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 .
Status of Claims
This action is a non-final rejection
Claims 1-20 are pending
Claims 16-20 are withdrawn
Claims 1-15 are rejected under 35 USC § 101
Claims 1-15 are rejected under 35 USC § 103
Priority
Acknowledgement is made of Applicant’s claim for a foreign priority date of 3-18-2022
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10-23-2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Election/Restrictions
Applicant’s election without traverse of invention I, claims 1-15 in the reply filed on 1-20-2026 is acknowledged.
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 1-15 are not patent eligible because the claimed invention is directed to an abstract idea without significantly more.
Analysis
First, claims are directed to one or more of the following statutory categories: a process, a machine, a manufacture, and a composition of matter. Regarding claims 1-15 the claims recite an abstract idea of “multi-modal patient representation”.
Independent Claims 1 and 15 are rejected under 35 U.S.C 101 based on the following analysis.
-Step 1 (Does the claim fall within a statutory category? YES): Claims 1 and 15 recite a method, and a system respectively of “multi-modal patient representation“.
-Step 2A Prong One (Does the claim fall within at least one of the groupings of abstract ideas?: YES): The claimed invention:
accessing a set of medical data associated with a patient, wherein the set of medical data includes a plurality of modalities of medical data, wherein each of the modalities, consists of one data type and is associated with one data source;
inputting one or more of the modalities each having a data type of laboratory testing data into a .. model ..to generate a first vector representation;
inputting another one of the modalities of medical data, into a .. model ..to generate a second vector representation of the second modality of medical data, wherein the second modality of medical data consists of a second data type;
generating a combined vector representation based on the first vector representation and the second vector representation; and
storing the combined vector representation.
belonging to the grouping of certain methods of organizing human activity under managing personal behavior or relationships or interactions between people as it recites “multi-modal patient representation”. (refer to MPP 2106.04(a)(2)). Accordingly this claim recites an abstract idea.
-Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO).
Claims 1, 15 recite:
first machine-learning model trained to generate a first vector representation;
second machine- learning model trained to generate a second vector representation;
storing the combined vector representation to a database associated with the one or more computing devices;
Claim 15 recites:
one or more non-transitory computer-readable storage media including instructions;
one or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions and cause the system to access a set of medical data associated with a patient.
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, these additional elements, when considered separately and as an ordered combination do not integrate the judicial exception/abstract idea into a “practical application” of the judicial exception because they do not impose any meaningful limit on practicing the judicial exception.
-Step 2B (Does the additional elements of the claim provide an inventive concept?: NO. As discussed previously with respect to Step 2A Prong Two:
Claims 1, 15 recite:
first machine-learning model trained to generate a first vector representation;
second machine- learning model trained to generate a second vector representation;
storing the combined vector representation to a database associated with the one or more computing devices;
Claim 15 recites:
one or more non-transitory computer-readable storage media including instructions;
one or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions and cause the system to access a set of medical data associated with a patient..
Amounting to mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)) Accordingly, even when viewed as a whole the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible.
Dependent Claims:
Step 2A Prong One: The following dependent claims recites additional limitations that further define the abstract idea of “managing exposure of confidential user information in a user data record to a third party”. The claim limitations include:
Claim 2: wherein a data type consists of whole slide images, radiological images, medical graph images, other medical images, genomics data, proteomics data, transcriptomics data, metabolomics data, radiomics data, toxigenomics data, multi-omics data, medication data, medical diagnostics data, medical procedures data, medical symptoms data, demographics data, patient lifestyle data, physical activity data, body mass index (BMI) data, family history data, socioeconomics data, geographic environment data, or another type of digital medical data relating to the patient.
Claim 3: wherein a data source consists of a randomized controlled trial for medical treatment, a provider of real-world medical data, or a provider of patient knowledge graphs.
Claim 4: wherein at least one of the plurality of modalities of medical data, comprises a longitudinal dataset of medical data.
Claim 5: wherein the first vector representation comprises a first dimensionless value representative of a first plurality of datasets of the first data type.
