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 .
DETAILED ACTION
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because it just recites transmitting and displaying data.
Independent claim 20:
With regards to step 1 of the eligibility analysis, claim 20 recites “A method for displaying flow-cytometry data in real-time”, thus claim 20 is eligible.
With regards to step 2A, Prong I, claim 20 recites transmitting and displaying data. Although claim 20, recites a user-interface, it is merely the destination of data which is indicated and does not indicate improvement in functionality. MPEP 2106.05 (a)(1).iv details that recording or transmitting data by use of conventional or generic technology in a nascent but well-known environment, without any assertion that the invention reflects an inventive solution are not sufficient to show an improvement in computer-functionality.
With regards to Step 2B, the claim does not include additional elements that are significant enough to amount to more than data transmission. The cell sorter and the user interface are the origin and destination, respectively of the data. , MPEP 2106.05(d)(II) (i) provides that the courts have recognized receiving and transmitting data as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
Furthermore, the recitation of a “a machine learning model” also does not amount to more than data transmission. The claim does not provide any details about how the classification machine learning model operates.
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.
(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.
Claim(s) 1-4, 6-17 and 19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Lin et al (US 20200072727), hereafter Lin.
With regards to claim 1, Lin discloses a system (Fig. 1) comprising: a cell sorting device (P0042), and a computing device configured to perform the steps of: collecting first cell sorter data associated with a first portion of a sample of one or more cells(P0116-117); training, based on one or more of the first cell sorter data, user input, a first representation of the first cell sorter data or a combination thereof, a machine learning model (P0116,P0122); receiving one or more gating instructions from a user input based on the first cell sorter data, the machine learning model, or a combination thereof (P0127); receiving second cell sorter data associated with a second portion of the sample of one or more cells (P0138); determining, based on the trained machine learning model and the user-inputted gating instructions, a classification of the second cell sorter data, a representation of the second cell sorter data, or a combination thereof (P0138); and sorting, based on the classification, a portion of the sample (P0138).
With regards to claim 2, Lin discloses, a method of sorting a sample of particles (Abstract), the method comprising: collecting first cell sorter data associated with a first portion of a sample of one or more particles comprising cells(P0116-117); training, based on one or more of the first cell sorter data, user input, a first representation of the first cell sorter data or a combination thereof, a machine learning model (P0116,P0122); receiving one or more gating instructions from a user input based on the first cell sorter data, the machine learning model, or a combination thereof (P0127); receiving second cell sorter data associated with a second portion of the sample of one or more cells(P0138); determining, based on the trained machine learning model and the user-inputted gating instructions, a classification of the second cell sorter data(P0138), a representation of the second cell sorter data, or a combination thereof; and sorting, based on the classification, a portion of the sample(P0138).
With regards to claim 3, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the one or more gating instructions are determined based on one or more of data output from the machine learning model, an embedding output from the machine learning model, a parametric embedding of the cell sorter data generated using the machine learning model, or a combination thereof (P0127).
With regards to claim 4, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the user input comprises gating instructions, sorting instructions, or a combination thereof indicating one or more groupings associated with the first cell sorter data (P0056).
With regards to claim 6, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the cell sorter comprises a field-programmable gate array (FPGA), wherein the FPGA is programmed with parameters of the trained machine learning model and configured to analyze a plurality of portions of the sample using the trained machine learning model, and configured to cause the FPGA to store or comprise one or more trained weights of the trained machine learning model (P0046).
With regards to claim 7, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the first representation of the first cell sorter data is determined based on applying a mapping process to the first cell sorter data, the mapping process comprising one or more of a clustering process, a dimensionality reduction process, an embedding, a non-parametric embedding, or a combination thereof (P0041).
With regards to claim 8, Lin discloses all the elements of claim 7 as outlined above. Lin further discloses wherein the embedding comprises a uniform Manifold Approximation and Projection (UMAP), a t-distributed Stochastic Neighbor Embedding (t-SNE), another nonlinear embedding, or a combination thereof (P0127).
With regards to claim 9, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the machine learning model transforms cell sorter data having a higher number of dimensions to a representation of the transformed cell sorter data having a lower number of dimensions (P0132).
With regards to claim 10, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the cell sorter data comprises quantitative fluorescence data expressed as one or more of antibodies bound per cell, antibody binding capacity (ABC), or molecules of equivalent soluble fluorochrome (MESF), one or more other quantitative indicators of fluorescence, or one or more combinations thereof (P0101).
With regards to claim 11, Lin discloses all the elements of claim 10 as outlined above. Lin further discloses wherein the fluorescence signals are derived from one or more fluorescent proteins, one or more fluorescent dyes, one or more fluorescently conjugate antibodies, one or more populations of fluorescent beads or fluorescently labeled beads, or one or more combinations thereof (P003).
With regards to claim 12, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the machine learning model comprises a neural network (P0116).
With regards to claim 13, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the machine learning model is trained without prior determination of a first representation of the first cell sorter data (P0121).
With regards to claim 14, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses, wherein the neural network is trained to learn the mechanism of a mapping process for generating one or more representations of the cell sorter data for a cell sorter measurement session (P0116).
With regards to claim 15, Lin discloses all the elements of claim 12 as outlined above. Lin further discloses wherein the neural network comprises one or more of an artificial neural network, a convolutional neural network, a recurrent neural network, or one or more combinations thereof (P0039).
With regards to claim 16, Lin discloses all the elements of claim 12 as outlined above. Lin further discloses wherein the neural network processes cell sorter data enabling cell sorting events equal to or greater than 100,000 events per second (P0050).
With regards to claim 17, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses wherein the sample comprises a biological sample comprising a plurality of cells (P0092).
With regards to claim 19, Lin discloses all the elements of claim 2 as outlined above. Lin further discloses, wherein the cell sorter comprises one or more processors (controller 190), wherein the one or more processors pass the classification of the second cell sorter data to the cell sorter for sorting the portion of the sample (P0138).
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.
Claim(s) 5 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin.
With regards to claim 5, Lin discloses all the elements of claim 4 as outlined above. Lin does not directly disclose wherein the one or more groupings comprise one or more signal clusterings or gatings for one or more parameters comprising: removal of doublets, cell viability, light scatter, expression of one or more specific lineage markers, or one or more combinations thereof. However, the examiner takes Official Notice that it is known in the art to remove light scatter, doublets etc. and is therefore it is rendered obvious to a person with ordinary skill in the art before the effective filing date of the invention to remove these features in order to ensure a more accurate sort.
With regards to claim 18, Lin discloses all the elements of claim 2 as outlined above. Lin does not directly disclose wherein the classification is based on one or more cellular phenotypic markers. However, the examiner takes Official Notice that the use of phenotypic markers is known in the art and therefore obvious to a person with ordinary skill in the art before the effective filing date of the invention based on the specific properties of the particles being sorted in order to ensure a more accurate sort.
Examiner’s Comment
The applicant is advised that the patentability of claim 20 could not be determined based on the extent of the 35 U.S.C 101 rejection.
Prior Art Not Relied Upon
US-20220390349, US-20230296492, and US-20240288354 disclose similar transformations of cell sorting data and particle sorting apparatuses.
Conclusion
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/J.L.B./Examiner, Art Unit 3653
/MICHAEL MCCULLOUGH/Supervisory Patent Examiner, Art Unit 3653