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 .
Applicant’s Response
In Applicant’s Response dated 12/30/25, the Applicant amended Claim 7, canceled Claim 10 and argued claims previously rejected in the Office Action dated 9/30/25. Claims 7-9, 11, 14 and 15 are pending examination.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Claim Rejections - 35 USC § 103
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 7-9, 11, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Saudagar, “Minimize the percentage of Noise in Biomedical Images using Neural Networks”, The Scientific World Journal, 2014, in view of Zafar et al., United States Patent Publication No 20220215511 (hereinafter “Zafar”), in further view of Sugihara, United States Patent Publication 2011/0091107.
Claim 7:
Saudagar discloses:
A telemedicine data transfer system configured to operate in a normal operation mode, the system comprising:
an image compression circuit including a first processing circuit programmed to receive telemedicine data from a data source during the normal operation mode and to produce compressed telemedicine data from the received telemedicine data during the normal the operation mode (see page 2 section Introduction, pp 1 and page 3 section 2 Methodology pp 1). Saudagar teaches receiving telemedicine data from a source and produce compressed data by ANN based compression system the image is coded with respect to its pixel values and pixel coordinate; and
an image decompression circuit including a second processing circuit programmed to receive the compressed telemedicine data via a network during the normal operation mode and produce decompressed and enhanced telemedicine data from the compressed telemedicine data during the normal operation mode, the decompressed and enhanced telemedicine data including an enhancement in data quality over the telemedicine data received from the data source (see page 2 pp5). Saudagar teaches receiving compressed telemedicine data and producing decompressed data by lowering the noise in the image , and
Saudagar fails to expressly disclose a training mode for image compression and decompression using a neural network.
Zafar discloses:
operating in a training mode (see paragraph [0008). Zafar teaches training a neural network.
the image compression circuit and the image decompression circuit include neural networks having been trained using an objective function to evaluate a difference between a training input data provided to the image compression circuit and a training output data that is output from the image decompression circuit in the training mode (see paragraphs [0008], [0009], [0011] and [0012]). Zafar teaches a compression and decompression circuit using a first neural network to find hyperparameters such that the hyperparameters, when applied to the noise image using the first neural network, produce an approximation of the desired image within an error that is less than a pre-determined threshold.
wherein during the training mode and prior to the evaluation by the objective function, the training output data is made different from the training input data by a data enhancement operation performed on the training input data, the data enhancement operation being performed on the training input data during the training mode correlated to the enhancement in data quality produced by the image decompression circuit during the normal operation mode (see paragraphs [0030], [0070]). Zafar teaches train a metanetwork with a set of training images, and used to compress and find filters that may be used to transform a noise image into an approximation of an input image, according to a preferred embodiment..
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to modify the method disclosed by Saudagar to include training a neural network to transform the image into a less noisy image similar to the input for the purpose of efficiently trained to quickly find the structure of a neural network which may re-construct an approximation of an image, as taught by Zafar.
Saudagar and Zafar fails to expressly disclose giving a higher priority of compression/decompression to the important portions of the images.
Sugihara discloses:
the image compression circuit is further configured to assess an importance of different portions of an image included in the telemedicine data, to send to the image decompression circuit a higher importance portion of the image first, and to send to the image decompression circuit a lower importance portion of the image after sending the higher importance portion of the image (see paragraphs [0034, [0035] and [0039]-[0042]). Sugihara teaches determining the important portions of the images and compressing/decompressing only the important regions with high priority.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to modify the method disclosed by Saudagar and Zafar to include compressing and decompressing important portions of the images for the purpose of efficiently transmitting important portions of images, as taught by Zafar.
Claim 8:
Saudagar discloses:
wherein an amount of data enhancement performed on the training output data during training correspond to the amount of enhancement in data quality produced by the image decompression circuit (see Page 6 section 2.3, pp 6 NN Unit). Saudagar teaches the NN unit extracts the min-max value of given input and creates a feed-forward neural network taking the least mean learning algorithm. The network is created for converging to the error with a goal of 0.1 and with number of epochs = 50. The created network is trained with these coefficient values based on the given input and the created feed-forward network for decompression.
