DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-9, 11-15, 17, 20, 23-27 is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent 20190050534 to Apte et al. in view of U.S. Patent Application 2015/0259728 to Cutliffe et al.
As to claim 1, Apte discloses a method, comprising:
Obtaining a gut microbiome sample associated with a user of a plurality of users, wherein obtaining the gut microbiome sample comprises generating, based at least in part on a stool test and and a biological collection device, a biological sample associated with the user (Apte [0044] see “transform a biological sample into components able to be sequenced and analyzed to generate microorganism dataset and/or microbiome features usable for characterizing users in relation to one or more microorganism-related conditions (e.g., such as through use of microbiome characterization modules, next-generation sequencing systems, multiplex amplification operations; etc.).”; [0066] where a sample site includes gut; and [0069] see characteristics of users associated with a plurality of microorganism related conditions);
Generating DNA from the gut microbiome sample (Apte [0044] see “transform a biological sample into components able to be sequenced and analyzed to generate microorganism dataset and/or microbiome features usable for characterizing users in relation to one or more microorganism-related conditions (e.g., such as through use of microbiome characterization modules, next-generation sequencing systems, multiplex amplification operations; etc. and [0066] where a sample site includes gut).”;
Preprocessing raw sequencing data associated with the DNA to generate screened sequencing data (Apte [0074] see “determining a microorganism dataset (e.g., microorganism sequence dataset, microbiome composition diversity dataset such as based upon a microorganism sequence dataset, microbiome functional diversity dataset such as based upon a microorganism sequence dataset, etc.) associated with a set of subjects”)
Generating a quantitative functional potential profile (Apte [0074] see microbiome functional diversity dataset), strain level genomic profile (Apte [0074] see “Compositional and/or functional aspects can include one or more of aspects at the microorganism level (and/or other suitable granularity), including parameters related to distribution of microorganisms across different groups of kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable infraspecies taxon (e.g., as measured in total abundance of each group, relative abundance of each group, total number of groups represented, etc.).”) and a quantitative taxonomic profile (Apte [0074] see “ microbiome composition diversity dataset”) from the gut microbiome sample (Apte [0066] where a sample site includes gut), wherein generating the quantitative functional potential profile comprises mapping sequencing reads from the screened sequencing data against sequences of at least one of DNA or amino acids representing known genes, proteins, gene families or protein families, and wherein generating the quantitative taxonomic profile comprises mapping the sequencing reads from the screened sequencing data against sequences representing known microbial organisms (Apte [0074] see “ Compositional and/or functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.). Compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g. enzyme activities, transport functions, immune activities, etc.). Outputs of Block S110 can thus be used to facilitate determination of microbiome features (e.g., generation of a microorganism sequence dataset usable for identifying microbiome features; etc.)” and [0075] in totality);
Inputting the quantitative functional potential profile, the strain level genomic profile and the quantitative taxonomic profile into a first machine learning mechanism to generate a classification associated with the microbiome of the user (Apte [0068]-[0069] applying machine learning to data resulting from the microbiome characterization modules)
generating, based at least in part on the quantitative functional profile, the quantitative taxonomic profile, a microbiome fingerprint that identifies a microbiome of the user at a particular point in time (Apte [0074] see compositional and functional aspects); and
inputting the microbiome fingerprint into a second machine learning mechanism to generate a dietary fingerprint using the microbiome print (Apte claims 1 and 12 see first and second characterization modules and using machine learning to determine relevance scores and paragraph [0038]-[0039] see at least “generate and/or promote (e.g., provide; present; notify regarding; etc.) characterizations and/or therapies for one or more microorganism-related conditions, which can include one or more of: diseases, symptoms, causes (e.g., triggers, etc.), disorders, associated risk (e.g., propensity scores, etc.), associated severity, behaviors (e.g., caffeine consumption, habits, diets, etc.), and/or any other suitable aspects associated with microorganism-related conditions.”);
inputting the microbiome fingerprint into a third machine learning mechanism to generate one or more scores, wherein an individual score of the one or more scores corresponds to a respective similarity of the microbiome fingerprint to another microbiome fingerprint that is representative of at least one other user of the plurality of users (Apte [0069])
However, Apte does not teach providing the dietary fingerprint and the one or more scores to a remote computing device over one or more networks, the remote computing device associated with the user. Cutliffe discloses providing the dietary fingerprint and the one or more scores to a remote computing device over one or more networks, the remote computing device associated with the user (Cutliffe [0073], [0082] and [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention by applicant to transmit a report as in Cutliffe using the analysis of Apte to better apprise the user of their health status.
