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 .
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kumar et al. US 2019/0303760.
In regarding to claim 1 Kumar teaches:
1. A system, comprising: one or more processors configured to: retrieve sensor data regarding an object;
Kumar, 0006, 0016, 0102-0103
and apply the sensor data as input to a classification model to cause the classification model to determine a classification of the object,
Kumar, 0006, 0097, 0102-0103
the classification model configured based on training data comprising a plurality of clusters generated by dimensionality reduction of example data regarding example objects, at least one cluster of the plurality of clusters associated with the classification;
Kumar, 0109, 0114-0115
and output the classification of the object.
Kumar, 0172
In regarding to claim 2 Kumar teaches:
2. The system of claim 1, wherein the classification model is configured to process the sensor data in a variable space corresponding to a number of dimensions of the dimensionality reduction, the number of dimensions greater than or equal to one and less than or equal to about ten.
Kumar, 0109, 0114-0115
In regarding to claim 3 Kumar teaches:
3. The system of claim 1, wherein the one or more processors are configured to perform the dimensionality reduction as a clustering operation on the example data to generate the plurality of clusters.
Kumar, 0109, 0114-0115
In regarding to claim 4 Kumar teaches:
4. The system of claim 1, wherein the one or more processors are configured to generate the plurality of clusters without predetermined labels of the example data.
Kumar, 0114-0115
In regarding to claim 5 Kumar teaches:
5. The system of claim 1, wherein the sensor data and the example data each respectively comprise a time-series electrical signal representative of an electromagnetic wave detected regarding the respective object and example objects.
Kumar, 0005, 0062, 0182
In regarding to claim 6 Kumar teaches:
6. The system of claim 1, wherein the object comprises at least one of cellular material, nucleic acid material, biological material, or chemical material.
Kumar, 0006
In regarding to claim 7 Kumar teaches:
7. The system of claim 1, wherein the object comprises a cell, and the classification model is configured to detect the classification of the cell based on a gate distinguishing a first cluster of the plurality of clusters that is associated with the classification from a second cluster of the plurality of clusters that is unassociated with the classification.
Kumar, 0006-0007, 0049
In regarding to claim 8 Kumar teaches:
8. The system of claim 1, wherein a field programmable gate array (FPGA) comprises the one or more processors, the FPGA configured to receive the sensor data from a flow cytometer through which the object is flowed, wherein the flow cytometer is configured to operate in a fluorescent activated cell sorting (FACS) mode or a structured light mode to output a waveform representing the sensor data regarding the object.
Kumar, 0006, 0028-0029, 0234, 0291
In regarding to claim 9 Kumar teaches:
9. The system of claim 1, wherein the object comprises a cell, and the classification indicates a cell type of the cell.
Kumar, 0006-0007, 0049
In regarding to claim 10 Kumar teaches:
10. A system, comprising: a flow cytometer configured to direct a fluid flow comprising an object through a field of view of a photosensor and cause the photosensor to detect sensor data regarding the object;
Kumar, 0006, 0016, 0102-0103
and one or more processors configured to apply the sensor data as input to a classification model to cause the classification model to detect a classification of the object,
Kumar, 0006, 0097, 0102-0103
the classification model configured based on training data comprising a plurality of clusters generated by dimensionality reduction of example data regarding example cells, at least one cluster of the plurality of clusters associated with the classification.
Kumar, 0006, 0109, 0114-0115
In regarding to claim 11 Kumar teaches:
11. The system of claim 10, wherein the one or more processors are configured to perform the dimensionality reduction as a clustering operation on the example data to generate the plurality of clusters.
Kumar, 0114-0115
In regarding to claim 12 Kumar teaches:
12. The system of claim 10, wherein the one or more processors are configured to generate the plurality of clusters without predetermined labels of the example data.
Kumar, 0114-0115
In regarding to claim 13 Kumar teaches:
13. The system of claim 10, wherein the sensor data and the example data respectively correspond to an electrical signal representative of a waveform regarding the respective object and example objects.
Kumar, 0005, 0062, 0182
In regarding to claim 14 Kumar teaches:
14. The system of claim 10, wherein the one or more processors are configured to use the classification model to detect the classification of the object based on a gate distinguishing a first cluster of the plurality of clusters that is associated with the classification from a second cluster of the plurality of clusters that is unassociated with the classification.
Kumar, 0006-0007, 0049
In regarding to claim 15 Kumar teaches:
15. The system of claim 10, wherein a field programmable gate array (FPGA) comprises the one or more processors.
Kumar, 0272
In regarding to claim 16 Kumar teaches:
16. The system of claim 10, wherein: the flow cytometer is to operate, to detect the sensor data regarding the object, in one of a fluorescent activated cell sorting (FACS) mode or a structured light mode.
Kumar, 0069, 0084
In regarding to claim 17 Kumar teaches:
17. A method, comprising: receiving, by one or more processors, a plurality of sensor data representations of a plurality of objects, wherein the plurality of objects comprise at least one of cellular material, nucleic acid material, biological material, or chemical material;
Kumar, 0006, 0016, 0102-0103
performing, by the one or more processors, dimensionality reduction of the plurality of sensor data representations to assign each object of the plurality of objects to a corresponding cluster of a plurality of clusters;
Kumar, 0006, 0016, 0102-0103
assigning, by the one or more processors, an identifier of a type of a given object of the plurality of objects to the corresponding cluster of the plurality of clusters to which the given object is assigned;
Kumar, 0006, 0109, 0114-0115
and configuring, by the one or more processors, a classification model based on the plurality of clusters and the identifier of the type.
Kumar, 0109, 0114-0115
In regarding to claim 18 Kumar teaches:
18. The method of claim 17, wherein performing the dimensionality reduction comprises applying the plurality of sensor data representations as input to at least one of a dimensionality reduction process or a clustering process.
In regarding to claim 19 Kumar teaches:
19. The method of claim 17, wherein performing the dimensionality reduction operation comprises performing, by the one or more processors, the dimensionality reduction operation without any identifier of types of the plurality of objects.
Kumar, 0114-0115
In regarding to claim 20 Kumar teaches:
20. The method of claim 17, wherein the plurality of clusters comprises a first cluster associated with a first type and a second cluster associated with a second type different from the first type.
Kumar, 0006-0007, 0049
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T TEKLE whose telephone number is (571)270-1117. The examiner can normally be reached Monday-Friday 8:00-4:30 ET.
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/DANIEL T TEKLE/Primary Examiner, Art Unit 2481
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