DETAILED ACTION
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 Amendment
Applicant’s amendment filed December 9th 2025 has been entered and made of record. No claims are amended. New claims 19-20 are added. Claims 11-18 are withdrawn in response to the restriction requirement. Claims 1-20 are pending. Claims 1-10 and 19-20 are examined below.
Applicant’s remarks in view of the newly presented amendments have been considered but are not found to be persuasive for at least the following reasons:
A. Applicant argues that Holmsten does not disclose the claim language:
“output up to eight digits and an indicator of a number of digits detected in the display.”
Examiner disagrees. Interpreted fairly broadly, the limitation of “an indicator of a number of digits detected in the display” reads on the disclosure of Holmsten who discloses bounding boxes as an indicator of a number of digits detected in the display. Bounding boxes are shown in Figs. 16, 18 and 21A for example. The bounding boxes are considered visual indicators of a number of digits detected in the display. Holmsten disclose that bounding boxes are output by the machine learning engine 118 in Fig. 1 and the bounding boxes appear in Fig. 21A (paragraphs [0064]-[0065]). Holmsten further discusses bounding boxes with regard to Fig. 18 (paragraph [0145]). Broadly interpreted, the bounding boxes of Holmsten serve as “an indicator of a number of digits detected in the display.” The rejection is accordingly maintained.
B. Applicant argues that Holmsten does not discloses the claim language:
“perform processing using two layers of trained non-linear units”
Examiner disagrees. Cited Fig. 14 is an image of a Faster R-CNN object detection approach utilized for identifying objects. CNNs used for image object detection are generally non-linear operations. A Faster R-CNN is inherently a system comprising non-linear units or layers and is considered a non-linear system overall. In the case of Fig. 14, the Faster R-CNN has both a region proposal network layer which is considered non-linear and a real pooling layer which is also non-linear. Both the disclosed R-CNN and the layers of region proposal network and the real pooling layer are considered to be non-linear as they enable the CNN to learn the complex non-linear task of object and digit recognition. The rejection in view of Holmsten is therefore maintained and accordingly made FINAL.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-10 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by USPN 2024/0304014 to Holmsten et al.
With regard to claim 1, Holmsten discloses a system comprising:
at least one processor (paragraph [0045] and Fig. 1, computing device 110);
at least one memory (Fig. 1, memory 120), wherein the at least one memory has stored thereon instructions that, when executed by the at least one processor, cause the system at least to:
receive an input image having a display of digits included therein (Fig. 3 and Figs. 8-13);
extract features from the input image using a trained feature generating network to identify digits in the display (paragraphs [0116], features are extracted to create a feature map using a RCNN. See Fig. 14, feature maps obtained from the core layers neural network. See also paragraphs [0046]-[0049], OCR and machine learning engines process the images of numerical characters);
perform processing using two layers of trained non-linear units (Fig. 14 shows a visual indication of different trained non-linear unit layers used in recognizing the numerical characters or digits. Cited Fig. 14 is an image of a Faster R-CNN object detection approach utilized for identifying objects. A Faster R-CNN is inherently a system comprising non-linear units or layers and is considered a non-linear system overall. In the case of Fig. 14, the Faster R-CNN has both a region proposal network layer which is considered non-linear and a real pooling layer which is also not linear. Both the disclosed R-CNN and the layers of region proposal network and the real pooling layer are considered to be non-linear as they enabled the CNN to learn the complex non-linear task of object and digit recognition); and
output up to eight digits and an indicator of a number of digits detected in the display (Figs. 12 and 13 show examples of several digits being recognized. See also paragraphs [0068]-[0070]. In the example of blood pressure, diastolic, systolic and pulse fields would contain 8 digits and an indication of the recognized fields of digits. Interpreted fairly broadly, the limitation of “an indicator of a number of digits detected in the display” reads on the disclosure of Holmsten who discloses bounding boxes as an indicator of a number of digits detected in the display. Bounding boxes are shown in Figs. 16, 18 and 21A for example. The bounding boxes are considered visual indicators of a number of digits detected in the display. Holmsten disclose that bounding boxes are output by the machine learning engine 118 in Fig. 1 and the bounding boxes appear in Fig. 21A (paragraphs [0064]-[0065]). Holmsten further discusses bounding boxes with regard to Fig. 18 (paragraph [0145]). Broadly interpreted, the bounding boxes of Holmsten serve as “an indicator of a number of digits detected in the display.” ).
With regard to claim 2, Holmsten discloses the system as set forth in claim 1, wherein the feature generating network is a convolutional network (paragraph [0047], the neural network is an R-CNN).
With regard to claim 3, Holmsten discloses the system as set forth in claim 1, wherein the feature generating network is followed by the two layers of trained non-linear units that are fully connected (Fig. 14 and paragraph [0116], Fig. 14 shows a visual indication of different trained non-linear unit layers used in recognizing the numerical characters or digits. See paragraph [0116] and Fig. 14, In the example of the RCNN, the features that are extracted are in the feature maps. The feature maps are then used in the next layers of non-linear units of the region proposal network and the pooling layer to then classify the identified digits from the feature map areas).
With regard to claim 4, Holmsten discloses the system as set forth in claim 1, wherein the digits are output as eight independent and trained categorical outputs (Figs. 12 and 13 show examples of several digits being recognized. See also paragraphs [0068]-[0070]. In the example of blood pressure, diastolic, systolic and pulse fields would contain 8 digits and an indication of the recognized fields of digits).
With regard to claim 5, Holmsten discloses the system as set forth in claim 1, wherein the indicator of the number of digits detected in the display is output in one linear unit (Fig. 21B.
With regard to claim 6, the discussion of claim 1 applies.
With regard to claims 7-10, the discussions of claims 2-5 apply respectively.
With regard to claim 19, Holmsten discloses the system of claim 1 wherein the output of up to eight digits and the indicator of the number of digits further includes at least one placeholder for no detected digit (paragraph [0111] and Fig. 12, Holmsten gives an example of when no digits are detected in a similar system. The device of Holmsten would readily recognize a scenario where no digits are recognized. For example, if the blood pressure device display shown in Fig. 13 was turned off, then no digits would likely be recognized. When the recognition is unsuccessful, Holmsten give an indication that no digits were detected as shown in Fig. 12. The term “placeholder” is interpreted as whatever memory is used to store and produce a determination that no digits are detected).
With regard to claim 20, Holmsten discloses the system of claim 1 wherein the instructions further cause the system to perform the processing on the identified digits in the display using the two layers of trained non-linear units after the features are extracted from the input image using the trained feature generating network (paragraph [0116] and Fig. 14, In the example of the RCNN, the features that are extracted are in the feature maps. The feature maps are then used in the next layers of non-linear units of the region proposal network and the pooling layer to then classify the identified digits).
FINAL REJECTION
Applicant’s amendment necessitated the new grounds of rejection presented in the 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 37CFR 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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WESLEY J TUCKER whose telephone number is (571)272-7427. The examiner can normally be reached 9AM-5PM Monday-Friday.
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/WESLEY J TUCKER/Primary Examiner, Art Unit 2661