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 .
Claims 1-13 are pending and under examination.
This application is a National Stage Application of PCT/KR2020/008776, filed 7/6/2020.
This application claims priority to two KR priority documents. The certified copies of the priority documents have been received. No certified translations of the priority documents have been received. Therefore, the effective filing date for the examined claims is that of the PCT: 7/6/2020.
This application has published as US PG-Pub 2022/0293220 A1.
The Examiner has reviewed all papers related to the KR PCT filing, including the ISR and Written Opinion.
The preliminary amendment to the specification, filed 1/5/2022 has been entered.
The Drawings as filed 1/5/2022 are suitable for examination.
The IDS filed 1/5/2022 has been entered and considered.
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
See at least p41, lines 4-5 and line 17. Applicant is requested to review all the disclosure for compliance.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they are drawn to “reasoning apparatus” that has no physical or tangible elements, and can be completely met by software, transmitted or embodied in transitory signals. The “apparatus” merely comprises various key information “units”, key database information, recommendation software, and a reasoning software.
For the purpose of compact prosecution, these claims will be evaluated for subject matter eligibility, however the claims must be amended to fall within the four categories of patent eligible material.
With respect to claims 1-6, they include physical but transitory forms of
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signal transmission such as radio broadcasts, electrical signals, and light pulses through fiber-optic cable that convey information encoded in manner disclosed and are not directed to
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statutory
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subject matter under 35 U.S.C. §101, since claimed
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signal
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is not “process,” in that “process” requires some kind of action, and claims at issue, although potentially product-by-process claims, do not cover act or series of acts, since claimed
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is not “machine,” in that transitory
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signal
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made of electrical or electromagnetic variances, although physical and real, is not made of “parts” or “devices” in any mechanical sense, and thus does not possess concrete structure, since “manufacture,” for purposes of Section 101, is properly defined as tangible article or commodity resulting from manufacture, and transient electrical or electromagnetic transmission does not fit that definition, and since claimed
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is not “composition of matter,” in that
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signal comprising fluctuation in electric potential or electromagnetic fields is not “chemical union,” gas, fluid, powder, or solid. In re Nuijten, 84 USPQ2d 1495 (Fed. Cir. 2007)
Claims 1-13 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental steps, mathematic concepts, organizing human activity, or a natural law without significantly more.
Applicant is directed to MPEP 2106 and the Federal Register notice (FR89, no 137 (7/17/2024) p 58128-58138) for the most current and complete guidelines in the analysis of patent- eligible subject matter. The current MPEP is the primary source for the USPTO’s patent eligibility guidance.
With respect to step (1): YES, claims 7-13 are drawn to statutory categories: processes. Claims 1-6 are analyzed for the purposes of compact prosecution, but do not fall within the statutory categories.
With respect to step (2A) (1): YES. The claims recite an abstract idea, law of nature and/or natural phenomenon. The claims explicitly recite elements that, individually and in combination, constitute one or more judicial exceptions (JE). (MPEP 2106.04(a))
Mathematic concepts, Mental Processes or Elements in Addition (EIA) in the claim(s) include:
What is claimed is:
1. A biological information reasoning apparatus utilizing a species identification, comprising:
(Preamble: indicating the intended invention is an apparatus that “uses” “species identification” which is not a tangible element of an apparatus. The apparatus has no parts, processors, inputs/outputs, memory or other typical limitations.)
an identification key collection unit configured for collecting a species identification key set;
(EIA- a data gathering “unit” intended to gather “species identification key sets”. P42 of the specification indicates “species identification key sets” are a set of Directed Acyclic Graphs.)
an identification key database configured for hierarchically managing a set of questions of the collected species identification key set;
(EIA- a data gathering element: A database or table (or sets of databases and/or tables) comprising a set of questions, and logical node table/ number/ text (specification p3), and hierarchical information related to the identification keys. The database has no particular features, beyond the description of the data it is configured to manage. P46 of the specification indicates the database comprises tables and columns.
The intended use of managing information is a mental process. As set forth, “managing” has no particular steps, aspects, limitations or requirements. MPEP 2106.04(a)(2) Section III. The specification states at p8 the “information management device 110 is configured for constructing biological system information that can be a base for bio-inspired design.” Table 2 is an example (p14). “managing” has no particular limitations which cannot be carried out by the human mind.)
a recommendation system configured for recommending a biological system according to a user's question through a user terminal; and
(Mental Processes: mental processes of observing a question, considering the question, and making a judgement as to what “biological system” to recommend to the user. MPEP 2106.04(a)(2) section III. This step does not require anything for which the human mind is not equipped. The “through a user terminal” limitation is a generic input to any system, and is not clearly a positive active limitation of the apparatus.)
a reasoning system configured for reasoning characteristics of the biological system from the set of questions of the collected species identification key set regarding a species corresponding to the biological system and a pre-built information system of biological system.
(Mental Processes of considering the set of questions and identification key data, and considering the “pre-built” information system, to make a reasoned judgement as to characteristics of the species corresponding to the “recommended biological system”. MPEP 2106.04(a)(2) section III. This step does not require anything for which the human mind is not equipped.
The “pre-built” information system was built outside the bounds of the claim, and has no particular characteristics, or elements.)
2. The biological information reasoning apparatus of claim 1, wherein the species identification key set consists of DAG graphs.
(EIA: Data Gathering limitation)
3. The biological information reasoning apparatus of claim 1, wherein the species identification key set comprises a logical node, which is a logical question that can be answered with yes or no; and a chain of logical connections of the logical questions, wherein the species identification key set comprises one starting point and a plurality of endpoints as the logical nodes.
(Mental processes, of observing a logical node with a question, considering the data and the question, and making a judgement as to whether to answer yes or no. MPEP 2106.04(a)(2) Section III. This limitation does not require any element for which the human mind is not equipped.)
4. The biological information reasoning apparatus of claim 3, wherein in the species identification key set, only one yes or no logical connection is derived from each logical node, the logical node receives connections from multiple logical connections, the chain connection of logical connections is acyclic connection, and only one biological species is assigned to one endpoint.
(Mental process of describing the structure of the key set and connections to be considered as part of answering the question of claim 3. MPEP 2106.04(a)(2) Section III. This step does not require anything for which the human mind is not equipped.)
5. The biological information reasoning apparatus of claim 3, wherein the identification key database comprises:
a logical node table having a logical node unique number and a logical node text; and
a logical connection table having a biological species unique number and a logical connection graph of logical node numbers.
(EIA- data gathering limitation, describing the data collected by the identification key database.)
6. The biological information reasoning apparatus of claim 5, wherein the reasoning system reasons a feature information of an organism with the logical node text of the logical node table, and the species unique number and the logical connection graph of the logical connection table.
(Mental process of reasoning, or considering information about an organism with previous mental processes of observation, consideration or comparison, and judgement. MPEP 2106.04(a)(2) Section III. This step does not require anything for which the human mind is not equipped.)
7. A biological information reasoning method being performed by a biological information reasoning apparatus utilizing a species identification, comprising:
(Preamble: setting forth a method, and implying or the intended use of a computer-based apparatus “utilizing” species identification. The apparatus “uses” “species identification” which is not a tangible element of an apparatus. The apparatus has no parts, processors, inputs/outputs, memory or other typical limitations.)
transmitting a user question from a user terminal to a recommendation system;
(EIA- routine data transmission within an implied computing system. The transmission has no particular aspects, beyond what it transmits: a question. This also an EIA: a step of Data Gathering, which gathers the question to be answered. The “recommendation system” has no tangible embodiments, such as input/ output, display, memory, processors, et al.)
analyzing, by the recommendation system, the user question;
(Mental process of analysis: which requires mental processes of observation, comparison, and judgement. The point of the analysis is not identified. The nature of the question is unlimited. This limitation does not require any element for which the human mind is not equipped. MPEP 2106.04(a)(2) Section III. The “recommendation system” has no tangible embodiments, such as input/ output, display, memory, processors, et al.)
driving a reasoning system when a relevant biological system information exists as a result of the analysis exists;
(Mental Process of reasoning, which requires observation of “relevance”, comparison and judgement. The relevance is not specified. The particular result of the analysis is not provided. MPEP 2106.04(a)(2) Section III. This limitation does not require any element for which the human mind is not equipped. The “when a relevant biological system information exists” is a contingent limitation. MPEP 2111.04. There is no consequence for a lack of relevance.)
searching for, by the reasoning system, a similar identification key from an identification key database; and
(Mental Process of observing data, and making comparisons with “similar identification key information” to make a judgement. MPEP 2106.04(a)(2) Section III This limitation does not require any element for which the human mind is not equipped.)
linking to a biological system information database and recommending a related biological system.
