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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) received on January 9, 2026, has been considered by examiner.
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.
Claims 1-30 are rejected under 35 U.S.C. 101 because the claims are not directed to patent eligible subject matter.
Claims 1-30 do fall within at least one of the four categories of patent eligible subject matter because the claims recite a machine (i.e., system) and process (i.e., a method).
Although claims 1-30 fall under at least one of the four statutory categories, it should be determined whether the claim wholly embraces a judicially recognized exception, which includes laws of nature, physical phenomena, and abstract ideas, or is it a particular practical application of a judicial exception (See MPEP 2106 I and II).
Claims 1-30 are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Part I: Step 2A, Prong One: Identify the Abstract Idea
Under step 2A, Prong One of the Alice framework, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP §2106.04(a). The determination consists of a) identifying the specific limitations in the claim that recite an abstract idea; and b) determining whether the identified limitations fall within at least one of the three subject matter groupings of abstract ideas (i.e., mathematical concepts, mental processes, and certain methods of organizing human activity).
The identified limitations of independent claim 2 (representative of independent claims 1 and 28) recite:
obtaining blood parameters of a blood sample by a hematology analyzer, wherein the blood parameters comprise quantitative and qualitative measurement variables, wherein the measurement variables comprise the properties of individual cells, wherein the individual cells comprise blood cells, wherein the blood cells comprise white blood cells, red blood cells and platelets, wherein the white blood cells comprise monocytes, lymphocytes, basophils, eosinophils and neutrophils;
creating at least one scatterplot having at least two axes, each axis of the scatterplot comprising a different measurement variable from the obtaining blood parameters of a blood sample;
determining at least one in vivo and/or in vitro and/or post-mortem state by at least one deep learning model, wherein the input variable for the at least one deep learning model comprises at least one scatterplot from the creating at least one scatterplot; and/or
at least one machine learning model, wherein the input variable for the at least one machine learning model comprises at least one 1D vector, wherein the 1D vector is created by vectorizing the at least one scatterplot from the creating at least one scatterplot;
automatically generating a report which comprises at least one result regarding the determination of the at least one state; and
transmitting the report from the automatically generating the report using a data signal receiving the transmitted report
The identified limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind (including observation, evaluation, judgement or opinion) but for the recitation of computer components of nominal expected use. That is, other than reciting deep and machine learning models (i.e., computer environment of nominal expected use), nothing in the claim elements precludes the steps from practically being performed in the mind. For example, the identified limitations encompass collecting and analyzing blood samples, plotting the blood samples as a scatterplot to generate a report (e.g., diagnosis). The claim limitations fall within the Mental Processes groupings of abstract ideas. Thus, the claimed invention recites a judicial exception.
Part I: Step 2A, prong two: additional elements that integrate the judicial exception into a practical application
Under step 2A, Prong Two of the Alice framework, the claims are analyzed to determine whether the claims recite additional elements that integrate the judicial exception into a practical application. In particular, the claims are evaluated to determine if there are additional elements or a combination of elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claims are more than a drafting effort designed to monopolize the judicial exception.
This judicial exception is not integrated into a practical application. As a whole, the deep and machine learning models in the steps are recited as nominal expected use (i.e., learning models functioning as required without improvements to the technology itself) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Dependent claims 3-27, 29, and 30, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea.
Since these claims are directed to an abstract idea, the Office must determine whether the remaining limitations “do significantly more” than describe the abstract idea.
Part II. Determine whether any Element, or Combination, Amounts to“Significantly More” than the Abstract Idea itself
Under Part II, the steps of the claims, when considered individually and as an ordered combination, do not improve another technology or technical field, do not improve the functioning of the computer itself, and are not enough to qualify as "significantly more". As a whole, the deep and machine learning models in the steps are recited as nominal expected use (i.e., learning models functioning as required without improvements to the technology itself) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Therefore, based on the two-part Mayo analysis, there are no meaningful limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself. Claims 1-30, when considered individually and as an ordered combination, are rejected as ineligible subject matter under 35 U.S.C. 101.
Dependent claims 3-27, 29, and 30, when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional claims do no recite significantly more than an abstract idea.
