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
Objections
Clams 1 and 19-20 are objected to because of the following informality: the sixth limitation recites “determining associations, based at least in part on the sub-populations or populations, between subcombinations of features and response or outcomes features,” and since response or outcomes features are recited as features in the first limitation, the antecedent basis of “features and response or outcomes features” in the subcombinations with is unclear i.e. is it subcombinations of response or outcomes features and ither response or outcomes features?
Rejections under 35 U.S.C. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1 and 19-20 each recites aggregating features from the received plurality of features based at least in part on one or more of the identified features; identifying a plurality of populations of subjects associated with the aggregated features; identifying, within the plurality of populations, sub-populations of subjects associated with features from the response or outcomes features; and determining associations, based at least in part on the sub-populations or populations, between subcombinations of features and response or outcomes features, wherein the subcombinations of features are associated with the response and outcomes features within the respective subpopulations of populations. Aggregating features is recited broadly and is a mental process accomplishable in the human mind or on paper; and identifying populations and sub-populations and determining associations between subcombinations of features are each evaluating and are mental processes. Each claim recites additional elements of receiving a plurality of features from a subject data store, including clinical features, therapeutic features, and at least one of response or outcomes features; receiving an identification of one or more of the plurality of features, which are both data-gathering steps and insignificant extra-solution activity; and providing a summary of the determined associations, which is an output step and also insignificant extra-solution activity. Claim 19 recites a computer including a processing device and claim 20 recites a non-transitory computer-readable medium, which are each generic components of a computer system. Examiner notes specification paragraph 0002 states “there exists an unmet need in the biomedical market space for a platform that enables accelerated discovery of actionable knowledge.” Paragraph 0004 discusses how patient records exist in numerous formats and on different storage mediums and discusses difficulties in searching such records. Paragraph 0005 discusses how correlation analysis before more complex with the increase in the number of features being analyzed; and “what is needed is a more targeted generation process and/or filtering mechanism for directing the correlation discovery process to reduce the number of computations to include those which are most likely to reveal promising correlations;” and “what is needed is a directed generation process, filtering and ranking system and mechanism for prioritizing meaningful, undocumented biomarkers over spurious correlations which are already accepted in the field or containing relationships which are not meaningful as biomarkers for research, treatment, or other applications to particular diseases.” Paragraphs 0009-0014 discuss techniques in the invention to address these needs but such techniques are not claimed, also the claim steps are broad and do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the input steps and output step are each recited broadly and amount to sending and receiving data across a network per figure 1 and paragraph 0174, which are routine and conventional activities per the list of such activities in MPEP 2106.05(d) part II. The computer including a processing device and non-transitory computer-readable medium are each still generic components of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claim 2 recites wherein the method is implemented in conjunction with a large language model, which is applying the large language model and is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claim 3 recites wherein subjects in respective populations of the plurality of populations have at least one feature in common, the at least one feature corresponding to a disease state, a therapy, a response, or an outcome, and subjects having features is data and a mental process accomplishable in the human mind or on paper. Claim 4 recites wherein identifying sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects using one or more of a prognostic, diagnostic, adverse effect, or therapeutic feature, and associating data is evaluating and a mental process. Claim 5 recites wherein identifying sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects based on response to a therapy or based on length of time between a therapy and a subsequent event, and associating data is evaluating and a mental process.
Claim 6 recites determining a likelihood of correlation among candidate subcombinations with respect to one or more of a disease state, a therapy, a response, or an outcome, and determining a likelihood is evaluating and a mental process; and selecting candidate relationships having the greatest likelihood of correlation, and selecting relationships is evaluating and a mental process. Claim 7 recites wherein the summary comprises a ranked list of the determined associations, and a summary comprising data is a mental process accomplishable in the human mind or on paper. Claim 8 recites wherein aggregating features from the received plurality of features comprises semantically relating entities in a heterogeneous knowledge base, wherein a relationship for at least a subset of the semantically related entities comprises a temporal element, and relating entities is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 9 recites inputting the heterogenous knowledge base into a neural network, the associations between subcombinations of features and response or outcomes features comprising outputs of the neural network, and inputting data into a neural network is a mental process accomplishable in the human mind or on paper.
