DETAILED ACTION
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 .
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.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5-15 and 18-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Ceballos Lentini et al (11,727,564)
Regarding claims 1, 15 and 19 Ceballos Lentini discloses
Accessing a slide image associated with a tissue for a medical analysis (note col. 4 lines 13-27 and 56-59, image of patient slides of cytology specimen, disease detection);
Segmenting the slide image into a plurality of tiles (note col. 5 lines 55-60, pixel mask segments the image);
Selecting, by one or more machine-learning models based on one or more criteria associated with the medical analysis, one or more tiles from the plurality of tiles (note step 204 and col. 6 lines 10-30, cites machine learning segmentation prediction);
Displaying, via a user interface, the one or more selected tiles for user review (note col. 6 lines 15-20 and 35-40, input collection and visual output based on type of user results may be displayed);
Receiving, via the user interface, one or more user inputs associated with the one or more tiles (note col. 6 lines 15-20, inputting collection); and
Generating, based on the one or more user inputs and the one or more tiles, an analysis result for the medical analysis (note col. 7 lines -55- col. 8 lines 5, predict whether certain groups of cells have the membrane antigen).
Regarding claims 5, 18 and 20 Ceballos Lentini
One or more of a high-attention value or a high representativeness of an illness targeted by the medical analysis (note col. 4 lines 54-56, disease detection platform, analysis detect diseases examiner interprets as illness).
Regarding claim 6 Ceballos Lentini discloses,
Wherein the tissue is associated with a patient having a tumor (note fig. 3A-3D, and col. 7 lines 5-11, illustrates tumors located, shows region comprises a tumor), and wherein the method further comprises: generating, for each of the one or more selected tiles, segmentations comprising nuclei (note col. 3 lines 18-22, target clusters that may be used to differentiate clinically relevant tissue types, from cells)
Regarding claim 7 Ceballos Lentini discloses,
Wherein the medical analysis comprises determining one or more of a recurrence of an illness (note col. 11 lines 38-40, recurrent cases of cancer) or a resistance to a treatment.
Regarding claim 8 Ceballos Lentini discloses,
Wherein the one or more user inputs comprise one or more of an approval of a tile, a rejection of a tile, or a score for a tile (note col. 10 lines 50-55, tile embedding).
Regarding claim 9 Ceballos Lentini discloses,
Wherein the one or more user inputs comprise one or more approvals of one or more tiles, wherein the method further comprises: determining a number of the one or more approved tiles reaches a predetermined number, wherein the analysis result is automatically generated based on the number of the one or more approved tiles reaching the predetermined number (note col. 11 lines 26-32).
Regarding claim 10 Ceballos Lentini discloses,
Wherein the one or more user inputs comprise one or more rejections of one or more tiles, wherein the method further comprises: selecting, by the one or more machine-learning models based on the one or more criteria associated with the medical analysis, one or more additional tiles from the plurality of tiles for the user review (note col. 12 lines 1-24, training may be supervised learning, weakly supervised learning and/or semi supervised learning base on an analysis).
Regarding claim 11 Ceballos Lentini discloses,
Wherein the analysis result comprises one or more of a risk score indicating a likelihood for a recurrence of an illness, a risk score indicating a likelihood for a resistance to a treatment, or a probability indicating a risk of relapse or refractory at a particular time point (note col. 11 lines 33-40, cites likelihood and recurrent cases).
Regarding claim 12 Ceballos Lentini discloses,
Displaying, via the user interface, one or more locations of the one or more selected tiles with respect to the tissue, respectively (note col. 6 lines 15-20 and 35-40, input collection and visual output based on type of user results may be displayed).
Regarding claim 13 Ceballos Lentini discloses,
Wherein the tissue is stained based on a H & E stain, wherein the method further comprises: generating, by the one or more machine-learning models, a virtual DAB stain for associated with the tissue, wherein the user interface is operable for adjusting the display of each of the one or more selected tiles based on one or more of the H & E stain or the virtual DAB stain (note col. 8 lines 30-40 and 48-65, test presence of cancer by staining and training for different staining expressions).
Regarding claim 14 Ceballos Lentini discloses,
Receiving, via the user interface, one or more additional user inputs comprising one or more of an approval of the analysis result, an adjustment of the analysis result, or an override of the analysis result (note col. 6 lines 15-20, inputting collection);
Generating, based on the one or more additional user inputs, a medical report (note col. 6 lines 1-9, receiving WSI or Metadata which includes patient information interpreted as medical report); and receiving a sign-off of the medical report (note col. 6 lines 41-48, respond to request of user).
Allowable Subject Matter
Claims 2-4 and 16-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter for dependent claims 2 and 16. Prior art could not be found for the features generating a first subset from the plurality of tiles by filtering out one or more first tiles from the plurality of tiles, wherein each of the one or more first tiles comprises an artifact, and wherein the one or more tiles are selected from the first subset. These features in combination with other features could not be found in the prior art. Claims 3-4 and 17 depend on claims 2 and 16, respectively. Therefore are also objected.
Relevant Prior Art
Bentaieb et al (11,727,674) Accessing a slide image associated with a tissue for a medical analysis (note col. 6 lines 55-67, assessment of multiple biomarkers from a single sample, or single slide image).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am-3:00pm.
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G.D.
September 10, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676
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