Claim Rejections - 35 USC § 102
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.
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 1-30 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Pushkin et al. (US 2024/0420842 A1).
The applied reference has a common inventor with the instant application. Based upon the earlier effectively filed date of the reference, it constitutes prior art under 35 U.S.C. 102(a)(2). This rejection under 35 U.S.C. 102(a)(2) might be overcome by: (1) a showing under 37 CFR 1.130(a) that the subject matter disclosed in the reference was obtained directly or indirectly from the inventor or a joint inventor of this application and is thus not prior art in accordance with 35 U.S.C. 102(b)(2)(A); (2) a showing under 37 CFR 1.130(b) of a prior public disclosure under 35 U.S.C. 102(b)(2)(B) if the same invention is not being claimed; or (3) a statement pursuant to 35 U.S.C. 102(b)(2)(C) establishing that, not later than the effective filing date of the claimed invention, the subject matter disclosed in the reference and the claimed invention were either owned by the same person or subject to an obligation of assignment to the same person or subject to a joint research agreement.
Regarding claims 1, 2, and 28, Pushkin discloses a computer-implemented method for automatically generating a report comprising at least one result on the determination of states in vivo, in vitro and/or post-mortem by analyzing blood parameters measured in a hematology analyzer, the method comprising:
obtaining blood parameters of a blood sample by a hematology analyzer, wherein the blood parameters comprise quantitative and qualitative measurement variables, wherein the measurement variables comprise the properties of individual cells, wherein the individual cells comprise blood cells, wherein the blood cells comprise white blood cells, red blood cells and platelets, wherein the white blood cells comprise monocytes, lymphocytes, basophils, eosinophils and neutrophils (Paragraphs [0014]: the clinical blood examination is carried out on the automatic hematology analyzer e.g. CELL-DYN Sapphire (Abbott Laboratories, USA) in open mode. In the process, the individual cells of the total blood count are measured in a high-dimensional manner, wherein the measurements comprise the properties of the individual cells, [0015]: The measurements are copied by the analyzer e.g. as FCS files or in another format and transferred to an accessible PC or mobile computing device or a cloud for machine processing. These measurements contain blood parameters as properties of leukocytes, wherein leukocytes comprise neutrophils, eosinophils, basophils, lymphocytes and monocytes );
creating at least one scatterplot having at least two axes, each axis of the scatterplot comprising a different measurement variable from the obtaining blood parameters of a blood sample (Fig. 3; Paragraph [0016]: The machine processing consists of the automatic differentiation of at least three subpopulations (neutrophils, lymphocytes, monocytes) by plotting the measured properties against each other in at least one scatter plot );
determining at least one in vivo and/or in vitro and/or post-mortem state by at least one deep learning model, wherein the input variable for the at least one deep learning model comprises at least one scatterplot from the creating at least one scatterplot; and/or at least one machine learning model, wherein the input variable for the at least one machine learning model comprises at least one 1D vector, wherein the 1D vector is created by vectorizing the at least one scatterplot from the creating at least one scatterplot (Paragraph [0050]: ascertain by means of the computing unit at least one disease using an ensemble, wherein the ensemble comprises at least one machine learning model and at least one deep learning model, wherein the machine learning model receives at least one reduced global vector as input variable and the at least one deep learning model receives at least one scatter plot image as input variable);
automatically generating a report which comprises at least one result regarding the determination of the at least one state (Paragraph [0050]: automatic generation of a report which comprises at least one result of the diagnosis of at least one disease using the computing unit); and
transmitting the report from the automatically generating the report using a data signal receiving the transmitted report (Paragraph [0064] the diagnosis is output on a graphical interface or in the form of an automatically created report).
Regarding claim 3, Pushkin discloses the computer-implemented method according to claim 1, wherein the measurement variables of the individual cells include the number of cells, size, shape, volume, complexity, granularity, electrical conductivity, light scattering at different angles, mean corpuscular volume (MCV), mean corpuscular hemoglobin content (MCH), mean corpuscular hemoglobin concentration (MCHC), red blood cell distribution width (RDW), mean platelet volume (MPV), and/or platelet distribution width (Paragraphs [0050] and [0067]).