Claim 6: wherein the first .. model was trained by:
inputting a first plurality of datasets to the first .. model, the first plurality of datasets corresponding to a first modality of medical data and utilizing the first .. model to encode the first plurality of datasets into the first vector representation; and
wherein a dimension of the first vector representation is reduced with respect to a dimension of the first plurality of datasets
Claim 7: wherein the second vector representation comprises a second dimensionless value representative of a second plurality of datasets of the second data type.
Claim 8:
inputting a second plurality of datasets to the second machine-learning model, the second plurality of datasets corresponding to the second modality of medical data and utilizing the second machine-learning model to encode the second plurality of datasets into the second vector representation and
wherein a dimension of the second vector representation is reduced with respect to a dimension of the second plurality of datasets
Claim 9:
prior to generating the combined vector representation, inputting a third modality of medical data of the plurality of modalities of medical data, (104), into a third machine-learning model trained to generate a third vector representation of the third modality of medical data, wherein the third modality of medical data consists of a third data type; and
generating the combined vector representation based on the first vector representation, the second vector representation, and the third vector representation
Claim 10: wherein generating the combined vector representation comprises generating a comprehensive data representation of a biomedical of the patient.
Claim 11: wherein generating the combined vector representation comprises generating a reduced-dimension dataset as compared to the set of medical data.
Claim 12: wherein generating the combined vector representation further comprises:
inputting the first vector representation and the second vector representation to a fourth machine-learning model; and
generating the combined vector representation by combining the first vector representation and the second vector representation utilizing the fourth machine- learning model
Claim 13:
in response to receiving one or more requests for medical data associated with the patient, retrieving the combined vector representation ..; and
performing one or more personalized healthcare (PHC) tasks for the patient based on the combined vector representation, the one or more PHC tasks being performed to satisfy the one or more requests
Claim 14: wherein performing the one or more PHC tasks comprises generating a predicted survivability for the patient, generating a predicted future disease development for the patient, generating a predicted treatment response for the patient, generating a predicted diagnosis for the patient, or identifying a precision cohort associated with the patient.
Step 2A Prong Two (Are there additional elements in the claim that imposes a meaningful limit on the abstract idea? NO). The following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claims as a whole do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims include:
Claim 6: the first machine-learning model.
Claim 8: second machine-learning model.
Claim 13: database.
Step 2B (Does the additional elements of the claim provide an inventive concept?: NO). As discussed previously with respect to Step 2A Prong Two, the following dependent claims recite mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to implement the abstract idea. (refer to MPEP 2106.05(f)). Accordingly, the claim does not provide an inventive concept (significantly more than the abstract idea) and hence the claim is ineligible. The claims include:
Claim 6: the first machine-learning model.
Claim 8: second machine-learning model.
Claim 13: database.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
non-obviousness.
Claims 1-4, 9-10, 12-15 are rejected under 35 U.S.C. 103 as being un-patentable by Liu et.al. (EP 4199002 A1) hereinafter “Liu” in view of Rahman et.al (US 20240168952 A1) hereinafter “Rahman”.
Regarding claims 1, 15 Liu teaches:
one or more non-transitory computer-readable storage media including instructions and one or more processors coupled to the one or more storage media, the one or more processors configured to execute the instructions and cause the system to access a set of medical data associated with a patient (See at least [Page 5, lines 26-31] via: “..The processor may be associated with one or more non-transitory storage media such as volatile and non-volatile computer memory such as RAM, PROM, EPROM, and EEPROM. The non-transitory storage media may be encoded with one or more programs that, when executed on one or more processors and/or controllers, perform the required functions. Various storage media may be fixed within a processor or controller or may be transportable, such that the one or more programs stored thereon can be loaded into the signal processing unit, property determination unit and/or processor...”)