(Mental process of data annotation within the database, and a mental process of judgement and recommendation. MPEP 2106.04(a)(2) Section III. This limitation does not require any element for which the human mind is not equipped.)
8. The biological information reasoning method of claim 7, wherein in the case of linking to the biological system information database, a part and organ of the biological relationship according to a biological system information causal model of the biological system information database have an additional connection relationship with species identification key information, and an ecological behavior of the ecological relationship has an additional connection with the species identification key
information.
(Mental process of observation of additional connected relationships in the data structure when carrying out the data annotation. MPEP 2106.04(a)(2) Section III. This limitation does not require any element for which the human mind is not equipped.)
9. The biological information reasoning method of claim 7, wherein the identification key database hierarchically manages a set of questions of the species identification key set, wherein the species identification key set consists of DAG graphs.
(EIA Data gathering limitation describing the data gathered in the database.)
10. The biological information reasoning method of claim 9, wherein the species identification key set comprises a logical node, which is a logical question that can be answered with yes or no; and
a chain of logical connections of the logical questions, wherein the species identification key set comprises one starting point and a plurality of endpoints as the logical nodes.
(EIA- Data Gathering: describing the data gathered by the identification key set.)
11. The biological information reasoning method of claim 10, wherein in the species identification key set, only one yes or no logical connection is derived from each logical node, the logical node receives connections from multiple logical connections, the chain connection of logical connections is acyclic connection, and only one biological species is assigned to one endpoint.
(EIA- Data Gathering: describing the data gathered by the identification key set.)
12. The biological information reasoning method of claim 11, wherein the identification key database comprises:
a logical node table having a logical node unique number and a logical node text; and
a logical connection table having a biological species unique number and a logical connection graph of logical node numbers.
(EIA- Data Gathering: describing the data gathered by the identification key set.)
13. The biological information reasoning method of claim 12, wherein the reasoning utilizes the logical node text of the logical node table and the species unique number and the logical connection graph of the logical connection table to reason a feature information of an organism.
(Mental Process of considering certain text, numbers and connections within the reasoning step, to make comparisons and judgements as to the feature information of an organism. MPEP 2106.04(a)(2) Section III. This limitation does not require any element for which the human mind is not equipped.)
With respect to step 2A (2): NO, the claims do not integrate any JE into a practical application (MPEP 2106.04(d)). The claimed additional elements are analyzed alone, or in combination to determine if the JE is integrated into a practical application (MPEP 2106.05(a-c, e, f and h)).
Claim(s) 1, 2, 5, 7, and 9-12 each recite the additional non-abstract element(s) of data gathering, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantec Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Dependent claim(s) 3, 4, 6, 8 and 13 each recite(s) an abstract limitation to the JE reciting additional mathematic concepts, or mental processes. Additional abstract limitations cannot provide a practical application of the JE as they are a part of that JE.
In combination, the limitations of data gathering, for the purpose of carrying out the JE, using a general-purpose computer merely provide extra-solution activity, and fail to integrate the JE into a practical application.
With respect to step 2B: NO. The claims recite a JE, do not integrate that JE into a practical application, and thus are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). The additional elements were considered individually and in combination to determine if they provide significantly more than the judicial exception. (MPEP 2106.05.A i-vi).
With respect to claim(s) 1, 2, 5, 7 and 9-12: The limitation(s) identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception.
Lally (2017) provides elements meeting the BRI of “key collection units” which comprise DAG, tabular data, decision trees and questions.
Hamilton (2018) provides elements meeting the BRI of key collection units, which comprise DAG, tabular data, decision trees and questions.
Brumel (2019) provides elements meeting the BRI of key collection units, which comprise DAG, tabular data, decision trees and questions.
Aravamudan (2018) provides elements meeting the BRI of key collection units, which comprise DAG, tabular data, decision trees and questions.
These elements meet the BRI of the identified data gathering limitations. As such, the prior art recognizes that this data gathering element is routine, well understood and conventional in the art (as in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook).
In the specification at [p42] it is disclosed that the key collection unit collects DAG graphs, wherein the DAG graphs have no particular characteristics. At page 45, the key collection unit collects the following data “logical node” data that is a “logical question that can be answered with “yes” or “no”, “chain” of yes/no connections, an initial node, and a final endpoint. In the specification at [p3 and p46] it is disclosed that the identification database is a database or table (or sets of databases and/or tables) comprising a set of questions, and logical node table/ number/ text (specification p3), and hierarchical information related to the identification keys. The database has no particular features, beyond the description of the data it is configured to manage. P46 of the specification indicates the database comprises tables and columns. The specification refers to public databases of species information such as National Institute of Biological Resources, and Delta Project’s IntKey to provide some key information.
Activities such as data gathering do not improve the functioning of a computer itself, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not effect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
Dependent claim(s) 3, 4, 6, 8 and 13 each recite a limitation requiring additional mathematic concepts or mental processes. Additional abstract limitations cannot provide significantly more than the JE as they are a part of that JE (MPEP 2106.05).
In combination, the data gathering steps providing the information required to be acted upon by the JE, performed in a generic computer or generic computing environment fail to rise to the level of significantly more than that JE. The data gathering steps provide the data for the JE, which is carried out by the general-purpose computers. No non-routine step or element has clearly been identified.
The claims have all been examined to identify the presence of one or more judicial exceptions. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether the additional limitations integrate the judicial exception into a practical application. Each additional limitation in the claims has been addressed, alone and in combination, to determine whether those additional limitations provide an inventive concept which provides significantly more than those exceptions. For these reasons, the claims, when the limitations are considered individually and as a whole, are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. This is not a rejection; this is an analysis.
Such claim limitation(s) is/are:
In claims 1 and 7 the generic placeholder is “a biological information reasoning apparatus”, and the specialized function is: “utilizing a species identification”.
In claim 1 the generic placeholder is “identification key collection unit”, and the specialized function is: “collecting a species identification key set”.
In claim 1 the generic placeholder is “identification key database”, and the specialized function is: to “hierarchically managing a set of questions”.
In claims 1 and 7 the generic placeholder is “a recommendation system”, and the specialized function is: “recommending a biological system”.
In claims 1 and 7 the generic placeholder is “a reasoning system”, and the specialized function is: “reasoning characteristics of the biological system”.
Dependent claims recite aspects of the same generic placeholders.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 USC 112(b) or 112 (second paragraph) as they fail to particularly point out and distinctly claim the subject matter which applicant regards as his invention.
As set forth above, claim limitations identified above invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
In claims 1 and 7 the generic placeholder is “a biological information reasoning apparatus”, and the specialized function is: “utilizing a species identification”.
Claims 1 and 7 fail to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing that specialized function. The recited “reasoning apparatus” has no elements characteristic of any particular apparatus. The limitation fails to set forth how any species identification information is “utilized” by the apparatus. It is entirely unclear what the apparatus physically comprises, in addition to the software and data collected. The point of the method is not identified such that one of skill could possibly identify any way to use “species identification” or identify the particular data, and algorithmic processes required. “Utilizing” does not set forth any particular way it is intended to be used, nor does it provide any structure, or step-by-step directions. While breadth of the claim is not the same as indefiniteness, one of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result: a recommendation. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 1 the generic placeholder is “identification key collection unit”, and the specialized function is: “collecting a species identification key set”.
