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 .
Specification
The disclosure is objected to because of the following informalities:
In ¶ 0044, “determining if an emergency alert must be send” seems to be a grammatical error. Examiner recommends amending it to read -- determining if an emergency alert must be sen[[d]]t --.
Appropriate correction is required.
Claim Objections
Claims 1, 4, 6-7, 10-11, 13 is/are objected to because of the following informalities:
In claims 1, 6, 10-11, 13, line(s) 3, “of the each individual” seems to be a grammatical error. Examiner recommends amending it to read -- of [[the]] each individual of the set of individuals --.
In claims 1, 4, 11, 13 throughout the claims, “the each node” seems to be a grammatical error. Examiner recommends amending it to read – [[the]] each node of the first graph --.
In claims 1, 4, 11, 13 throughout the claims, “said each node” seems to be a grammatical error. Examiner recommends amending it to read – [[said]] each node of the first graph --.
In claim 7, line(s) 1-2, “wherein said computer implemented method is performed via non-transitory computer” seems to be a grammatical error. Examiner recommends amending it to read -- wherein said computer implemented method is performed via a non-transitory computer --.
Appropriate correction is required.
Subject Matter Free of Prior Art
Claim(s) 1-15 are allowable over prior art because the prior art of record fail to expressly teach or suggest, either alone or in combination, the features found within the independent claims, in particular: “a connection between two nodes of the each node and the at least one other node is added between said two nodes if 80% of data of said two nodes are of same values within a predetermined range, applying a node embedding algorithm to the first graph, the node embedding algorithm comprising an encoding of said each node of the first graph as a low-dimensional vector comprising the health data and the health condition information of the each node, a position of the each node in the first graph and a structure of a local first graph neighborhood of the each node, therefore obtaining a first set of low-dimensional vectors, and learning a neural network taking as input a low-dimensional vector of the first set of low-dimensional vectors and configured to determine a future health condition of the each individual, the learning being performed with an unsupervised training based on the first set of low-dimensional vectors and comprising applying a similarity algorithm to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors.” Because the prior art does not teach or disclose the above features in the specific manner and combinations recited in independent claims 1, 11, 13, claims 1, 11, 13 are hereby deemed to be allowable over prior art. Originally numbered dependent claims 2-10, 12, 14-15 incorporate the allowable features of originally numbered independent claim 31, through dependency.
However, the claims are still rejected under 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 11, 13 recites "applying a node embedding algorithm to the first graph, the node embedding algorithm comprising an encoding of said each node of the first graph as a low-dimensional vector comprising the health data and the health condition information of the each node, a position of the each node in the first graph and a structure of a local first graph neighborhood of the each node, therefore obtaining a first set of low-dimensional vectors.” However, Applicant has provided no disclosure of how the node embedding algorithm actually works to obtain low-dimensional vectors.
The specification only mentions “The node embedding algorithm comprises an encoding of the nodes of the first graph. The encoding encodes each node as a low-dimensional vector… By applying at the step 40 a node embedding algorithm to the first graph, a first set of low-dimensional vectors is obtained” (¶ 0031) and does not further provide a specific description of the “node embedding algorithm.” Any rule, instruction, algorithm, or model associated with encoding could potentially read on the as-claimed invention. The claimed “node embedding algorithm” amounts to a black box into which information is inputted and a result is received; however, there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. See: MPEP § 2161.01.
The Examiner prospectively notes that this written description rejection is not based on whether one skilled in the art would know how to program a computer to perform any form of the node embedding algorithm (i.e., an enablement rejection), but rather is directed to the Applicant’s lack of specificity as to how the node embedding algorithm is specifically performed with respect to the Applicant’s claimed invention.
Claim(s) 2-10 is/are rejected as being dependent on claim 1.
Claim(s) 12 is/are rejected as being dependent on claim 11.
Claim(s) 14-15 is/are rejected as being dependent on claim 13.
Claims 1, 11, 13 recites "the learning being performed with an unsupervised training based on the first set of low-dimensional vectors.” However, Applicant has provided no disclosure of how the unsupervised training actually works to learn a neural network.
The specification only mentions “During the learning phase, an unsupervised neural network tries to mimic the data it's given and uses the error in its mimicked output to correct itself, by correcting its weights and biases. Sometimes the error is expressed as a low probability that the erroneous output occurs, or it might be expressed as an unstable high energy state in the network. One of the advantages of an unsupervised neural network is its ability to learn patterns from untagged data” (¶ 0006) and does not further provide a specific description of the “unsupervised training based on the first set of low-dimensional vectors.” Any rule, instruction, algorithm, or model associated with unsupervised training could potentially read on the as-claimed invention. The claimed “unsupervised training” amounts to a black box into which information is inputted and a result is received; however, there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. See: MPEP § 2161.01.
The Examiner prospectively notes that this written description rejection is not based on whether one skilled in the art would know how to program a computer to perform any form of the unsupervised training (i.e., an enablement rejection), but rather is directed to the Applicant’s lack of specificity as to how the unsupervised training is specifically performed with respect to the Applicant’s claimed invention.