accessing a set of medical data associated with a patient, wherein the set of medical data includes a plurality of modalities of medical data, wherein each of the modalities, consists of one data type and is associated with one data source; (See at least [Page 2, lines 28-29] via: “...receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type...”; in addition see at least [Page 5, lines 6-11] via: “... plurality of patient data records comprises: (i) a first patient data record comprising at least one medical image (i.e. medical imaging data); (ii) a second patient data record comprising a non-image medical data comprising a physiological signal; and (iii) a third patient data record comprising text-based medical data. This combination of data is traditionally particularly difficult to combine but the disclosed methods provide improved pre-processing that enables the more accurate determination of health parameters using these three data types...”; in addition see at least [Page 6, lines 30-36] via: “... the data received is heterogeneous. In this embodiment, the medical imaging data and non-medical imaging data are related in that they have relevance to a health condition under investigation. The input data may be extracted or received from a healthcare database or plural databases. For example, this data may be extracted or received from EMRs, Radiology Information System, Cardiology Information System and/or PACS. Alternatively, this may be extracted or received from the devices or instruments used to obtain the data, such as a medical device or sensor. Interfacing with clinical, operational, and demographic data sources can be done with any available state-of-art IT communication protocol such as HL7, DICOM and FHIR...”; in addition see at least [Page 4, lines 15-23] via: “...the plurality of patient data records comprise patient data records obtained at a plurality of points in time; and wherein analyzing the vectors using a machine learning model comprising applying a weighting to each patient data record based on the point in time that the record was created. The weighting may apply more weight to the more recently obtained data, in some embodiments. An example of this may be weighting a scan that has been taken recently more heavily than a scan that was taken at an earlier point in time. This enables the accuracy of predictions pertaining to the diagnosis, onset and/or progression of a disease to be improved. Although some risk scores have developed (e.g. Framingham risk score) for disease prediction, these are general and only depend on one study or investigation. Embodiments taking into account longitudinal data for disease prediction provide improved accuracy over these by taking into account data obtained over time and monitoring the changes...”..)
inputting one or more of the modalities each having a data type of laboratory testing data into a first machine-learning model trained to generate a first vector representation; (See at least [Page 5, lines 38-41] via: “...generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model...”; in addition see at least [Page 5, lines 6-7] via: “... plurality of patient data records comprises: (i) a first patient data record comprising at least one medical image (i.e. medical imaging data)..”; in addition see at least [Page 6, lines 37-41] via: “...generating a first vector 120 for the medical imaging data received in the first step 110. Generating the first vector 120 comprises processing each patient data record (i.e. the medical imaging data in this case) using an encoding algorithm which is optimized and specific to medical imaging data. The encoding algorithm may be an AI or machine learning algorithm or model. ..”; )..”; in addition see at least [Page 7, lines 3-4] via: “... for each type of patient data, a model that is suitable for the data type can be built. These model wills convert the data into vectors from different data sources...”)
inputting another one of the modalities of medical data, into a second machine- learning model trained to generate a second vector representation of the second modality of medical data, wherein the second modality of medical data consists of a second data type; (See at least [Page 2 lines 28-32] via: “...receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type... generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model...”; in addition see at least [Page 5, lines 6-8] via: “... plurality of patient data records comprises: ... (ii) a second patient data record comprising a non-image medical data comprising a physiological signal..”; in addition see at least [Page 6, lines 43-46 and Page 7, lines 1-2] via: “...generating a second vector 120' for the non-medical imaging data received in the first step 110'. In this case, generating the vector 120' comprises processing each patient data record (i.e. the non-medical imaging data in this case) using an encoding algorithm which is optimized and specific to the specific data type. This is a different encoding algorithm to that used to encode the first vector such that the data extraction is optimized for each data type thereby providing an output that is especially accurate and that key aspects and information in each particular data type is retained and taken into account in subsequent processing...”)
generating a combined vector representation based on the first vector representation and the second vector representation; (See at least [Page 3, lines 43-47] via: “... combining the vectors prior to analyzing the vectors. Combining the output vectors can be carried out using a number of different ways, such as stacking the vectors as a one-dimensional vector or multidimensional vectors. Accordingly, in an embodiment of the computer implemented method, combining the vectors comprises combining the vectors to form a one-dimensional or multidimensional vector. ..”)