Claim 1 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing that specialized function. The “identification key collection unit” has no particular limitations as to how it particularly collects data, and what data, particularly is to be collected. The collection unit is not linked to any particular algorithm, process, or set of step-by-step instructions which provide the appropriate data for carrying out the method and providing the desired result. While breadth of the claim is not the same as indefiniteness, one of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. It is entirely unclear what particular identification key data is required for carrying out the claimed method, where to find that data, and how, in particular, to collect it for the “identification key database” of the next step. There are no steps of acquiring particular data, formatting data, or performing particular data transformations. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claim 1 the generic placeholder is “identification key database”, and the specialized function is: to “hierarchically managing a set of questions”.
Claim 1 fails to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing that specialized function. It is entirely unclear how a database, structured as tables with columns, in DAG “manages” any information. Generically described databases, such as the “identification key database” have the plain meaning of acting to store data. While breadth of the claim is not the same as indefiniteness, one of skill would not be apprised as to what particular functions Applicant applies to “manage” the collected data, beyond storage. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claims 1 and 7 the generic placeholder is “a recommendation system”, and the specialized function is: “recommending a biological system”.
Claims 1 and 7 fail to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing that specialized function. The recommendation system has no particular elements, algorithms, or step-by-step instructions as to how the data at hand is to be acted upon to provide any specific recommendation. The claims fail to set forth any particular way to look at the data, and make any conclusion as to what the user intends. It is entirely unclear how to identify a biological species or system based solely on the limitations of claims 1 or 7. One of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
In claims 1 and 7 the generic placeholder is “a reasoning system”, and the specialized function is: “reasoning characteristics of the biological system”.
Claims 1 and 7 fail to particularly point out and distinctly claim the algorithms, structures, or step-by-step instructions for performing that specialized function. The reasoning system has no particular tracks of reasoning from which one could ascertain any “characteristics” of any “biological system” or “biological species.” The reasoning system has no particular steps of comparison, or algorithmic calculations which would “drive” the system through the (implied) DAG or tables to reach an answer, or identify additional information linked to that answer. It is entirely unclear how to achieve the desired goal. While breadth of the claims is not the same as indefiniteness one of skill would not be apprised as to what particular functions Applicant applies to achieve the desired result. While the claims are read in light of the specification, limitations from the specification cannot be read into the claims.
The dependent claims which recite the same generic placeholders also fail to provide the specific algorithms, or specific functions to be carried out.
Dependent claims 2-4 and 10-12describe the data collected for the information key set, but not how to search for and collect that data. Some aspects of claims 2-4 appear to describe the structure of the key identification database but the claims state they are modifying the identification key set.
Dependent claims 5 and 9 set forth some structure of the database, however it fails to set forth how any of the structure “manages” any data, as required.
Dependent claim 6 further describes data to be collected, but not now the data is to be collected or managed.
Dependent claim 8 further describes information about a database, and that it may be linked to the method, but not how it is linked, nor how any additional connections are realized, nor how any connections affect the final recommendation.
Dependent claim 13 states that the reasoning “utilizes the logical node text… to reason a feature information…” however it fails to set forth how any text is acted upon to derive any further specific information.
The claims overall use results-based language which fail to set forth what is required, and fail to identify how the desired results are to be achieved.
MPEP 2181.II.B: “For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (b). See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008).” Additionally, “To claim a means for performing a specific computer-implemented function and then to disclose only a general-purpose computer as the structure designed to perform that function amounts to pure functional claiming. Aristocrat, 521 F.3d 1328 at 1333, 86 USPQ2d at 1239.” Finally, “Mere reference to a general-purpose computer with appropriate programming without providing an explanation of the appropriate programming, or simply reciting "software" without providing detail about the means to accomplish a specific software function, would not be an adequate disclosure of the corresponding structure to satisfy the requirements of 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph. Aristocrat, 521 F.3d at 1334, 86 USPQ2d at 1239...”
Therefore, claims 1-13 are indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claims 1-13 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. This is a written description rejection.
The function of the written description requirement is to ensure that the inventor had possession of the specific subject matter later claimed as of the filing date of the application relied on... In re Herschler, 591 F.2d 693, 700-01, 200 USPQ 711, 717 (CCPA 1979), further reiterated in In re Kaslow, 707 F.2d 1366, 217 USPQ 1089 (Fed. Cir. 1983); see also MPEP §§ 2163 - 2163.04.
Original, amended, or new claims are each given their broadest reasonable interpretation in light of, and consistent with the written description of the invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or how the result is to be achieved. In other words, the algorithm or steps or procedures taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed to achieve the desired results. See MPEP §§ 2163.02 and 2181, subsection IV.
Functional claim limitations may be adequately described if: (1) The written description adequately links or associates adequately described particular structure, material, or acts to perform the function recited; or (2) it is clear based on the facts of the application that one skilled in the art would have known what specific structure, material, or acts disclosed in the specification perform the specialized function. See Aristocrat Techs. Australia PTY Ltd. v. Int’l Game Tech., 521 F.3d 1328, 1336-37, 86 USPQ2d 1235, 1242 (Fed. Cir. 2008)
Whether the specification shows that applicant was in possession of the claimed invention is not a single, simple determination, but rather is a factual determination reached by considering a number of factors. For example, in Atmel Corp. v. Information Storage Devices, Inc., 198 F.3d 1374, 1380[, 53 USPQ2d 1225, 1230] (Fed. Cir. 1999), the court embraced the proposition that ‘consideration of the understanding of one skilled in the art in no way relieves the patentee of adequately disclosing sufficient structure in the specification.’ It is not enough for the patentee simply to state or later argue that persons of ordinary skill in the art would know what structures to use to accomplish the claimed function. The court in Biomedino, LLC v. Waters Technologies Corp., 490 F.3d 946, 953[, 83 USPQ2d 1118, 1123] (Fed. Cir. 2007), put the point this way: "The inquiry is whether one of skill in the art would understand the specification itself to disclose a structure, not simply whether that person would be capable of implementing that structure."
An invention described solely in terms of a method of its desired function lack written descriptive support where there is no described or art-recognized correlation between the disclosed function and the structure(s) responsible for the function. The description needed to satisfy the requirements of 35 U.S.C. 112 "varies with the nature and scope of the invention at issue, and with the scientific and technologic knowledge already in existence." Capon v. Eshhar, 418 F.3d at 1357, 76 USPQ2d at 1084. For inventions in emerging and unpredictable technologies, or for inventions characterized by factors not reasonably predictable which are known to one of ordinary skill in the art, more evidence is required to show possession.
Considering claims 1-13, the claimed technology of analyzing a question to the end of “recommending a biological system” is considered in the art to be unpredictable.
The level of the skill and knowledge of one skilled in the art of bioinformatics is high. Bioinformatics combines biological and technical knowledge with skills related to computers and sophisticated data analysis. In particular, the prior art of record relating to the claimed biological reasoning technology illustrates the high levels of skill and inventive decision making required in the art, the unpredictable nature of the technology, and underscores the requirement for a higher level of disclosure required.
For a computer-implemented 35 U.S.C. 112(f) claim limitation, the specification must disclose an algorithm for performing the claimed specific computer function... See Net MoneyIN, Inc. v. Verisign. Inc., 545 F.3d 1359, 1367 (Fed. Cir. 2008). This includes the particular disclosure of the algorithms, structures, or step-by step processes specifically linked to particular specialized functions in the claims. MPEP 2181: The structure disclosed in the written description of the specification is the corresponding structure only if the written description of the specification or the prosecution history clearly links or associates that structure to the function recited in a means- (or step-) plus-function claim limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. See B. Braun Medical Inc., v. Abbott Laboratories, 124 F.3d 1419, 1424, 43 USPQ2d 1896, 1900 (Fed. Cir. 1997).
