DETAILED ACTION
Response to Amendment
1. This office action is in response to applicant’s communication filed on 04/23/2026 in response to PTO Office Action mailed 03/11/2026. The Applicant’s remarks and amendments to the claims and/or the specification were considered with the results as follows.
2. In response to the last Office Action, claims 1, 2, 5, 11 and 12 are amended. Claims 3, 4, 6, 7 are canceled. As a result, claims 1, 2 and 8-12 are pending in this office action.
Response to Arguments
3. Applicant's arguments with respect to 35 USC 103 rejections have been fully considered but are moot in view of new ground (s) of rejection.
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 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, 5, 9, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable by Sethi et al. (US 2018/0232883 A1), hereinafter Sethi and in view of Iqbal et al. (“Balancing Prediction Errors for Robust Sentiment Classification”, 2019), hereinafter Iqbal.
Referring to claims 1, 11 and 12, Sethi discloses an information processing apparatus comprising: a camera (See para. [0046], a system comprises one or more digital cameras for image capturing images); a display, at least one memory configured to store instructions (See para. [0046] and para. [0047], a computing system includes one or more processors 132, a memory 134, a display 136, a user interface 138); and at least one processor (See para. [0004], a processor) configured to execute the instructions to:
sequentially acquire a plurality of elements included in sequential data, each element being an image signal acquired via the camera (See para. [0006], para. [0007] and para. [0046], the system’s imaging apparatus comprises a microscope with optical zoom and a camera for capturing one or more images of the patient tissue)
calculate, based on at least two elements of the plurality of elements, a classification indicator indicating which one of a plurality of classes the sequential data belongs to (See para. [0071], the system’s POI classifier learns to predict labels for central pixel of input sub-image windows of pre-processed tissue image);
execute a first process of setting the classification indicator to a predetermined value […] (See para. [0077], the system sets label at each POI location to a disease class); and
determine an interval including an element of a detection-target class, based on the classification indicator; output a result to the display showing that the predetermined condition is that the classification indicator crosses a threshold value, wherein the threshold value determines that the classification indicator leans to a detection-target class or a class other than the detection-target class (See para. [0081], para. [0083] and Figure 7, one or more classification maps 208 may be given as input to a nearest neighbor graph formation module 602 within the class aggregator 210, which connects two POIs using the edge of a graph 603 if one of them is among the nearest k neighbors, where k is usually around, the system examines each vertex or POI in the graph 603 and assigns it a class and confidence based on the class with highest probability, for example, the disease class scores are arranged in a matrix whose rows are disease classes, and columns are confidence intervals, while each entry is the percent of POIs across one or more tissue images with that disease classification and confidence equal to or larger than the threshold for that column. Also, note in para. [0083], a local classification module samples multiple sized sub-image windows 750 and 752 from the pre-processed image 722 using the location map 742, a local classifier may take each set of windows centered at each POI and produces class probability maps 760, in which a local classification probability for a POI is shown as a pie chart 762. such class probability maps can be aggregated using a disease class aggregator module into disease class scores 770, where proportion of POIs representing each disease class 772, 774, 776 (including “no cancer” 772), along with their confidence intervals 782, 784, 786, are shown).
Sethi does not explicitly disclose execute a first process of resetting the classification indicator to non-zero threshold value when a predetermined condition is satisfied.
Iqbal discloses execute a first process of resetting the classification indicator to non-zero threshold value when a predetermined condition is satisfied (See page 33:11, Section 5.2, the value of threshold δ that produces the smallest absolute PBR on the labeled data is selected. This is done through iterative line search. More precisely, when PBR is positive then the value of δ is increased by a fixed proportion and when PBR is negative then the value of δ is decreased by a fixed proportion. These steps are repeated until PBR becomes close to zero. Subsequently, this value is used while predicting the labels of new documents) and output a result to the display showing that the determined condition is that the classification indicator crosses the non-zero threshold value, wherein the non-zero threshold value determines that the classification indicator leans to a detection-target class or a class other than the detection-target class (See page 33:15, the shift in decision boundary learned by BAT over the training data (the threshold δ) makes the predictions over the test data less biased (PBR is close to zero) and more accurate. This result is similar to that observed for BAT combined with lexicon-based methods, hence confirming the effectiveness and generality of BAT for bias control in sentiment classification).
Therefore, it 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 was made to modify the classifier of Sethi to reset a classification indicator when a predetermined condition is satisfied, taught by Iqbal. Skilled artisan would have been motivated to make the predictions over the test data less biased and more accurate (See page 33:15). In addition, all references (Sethi and Iqbal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as classifying image data. This close relation between both references highly suggests an expectation of success.
As to claim 5, Sethi in view of Iqbal disclose wherein the predetermined value is an initial value of the classification indicator (See Iqbal, page 33:11, for each experiment of the BAT, the value of threshold δ that produces the smallest absolute PBR on the labeled data is selected. This is done through iterative line search. More precisely, when PBR is positive then the value of δ is increased by a fixed proportion and when PBR is negative then the value of δ is decreased by a fixed proportion. These steps are repeated until PBR becomes close to zero. Subsequently, this value is used while predicting the labels of new documents).