However, Liu is silent the following limitation that is taught by Rahman:
storing the combined vector representation to a database associated with the one or more computing devices. (See at least [0007] via: “...the generated context vector is stored in a database of the time series retrieval system..”; in addition see at least [0076] via: “...system 800 may comprise or be in remote or local communication with a database or data source 815 ..”; in addition see at least [0068] via: “...system 800 comprises one or more of a processor 820, memory 830, user interface 840, communications interface 850, and storage 860, interconnected via one or more system buses 812...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liu with Rahman. Liu teaches collating patient data for analysis comprising receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model. However, Liu fails to disclose storing the generated vector into a database as taught by Rahman. Combining Liu and Rahman is helpful to medical practitioners that may desire to review and analyze the collated plurality of patient data records obtained.
Regarding claim 2: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
wherein a data type consists of whole slide images, radiological images, medical graph images, other medical images, genomics data, proteomics data, transcriptomics data, metabolomics data, radiomics data, toxigenomics data, multi-omics data, medication data, medical diagnostics data, medical procedures data, medical symptoms data, demographics data, patient lifestyle data, physical activity data, body mass index (BMI) data, family history data, socioeconomics data, geographic environment data, or another type of digital medical data relating to the patient. (See at least [Page 5, lines 6-9] via: “...the plurality of patient data records comprises: (i) a first patient data record comprising at least one medical image (i.e. medical imaging data); (ii) a second patient data record comprising a non-image medical data comprising a physiological signal; and (iii) a third patient data record comprising text-based medical data...”)
Regarding claim 3: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
wherein a data source consists of a randomized controlled trial for medical treatment, a provider of real-world medical data, or a provider of patient knowledge graphs. (See at least [Page 6, lines 30-35] via: “...the medical imaging data and non-medical imaging data are related in that they have relevance to a health condition under investigation. The input data may be extracted or received from a healthcare database or plural databases. For example, this data may be extracted or received from EMRs, Radiology Information System, Cardiology Information System and/or PACS. Alternatively, this may be extracted or received from the devices or instruments used to obtain the data, such as a medical device or sensor...”)
Regarding claim 4: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
wherein at least one of the plurality of modalities of medical data, comprises a longitudinal dataset of medical data. (See at least [Page 4, lines 10-11] via: “... data used in the method has been obtained at different time points (i.e. longitudinal data) ...”; in addition see at least [Page 4, lines 22-23] via: “... Embodiments taking into account longitudinal data for disease prediction provide improved accuracy over these by taking into account data obtained over time and monitoring the changes...”; in addition see at least [Page 4, lines 24-25 ] via: “...there is almost always a vast amount of longitudinal information in a patient's medical records, and the healthcare system more generally, which can be used..”; in addition see at least [Page 4, lines 26-29] via: “ ...the patient data comprises data obtained at plural points in time (e.g. different days or studies), each patient data comprises time stamps associated therewith and generation of the vector comprises time-resolved feature vectors based on the time stamps. In such embodiments, subsequent processing determines the disease onset/progress prediction based on a plurality of time-resolved feature vectors..”; in addition see at least [Page 5, lines 1-3] via: “... the plurality of patient data records comprise patient data records obtained at a plurality of points in time; and generating a vector for each of the plural patient data record comprises generating time-resolved feature vectors...”)
Regarding claim 9: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
prior to generating the combined vector representation, inputting a third modality of medical data of the plurality of modalities of medical data, (104), into a third machine-learning model trained to generate a third vector representation of the third modality of medical data, wherein the third modality of medical data consists of a third data type; (See at least [Page 8, lines 45-47] via: “...generating a third vector for the text-based medical data 323 using a text-based data encoding model (i.e. a model adapted or configured for converting text-based data into a vector)..”) and
generating the combined vector representation based on the first vector representation, the second vector representation, and the third vector representation. (See at least [Page 8, lines 41-49] via: “...generating vectors for the first data set 320, which itself comprises generating a first vector for the medical imaging data 321 using a medical imaging encoding model (i.e. a model adapted or configured for converting medical imaging data into a vector), generating a second vector for the medical data relating to a physiological signal 322 using a physiological signal encoding model (i.e. a model adapted or configured for converting physiological signal data into a vector) and generating a third vector for the text-based medical data 323 using a text-based data encoding model (i.e. a model adapted or configured for converting text-based data into a vector). These are subsequently combined 325 to provide a multi-dimensional or stacked vector encoding temporal information. The use of data-type-specific models ensures that all of the key information present in each of the data is retained and encoded into the vectors...”)