In order to practice the claimed invention one of skill in the art must create a biological reasoning apparatus/ system, using sets of information which allegedly discriminate between all species of living things, all biological systems, and all biological events. One must also collect and collate sets of informative questions which can be answered by the reasoning apparatus, such that the desired result, a recommended system/species, is achieved. The actual calculations, algorithms, or steps for specifically performing the steps and specialized functions of the claims are not clearly provided by the disclosure. While the specification provides some algorithms or flowcharts related to one or more processes or calculations, there is not a clear basis linking specific algorithmic processes to specific steps within the entire scope of the claim, and an issue exists as to whether the disclosure is adequate to perform the entire claimed function(s).
The specification provides the goal, that the invention relates to making biological inferences, using biological species identification, and a biological reasoning apparatus at p1. A high level, generic description of the invention is provided by the summary at p2-5.
The reasoning apparatus/ system does not have an overall theme, such as: recommending a species to be used in an experiment, vs recommending that a sample be tested for the recommended species possibly infecting a patient vs recommending a species which reduces biofilms on boat hulls vs recommending a species of goat to forage in a specific climate zone.
Generic description of possible data, possible interpretations, possible applied algorithms and possible outcomes are set forth in the Summary. The summary sets forth that information sufficient to identify the species of an organism is to be collected, however does not set forth or identify what, exactly that information is or must comprise. The database comprises sets of DAG, without specifying how the DAG is structured, to store and manage the data sufficient to discriminate between all species of all lifeforms, all biological systems and all biological events. The DAG graphs may comprise binary decision trees, however, how the information is applied to the trees which are then applied to the DAG is not set forth. Additional information about organisms is linked to the decision trees/DAG, or to the species identification key, without particularity as to what the information is, or how it affects any recommendation. The set of questions to be managed is mentioned, but not elaborated upon.
Figures 1-9 provide flow charts and other black box depictions) which provide only the same results-based language of the claims, and do not link specific algorithms or step by step directions to any particular limitation of the claims. Figure 1 depicts an overall view of the units, databases, and possible directions of information flow, however not all the elements depicted in Figure 1 are present in the independent claims. Figure 3 depicts a generalized flowchart for answering an unspecified question, following the depiction of Figure 2, however, the data required for each dataset, the reasoning behind each answer, and how the results are achieved by “generate an out network graph”, are not provided. This flowchart describes elements not present in the independent claims. Figure 4 depicts a possible database structure. Figure 5 appears to represent a screenshot of a possible embodiment using a GUI. It appears to include a literature citation, images, a graph of known activities, and a scientific name of a species- none of which are required or provided for the independent claims. Fig 6 depicts a generic computer setup with a user interface. Fig 7 is a high-level, generic flowchart of the idea of the invention. Figure 8 provides an example flowchart, where the “topic” provided by the user appears to be “Gymnosperms” with no other aspect to the topic or area of questioning. Two levels of additional questions are provided, with examples of possible answers by the reasoning system. However, the actual data and reasoning required to make the species key identification for this example are not provided, nor clearly required by the independent claims. Figure 9 depicts a generic example of a DAG, a directed acyclic graph, with no particular invention-related information applied to it, nor does it indicate how the directionality is determined, what data is required, what questions can be asked, and what the answers are intended to encompass.
The Detailed Description provides generic characterizations of the elements of the system starting at page 6. Biological system information, required to make up the species identification key:
“… specifies physical phenomena, biochemical phenomena and so on in an individual organism that is a subject of mimicking and application as physical relations, ecological relations, and biological relations. Biological system information can be extended an interaction between entities or an interaction between a plurality of species.” (p8)
However, this does not specifically identify what biological, physical, biochemical, or ecological data is necessary and sufficient to carry out the claimed invention. The specification continues on p8 to note that:
“… biological system information can encompass biological phenomena in individual organism or interactions between organisms or species in order for designers to conceive various ideas in wider range. For example, if biological information about European-starling having an enzyme that can catalyze alcohol decomposition for alcohol detoxification is stored and managed, a designer who is trying to develop a product for catalyzing alcohol decomposition can access the information management device 110 by using the information using device 150, and can retrieve and utilize information about European starling by searching biological system information about catalyzing alcohol decomposition.”
The claims do not clearly require any information particular to biochemical reactions such as “catalyzing alcohol decomposition” nor do the claims clearly require any other specific aspects or specific information about every known and named biological species, every biological system and every biological or biochemical event. The description carries on with various sub units possibly incorporated into the reasoning system which are not required for the independent claims. For example, it appears the “document gathering unit” is critical to achieve at least the results of Figure 5: where a literature citation is provided, along with certain information from that citation. The claims do not set forth using literature citations or a document gathering unit, to provide the required information. The specification states that:
“The document gathering unit 112 collects BS (Biological structure) documents constituting of natural language. BS documents may be, for example, natural- language based HTML document arranged by biologists. Of course, author or type of BS documents should not be limited to the aforementioned, but any documents available for categorizing physical relations, ecological relations, and/or biological relations, and creating a causal model would be enough.” P9
The claims do not require any of this specific information, nor how to judge whether a citation is useful for creating causal models for any given species, any given biological system, any given biological event, or any given question. Various named ontologies, taxonomies, dictionaries or terms are allegedly incorporated into the invention, but are not a part of the independent claims. Use of named programs such as “SCRAPY parsing unit” are allegedly applied, with no details as to what parameters were used or modified, nor how the program acts on the data at hand to achieve any result. (p9-10)
The specification names a prior art causality model allegedly used in the reasoning apparatus, but it is not clearly defined, nor claimed by the independent claims, nor does the specification set forth how this SAPPhIRE model acts on any of the data provided by the claims, to reason out any particular answers. (p10). The citation of a program, without a source, without parameters and without how it was particularly used is akin to citing a trademark as the source of goods, but it doesn’t describe the goods themselves. This model is allegedly intended to be a part of the “index processing unit” which apparently works with the “document processing unit” and the “term dictionary database” and the unspecified indexes.
“… indexes each of physical relations and ecological relations among biological system of the organism based on terms representing function, material, energy, and/or signal respectively. Biological system information may be derived from a triple form of subject predicate- object, but, as shown in FIG. 2, may be structured to combine physical relations, ecological relations and/or biological relations that represent a mechanism of the organism and a causality manifested through the mechanism.” (p10-11)
None of these elements are required by the claims.
Continuing with the specification, a description of Fig 2 is set forth that discusses elements of the figure, but not how each works, and not what data is necessary and sufficient to answer any question about any known species, any known biological system or any known biological event. Page 12 attempts to illustrate the invention using the information about the European Starling, to “develop an alcohol addiction treatment” using “an ecological relation”. Table 1 is a mock-up of index terms and how they may be indexed in the European Starling example, however, it fails to clarify or support the full scope of the claims. Table 2 allegedly sets forth characteristics of the European Starling, and index terms related to the characteristics. However, these data are not clearly provided by the claims for all organisms and all species of those organisms, nor are appropriate terms for all biological systems, or all biological/ biochemical events provided. Categories such as “air resistance” are not applicable or useful for all organisms, systems, or events. The reasoning apparatus does not suggest the actual treatment for alcohol addiction. It doesn’t clearly suggest an ecological relationship system. Page 15 provides some insight into some of the additional information required, in that every organism must be indexed for each organ and part of the organism, however the specification does not provide this information for all organisms, nor does it provide a data structure able to carry out such indexing.