Claim 10 recites receiving a plurality of features from a background knowledge data store, which is recited broadly and amounts to receiving data across a network per figure 1 and paragraph 0174, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II, wherein aggregating features comprises aggregating from the subject data store and the background knowledge data store, and aggregating features is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 11 recites receiving the plurality of features from a plurality of background knowledge data stores, which is recited broadly and amounts to receiving data across a network per figure 1 and paragraph 0174, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II; and ranking features based on which of the plurality of background knowledge data stores they are received from, and ranking features is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 12 recites referencing a plurality of established associations to identify first associations to be excluded from the determined associations, and referencing an association is evaluating and a mental process; and excluding associations from the determined associations that are also present within the first associations, which is recited broadly and a mental process accomplishable in the human mind or on paper.
Claim 13 recites receiving a plurality of features from a background knowledge data store, which is recited broadly and amounts to receiving data across a network per figure 1 and paragraph 0174, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II, wherein aggregating features comprises aggregating from the subject data store and the background knowledge data store, and aggregating features is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 14 recites categorizing the plurality of features as source-type features and target-type features, which is recited broadly and is a mental process accomplishable in the human mind or on paper; and aggregating features, wherein the aggregation includes at least one source-type feature and at least one target-type feature from the identified features, which is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 15 recites wherein the aggregating is based at least in part on embeddings generated from the aggregation of features including the at least one source-type feature and the at least one target-type feature, which is recited broadly and is a mental process accomplishable in the human mind or on paper.
Claim 16 recites determining confidence scores, based at least in part on one or more of the populations or sub-populations, between the subcombinations of features and the response or outcomes features, and determining a confidence score us evaluating and a mental process; and ranking the confidence scores, and ranking is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 17 recites wherein providing the summary of the determined associations comprises providing at least a set of the ranked confidence scores and relationships identified from the generated embeddings, and providing scores and relationships is recited broadly and amounts to receiving data across a network per figure 1 and paragraph 0174, which is routine and conventional activity per the list of such activities in MPEP 2106.05(d) part II. Claim 18 recites wherein the source-type features and target-type features comprise semantically related entities in a heterogeneous knowledge base, wherein a relationship for at least a subset of the semantically related entities further comprises a temporal element, and features are data and a mental process accomplishable in the human mind or on paper.
Rejections under 35 U.S.C. 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.
Claims 1, 4-7, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes et al (US 20230352151), hereafter Barnes, in view of Masters et al (US 20220003785) hereafter Masters.
With respect to claims 1, 19, and 20, Barnes teaches:
receiving a plurality of features from a subject data store, including clinical features, therapeutic features, and at least one of response or outcomes features (paragraph 0016 images with features from clinical trial data and biological samples of patients, features include drug treatments, outcome data)
receiving an identification of one or more of the plurality of features (paragraph 0016 features are identified from samples);
aggregating features from the received plurality of features based at least in part on one or more of the identified features (paragraph 0016 features are identified from samples (compute features for a cohort population);
identifying a plurality of populations of subjects associated with the aggregated features (paragraphs 0010, 0016 identify cohorts associated with drug or treatment protocols);
identifying, within the plurality of populations, sub-populations of subjects associated with features from the response or outcomes features (paragraph 0018 stratifying patient cohorts, paragraph 0022 association with patient outcome data);
determining associations, based at least in part on the sub-populations or populations, between subcombinations of features and response or outcomes features, wherein the subcombinations of features are associated with the response and outcomes features within the respective subpopulations of populations (paragraph 0016 determining deriving diagnostic feature metric based on data from cohort population, paragraph 0111 association between outcome tracked and feature for each biomarker).
Barnes does not teach providing a summary of the determined associations. Masters teaches this with groupings summarized in a ranked list (Table 6) for acute exacerbations in a time period according to a treatment (paragraphs 0315-0322 Example 7). It would have been obvious to have combined the techniques for data analysis in Barnes with the providing a summary of data in Masters as Masters is in the same field of endeavor of studies of subject outcomes for diseases and treatments, and the combination would provide useful information to a user about results of a study
Regarding claim 19, Barnes teaches a computer including a processing device (paragraph 0052 figure 1 14).
Regarding claim 20, Barnes teaches a non-transitory computer-readable medium (paragraphs 0020, 0054 figure 2 201).