Regarding claim 10: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
wherein generating the combined vector representation comprises generating a comprehensive data representation of a biomedical of the patient. (See at least [Page 9, lines 10-14] via: “...The resultant vectors from this process are then combined and analyzed in a subsequent processing step 330. In particular, the vectors are used to train a machine learning model, which in this embodiment comprises a recurrent neural network which analyses and takes into account the temporal aspect of the data. The method in this embodiment includes applying a weighting to each patient data record based on the point in time that the record was created...”; in addition see at least [Page 9, lines 15-16] via: “...The result from the analysis is an output 340 in the form of a prediction relating to the onset of a condition for a patient, which takes account of the three studies and the data within each of the studies...”; in addition see at least [Page 3, lines 10-12] via: “... the use of a machine learning model or algorithm to provide an output relating to the health of the patient (e.g. a prediction relating to the onset or progression of a condition), such an output being more accurate and more sensitive to changes due to the data-type-specific pre-processing steps.)
Regarding claim 12: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
wherein generating the combined vector representation further comprises:
inputting the first vector representation and the second vector representation to a fourth machine-learning model; (See at least [Page 9, lines 5-8] via: “... generating a first vector for the medical imaging data 321" using a medical imaging encoding model and generating a second vector for the medical data relating to a physiological signal 322" using a physiological signal encoding model...”; in addition see at least [Page 7, lines 5-7] via: “...analyzing the vectors using a machine learning model 130. This includes analysis and combination of the vectors so as to provide an output containing information relating to a patient's health 140 which takes into account the plural different inputs received..”) and
generating the combined vector representation by combining the first vector representation and the second vector representation utilizing the fourth machine- learning model. (See at least [Page 9, lines 5-9] via: “... generating a first vector for the medical imaging data 321" using a medical imaging encoding model and generating a second vector for the medical data relating to a physiological signal 322" using a physiological signal encoding model. These are subsequently combined 325" to provide a multi-dimensional or stacked vector encoding temporal information..”; in addition see at least [Page 7, lines 5-7] via: “...analyzing the vectors using a machine learning model 130. This includes analysis and combination of the vectors so as to provide an output containing information relating to a patient's health 140 which takes into account the plural different inputs received...”)
Regarding claim 13: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
performing one or more personalized healthcare (PHC) tasks for the patient based on the combined vector representation, the one or more PHC tasks being performed to satisfy the one or more requests. (See at least [Page 3, lines 46-49] via: “...combining the vectors comprises combining the vectors to form a one-dimensional or multidimensional vector. The computer implemented method's ability, in some embodiments, to process multiple types of data including both image, signal and non-image data through time allows for significant improvements in accuracy to be made over previous attempts to predict disease onset or progression...”; in addition see at least [Page 4, lines 1-8] via: “... the output of the computer implemented method comprises a prediction pertaining to the diagnosis, onset and/or progression of a disease for a patient. Producing an output containing information regarding the diagnosis, onset and/or progression of disease is of benefit since it can be used by a medical practitioner to obtain information that would not have otherwise been easily obtainable. Having a predictive model that takes into account a vector that contains a plurality of patient data records rather than a single record allows for more accurate predictions to be made regarding the diagnosis, onset and/or progression of disease. In such embodiments, the method is accordingly for providing or producing a prediction pertaining to the diagnosis, onset and/or progression of a disease for a patient...”)
Nevertheless Liu is silent the following limitation that is taught by Rahman:
in response to receiving one or more requests for medical data associated with the patient, retrieving the combined vector representation from the database; (See at least [0007] via: “...To retrieve stored time series data, the system first receives a request for identification of one or more of the plurality of time series based on similarity to a time series query. The system identifies one or more of the stored generated context vectors based on similarity to the query time series context vector. The trained time series encoder/decoder of the time series retrieval system retrieves each stored time series associated with the identified one or more stored generated context vectors, and then provides the retrieved time series data to a user via a user interface of the time series retrieval system..”;
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liu with Rahman. Liu teaches collating patient data for analysis comprising receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model. However, Liu fails to disclose storing and retrieving the generated vector into a database as taught by Rahman. Combining Liu and Rahman is helpful to medical practitioners that may desire to review and analyze the collated plurality of patient data records obtained.