Some language processing instructions are provided for using the index terms in the causality model of the reasoning unit at pages 16-19. However, actual language processing is not required by the independent claims. Additionally, the disclosure fails to set forth how these language processing syntaxes are to be particularly applied to the question, the data at hand, the indexes and the additional information to provide the final desired result. These appear to be standard NLP syntax arrangements for causality. The reasoning unit may comprise a “similarity assessing unit” according to p19; the basis of the similarity is not clearly defined, nor is it clear how it is to be assessed over the genus of all known and named species, all biological systems and all biological/ biochemical events. Multiple additional “units” are described but not required by the claims. P20-21 generically describe tokenizing data from the query. This requires the use of natural language processing (NLP), the query parsing unit, terms in the term dictionary database, the information management device: none of which are required by the independent claims. Various ways data could be indexed, or could have causal relationships identified are set forth, with no particularity, at p22-24. Actions possibly taken by the causality model are discusses with respect to the canvas unit, and the similarity unit are discussed ag p25-30. “n” number of biological systems about each organism are stored. The similarity unit allegedly employs Term Frequency-Inverse Document Frequency, a prior art known document comparison strategy, for comparing the indexed terms for each topic.
There are no limits to the organisms, such that they include all organisms; there are no limits to the systems associated with all organism, there are no limits to any biological or biochemical events. The claims encompass all knowledge about all organisms, without providing the particular data structures required to store and manage that information.
Additional generic NLP processes are described at p31. The claims do not explicitly require any NLP process. Example of “measuring derivativity” for certain indices or terms are set forth at p32-37. These generic examples are not clearly linked to any part of the independent claims, nor are they dispositive for discriminating between all organisms, or for reasoning the suggestion of a particular organism or system.
The specification addresses some aspects of species identification beginning at p38:
“Species identification refers to the act of revealing the scientific name and common name of an organism to which taxon it belongs. In other words, it refers to the act of clarifying the collected unknown organism is. Identifying the fossils found, or the pollen collected is also called as identification. From the academic point of view, the species identification key in the current biology field is being used to accurately identify the scientific names ('species' and 'genus' names) of observed organisms in nature, and objectively (scientifically) clearly identify what kind of organism the discovered organism is.”
The specification further notes:
“The species identification key is a set of questions used to identify the biological species. In general, the species identification key is a set of questions with a hierarchical structure. The questions used for identification are called 'identification keys'. By answering the identification key, it will be possible to identify the organism. For example, if you want to specify what kind of shrimp the discovered shrimp is, you can answer the 'identification key' with a set of species identification keys for shrimp (the number of the identification key for shrimp is 148). The identification key was generally used in the form of step-by-step questions of a tree structure. However, recent advances in genetics and molecular biology have made it possible to identify species more accurately, and thus the species identification key has become more complicated.”
“Therefore, although the structure is more complicated than the decision graph of the old tree structure, it has an acyclic structure so that one specific organism can be specified in the end (a tree-structured decision graph is a type of DAG graph). Therefore, by answering a series of questions (identification keys), it becomes finally possible to specify the type of organism (like twenty questions, according to the answers to the questions, the possible types of living organism can be narrowed), which is the scientific basis for making the researchers to specify the name of the observed organism.” P39.
However, those questions which definitively identify every organism, system or event encompassed by the claims are not provided, nor clearly claimed. Further, there is no clear way to incorporate any of the genetic information, or molecular biology information, into the structures of the independent claims. The specification refers to public databases of species information (National Institute of Biological Resources, Delta Project’s IntKey), without setting forth what information from these sources is necessary and sufficient to generate the discriminatory questions for every organism, for every possible biological or biochemical system, aspect, or question. The specification appears to acknowledge this at p43, but provides no solution, nor the data required:
“The species identification key has not yet been unified internationally…. An identification key repository or database for all biological species has not yet been completed…. In addition, the scale of the identification key set is not unified for each biological group. Therefore, they are bound to be different. Some types of identification key sets are for an entire 'Order', and some types of identification key sets are limited to specific 'Genus'.”
The keys required for each organism are not known are not all the same, and are not provided by the specification. Which “Genus” requires which key, and how does one of skill know what keys to collect? If they are all expected to be different, there is no expectation that any one data structure will provide all the necessary and sufficient data, instructions, and processing sufficient for the scope of the claims: all questions about all biological aspects of all organisms.
The examples of the disclosure comprise a variety of steps and algorithms not clearly required in the independent claims to achieve the desired goals. Therefore, there is no clear linkage between this disclosure and each required specialized function. The claims fail to set forth the minimally sufficient set of steps required for achieving the specialized function, to meet the ultimate goal of the invention. The disclosure fails to link specific algorithms, structures or step-by-step instructions to the specialized function(s) and therefore the claims lack sufficient written description for the specialized functions.
The state of the art of biological reasoning models and NLP technology can be represented by Zadeh, Brumel, Lally, and/or Hamilton.
Zadeh et al. (US 2018/0204111 A1) attempts to model big data, including:
“new algorithms, methods, and systems for artificial intelligence the first application of General-AI (versus Specific, Vertical, or Narrow-AI) (as humans can do); addition of reasoning, inference, and cognitive layers/engines to learning module/engine/layer; soft computing; Information Principle; Stratification; Incremental Enlargement Principle; deep-level/detailed recognition… relationship, position, pattern, and object); Big Data analytics; machine learning; crowd-sourcing; classification; clustering; SVM; similarity measures; Enhanced Boltzmann Machines; Enhanced Convolutional Neural Networks; optimization; search engine; ranking; semantic web; context analysis; question-answering system; soft, fuzzy, or un-sharp boundaries/ impreciseness/ ambiguities/ fuzziness in class or set, e.g., for language analysis; Natural Language Processing (NLP); Computing-with-Words (CWW); parsing; machine translation… medical diagnosis; genetics; drug discovery; biomedicine; data mining; event prediction…” (abstract)
Each of these activities requires specific data, specific data structures, specific algorithmic and statistical operations, to answer specific questions.
Brumel et al. (US 2018/0018375 A1) provides hierarchical execution of questions or queries to a database comprising nodes, edges and a directed acyclic graph.
“Structural grouping is applied to relational algebra to provide concise syntax to express a class of useful computations. Algorithms are also provided to evaluate such structural groupings efficiently by exploiting available indexing schemes.”
The specific syntax, algorithms and specific data required are used to organize and query specific topics. For each topic, differing data and differing processes are required. The role of pattern matching or similarity indices, and the ability to discriminate between closely aligned “species” is discussed at length, indicting how and when pattern matching provides relevant results.
Hamilton (2018 NeurIPS) discloses known problems in embedding logical queries on knowledge graphs, particular when complex relationships, such as biological relationships, are to be analyzed.
“Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict “what drugs are likely to target proteins involved with both diseases X and Y?” a query that requires reasoning about all possible proteins that might interact with diseases X and Y…. Valid answers to such a query correspond to subgraphs. However, since any edge in this biological interaction network might be unobserved, naively answering this query would require enumeration over all possible diseases.”
The difficulty with the amount of information, the amounts of connections, and the possible queries is addressed by Hamilton:
“In general, the query prediction task—where we want to predict likely answers to queries that can involve unobserved edges—is difficult because there are a combinatorial number of possible queries of interest, and any given conjunctive query can be satisfied by many (unobserved) subgraphs (Figure 1). For instance, a naive approach to make predictions about conjunctive queries would be the following: First, one would run an edge prediction model on all possible pairs of nodes, and—after obtaining these edge likelihoods—one would enumerate and score all candidate subgraphs that might satisfy a query. However, this naive enumeration approach could require computation time that is exponential in the number of existentially quantified (i.e., bound) variables in the query [12].”
Hamilton uses the idea Graph Query Embeddings, which appear to be similar to the Species Identification Keys of the claims. Hamilton applies their solution to specific topics:
“new interactions in a biomedical drug interaction network (e.g. “predict drugs that might treat diseases associated with protein X…”).
Lally (2017, AAAI) provides WatsonPaths, using the IBM Watson as a basis to infer answers using unstructured information.