With respect to claim 4, all the limitations in claim 1 are addressed by Barnes and Masters above. Barnes also teaches wherein identifying sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects using one or more of a prognostic, diagnostic, adverse effect, or therapeutic feature (paragraph 0018 uses diagnostic feature metric).
With respect to claim 5, all the limitations in claim 1 are addressed by Barnes and Masters above. Barnes also teaches wherein identifying sub-populations of subjects comprises associating one or more subjects within one or more of the plurality of populations with one or more other subjects based on response to a therapy or based on length of time between a therapy and a subsequent event (paragraphs 0059-0060 associating patients with other patients based on response to cancer therapy(survival rate), length of time between treatment (therapy) and reoccurrence/death (event)).
With respect to claim 6, all the limitations in claim 1 are addressed by Barnes and Masters above. Barnes also teaches wherein determining associations between subcombinations of features comprises: determining a likelihood of correlation among candidate subcombinations with respect to one or more of a disease state, a therapy, a response, or an outcome (paragraphs 0006-0007 likelihood of success among patients for therapy tested in Phase III trials); and
selecting candidate relationships having the greatest likelihood of correlation (paragraphs 0006-0007 selecting candidates based on greatest success with therapy (effectiveness, success of Phase III trials)).
With respect to claim 7, all the limitations in claim 1 are addressed by Barnes and Masters above. Barnes does not teach wherein the summary comprises a ranked list of the determined associations. Masters teaches this in Table 6 showing a summary of in a ranked list (Table 6) for acute exacerbations in a time period according to a treatment (paragraphs 0315-0322 Example 7).
Claims 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters in further view of Boussios et al (US 20240153647), hereafter Boussios.
With respect to claim 2, all the limitations in claim 1 are addressed by Barnes and Masters above. The combination of Barnes and Masters does not teach wherein the method is implemented in conjunction with a large language model. Boussios teaches this in using a neural language model such as Word2Vec for identifying co-occurrence relations of medical codes using patient histories (paragraph 0082). It would have been obvious to have combined the use of a language model in Boussios with the analysis techniques in Barnes and Masters to look for matches in codes for patients and provide greater accuracy with modeling.
With respect to claim 3, all the limitations in claim 1 are addressed by Barnes and Masters above. The combination of Barnes and Masters does not teach wherein subjects in respective populations of the plurality of populations have at least one feature in common, the at least one feature corresponding to a disease state, a therapy, a response, or an outcome. Boussios teaches this in using a neural language model such as Word2Vec for identifying co-occurrence relations of medical codes (features in common) using patient histories (paragraph 0082).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters in further view of Russell et al (US 10,997,244), hereafter Russell.
With respect to claim 8, all the limitations in claim 1 are addressed by Barnes and Masters above. The combination of Barnes and Masters does not teach wherein aggregating features from the received plurality of features comprises semantically relating entities in a heterogeneous knowledge base, wherein a relationship for at least a subset of the semantically related entities comprises a temporal element. Russell teaches this in searching metaset databases in knowledge graphs that use semantic searching to determine matches and finds matches on temporal attributes (column 12 lines 15-46). It would have been obvious to have combined the function of semantically relating entities in a knowledge base in Russell with the analysis techniques in Barnes and Masters top provide more information for a user about the analyses performed, making the combination more user-friendly.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters and Russell and further in view of Prathap et al (US 20220366331), hereafter Prathap.
With respect to claim 9, all the limitations in claims 1 and 8 are addressed by the combination of Barnes, Masters, and Russell above. The combination of Barnes, Masters, and Russell does not teach inputting the heterogenous knowledge base into a neural network, the associations between subcombinations of features and response or outcomes features comprising outputs of the neural network. Prathap teaches this in a semantic parser for entities in a database which are fed into a neural network to get matches output (paragraph 0041). It would have bene obvious to have combined the function of inputting a knowledge base into a neural network an in Prathap with the analysis techniques in Barnes, Masters, and Russell to provide more information for the user, making the combination more user-friendly, and to use the network for more accuracy of results.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters and further in view of Rahman et al (US 20210118550), hereafter Rahman.
With respect to claim 10, all the limitations in claim 1 are addressed by Barnes and Masters above. The combination of Barnes and Masters does not teach receiving a plurality of features from a background knowledge data store, wherein aggregating features comprises aggregating from the subject data store and the background knowledge data store. Rahman teaches this in using a plurality of features in another database to develop a matrix of correlations between a features database and another database (paragraph 0040). It would have been obvious to have combined the function of using a background database in Rahman with the analysis techniques in Barnes and Masters to provide more information for a user from additional knowledge bases on patients and features.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters and further in view of Ahmad et al (US 20200258629), hereafter Ahmad.