Regarding claim 14: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1 & 13. Liu also teaches:
wherein performing the one or more PHC tasks comprises generating a predicted survivability for the patient, generating a predicted future disease development for the patient, generating a predicted treatment response for the patient, generating a predicted diagnosis for the patient, or identifying a precision cohort associated with the patient. (See at least [Page 4, lines 1-8] via: “...the output of the computer implemented method comprises a prediction pertaining to the diagnosis, onset and/or progression of a disease for a patient. Producing an output containing information regarding the diagnosis, onset and/or progression of disease is of benefit since it can be used by a medical practitioner to obtain information that would not have otherwise been easily obtainable. Having a predictive model that takes into account a vector that contains a plurality of patient data records rather than a single record allows for more accurate predictions to be made regarding the diagnosis, onset and/or progression of disease. In such embodiments, the method is accordingly for providing or producing a prediction pertaining to the diagnosis, onset and/or progression of a disease for a patient...”)
Claims 5, 7, 11 are rejected under 35 U.S.C. 103 as being un-patentable by Liu in view of Rahman, in further view of Chen et.al (CN 114550946 A) hereinafter “Chen”
Regarding claim 5: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu and Raman are silent the following claim that is taught by Chen:
wherein the first vector representation comprises a first dimensionless value representative of a first plurality of datasets of the first data type. (See at least [Page 11, lines 40-43] via: “...perform softmax normalization processing to the vector, so as to obtain the numerical value vector. by normalization processing, so that each component in the numerical value vector is defined in a certain interval, for example, [0, 1], the numerical value vector is the normalized vector of the numerical value data...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liu and Rahman with Chen. Liu teaches collating patient data for analysis comprising receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model. However, Liu fails to disclose normalizing each component of the numerical value vector (equivalent to a dimensionless representation of a vector) as taught by Chen. Combining Liu and Chen is helpful in combining multiple vectors by ensuring that each vector contributing to the sum has equal weight.
Regarding claim 7: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu and Raman are silent the following claim that is taught by Chen:
wherein the second vector representation comprises a second dimensionless value representative of a second plurality of datasets of the second data type. (See at least [Page 11, lines 40-43] via: “...perform softmax normalization processing to the vector, so as to obtain the numerical value vector. by normalization processing, so that each component in the numerical value vector is defined in a certain interval, for example, [0, 1], the numerical value vector is the normalized vector of the numerical value data...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liu and Rahman with Chen. Liu teaches collating patient data for analysis comprising receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model. However, Liu fails to disclose normalizing each component of the numerical value vector (equivalent to a dimensionless representation of a vector) as taught by Chen. Combining Liu and Chen is helpful in combining multiple vectors by ensuring that each vector contributing to the sum has equal weight.
Regarding claim 11: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu and Raman are silent the following claim that is taught by Chen:
wherein generating the combined vector representation comprises generating a reduced-dimension dataset as compared to the set of medical data. (See at least [Page 3, lines 18-19] via: “...fusing the first weight vector and the second weight vector to obtain the disease analysis vector corresponding to the medical record data...”; in addition see at least [Page 11, lines 40-43] via: “...perform softmax normalization processing to the vector, so as to obtain the numerical value vector. by normalization processing, so that each component in the numerical value vector is defined in a certain interval, for example, [0, 1], the numerical value vector is the normalized vector of the numerical value data...” )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liu and Rahman with Chen. Liu teaches collating patient data for analysis comprising receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model. However, Liu fails to disclose normalizing each component of the numerical value vector (equivalent to a dimensionless representation of a vector) and subsesquently fusing or combining multiple vectors as taught by Chen. Combining Liu and Chen is helpful in combining multiple vectors each representing a set of medical data into one by ensuring that each vector contributing to the sum has equal weight resulting in a reduced dimension medical representation of a patient.