“The main challenges for a factoid question-answering system are retrieving the correct document, and then extracting the correct answer from the document. At the core of Watson’s question answering is a suite of algorithms that match passages containing candidate answers to the original question.” (p59)
In particular, scenario-based questions can be more difficult to answer.
“In these types of questions, it is not generally the case that the answer and supporting evidence can be contained in one document. Rather, for many scenario-based questions, information from multiple documents and other sources must generally be retrieved and then integrated to answer the questions properly. Furthermore, we must often apply general knowledge to a specific case, as in a medical scenario about a patient.”
Lally describes WatsonPaths as:
“a system that builds on Watson to answer scenario-based questions. The core idea is to break the question down into parts, over which we can ask and answer factoid subquestions using Watson, then integrate these answers into a graphic model that can be used to answer the larger scenario-based question.” P60-61.
The particular data and algorithmic structures of Watson, the particular parsing of questions, the generation of particular questions and subquestions are all critical to obtaining the correct answer.
The skilled practitioner would first turn to the instant specification to identify the specific algorithms or steps required by the specialized functions recited by the claims. However, the instant disclosure does not provide a written description of those specialized functions, and fails to link the particular algorithms or processes disclosed by the specification to specific functional limitations of the claims. As such, the skilled practitioner would turn to the prior art for guidance as to any known correlations between the disclosed structures or algorithms, and the specialized functions of the claims, however, the prior art shows that significant levels of detailed data, detailed language processing, detailed and specific algorithmic and statistical processes are required, all dependent on the question to be answered. As such, the claims lack adequate written description.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 9-13 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Hamilton (2018).
Hamilton, W. L. et al. (2018) Embedding Logical Queries on Knowledge Graphs. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018) Montreal Canada, 12 pages.
Hamilton is directed to: “a framework to efficiently make predictions about conjunctive logical queries—a flexible but tractable subset of first-order logic—on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.”
With respect to claim 1 and:
“A biological information reasoning apparatus utilizing a species identification, comprising:
an identification key collection unit configured for collecting a species identification key set;”
Hamilton provides a unit that collects and embeds key species identification information as Graph Query Embeddings (GQE), which comprise DAG, and tabular data, and decision trees for answering questions.
With respect to Claim 1 and:
“an identification key database configured for hierarchically managing a set of questions of the collected species identification key set;”
Hamilton provides a database structure, with hierarchical structure and sets of questions for the GQE. See Fig 2, and p3:
“The primary distinction between our work and probabilistic databases is the following:
Whereas probabilistic databases take a database containing probabilistic facts and score queries, we seek to predict unobserved logical relationships in a knowledge graph. Concretely, a distinguishing challenge in our setting is that while we are given a set of known edge relationships (i.e., facts), all missing edge relationships could possibly be true…
“Example1: Drug interactions (Figure 2.a). A knowledge graph derived from a number from public biomedical databases (Appendix B). It consists of nodes corresponding to drugs, diseases, proteins, side effects, and biological processes. There are 42 different edge types, including multiple edge types between proteins (e.g., co-expression, binding interactions), edges denoting known drug-disease treatment pairs, and edges denoting experimentally documented side-effects of drugs. In total this dataset contains over 8 million edges between 97,000 nodes.”
With respect to claim 1 and: “a recommendation system configured for recommending a biological system according to a user's question through a user terminal; and”
Hamilton provides Query processing, and elements meeting the BRI of recommendation systems:
“For a query to be valid, its dependency graph must be a directed acyclic graph (DAG), with the anchor nodes as the source nodes of the DAG and the query target as the unique sink node. The DAG structure ensures that there are no contradictions or redundancies. Note that there is an important distinction between the query DAG, which contains variables, and a subgraph structure in the knowledge graph that satisfies this query, i.e., a concrete assignment of the query variables (see Figure 1).” P4
“we seek to discover or predict unobserved relationship and not just answer queries that exactly satisfy a set of observed edges. Formally, we assume that every query q ∈ Q(G) has some unobserved denotation set q that we are trying to predict, and we assume that q is not fully observed in our training data. To avoid confusion on this point, we also introduce the notion of the observed denotation set of a query, denoted q train, which corresponds to the set of nodes that exactly satisfy q according to our observed, training edges. Thus, our goal is to train using example query answer pairs that are known in the training data, i.e., (q,v∗),v∗ ∈ q train, so that we can generalize to parts of the graph that involve missing edges, i.e., so that we can make predictions for query-answer pairs that rely on edges which are unobserved in the training data (q,v∗),v∗ ∈ q \ q train.” P4
Further, section 4 sets forth the overall structure of the system, and the core Algorithm of their process. In the section of Query inference, p6 and Theoretical Analysis, p6-7 additional details are provided.
“To generate a query embedding, we start by projecting the anchor node embeddings according to their outgoing edges; then if a node has more than one incoming edge in the query DAG, we use the intersection operation to aggregate the incoming information, and we repeat this process as necessary until we reach the target variable of the query.” P6.
With respect to claim 1 and: “a reasoning system configured for reasoning characteristics of the biological system from the set of questions of the collected species identification key set regarding a species corresponding to the biological system and a pre-built information system of biological system.”
Hamilton provides reasoning systems for reasoning characteristics at Section 5: experiments.
“To test our approach, we sample sets of train/test queries from a knowledge graph, i.e., pairs (q, v∗), where q is a query and v∗ is a node that satisfies this query. In our sampling scheme, we sample a fixed number of example queries for each possible query DAG structure (Figure 4, bottom). For each possible DAG structure, we sampled queries uniformly at random using a simple rejection sampling approach…” p7
“For a test query q we evaluate how well the model ranks a node v∗ that does satisfy this query
v∗ ∈ q compared to negative example nodes that do not satisfy it, i.e., vN /∈ q . We quantify this performance using the ROC AUC score and average percentile rank (APR).” p8
“This demonstrates that performing logical operations in the embedding space is not only more efficient, it is also an effective alternative to enumerating the product of edge-likelihoods, even in cases where the latter is feasible.” P9
“This shows that training on complex queries is a useful way to impose a meaningful
logical structure on an embedding space and that optimizing for edge prediction alone does not
necessarily lead to embeddings that are useful for more complex logical queries.” P9.
With respect to claim 7, the method of using the program/system of Hamilton is also described at the same places. The biological based example of Hamilton meets the BRI of all of the following:
“A biological information reasoning method being performed by a biological information reasoning apparatus utilizing a species identification, comprising:
transmitting a user question from a user terminal to a recommendation system;
analyzing, by the recommendation system, the user question;
driving a reasoning system when a relevant biological system information exists as a result of the analysis exists;
searching for, by the reasoning system, a similar identification key from an identification key database; and linking to a biological system information database and
recommending a related biological system.”
With respect to dependent claims 2 -4, 9-11 Hamilton uses DAG to embed the GQE meeting the BRI of the DAG and the logical nodes, questions and a path with one starting point and one ending point, and the questions can have yes/no answers.
With respect to dependent claims 5, and 12 The databases of Hamilton containing the GQE embeddings meet this limitation as they comprise logical node tables and logical node connections.
With respect to dependent claim 6, the specific “species” of drugs have ID numbers and connections.
With respect to dependent claim 13, the reasoning uses the logical node text, species ID and connections.
Claim(s) 1 -13 are is/are rejected under 35 U.S.C. 102a1 as being anticipated by Aravamudan et al (2018).
Aravamudan, M. et al. Systems, methods and computer readable media for visualization of semantic information and inference of temporal signals indicating salient associations between life science entities. US 2018/0082197 A1, published 3/22/2018.
Aravamudan is directed to:
“Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths.”