Claim(s) 2-10 is/are rejected as being dependent on claim 1.
Claim(s) 12 is/are rejected as being dependent on claim 11.
Claim(s) 14-15 is/are rejected as being dependent on claim 13.
Claims 1, 11, 13 recites "applying a similarity algorithm to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors.” However, Applicant has provided no disclosure of how the similarity algorithm actually identifies similar vectors.
The specification only mentions “the second set of low-dimensional vectors is used to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors. By using the second set of low-dimensional vectors, the learning is more efficient to identify, the similar vectors of the first set of low-dimensional vectors” (¶ 0034) and does not further provide a specific description of the “similarity algorithm [applied] to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors.” Any rule, instruction, algorithm, or model associated with similarities could potentially read on the as-claimed invention. The claimed “similarity algorithm” amounts to a black box into which information is inputted and a result is received; however, there is no disclosure as to what occurs in the box. As such, the claimed invention lacks adequate written description. See: MPEP § 2161.01.
The Examiner prospectively notes that this written description rejection is not based on whether one skilled in the art would know how to program a computer to perform any form of the similarity algorithm (i.e., an enablement rejection), but rather is directed to the Applicant’s lack of specificity as to how the similarity algorithm is specifically performed with respect to the Applicant’s claimed invention.
Claim(s) 2-10 is/are rejected as being dependent on claim 1.
Claim(s) 12 is/are rejected as being dependent on claim 11.
Claim(s) 14-15 is/are rejected as being dependent on claim 13.
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.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Claim 1 is drawn to a method which is within the four statutory categories (i.e., method). Claim 11 is drawn to a computer program which is interpreted to be within the four statutory categories (i.e., manufacture) for subject matter eligibility analysis purposes. Claim 13 is drawn to a system which is within the four statutory categories (i.e., machine).
Independent claim 13 (which is representative of independent claims 1, 11) recites… providing, for a set of individuals, health data that is one or more of unstructured and semi structured, said health data comprising pathology reports and at least one or more of family history health, genetic information, individual nature, body structure, face structure, obtaining, for each individual of the set of individuals, health condition information from the health data, generating a first graph based on the health data and the health condition information, each node of the first graph comprising the health data and the health condition information of one individual of the set of individuals and being connected to at least one other node of the first graph, a connection between two nodes of the each node and the at least one other node is added between said two nodes if 80% of data of said two nodes are of same values within a predetermined range, applying a node embedding algorithm to the first graph, the node embedding algorithm comprising an encoding of said each node of the first graph as a low-dimensional vector comprising the health data and the health condition information of the each node, a position of the each node in the first graph and a structure of a local first graph neighborhood of the each node, therefore obtaining a first set of low-dimensional vectors…
Under its broadest reasonable interpretation, the limitations noted above, as drafted, covers certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people…following rules or instructions), but for the recitation of generic computer components. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to collect data, analyze the collected data, and output relevant data based on the analysis accordingly in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “providing,” “obtaining,” “generating,” “applying,” “learning,” as indicated supra. That is, other than reciting generic computer components (discussed infra) (i.e., a “computer”), the claim amounts to managing personal behavior or relationships or interactions between people following rules or instructions. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Independent claim 13 (which is representative of independent claims 1, 11) further recites…learning a [model] taking as input a low-dimensional vector of the first set of low-dimensional vectors and configured to determine a future health condition of the each individual, the learning being performed…based on the first set of low-dimensional vectors and comprising applying a similarity algorithm to identify, for each vector of the first set of low-dimensional vectors, similar vectors in the first set of low-dimensional vectors.
Under the broadest reasonable interpretation, the limitations noted above, as drafted, covers mathematical relationships, but for the recitation of generic computer components. When given its broadest reasonable interpretation in light of the disclosure, learning a model represents the creation of mathematical interrelationships between data. See Example 47, Claim 2. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
For purposes of the following analysis, the aforementioned types of identified abstract ideas are considered together as a single abstract idea. See MPEP § 2106.04(II)(B).
Claim 1 recites additional elements (i.e., computer to implement the method; a neural network; unsupervised training). Claim 11 recites additional elements (i.e., A non-transitory computer program comprising instructions; a computer; a neural network; unsupervised training). Claim 13 recites additional elements (i.e., A system comprising: a processor coupled to a memory, the memory having recorded thereon a non-transitory computer program comprising instructions; a computer; a neural network; unsupervised training). Looking to the specifications, a computer having a non-transitory computer program comprising instructions, processor, memory is described at a high level of generality (¶ 0051), such that it amounts to no more than mere instructions to apply the exception using generic computer components. Also, “learning a neural network” and “unsupervised training” is described at a high level of generality and is only used to generally apply the abstract idea without placing any limits on how the unsupervised training functions and does not include details about how “learning a neural network” is accomplished (i.e., no description of the mechanism for accomplishing the result), such that learning a neural network via unsupervised training amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea.