Claims 6, 8 are rejected under 35 U.S.C. 103 as being un-patentable by Liu in view of Rahman, in further view of Molero et.al (WO 2021236702 A1) hereinafter “Molero”
Regarding claim 6: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
inputting a first plurality of datasets to the first machine-learning model, the first plurality of datasets corresponding to a first modality of medical data and utilizing the first machine-learning model to encode the first plurality of datasets into the first vector representation; (See at least [Page 5, lines 38-41] via: “...generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model...”; in addition see at least [Page 5, lines 6-7] via: “... plurality of patient data records comprises: (i) a first patient data record comprising at least one medical image (i.e. medical imaging data)..”; in addition see at least [Page 6, lines 38-41] via: “...generating a first vector 120 for the medical imaging data received in the first step 110. Generating the first vector 120 comprises processing each patient data record (i.e. the medical imaging data in this case) using an encoding algorithm which is optimized and specific to medical imaging data. The encoding algorithm may be an AI or machine learning algorithm or model. ..”; in addition see at least [Page 7, lines 3-4] via: “... for each type of patient data, a model that is suitable for the data type can be built. These model wills convert the data into vectors from different data sources...”)
Nevertheless Liu and Rahman are silent the following limitation that is taught by Molero:
wherein a dimension of the first vector representation is reduced with respect to a dimension of the first plurality of datasets. (See at least [0030] via: “... data elements that include images (e.g., MRIs) or image frames of a video (e.g., a video of an ultrasound), each image or image frame may be transformed into a numerical representation (e.g., vector) using a trained auto-encoder neural network, which is trained to generate a latent-space representation of an input image. The condensed representation of the input image (e.g., the latent-space representation) may serve as the vector that numerically represents the input image...”; in addition see at least [0028] via: “...instead of generating a vector to numerically represent each data element of a subject record, techniques may be executed to reduce the dimensionality of the subject record by identifying and selecting a subset of data elements from the set of data elements. The subset of data elements may represent the “important” data elements, where “importance” of a data element is determined based on a prediction using feature extraction techniques, such as Singular Value Decomposition (SVD). For example, transforming a subject record into a transformed representation that is consumable by machine-learning and artificial-intelligence models may include performing one or more feature extraction techniques on the non-numerical values included in the data elements of a subject record to generate a feature vector that numerically represents a decomposed version of the non-numerical values. In some implementations, feature extraction techniques may include, for example, reducing the dimensionality of a set of data elements of a subject record (e.g., each data element representing a feature or dimension of a subject) into an optimal subset of features that can be used to, for example, predict an outcome or event. Reducing the dimensionality of the set of data elements may include reducing N data elements into a subset of M elements, where M is smaller than N. In these implementations, each element of the subset of M elements may be transformed into a numerical value. In some implementations, a feature vector may be generated to represent the N data elements of a subject record. The feature vector may include a vector for each data element of the set of data elements...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liu and Rahman with Molero. Liu teaches collating patient data for analysis comprising receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model. However, Liu fails to disclose techniques to reduce the dimensionality of the subject record by identifying and selecting a subset of data elements from the set of data elements, whereby reducing the dimensionality of the set of data elements may include reducing N data elements into a subset of M elements, where M is smaller than N as taught by Molero. Combining Liu and Molero is helpful reducing vector dimensionality which enhances processing by improving computational efficiency, reducing storage costs, and mitigating the "curse of dimensionality". It speeds up machine learning training and inference, reduces noise, and prevents overfitting by focusing on key, informative features, ultimately enhancing model accuracy and performance.
Regarding claim 8: Liu and Rahman teach the invention as claimed and detailed above with respect to claim 1. Liu also teaches:
wherein the second machine-learning model was trained by:
inputting a second plurality of datasets to the second machine-learning model, the second plurality of datasets corresponding to the second modality of medical data and utilizing the second machine-learning model to encode the second plurality of datasets into the second vector representation (See at least [Page 2, lines 28-32] via: “...receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type... generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model...”; in addition see at least [Page 5, lines 6-8] via: “... plurality of patient data records comprises: ... (ii) a second patient data record comprising a non-image medical data comprising a physiological signal..”; in addition see at least [Page 6, lines 43-46 and Page 7, lines 1-2] via: “...generating a second vector 120' for the non-medical imaging data received in the first step 110'. In this case, generating the vector 120' comprises processing each patient data record (i.e. the non-medical imaging data in this case) using an encoding algorithm which is optimized and specific to the specific data type. This is a different encoding algorithm to that used to encode the first vector such that the data extraction is optimized for each data type thereby providing an output that is especially accurate and that key aspects and information in each particular data type is retained and taken into account in subsequent processing...”)