With respect to claim 1 and:
“A biological information reasoning apparatus utilizing a species identification, comprising:
an identification key collection unit configured for collecting a species identification key set;”
Aravamudan provides identification keys within the structure databases, at [0153]. The data collected can be from multiple sources including public databases with information about species, biochemicals drugs, actions, trials and events [0154-0163, 0165-0168]. Scraping of public literature and biomedical literature is disclosed at [0160]. These public sources comprise certain structured index terms or ontologies.
With respect to Claim 1 and:
“an identification key database configured for hierarchically managing a set of questions of the collected species identification key set;”
Aravamudan provides queries, and a database to manage queries in the sematic search system, such as described at [0164]:
“In some embodiments, a semantic search system can provide “summary answers” to a range of queries about the “temporal status” of drug or therapeutic entities. The temporal status can indicate the stage of development (e.g., preclinical, phase 1, phase 2, phase 3, marketed) of the drug. In some embodiments, the temporal status can be automatically mapped to ail “entity” and/or “intersection of one or more entities” in a semantic bio-knowledge graph (e.g., as shown in FIG. 8).”
At [0176] Aravamudan provides data structures and algorithms for storing and managing queries, such as known neural network structures:
“In some embodiments, the system store 114 can capture information extracted from two or more source paths (e.g., 103a and 105a) in different forms to facilitate the synthesis of information and/or enable subsequent information extraction through different pathways (e.g., pathways 103a and 105a). The system store 114 can include information stored in a structured semantic database 106 (which can be a traditional database); a knowledge graph(s) 107 (which can be directed graphs of labeled (extracted from both paths 101a and 102a) and/or unlabeled entities (extracted from the 102a path)); word embeddings 108 (which can include word(s) and/or sentence(s)), document/ paragraph/ sentence embeddings 109; and sequence representations of unstructured data 110. In some embodiments, an example of word embedding can be word2vec. In some embodiments, an example of document/ paragraph/ sentence embedding can be doc2vec. In some embodiments, an example of sequence representations 110 can be Memory Neural Network (MemNN). In some embodiments, MemNN can be used for “Question and Answer” style discovery, where MemNN can be trained on questions to generate responses/ follow-up questions. In some embodiments, these responses and/or follow-up questions can be used in case of ambiguity. For example, there may be ambiguity as to what an entity may refer to.”
With respect to claim 1 and: “a recommendation system configured for recommending a biological system according to a user's question through a user terminal; and”
Aravamudan provides a discrimination engine that includes dialog/ query analyzer elements, a response synthesizer, and response template generators et al. beginning at [0180].
“The system store 114 can power a discrimination engine 116 that can include a dialog/query analyser 111 (which can rely largely on sequence representations 110), a response synthesizer 112, and a response templates generator/chooser 115. The response template generator/chooser 115 can power user interfaces 113 through 116a. In some embodiments, the dialog/query analyzer 111 can analyze user input, such as a search term and filter criterion. For example, if a user searches the term “AML” on an interface (e.g., the interface in FIG. 8), the dialog/query analyzer 111 can receive and analyze this search term, and pass the search term to the response synthesizer 112 for further processing. In some embodiments, the dialog/query analyzer 111 can receive data from the system store 114 through 114a for the analysis function. The response synthesizer 112 can also receive data from the system store 114 through 114b, and use this data to synthesize responses that are relevant for producing results for the user's search action.
[0181] The response template generator/chooser 115 can generate/choose an appropriate template to be used for presenting search results to the user through an interlace. Different types of templates can be used to generate different types of bio-knowledge graphs, such as the bulbs eye bio-knowledge graph in FIG. 7 and, the pipeline bio-knowledge graph in FIG. 8. In some embodiments, the response template generator/chooser 115 can generate a template based on the labels for the entities that are being presented on an interface. These entities can be selected based, on their entity distribution. In some embodiments, the response template generator/chooser 115 can choose a template from a set of hard-coded templates. In some embodiments, a hard-coded template can be generated through training (e.g., a system can generate a template by learning certain types of entities and their labels from a corpus). In other embodiments, a hard-coded template can be manually generated. In some embodiments, a user can override a portion or all of the view in an automatically chosen/generated template. For example, a user can replace the drug information with the indication information by using filters.”
With respect to claim 1 and: “a reasoning system configured for reasoning characteristics of the biological system from the set of questions of the collected species identification key set regarding a species corresponding to the biological system and a pre-built information system of biological system.”
Aravamudan provides reasoning systems for reasoning additional characteristics beginning at [0189-0196, 0201-0211] including an “information box” at [0218], [0255], etc.
With respect to claim 7, the method of using the program/system of Aravamudan is also described at the same places. A biological based example of Aravamudan such as that at [0256] meets the BRI of all of the following:
“A biological information reasoning method being performed by a biological information reasoning apparatus utilizing a species identification, comprising:
transmitting a user question from a user terminal to a recommendation system;
analyzing, by the recommendation system, the user question;
driving a reasoning system when a relevant biological system information exists as a result of the analysis exists;
searching for, by the reasoning system, a similar identification key from an identification key database; and linking to a biological system information database and
recommending a related biological system.”
Aravamudan provides a descriptive analysis of the method beginning, for example at [0256].
“[0256] Disclosed systems and methods establish that the discovery of novel biological associations can be achieved through temporal analysts of-be semantic neighborhood (e.g., in all documents found in Pub Med) of a given pair of entities (words or phrases). These pairs can be of any entity type used in the Life Science literature (e.g., gene-gene or gene-disease) leading to hypothesis generation that can have a profound impact in strategic decision making. The complex set of phrases that constitute life science entities (e.g., diseases, genes) are often constituted of multiple words, and preserving such phrases is central to maximizing the value of Natural Language Processing (NLP) in the Life Sciences.
[0257] According to embodiments, temporal analysis of semantic association strengths or scores can enable identification of novel associations that predate or coincide with a seminal biological discovery published in the scientific literature. The strong semantic association score signal can occur on the year of the seminal publication, or several years prior to such a seminal publication. Consequently, the semantic association scores (cosine distances) described herein can be used today to predict novel biological associations that have yet to be disclosed in the biomedical literature.
[0258] Disclosed systems and methods can identify and visualize, at the incipient stages, significant associations between life science entities (e.g., the gene EGFR is a life science entity). Sets of entities can be grouped into entity collections, which include but are not limited to the following: Biomolecules (e.g., genes, DNA or RNA polymers, proteins, lipids, metabolites, coding and non-coding RNA, peptides, antigens, mutations, etc.), Bio-entities (e.g., cells, organs, etc.), Diseases (e.g., Non-small cell lung cancer, Rheumatoid Arthritis, Hypercholesterolemia, Multiple Sclerosis, Parkinson's disease, NASH, NAFLD, AIDS, Sepsis, etc.), Adverse Events, Microorganisms (e.g., H.pylori, influenza H1N1 virus. Hepatitis C Virus, Candida albicans, etc.), Assays (e.g., High throughput cell screening, Kinome profiling. Growth inhibition, mass spectrometry, etc.), Companies/Institutions (e.g., pharmaceutical, biotechnology, CROs, diagnostics/device manufacturers, hospitals, clinics, universities, etc.), People (e.g., researchers/scientists, doctors/physicians, physician names, NPI IDs of physicians, executives, etc.), Phenotypes (e.g., in-vitro, in-vivo observable/measurable/subjective, etc.), Drugs (e.g., compounds/small molecules, antibodies, cells, etc.), Medical instruments, Medical Procedures (e.g., surgery, transplantation, radiation etc.), and other entity collections that can be compiled by users of diverse Biomedical corpora (see FIG. 15). In some embodiments, the terms “knowledgebase” and “entity collection” are interchangeable.
[0259] FIG. 15 illustrates exemplary entity collections in accordance with some embodiments of the present disclosure. FIG. 15 highlights super-collections that include several smaller sub-collections, as well as collections that overlap across multiple other entity collections in accordance with some embodiments of the present disclosure. The superset of all collections in the Life Science corpus itself may be construed as a “Master Entity Collection” (the collection of all collections and entities in the corpus). In some embodiments, custom collections that will be created by users of the system may also be labeled as Entity Collections. In the entity collection schematic visualized herein, diverse entity collections can be deposited, where entities can belong to multiple entity collections, and entity collections can be nested within one another or extend across other entity collections.