As to claims 2 and 11, see the discussion of claim 1, additionally, Apte discloses
performing a first comparison that compares the microbiome fingerprint with one or more second microbiome fingerprints that are representative of at least one other user, of the plurality of users, that is indicated to follow a first diet (Apte claim 1 and paragraph [0038])
performing a second comparison that compares the microbiome fingerprint with one or more third microbiome fingerprints that are representative of at least one other user, of the plurality of users, that is indicated to follow a second diet (Apte claim 1 and paragraph [0038]); and
determining that the microbiome fingerprint of the user is similar to the first diet or the second diet based on the first comparison and the second comparison (Apte claim 1 and paragraph [0038]).
As to claim 12 see the discussion of claim 2, additionally, Apte discloses
wherein the first diet is selected from one or more of a vegetarian diet and a ketogenic diet, and wherein the second diet is different from the first diet (Apte claim 1 and paragraph [0038]).
As to claims 4 and 13 see the discussion of claim 2, additionally, Apte discloses
further comprising performing a third comparison that compares the microbiome fingerprint with a fourth microbiome fingerprint that is representative of at least one other user, of the plurality of users, that is indicated to exhibit a characteristic, wherein the characteristic is selected from or a health characteristic (Apte claim 1 and paragraph [0038]).
As to claim 5, see the discussion of claim 1, additionally, Cutliffe discloses determining that one or more other users of the plurality of users are genetically related to the user’s microbiome based on a respective score being above a value (Cutliffe [0097]).
As to claim 6, see the discussion of claim 1, additionally, Cutliffe discloses wherein the third machine learning mechanism further identifies the microbiome fingerprint is associated with a geographic region, wherein the identifying includes comparing the microbiome fingerprint with other microbiome fingerprints associated with different users in different geographic regions (Cutliffe see cohort generalized microbiome and region of the world [0073]).
As to claim 7, see the discussion of claim 1, additionally, Cutliffe discloses the method further comprising generating a graphical user interface that includes user interface elements, causing the graphical user interface to be presented on a display of the remote computing device, and causing data associated with the microbiome fingerprint to be presenting on the display (Cutliffe [0018]).
As to claim 8, see the discussion of claim 1, additionally, Cutliffe discloses the method further comprising generating a nutritional recommendation based on the microbiome fingerprint and causing the nutritional recommendation to be provided to the remote computing device associated with the user (Cutliffe [0032] see subject-specific diet recommendation).
As to claims 9, Apte discloses the system, comprising:
a data ingestion service, including one or more processors, configured to access raw sequencing data associated with a microbiome of a user of a plurality of users (Apte [0044] see “transform a biological sample into components able to be sequenced and analyzed to generate microorganism dataset and/or microbiome features usable for characterizing users in relation to one or more microorganism-related conditions (e.g., such as through use of microbiome characterization modules, next-generation sequencing systems, multiplex amplification operations; etc.).”; and
a microbiome service, including one or more processors, configured to
preprocess the raw sequencing data to generate screened sequencing data (Apte [0074] see “determining a microorganism dataset (e.g., microorganism sequence dataset, microbiome composition diversity dataset such as based upon a microorganism sequence dataset, microbiome functional diversity dataset such as based upon a microorganism sequence dataset, etc.) associated with a set of subjects”)
generate a quantitative functional potential profile from the screened sequencing data wherein generating the quantitative functional potential profile comprising mapping sequencing reads from the screened sequencing data against sequences of at least one of DNA or amino acids representing known genes, proteins, gene families or protein families (Apte [0074] see microbiome functional diversity dataset and “Compositional and/or functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.). Compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g. enzyme activities, transport functions, immune activities, etc.”)
input the strain level genomic profile into a first machine learning mechanism to generate a classification associated with the microbiome of the user (Apte [0068]-[0069] applying machine learning to data resulting from the microbiome characterization modules); and
generate, based at least in part on the strain level genomic profile, the quantitative functional potential profile and the classification, a microbiome fingerprint associated with the user, wherein the microbiome fingerprint identifies the microbiome at a particular point in time (Apte [0074] see compositional and functional aspects);
inputting the microbiome fingerprint into a third machine learning mechanism to generate one or more scores, wherein an individual score of the one or more scores corresponds to a respective similarity of the microbiome fingerprint to another microbiome fingerprint that is representative of at least one other user of the plurality of users (Apte [0069])
Provide the dietary fingerprint to a remote computing device over one or more networks the computing device associated with the user (Cutliffe see cohort generalized microbiome and region of the world [0073]).
However, Apte does not teach providing the dietary fingerprint and the one or more scores to a remote computing device over one or more networks, the remote computing device associated with the user. Cutliffe discloses providing the dietary fingerprint and the one or more scores to a remote computing device over one or more networks, the remote computing device associated with the user (Cutliffe [0073], [0082] and [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention by applicant to transmit a report as in Cutliffe using the analysis of Apte to better apprise the user of their health status.