Regarding claim 4, Pushkin discloses the computer-implemented method according to claim 1, wherein scatterplots are subjected to processing before being analyzed by the at least one deep learning model, the processing comprising size matching, normalization, standardization, noise reduction, test time augmentation (TTA), clustering, contrast matching, and/or filtering individually or in combination (Paragraph [0042]).
Regarding claim 5, Pushkin discloses the computer-implemented method according to claim 1, wherein the at least one deep learning model comprises Convolutional Neural Networks, Generative Adversarial Networks, Recurrent Neural Networks, Long Short-Term Memory Networks, Transformer Networks, 3D Convolutional Neural Networks, and/or 4D Convolutional Neural Networks (Paragraph [0024]).
Regarding claim 6, Pushkin discloses the computer-implemented method according to claim 1, wherein the at least one 1D vector is subjected to processing prior to analysis by the at least one machine learning model, the processing comprising normalization, standardization, scaling, dimensionality reduction, noise reduction, and feature selection individually or in combination (Paragraph [0070]).
Regarding claim 7, Pushkin discloses the computer-implemented method according to claim 6, wherein the dimensionality reduction and/or the feature selection of the at least one 1D vector comprises at least one processing method from a group of processing methods comprising the group of processing methods: principal component analysis, T-distributed stochastic neighbor embedding, linear discriminant analysis, truncated singular value decomposition, uniform manifold approximation and projection, independent component analysis, sparse representation, partial least squares regression and kernel principal component analysis (Paragraph [0020]).
Regarding claim 8, Pushkin discloses the computer-implemented method according to claim 1, wherein the at least one machine learning model comprises K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, Multi-Layer Perceptrons, Adaboost Models, Gradient Boosting Models, Naive Bayes, One-Class Support Vector Machines, Isolation Forests, Local Outlier Factors and/or Support Vector Data Descriptions (Paragraph [0023]).
Regarding claim 9, Pushkin discloses the computer-implemented method according to claim 1, wherein more than two deep learning models and/or more than two machine learning models and/or a combination of at least one deep learning model and at least one machine learning model comprise an ensemble (Paragraph [0023]).
Regarding claim 10, Pushkin discloses the computer-implemented method according to claim 9, wherein the determining of the at least one state is performed using an ensemble technique, the ensemble technique comprising bagging, boosting, stacking, hard voting, soft voting, random subspace, mixture of experts, and/or Bayesian model averaging (Paragraph [0044]).
Regarding claim 11, Pushkin discloses the computer-implemented method according to claim 1, wherein in vitro states are based on processes outside a living organism, including changes in the morphology of the blood cells, cell composition, cell function or other features of the individual cells as a result of storage, handling and/or analysis (Paragraph [0038]).
Regarding claim 12, Pushkin discloses the computer-implemented method according to claim 1, wherein in vivo states are based on processes within a living organism, including physiological and pathological states such as diseases, biological age, pregnancy, drug action, state of health, nutritional deficiency, hereditary disorders, dehydration, blood clotting disorders, infections and/or anemia (Paragraph [0003]).
Regarding claim 13, Pushkin discloses the computer-implemented method according to claim 1, wherein post-mortem states are based on processes of a dead organism, including changes in morphology, cell composition, cell function or other characteristics of the individual cells as a result of diseases, presence of drugs, health status before death, drugs, poisons and/or toxic substances, as well as changes caused by the decay and autolysis of cells and tissues after death (Paragraph [0006]).
Regarding claim 14, Pushkin discloses the computer-implemented method according to claim 1, wherein the at least one deep learning model and/or the at least one machine learning model are trained and/or validated on the basis of a prefabricated database, the database comprising measured blood parameters and/or scatterplots, the database being extensible with new measured blood parameters and/or scatterplots for improving performance and accuracy, the database comprising information about known states, diseases or other relevant information contributing to the interpretation and analysis of the measured blood parameters and/or scatterplots (Paragraph [0034]).
Regarding claim 15, Pushkin discloses the computer-implemented method according to claim 1, wherein the method is performed to enable integration and use of external data sources, including clinical data, demographic information, medical history and/or genetic data, to provide additional context and improved predictive accuracy in the determination of states (Paragraph [0014]).