Claim 9:
Saudagar discloses:
wherein the image decompression circuit is further configured to produce the decompressed and enhanced telemedicine data including the enhancement in data quality including one or more of a contrast enhancement, a denoise enhancement, a resolution increase, a channel reduction, a skeletonized output, and a segmented output with different regions having different compression levels (see page 1, Abstract and page 2 pp 5). Saudagar teaches decompressing the image and the enhanced image by including the denoise enhancement to the image.
Claim 11:
Saudagar discloses:
wherein the telemedicine data includes at least one of a 2D image and a 3D image (see page 1, Abstract). Saudagar teaches the biomedical images are X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) which contain 2D and 3D images.
Claim 14:
Saudagar discloses:
wherein the data enhancement operation performed on the training output data prior to the evaluation by the objective function includes at least one of a denoise enhancement, contrast enhancement, color tuning, and structural enhancements (see page 1, Abstract and page 2 pp 5). Saudagar teaches enhancing the image by a denoise enhancement to the image.
Claim 15:
Saudagar fails to expressly disclose
Zafar discloses:
wherein, during the normal operation mode, the image decompression circuit is configured to perform data decompression and the enhancement in data quality in a same operation so that the image decompression circuit outputs the decompressed and enhanced telemedicine data from the compressed telemedicine data, without requiring a subsequent enhancement operation after decompression (see paragraph [0011]). Zafar teaches during normal operation performing all steps in one operation to achieve the desired results without multiple enhancements.
Accordingly, it would have been obvious to one having ordinary skill in the art before the effective date of the claimed invention to modify the method disclosed by Saudagar to include training a neural network to transform the image into a less noisy image similar to the input for the purpose of efficiently trained to quickly find the structure of a neural network which may re-construct an approximation of an image, as taught by Zafar.
Response to Arguments
Applicant’s arguments, see REM, filed 12/30/25, with respect to the rejections of claims 1, 7-9, 11, 14 and 15 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Saudagar, Zafar and Sugihara.
103 Rejections
Applicant argues As acknowledged by the Office Action at page 8, Saudagar and Zafar fail to disclose compressing high priority portions of the images. Furthermore, Applicant respectfully submits that Saudagar and Zafar fail to disclose or suggest that the image compression circuit assesses "an importance of different portions of an image included in the telemedicine data, to send to the image decompression circuit a higher importance portion of the image first, and to send to the image decompression circuit a lower importance portion of the image after sending the higher importance portion of the image," as recited by amended Claim 7.
The Examiner agrees.
Saudagar and Zafar do not teach the amended portions of the claim. The Examiner introduced new art, Sugihara, to teach the newly amended portions of the claim.
Applicant argues Ansari merely indicates that high and lower importance portions are separately encoded. However, according to Ansari, those separately encoded portions are merged together to produced the compressed image data before that image compressed image is sent, in its entirety, to the decoder. Accordingly, Ansari fails to suggest that a higher importance portion of an image is sent separately from a lower importance portion of the image, and Ansari fails to suggest that a higher importance portion is sent before a lower importance portion.
The Examiner agrees.
The Examiner no longer used Ansari to teach to the newly amended portions of Claim 7.
Applicant argues Saudagar in view of Zafar and Ansari fails to teach or suggest "the image compression circuit is further configured to assess an importance of different portions of an image included in the telemedicine data, to send to the image decompression circuit a higher importance portion of the image first, and to send to the image decompression circuit a lower importance portion of the image after sending the higher importance portion of the image," as recited by amended Claim 7.
The Examiner disagrees.
The Examiner uses Sugihara to teaches determining important sections of the images. Sugihara teaches determining the important portions of the images and compressing/decompressing only the important regions with high priority (see paragraphs [0034, [0035] and [0039]-[0042]). Thus, the combination of art teaches the limitations of the claims.
Conclusion
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 TIONNA M BURKE whose telephone number is (571)270-7259. The examiner can normally be reached M-F 8a-4p.
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/TIONNA M BURKE/Examiner, Art Unit 2178 3/25/26
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178