Therefore, it 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 was made to modify the classifier of Sethi to reset a classification indicator when a predetermined condition is satisfied, taught by Iqbal. Skilled artisan would have been motivated to make the predictions over the test data less biased and more accurate (See page 33:15). In addition, all references (Sethi and Iqbal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as classifying image data. This close relation between both references highly suggests an expectation of success.
As to claim 9, Sethi in view of Iqbal discloses wherein the predetermined condition is that a predetermined number of elements are acquired (See Iqbal, page 33:13, page 33:16 and Figure 3, the size of the data set is required to determine the threshold).
Therefore, it 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 was made to modify the classifier of Sethi to reset a classification indicator when a predetermined condition is satisfied, taught by Iqbal. Skilled artisan would have been motivated to make the predictions over the test data less biased and more accurate (See page 33:15). In addition, all references (Sethi and Iqbal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as classifying image data. This close relation between both references highly suggests an expectation of success.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi (US 2018/0232883 A1) and in view of Iqbal (“Balancing Prediction Errors for Robust Sentiment Classification”, 2019) and further in view of Unser (US 2015/0332414 A1).
As to claim 2, Unser discloses determine that interval is the interval including an element of the detection-target class when the classification indicator exceeds a first threshold value, and to determine that interval ceases to be the interval including an element of the detection-target class when the classification indicator falls below a second threshold value after the classification indicator exceeds the first threshold value (See para. [0035], para. [0051]-para. [0053] and para. [0057], determining that the transaction data which associated with a particular time period has probability scores larger than a threshold for an associated score and the scores that exceed the threshold value are selected for further analysis, the differential of the selected scores if determine along with a threshold variance for the probability of each score, if the score differential exceeds the threshold variance, then the highest score is selected, and the score’s associated item type or category within the particular time period is identified as the object of the transaction. If the differential does not exceed the threshold variance [e.g., the second threshold], no prediction is made [e.g., ceases to be target class including the particular time interval]).
Therefore, it 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 was made to modify the system of Sethi to further determine an interval including an element of the detection-target class when the classification indicator exceeds a first threshold value, as taught by Unser. Skilled artisan would have been motivated to provide user with accurate predictions (See Unser, para. [0006]). In addition, all references (Unser, Sethi and Iqbal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as data classification. This close relation between all references highly suggests an expectation of success.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi (US 2018/0232883 A1) and in view of Iqbal (“Balancing Prediction Errors for Robust Sentiment Classification”, 2019) and further in view of Baker (2020/0143240 A1), hereinafter Baker.
As to claim 8, Sethi does not explicitly disclose wherein the predetermined condition is that a slope of the classification indicator exceeds a fifth threshold value.
Baker discloses the predetermined condition is that a slope of the classification indicator (See para. [0135] and Figure 12, detecting a specified slope of a data item exceeds a specification threshold which indicates the data item is misclassified).
Therefore, it 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 was made to modify the system of Unser to include a slope predetermined condition, taught by Baker. Skilled artisan would have been motivated to classify data items correctly (See Baker, para. [0004]). In addition, all references (Baker, Sethi and Rainer) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as analyzing data using predictive analytic systems. This close relation between both references highly suggests an expectation of success.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi (US 2018/0232883 A1) and in view of Iqbal (“Balancing Prediction Errors for Robust Sentiment Classification”, 2019) and further in view of Lin (US 2007/0091187 A1).
As to claim 10, Unser discloses calculate a likelihood ratio indicating a likelihood that each of the plurality of elements belongs to a class of the plurality of classes (See para. [0057] and para. [0058], provide a likelihood indicator that the particular category of product purchased in transaction was a computer, the higher value likelihood indicator represents a game station).
Unser does not explicitly disclose calculate, as the classification indicator, a consolidated likelihood ratio indicating a likelihood that the data belongs to a class of the plurality of classes, based on the likelihood ratios.
Lin discloses calculate, as the classification indicator, a consolidated likelihood ratio indicating a likelihood that the data belongs to a class of the plurality of classes, based on the likelihood ratios (See para. [0005], obtaining an accumulated likelihood ratio to determine whether the data belongs to a defective class or a normal class).
Therefore, it 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 was made to modify the system of Unser to include consolidated likelihood ratio, taught by Lin. Skilled artisan would have been motivated to avoid estimation bias (See Lin, para. [0005]). In addition, all references (Lin, Sethi and Iqbal) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as analyzing data using predictive analytic systems. This close relation between all references highly suggests an expectation of success.
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 YUK TING CHOI whose telephone number is (571)270-1637. The examiner can normally be reached Monday-Friday 9am-6pm.
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/YUK TING CHOI/Primary Examiner, Art Unit 2164
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