Reevaluated under step 2B, the additional elements noted above do not provide “significantly more” when taken either individually or as an ordered combination. The use of a general purpose computer or computers (i.e., a computer having a non-transitory computer program comprising instructions, processor, memory) amounts to no more than mere instructions to apply the exception using generic computer components and does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, “learning a neural network” and “unsupervised training” is described at a high level of generality and is only used to generally apply the abstract idea without placing any limits on how the unsupervised training functions and does not include details about how “learning a neural network” is accomplished (i.e., no description of the mechanism for accomplishing the result), such that learning a neural network via unsupervised training amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook; similarly, the current invention merely limits the claimed calculations to the healthcare industry which does not impose meaningful limits on the scope of the claim. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
Dependent claims 2-10, 12, 14-15 include all the limitations of the parent claims and further elaborate on the abstract idea discussed above and incorporated herein.
Claims 2-10, 12, 14-15 further define the analysis and organization of data for the performance of the abstract idea and do not recite any additional elements. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 2 further recites the additional elements of “extracting the health condition information from at least one of the health data using one or more of optical character recognition, intelligent character recognition, natural language processing technologies.” Claim 4 further recites the additional elements of “extracting medical knowledges from at least a one of the at least one medical knowledge repository using one or more of optical character recognition, intelligent character recognition, natural language processing technologies.” Extracting data from “optical character recognition, intelligent character recognition, natural language processing technologies” only provides input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., data gathering). Reevaluated under step 2B, electronically scanning or extracting data from a physical document has been recognized by the courts as well-understood, routine, and conventional elements/functions. See: MPEP § 2106.05(d)(II). Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 2 further recites the additional elements of “accessing and retrieving the health condition information from a healthcare organization database based on historical data or records.” Claim 4 further recites the additional elements of “obtaining at least one medical knowledge repository.” Receiving data from a database or repository only invokes the database and repository merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent), and only provides the input data for the performance of the abstract idea, and as such, amounts to insignificant extrasolution activity (i.e., mere data gathering), which does not impose meaningful limits on the scope of the claim. Reevaluated under step 2B, the database and repository are invoked merely as a tool in its ordinary capacity to perform an existing process (i.e., receiving, storing, providing data), which does not impose meaningful limits on the scope of the claim and amounts to no more than a recitation of the words "apply it" (or an equivalent). Furthermore, receiving or transmitting data over a network has been recognized by the courts as well-understood, routine, and conventional elements/functions. See: MPEP § 2106.05(d)(II). Also, functional limitations further define the analysis and organization of data for the performance of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Claim 5 further recites the additional elements of “wherein additional health data that is one or more of structured and semi structured are obtained and used to update, at a predefined interval of time, the neural network that is learned.” The specification further describes the updating as: “steps 10 to 50 may be performed with the additional unstructured and/or semi structured health data” (¶ 0035), which have been analyzed supra to be part of the abstract idea, and only further define the analysis and organization of data for the performance of the abstract idea. Furthermore, see Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 12 (Fed. Cir. April 18, 2025) (finding that “[i]terative training using selected training material…are incident to the very nature of machine learning.”). Furthermore, the “neural network” is still described at a high level of generality, such that it amounts to no more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer, and only generally links the use of a judicial exception to a particular technological environment or field of use (i.e., machine learning), which does not impose meaningful limits on the scope of the claim. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. Thus, the claims as a whole do not integrate the abstract idea into a practical application and do not provide “significantly more.”
Although the dependent claims add additional limitations, they only serve to further limit the abstract idea by reciting limitations on what the information is and how it is received and used. These information characteristics do not change the fundamental analogy to the abstract idea groupings and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Claim(s) 11 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to a “non-transitory computer program.”
The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319(Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a “program” may cover forms of software per se in view of the ordinary and customary meaning of “program,” particularly when the specification is silent. See MPEP 2111.01.
When the broadest reasonable interpretation of a claim covers a software per se, the claim must be rejected under 35 U.S.C. §101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility under 35 U.S.C. §101, Aug. 24, 2009; p. 2.
The USPTO recognizes that applicants may have claims directed to a system that covers software per se, which the USPTO must reject under 35 U.S.C. §101 as covering both non-statutory subject matter and statutory subject matter. In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. §101 in this situation, the USPTO suggests the following approach. A claim drawn to such an application that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. §101 by adding the limitation "non-transitory" to the claim. Cf. Animals – Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) (suggesting that applicants add the limitation "non-human" to a claim covering a multi-cellular organism to avoid a rejection under 35 U.S.C. §101). Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes software per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a software per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
Examiner recommends amending the claims to read -- A non-transitory data storage medium comprising a computer program --.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2023/0317279 A1 teaches embedding graph nodes based on similarity of patient symptoms.
WO 2022/072785 A1 teaches associating a node to a knowledge graph using embeddings of medical data and a neural network.
“DUGRA: Dual-Graph Representation Learning for Health Information Networks” teaches representing patient records as vectors and embedding the vectors as neighboring nodes in an graph.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Emily Huynh whose telephone number is (571)272-8317. The examiner can normally be reached on M-Th 8-5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EMILY HUYNH/Primary Examiner, Art Unit 3683