Nevertheless Liu and Rahman are silent the following limitation that is taught by Molero:
wherein a dimension of the second vector representation is reduced with respect to a dimension of the second plurality of datasets. (See at least [0028] via: “...instead of generating a vector to numerically represent each data element of a subject record, techniques may be executed to reduce the dimensionality of the subject record by identifying and selecting a subset of data elements from the set of data elements. The subset of data elements may represent the “important” data elements, where “importance” of a data element is determined based on a prediction using feature extraction techniques, such as Singular Value Decomposition (SVD). For example, transforming a subject record into a transformed representation that is consumable by machine-learning and artificial-intelligence models may include performing one or more feature extraction techniques on the non-numerical values included in the data elements of a subject record to generate a feature vector that numerically represents a decomposed version of the non-numerical values. In some implementations, feature extraction techniques may include, for example, reducing the dimensionality of a set of data elements of a subject record (e.g., each data element representing a feature or dimension of a subject) into an optimal subset of features that can be used to, for example, predict an outcome or event. Reducing the dimensionality of the set of data elements may include reducing N data elements into a subset of M elements, where M is smaller than N. In these implementations, each element of the subset of M elements may be transformed into a numerical value. In some implementations, a feature vector may be generated to represent the N data elements of a subject record. The feature vector may include a vector for each data element of the set of data elements...”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Liu and Rahman with Molero. Liu teaches collating patient data for analysis comprising receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model. However, Liu fails to disclose techniques to reduce the dimensionality of the subject record by identifying and selecting a subset of data elements from the set of data elements, whereby reducing the dimensionality of the set of data elements may include reducing N data elements into a subset of M elements, where M is smaller than N as taught by Molero. Combining Liu and Molero is helpful reducing vector dimensionality which enhances processing by improving computational efficiency, reducing storage costs, and mitigating the "curse of dimensionality". It speeds up machine learning training and inference, reduces noise, and prevents overfitting by focusing on key, informative features, ultimately enhancing model accuracy and performance.
Prior Art Made of Record
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure, and is listed in the attached form PTO-892 (Notice of References Cited). Unless expressly noted otherwise by the Examiner, all documents listed on form PTO-892 are cited in their entirety.
Ribbing (WO 2011070461 A2) - DIAGNOSTIC TECHNIQUES FOR CONTINUOUS STORAGE AND JOINT ANALYSIS OF BOTH IMAGE AND NON-IMAGE MEDICAL DATA - teaches: a database (30) storing medical data including image medical data and non-image medical data for a plurality of patients; a digital processor (40) configured to (i) generate a features vector (56) comprising features indicative of a patient derived from patient medical data stored in the database including both patient image medical data and patient non-image medical data and (ii) perform multivariate analysis (64) on a features vector generated for a patient of interest to determine a proposed diagnosis for the patient of interest; and a user interface (42) configured to output a human perceptible representation of the proposed diagnosis for the patient of interest.
Kunz (US 20250299802 A1) - SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES FOR DETERMINING TREATMENT - teaches: processing digital pathology images, the method including receiving a plurality of digital pathology images of at least one pathology specimen, the pathology specimen being associated with a patient. The method may further include determining receiving metadata corresponding to the plurality of digital pathology images, the metadata comprising data regarding previous medical treatment of the patient. Next, the method may include providing the medical images and metadata as input to a machine learning system, the machine learning system having been trained by receiving as input historical treatment information and digital images labeled with a predicted treatment regimen. Lastly, the method may include outputting, by the machine learning system, a treatment effectiveness assessment.
Conclusion
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/PIERRE L MACCAGNO/Examiner, Art Unit 3687
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684