As to claim 11, see the discussion of claim 9, Cutliffe discloses wherein a first score that indicates a similarity between at least a portion of the microbiome data with second microbiome data that is representative of at least one other user that is indicated to exhibit a first health characteristic (Cutliffe [0062] and [0075]-[0076]);
a second score that indicates a similarity between at least a portion of the microbiome data with third microbiome data that is representative of at least one other user that is indicated to exhibit a second health characteristic (Cutliffe [0062] and [0075]-[0076]); and
the data ingestion service further configured to associate the user with one or more of the first health characteristic and the second health characteristic based, at least in part, on the first score and the second score (Cutliffe [0062] and [0075]-[0076]).
As to claim 12, see the discussion of claim 11, Cutliffe discloses the system wherein the first health characteristic and the second health characteristic are selected from an internal fat characteristic and a carbohydrate digesting characteristic (Cutliffe [0202] and obesity [0227] and [0028]
As to claim 14, see the discussion of claim 9, Cutliffe discloses the system wherein the respective similarity is associated with a geographic region, and wherein the plurality of different users are in different geographic regions (Cutliffe see cohort generalized microbiome and region [0062], [0073]).
As to claim 15, see the discussion of claim 9, Cutliffe discloses the system further comprising generating a nutritional recommendation based, at least in part, on the microbiome fingerprint and causing the nutritional recommendation to be provided to the remote computing device associated with the user (Cutliffe [0032] see subject-specific diet recommendation).
As to claim 17, Cutliffe discloses a non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by a computer, cause the computer to:
access data associated with a gut microbiome of a user of a plurality of users (Apte [0044] see “transform a biological sample into components able to be sequenced and analyzed to generate microorganism dataset and/or microbiome features usable for characterizing users in relation to one or more microorganism-related conditions (e.g., such as through use of microbiome characterization modules, next-generation sequencing systems, multiplex amplification operations; etc.).”; [0066] where a sample site includes gut; and [0069] see characteristics of users associated with a plurality of microorganism related conditions);
generate a quantitative taxonomic profile from the data, the quantitative taxonomic profile including data associated with at least one known microbial species of a plurality of known microbial species wherein generating the quantitative taxonomic profile comprises mapping sequencing reads from screened sequencing data associated with the gut microbiome against sequences representing a plurality of known microbial species (Apte [0074] see “ microbiome composition diversity dataset” and [0074] see “Compositional and/or functional aspects can include one or more of aspects at the microorganism level (and/or other suitable granularity), including parameters related to distribution of microorganisms across different groups of kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable infraspecies taxon (e.g., as measured in total abundance of each group, relative abundance of each group, total number of groups represented, etc.).”)
generate a quantitative functional potential profile from the data, the quantitative functional potential profile including data associated with at least one known microbial gene, protein, gene family, or protein family organisms (Apte [0074] see “ Compositional and/or functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.). Compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g. enzyme activities, transport functions, immune activities, etc.). Outputs of Block S110 can thus be used to facilitate determination of microbiome features (e.g., generation of a microorganism sequence dataset usable for identifying microbiome features; etc.)” and [0075] in totality); and
generate a strain-level genomic profile from the data, the strain-level genomic profile including data associated with a reconstruction of at least a part of a genome of at least one microbe (Apte [0068]-[0069] applying machine learning to data resulting from the microbiome characterization modules)
Inputting the quantitative taxonomic profile, the quantitative functional potential profile or the the strain level genomic profile into a first machine learning mechanism to generate a classification associated with the microbiome of the user (Apte [0068]-[0069] applying machine learning to data resulting from the microbiome characterization modules)
generating, based at least in part on the quantitative taxonomic profile, the quantitative functional potential profile, the strain level genomic profile or the classification, a microbiome fingerprint that identifies a microbiome of the user at a particular point in time (Apte [0074] see compositional and functional aspects); and
inputting the microbiome fingerprint into a second machine learning mechanism to generate a dietary fingerprint using the microbiome print (Apte claims 1 and 12 see first and second characterization modules and using machine learning to determine relevance scores and paragraph [0038]-[0039] see at least “generate and/or promote (e.g., provide; present; notify regarding; etc.) characterizations and/or therapies for one or more microorganism-related conditions, which can include one or more of: diseases, symptoms, causes (e.g., triggers, etc.), disorders, associated risk (e.g., propensity scores, etc.), associated severity, behaviors (e.g., caffeine consumption, habits, diets, etc.), and/or any other suitable aspects associated with microorganism-related conditions.”);
inputting the microbiome fingerprint into a third machine learning mechanism to generate one or more scores, wherein an individual score of the one or more scores corresponds to a respective similarity of the microbiome fingerprint to another microbiome fingerprint that is representative of at least one other user of the plurality of users (Apte [0069])
However, Apte does not teach providing the dietary fingerprint and the one or more scores to a remote computing device over one or more networks, the remote computing device associated with the user. Cutliffe discloses providing the dietary fingerprint and the one or more scores to a remote computing device over one or more networks, the remote computing device associated with the user (Cutliffe [0073], [0082] and [0018]).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention by applicant to transmit a report as in Cutliffe using the analysis of Apte to better apprise the user of their health status.