Regarding claim 16, Pushkin discloses the computer-implemented method according to claim 1, wherein the method comprises supplying real-time blood parameter data from the hematology analyzer to perform continuous monitoring and real-time analysis of states (Paragraph [0052]).
Regarding claim 17, Pushkin discloses the computer-implemented method according to claim 1, wherein the method comprises training the at least one deep learning model and/or the at least one machine learning model, wherein the training comprises supervised and/or unsupervised learning, wherein the supervised learning comprises using annotated data in a database to identify patterns and correlations, while the unsupervised learning enables recognition of patterns and correlations in the data of the database without prior annotation to identify novel insights and possibly previously unknown conditions or diseases (Paragraph [0033]).
Regarding claim 18, Pushkin discloses the computer-implemented method according to claim 17, wherein the training includes transfer learning, in which pre-trained models from related domains or applications are used as a starting point for training and adaptation to the specific blood parameters and/or scatterplots, in order to increase the efficiency and effectiveness of the training and to reduce the required amount of training data (Paragraph [0022]).
Regarding claim 19, Pushkin discloses the computer-implemented method according to claim 17, wherein the training comprises active learning in which the at least one deep learning model and/or the at least one machine learning model selectively search for examples in the database that can most improve their performance and accuracy (Paragraph [0047]).
Regarding claim 20, Pushkin discloses the computer-implemented method according to claim 19, wherein the training comprises receiving input from a user for annotation and/or confirmation of the examples to optimize the training process (Paragraph [0031]).
Regarding claim 21, Pushkin discloses the computer-implemented method according to claim 17, wherein the training comprises at least one ensemble learning method combining a plurality of learning methods (Paragraph [0023]).
Regarding claim 22, Pushkin discloses the computer-implemented method according to claim 17, wherein the training comprises incremental learning in which the deep learning model and/or the machine learning model are continuously and stepwise updated from newly added blood parameters and/or scatterplots in the database (Paragraph [0037]).
Regarding claim 23, Pushkin discloses the computer-implemented method according to claim 17, wherein the method performs at least one data augmentation process to increase the size and diversity of the training data in the database to reduce the risk of overfitting (Paragraph [0033]).
Regarding claim 24, Pushkin discloses the computer-implemented method according to claim 23, wherein the at least one data augmentation process has a synthetic generation of blood parameters and/or scatterplots which are based on existing data, stochastic methods, statistical models or artificial intelligence algorithms being used in order to generate realistic and representative data for the training of the models (Paragraph [0022]).
Regarding claim 25, Pushkin discloses the computer-implemented method according to claim 23, wherein the at least one data augmentation process has at least one transformation of existing blood parameters and/or scatterplots, wherein the at least one transformation has rotations, scales, reflections, shearings, noise and/or distortions, in order to increase the diversity of the training data and to increase the robustness of the determination of the at least one state (Paragraph [0037]).
Regarding claim 26, Pushkin discloses the computer-implemented method according to claim 23, wherein the at least one data augmentation process comprises a combination of blood parameters and/or scatterplots from different sources (Paragraph [0032]).
Regarding claim 27, Pushkin discloses the computer-implemented method according to claim 23, wherein the at least one deep learning model and/or the at least one machine learning model is adapted to adjust the degree of data augmentation on the basis of boundary conditions, such as the size of the existing database, the number of previous training iterations and/or the current performance and accuracy of the respective model, in order to improve the efficiency of training (Paragraph [0020]).
Regarding claim 29, Pushkin discloses the system according to claim 28, wherein the system has a communication interface for transmitting results and reports to other computer systems, laboratory information systems (LIS), hospital information systems (HIS) and/or electronic patient records (EPA) (Paragraph [0037]).
Regarding claim 30, Pushkin discloses a data signal transmitting the report automatically generated in the method according to claim 1 (Paragraph [0050]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHINYERE MPAMUGO whose telephone number is (571)272-8853. The examiner can normally be reached Monday-Friday, 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kambiz Abdi can be reached at (571) 272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685
Prosecution Insights