With respect to claim 12, all the limitations in claim 1 are addressed by Barnes and Masters above. The combination of Barnes and Masters does not teach:
referencing a plurality of established associations to identify first associations to be excluded from the determined associations; and
excluding associations from the determined associations that are also present within the first associations.
Ahmad teaches these things:
referencing a plurality of established associations to identify first associations to be excluded from the determined associations (paragraph 0070 determining correlations between features (associations) to remove due to biasing a model); and
excluding associations from the determined associations that are also present within the first associations (paragraph 0070 removing those correlations to prevent biasing the model).
It would have been obvious to have combined the function of excluding associations with the analysis techniques of Barnes and Masters to allow additional features/associations to come out in an analysis after certain associations are removed.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters and Ahmad and further in view of Rahman.
With respect to claim 13, all the limitations in claims 1 and 12 are addressed by Barnes, Masters, and Ahmad above. The combination of Barnes, Masters, and Ahmad does not teach:
receiving a plurality of features from a background knowledge data store,
wherein aggregating features comprises aggregating from the subject data store and the background knowledge data store.
Rahman teaches these things:
receiving a plurality of features from a background knowledge data store (paragraph 0040 receiving features from the feature database and from another database),
wherein aggregating features comprises aggregating from the subject data store and the background knowledge data store (paragraph 0040 determine correlations using both databases).
It would have been obvious to have combined the function of aggregating features from different data stores in Rahman with the analysis techniques in Barnes, Masters, and Ahmad to gather more information from both databases for analysis.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters and further in view of Ghiassian et al (US 12,523,318) hereafter Ghiassian.
With respect to claim 14, all the limitations in claim 1 are addressed by Barnes and Masters above. The combination of Barnes and Masters does not teach:
categorizing the plurality of features as source-type features and target-type features; and
aggregating features, wherein the aggregation includes at least one source-type feature and at least one target-type feature from the identified features.
Ghiassian teaches these things:
categorizing the plurality of features as source-type features and target-type features (column 2 lines 52-64 categorize subjects (source) per likely benefit for a therapy (target)); and
aggregating features, wherein the aggregation includes at least one source-type feature and at least one target-type feature from the identified features (columns 4-5 lines 60-3 aggregate features for therapies for patients).
It would have been obvious to have combined the function of categorizing source-type and target-type features in Ghiassian with the analysis techniques in Barnes and Masters to be more cost-effective for therapies being effective for a disease.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters and Ghiassian and further in view of Abraham et al (US 20220319658), hereafter Abraham.
With respect to claim 15, all the limitations in claims 1 and 14 are addressed by Barnes, Masters, and Ghiassian above. The combination of Barnes, Masters, and Ghiassian does not teach wherein the aggregating is based at least in part on embeddings generated from the aggregation of features including the at least one source-type feature and the at least one target-type feature. Abraham teaches this with a feature vector (embeddings) of biomarkers representing associations between biomarker data and outcome data (paragraph 0133). It would have been obvious to have combined the function of feature vector embeddings generated from associations between biomarkers and patient outcome data in Abraham with the analysis techniques in Barnes, Masters, and Ghiassian to provide predictive ability for a therapy involving the biomarker outcome.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Barnes and Masters and Ghiassian and further in view of Russell (US 10,997,244).
With respect to claim 18, all the limitations in claims 1 and 14 are addressed by Barnes, Masters, and Ghiassian above. The combination of Barnes, Masters, and Ghiassian does not teach wherein the source-type features and target-type features comprise semantically related entities in a heterogeneous knowledge base, wherein a relationship for at least a subset of the semantically related entities further comprises a temporal element. Russell teaches this with metasets from datasets (column 7 lines 48-64), semantic searching of metasets for entities, finds matches between temporal attributes (column 12 lines 15-46). It would have been obvious to have combined the function of semantically-related entities in a knowledge base in nRussell with the analysis techniques in Barnes, Masters, and Ghiassian to provide more information for the user, making the combination more user-friendly.
Inquiry
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 2/3/26