Tyrrell et al. teach using the camera to capture images of at least a part of an optical test strip having a test field, thereby obtaining training data on the analyte measurements (Page 2, Paragraph 0008: smart phone or other device used for capturing digital images to be used for collecting, analyzing, qualifying, quantifying, processing, validating, determining, and/or verifying test results (e.g. test strips)). Tyrrell et al. also teaches the utilization of GPS data (Page 2, Paragraph 0015), analyte data (Page 2, Paragraph 16: metabolite), information derived from a color reference card visible in the images (Page 4, Paragraph 0053: color swatches placed for calibration), color information (Page 4, Paragraph 0055: color coding can be matched), the users setup information (Page 4, paragraph 0053: determine if the hardware/camera is functioning), and health information related to the user (Page 1, paragraph 0002: a well-known application is the pregnancy test).
Regarding Claim 1.iii, Tyrrell et al. also teaches making specific suggestions to improve the accuracy of the results (Page 5, Paragraph 0063: control temperature when suggested or needed to help ensure accurate test results), that include timing sequence for performing the measurements (Page 5, Paragraph 0063: if cold, add an incubation time for the device), handling procedure (Page 11, Paragraph 0149: ensures proper orientation), and providing instructions that include setup and operation (Page 2, paragraph 0014: Providing instructions via a GUI).
Regarding Claim 5.a and 5.c, Tyrrell et al. teaches using the camera to capture images of an optical test strip having a test field, thereby obtaining training data on the analyte measurements (Page 2, Paragraph 0008: smart phone or other device used for capturing digital images to be used for collecting, analyzing, qualifying, quantifying, processing, validating, determining, and/or verifying test results (e.g. test strips)).
Regarding Claim 9.i, Tyrrell et al. teaches using the camera to capture images of an optical test strip having a test field, thereby obtaining training data on the analyte measurements (Page 2, Paragraph 0008: smart phone or other device used for capturing digital images to be used for collecting, analyzing, qualifying, quantifying, processing, validating, determining, and/or verifying test results (e.g. test strips)).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Tyrrell et al. and Shen et al. Tyrrell et al. and Shen et al. utilize metadata associated with images to alter images. Shen et al. uses data associated with images in combination with a machine learning framework to enhance generic images. Tyrrell et al. seeks to improve conditions associated with taking pictures of optical test strips in order to enhance determining the test result. It would be obvious by one of ordinary skill in the art at the time of the effective filing date to combine the analytical framework of Shen et al. with the test strip image system of Tyrrell et al. in order to harness the capabilities of self-learning algorithms to improve the reading of test results captured by the image of a test. Furthermore, one of ordinary skill in the art would predict that the methods taught by Shen et al. could be readily added to the methods of Tyrrell et al. with a reasonable expectation of success because they both utilize images and data associated with images as inputs in order to alter an image. Furthermore, Madabhushi and Lee (2016, Medical Image Analysis, Vol. 33, Pgs. 170-175, previous office action) teach the theories employed by Shen et al. (machine learning framework) and Tyrrell et al. (image calibration) are well established in the field of medicine (Page 170, Paragraph 1: The ability to mine sub-visual image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance) and therefore would have been more likely to succeed together.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Shen et al., as applied to claims 1-13 and 17 above, in view of Tyrrell et al., as applied to claims 1-13 and 17 above, and in further view of Dehais et al. (US 20160163037 A1). The italicized text corresponds to the reference art.
Applicable claims include:
Claims 1-13 and 17 are presented above.
Claim 14. The adjustment method according to claim 1, wherein the similarities identified in step ii) at least partially refer to the users' tremor when performing step i) and the profile-specific measurement adjustments at least partially refer to the tolerance range for admissible parameters when performing the analyte measurement.
Claim 15. The adjustment method according to claim 14, wherein the profile-specific measurement adjustments include an increased tolerance for motion blur.
Claim 16. The adjustment method according to claim 1, wherein the similarities identified in step ii) at least partially refer to the lighting conditions when performing step i) and the profile-specific measurement adjustments at least partially refer to camera adjustments when carrying out step i) including at least one of a sensitivity of the camera and an exposure time.
Regarding claims 1-13 and 17, these limitations are taught by Shen et al. and Tyrrell et al. as indicated above.
Shen et al. and Tyrrell et al. do not explicitly teach the limitations of claims 14-16.
Regarding Claim 14, Dehais et al. teach the similarities identified in step ii) at least partially refer to the users' tremor when performing step i) and the profile-specific measurement adjustments at least partially refer to the tolerance range for admissible parameters when performing the analyte measurement (Paragraph 0025: operations are continuously repeated until the acquisition of the first and/or the second image is considered as sufficient based on predefined thresholds associated with criteria comprising one or more of image quality, associated measurements of handshakes).
Regarding Claim 15, Dehais et al. teach the profile-specific measurement adjustments include an increased tolerance for motion blur (Paragraph 0048: Handshake generates motion blur, which can be formulated as a convolution kernel, this kernel may be automatically detected and then automatically compensated by using image denoising).