As to claim 20, see the discussion of claim 9, Cutliffe discloses the system wherein the respective similarity is associated with a geographic region and wherein the plurality of users are in different geographic regions (Cutliffe see cohort generalized microbiome and region [0073]).
As to claim 23, see the discussion of claim 17, Cutliffe discloses the system wherein operations further comprise:
a first score of the one or more scores indicates a similarity between the microbiome fingerprint and a second microbiome fingerprint of a second user that is indicated to one or more of follow a first diet and exhibit a first health characteristic (Cutliffe [0062] and [0075]-[0076]);;
a second score of the one or more score indicates a similarity between the microbiome fingerprint and a third microbiome fingerprint associated with a third user that is indicated to one or more of follow a second diet and exhibit a second health characteristic, the second diet different than the first diet (Cutliffe [0073]); and
and wherein the computer-executable instructions further cause the computer to associate the first user with the first diet, the second diet, and with one or more of the first health characteristic, and the second health characteristic based at least in part on the first score and the second score, wherein the first diet, the second diet, the first health characteristic, and the second health characteristic are selected from one or more of a health characteristic (Cutliffe [0073] and [0227]-[0228]).
Cutliffe discloses a quantitative score in relation to a dietary regimen [0052] and [0227]-[0028].
Wherein the first health characteristic and the second health characteristic are selected from one or more of a carbohydrate digesting characteristic (Cutliffe [0207]
However, the references do not teach the diets are mediterranean diet, fast food diet, and ketogenic diet. It would have been obvious to one of ordinary skill in the art before the effective filing of the invention by applicant to select from a finite number of known diets a Mediterranean, fast food, and ketogenic diet with the results being predictable, that the system can provide findings referencing well known diet types.
As to claim 24, see the discussion of claim 17, Cutliffe discloses the system wherein a first score of the one or more scores indicates a similarity between at least a portion of the microbiome fingerprint with second microbiome fingerprint that is representative of at least one other user that is indicated to follow a first diet: and a second score that indicates a similarity between at least a portion of the microbiome fingerprint with third microbiome fingerprint that is representative of at least one other user that is indicated to follow a second diet: and the method further comprising associating the user with one or more of the first diet or the second diet based, at least in part, on the first score and the second score (Cutliffe [0073] and [0227]-[0228]).
As to claim 25, see the discussion of claim 24, Cutliffe discloses a quantitative score in relation to a dietary regimen [0052] and [0227]-[0028].
However, the references do not teach the diets are mediterranean diet, fast food diet, and ketogenic diet. It would have been obvious to one of ordinary skill in the art before the effective filing of the invention by applicant to select from a finite number of known diets a Mediterranean, fast food, and ketogenic diet with the results being predictable, that the system can provide findings referencing well known diet types.
As to claim 26, see the discussion of claim 1, additionally, Cutliffe discloses wherein the data associated with at least one microbial gene, protein, gene family, or protein family are associated with sequences of DNA or amino acids (Cutliffe [0134] and [0091]-[0092]):
As to claim 27 see the discussion of claim 1, additionally, Cutliffe discusses generating, based at least in part on a stool test and a biological collection device, a biological sample associated with the user. (Cutliffe [0058]-[0059]).
As to claim 28, Apte discloses wherein the one or more scores are one or more first scores, and wherein inputting the microbiome fingerprint into the second machine learning mechanism further generates a second score corresponding to a similarity of the microbiome is to an index associated with a particular diet (Apte [0062], and [0052]-[0053]).
Response to Arguments
Applicant’s arguments regarding machine learning as well as the newly added features are moot in view of new grounds of rejection.
Applicant argues that Apte does not teach scores representing similarity of microbiome fingerprint of one person to a microbiome fingerprint that is representative of at least one other person. Apte discloses a degree of similarity between a microbiome profile for a plurality of conditions for a set of subjects. (Step 130 of figure 1).
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Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Eliza Lam whose telephone number is (571)270-7052. The examiner can normally be reached Monday-Friday 8-4:30PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Choi can be reached on 469-295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ELIZA A LAM/ Primary Examiner, Art Unit 3686
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