Regarding Claim 16, Dehais et al. teach the similarities identified in step ii) at least partially refer to the lighting conditions when performing step i) and the profile-specific measurement adjustments at least partially refer to camera adjustments when carrying out step i) including at least one of a sensitivity of the camera and an exposure time. (Paragraph 0026: operations are continuously repeated until the acquisition of the first and/or the second image is considered as sufficient based on predefined thresholds associated with criteria comprising one or more of image quality, resulting light pattern; Paragraph 0026: the image acquisition component has sensitivity inferior to 5 lux; Paragraph 0060: improves still image quality by allowing one to increase the exposure time).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date to combine Dehais et al. with Tyrrell et al. and Shen et al. Dehais et al. teach their methods improve image analysis on mobile devices (Paragraph 0012: the invention solve technical problems including optimizing processing algorithms to fit in the constraints of calculation power on the mobile devices and achieving effective noise reduction; Paragraph 0060: This technique improves still image quality by allowing one to increase the exposure time without blurring the image), which is a focus of Shen et al. and the instant application. Additionally, Dehais et al. teach their methods to improve patient treatment (Paragraph 0094: An additional benefit of using this intensive approach is to determine whether the patient's therapy parameters need to be altered), which is a focus of Tyrrell et al. and the instant application. Furthermore, one of ordinary skill in the art would predict that the methods could be readily combined with a reasonable expectation of success because all are within the same technical field – utilizing images and data associated with images in machine leaning models to make improved predictions.
Response to Arguments
Applicant asserts “the image processing which is influenced by the photo preference profile is ‘post-processing’ that is done after the image has been captured” (Page 9, Paragraph 5 of remarks) and “Claim 1, in contrast, calls for setup adjustments which ‘refer to at least one specific setting for carrying out the at least one analyte measurement by the user’” (Page 10, Paragraph 1 of remarks). The phrase “for carrying out the at least one analyte measurement by the user” is interpreted as an intended use as claimed. Therefore, it does not hold patentable weight over the prior art. Additionally, the phrase “for carrying out the at least one analyte measurement by the user” is not interpreted as indicated that that adjustments must be applied prior to taking the photograph but is interpreted as saying the adjustments have to be applied prior to the carrying out the analyte measurement. The broadest reasonable interpretation of a carrying out the analyte measurement encompasses reading the test. Therefore, the adjustment could be applied after the photo was taken but before the reading of the results from the image. Additionally, Paragraph 0015 of Shen et al. indicates the adjustment can be made “during capture or ‘post-processing’”. During capture is interpreted as before the image is captured. Therefore, it cannot be limited to post-processing. Other cited instances of postprocessing in Shen et al. represent non-limiting embodiments of the invention. Additionally, MPEP 2144.04 indicates changing the order of steps within a method is obvious, which is applicable given the rejection is a 35 USC 103 rejection. Therefore, the argument is unpersuasive.
Applicant asserts “such an external computing machine would be limited to post-processing of images captured by the camera-enabled device” (Page 10, last paragraph of remarks). The cited paragraph (0036) of Shen et al. is not a limiting embodiment of the invention. Additionally, Shen et al. indicates the camera device is in wireless communication with other devices (Paragraph 0019: the network interface can provide wireless communication). No evidence is presented as to why an external device wirelessly communicating with other device could only post process. Additionally, the issue of post processing is moot given the explanation in the first paragraph of the response to arguments. Therefore, the argument is unpersuasive.
Applicant asserts “Shen fails to teach or suggest adjustment of the camera module configuration and its settings, e.g., exposure, focus distance, zoom, aperture size, or shutter speed, which would have to be adjusted before capturing the image” (Page 11, Paragraph 2 of remarks). No section of the claims is interpreted as indicating an adjustment must be applied directly to the camera and it is unclear where the applicant considers this limitation to be recited. The camera adjustments recited by claims 4 and 16 are interpreted as a class of adjustments which could be applied to the images directly as these claims do not recite any required active step of modifying a camera. Additionally, the issue of post processing is moot given the explanation in the first paragraph of the response to arguments. Therefore, the argument is unpersuasive.
Applicant asserts “The suggestions of Tyrrell, however, are not associated with a specific user profile as called for by claim 1” (Page 11, Paragraph 4 of remarks). Shen teaches utilizing user profiles (see Shen teachings of claim 1.ii). Given that claim 1 was rejected as a 35 USC 103 rejection, it is the combination of Shen et al. and Tyrell et al. that rejects the claim (MPEP 2145: “One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references”). Applicant provides no evidence that Shen et al. and Tyrell et al. are an improper combination under MPEP 2143. Therefore, the argument is unpersuasive.
Applicant asserts, regarding claim 4, “Shen categorizes camera adjustments, e.g., exposure, focus distance, zoom, aperture size, or shutter speed, as context attributes and does not provide for the adjustment of these attributes” (Page 12, Paragraph 1 of remarks). Shen explicitly recites adjusting at least exposure and focus distance (see Shen et al. teachings of claims 4 in the rejection above). As previously explained, no section of the claims is interpreted as indicating an adjustment must be applied directly to the camera and it is unclear where the applicant considers this limitation to be recited. The camera adjustments recited by claims 4 and 16 are interpreted as a class of adjustments which could be applied to the images directly as these claims do not recite any required active step of modifying a camera. Therefore, the argument is unpersuasive.
Double Patenting
No double patenting issues were identified.
Conclusion
No Claims are allowed.
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.
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/B.H.E./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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