[0260] According to some embodiments, a set of industry specific entity collections can be created to provide a basis for the comparison of the evolution history of the “aggregated collection” against a singleton entity so that statistically robust inference can be made, for example, on the salience of the singleton entity's association with another entity over time.
[0261] Vector Space Models represent words in a continuous vector space where “semantic-ally” similar words are mapped to neighboring points (i.e., such words are embedded nearby each other in a synthetic high-dimensional space). Such techniques have a long, rich history in the field of Natural Language Processing (NLP), but all methods depend in some way or another on the Distributional Hypothesis, which states that words that appear in the same contexts share semantic meaning. The different approaches that leverage this principle can be divided into two categories: count-based methods (e.g., Latent Semantic Analysis), and Predictive methods (e.g., neural probabilistic language models). Count-based methods compute the statistics of how often some word co-occurs with its neighbor words in a large text corpus, and then map these count-statistics down to a small, dense vector for each word. Predictive models directly try to predict a word from its neighbors in terms of learned small, dense embedding vectors (considered parameters of the model). Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors; the Continuous Bag-of-Words model (CBOW) and the Skip-Grain model. (See Section 3.1 and 3.2 in Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, Efficient Estimation of Word Representations in Vector Space, ICLR Workshop, 2013 (“Mikolov et al.”)). Algorithmically, these models am similar, except that CBOW predicts target words (e.g., “mat”) from source context words (e.g., “the eat sits on the”), while the skip-gram does the inverse and predicts source context-words from the target words. This inversion might seem, like an arbitrary choice, but statistically it has the effect that CBOW smoothens over a lot of the distributional information (by treating an entire context as one observation). For the most part, this turns out to be useful for smaller datasets. However, skip-grain treats each context-target pair as a new observation, and this tends to do better for larger datasets, such as the gargantuan Life Sciences corpus summarized in Table 1 below.”
“[0263] As illustrated in FIG. 1 in accordance with some embodiments of the present disclosure, the system store 114 can capture information extracted from two or more source paths (e.g., 103a and 105a) in different forms to facilitate the synthesis of information and/or enable subsequent information extraction through different pathways (e.g., pathways 103a and 105a). In some embodiments. FIG. 1 includes the system store 114 that can be used to convert words into vectors and analysis of the resulting semantic BioKnowledge graph in accordance with some embodiments of the present disclosure. The system store 114 can include information stored in a structured semantic database 106 (which can be a traditional database); a knowledge graph(s) 107 (which can be directed graphs of labeled (extracted from both paths 101a and 102a) and/or unlabeled entities (extracted from the 102a path)); word embeddings 108 (which can include word(s) and/or sentence(s)), document/paragraph/sentence embeddings 109; and sequence representations of unstructured data 110. In some embodiments, tin example of word embedding can be word2vec. In some embodiments, an example of document/paragraph/sentence embedding can be doc2vec. In some embodiments, an example of sequence representations 110 can be Memory Neural Network (MemNN).”
“[0291] Nascent Life Sciences entity associations that are detected can be further characterized by their features that can be found in various proprietary and/or public datasets. For example, for gene entities, their expression in normal human tissues can be determined by using a dataset, such as the GTEx dataset from the Broad Institute (//gtexportal.org/home/), and correlate that to their Semantic Association Score. Similarly, gene and disease associations can be stress-tested for novelty by determining their association score in database, such as the OpenTargets database (//www.targetvalidation.org/), which should be low for our predicted nascent Life Sciences entity pairs.”
As such, Aravamudan anticipates the independent claims.
With respect to dependent claims 2 -4, 9-11 Aravamudan uses DAG to store the embeddings and information which meet the BRI of the DAG and the logical nodes, questions and a path with one starting point and one ending point, and the questions can have yes/no answers. Fig 1, 19, their descriptions in the text, the knowledge graphs, etc.
With respect to dependent claims 5, and 12 The databases of Aravamudan containing the various information, and embeddings meet this limitation as they comprise logical node tables and logical node connections.
With respect to dependent claim 6, the specific “species” of drugs have ID numbers and connections.
With respect to claim 8, connections between organism parts, organs, and events are all stored and used in the reasoning method to link to additional information, such as those provided by various public databases.
With respect to dependent claim 13, the reasoning uses the logical node text, species ID and connections.
Claim(s) 1, 7 is/are rejected under 35 U.S.C. 102a1 as being anticipated by Zadeh et al. (2018).
Zadeh, L. A. et al. System and method for extremely efficient image and pattern recognition and artificial intelligence platform. US 2018/02041111 A1, published 7/19/2018.
Zadeh is directed to: adding the use of causal modeling, reasoning, inference and cognitive engines to the analysis of all types of data. As set forth in the abstract, the methods are to be applied to biological data, medical diagnostics, biomedicine, drug discovery, among other topics. The types of modeling can include question-answering systems, NLP, computing with words, parsing queries, etc. Data storage and organization to carry out these methods are also disclosed.
“[0036] However, in addition, there are many other embodiments in the current disclosure that deal with other important and innovative topics/subjects, e.g., related to General AI, versus Specific or Vertical or Narrow AI, machine learning, using/requiring only a small number of training samples (same as humans can do), learning one concept and use it in another context or environment (same as humans can do), addition of reasoning and cognitive layers to the learning module (same as humans can do), continuous learning and updating the learning machine continuously (same as humans can do), simultaneous learning and recognition (at the same time) (same as humans can do), and conflict and contradiction resolution (same as humans can do), with application, e.g., for image recognition, application for any pattern recognition, … application for medical imaging and medical diagnosis and medical procedures and drug developments and genetics, application for control systems and robotics, application for prediction, forecasting, and risk analysis, e.g., for weather forecasting, economy, oil price, interest rate, stock price, insurance premium, and social unrest indicators/parameters, and the like.”
With respect to claim 1 and:
“A biological information reasoning apparatus utilizing a species identification, comprising:
an identification key collection unit configured for collecting a species identification key set;”
Zadeh provides a reasoning apparatus:
[0191] Please note that General-AI is also called/referred to as General Artificial Intelligence (GAI), or Artificial General Intelligence (AGI), or General-Purpose AI, or Strong Artificial Intelligence (AI), or True AI, or as we call it, Thinking-AI, or Reasoning-AI, or Cognition-A, or Flexible-AI, or Full-Coverage-AI, or Comprehensive-AI, which can perform tasks that was never specifically trained for, e.g., in different context/environment, to recycle/re-use the experience and knowledge, using reasoning and cognition layers, usually in a completely different or unexpected or very new situation/condition/environment (same as what a human can do).”
As well as various embodies throughout, including fig 109, a question-answer system, Fig 160 depicting a fuzzy reasoning inference engine, Fig 271-277, etc.
The reasoning apparatus of Zadeh collects and embeds key species identification information about desired entities or species. The knowledge base for the data comprises bodies of information collected about each topic or entity, such as described in [0846]. Specific information that answers specific questions are obtained, indexed and stored, for statistical and NLP processes. [0851-0852] the use of structured ontologies is identified at [0875]. Precisiated Natural Language, co-intensions and related topics begin at [1008]. Protoforms are addressed at [1115] which appear to meet the BRI of the identification key set.
“[1115] In CTPM, a concept which plays a key role in deduction is that of a protoform—an abbreviation for prototypical form. Informally, a protoform of an object is its abstracted summary. More specifically, a protoform is a symbolic expression which defines the deep semantic structure of an object such as a proposition, question, command, concept, scenario, or a system of such objects. In the following, our attention will be focused on protoforms of propositions, with PF(p) denoting a protoform of p. Abstraction has levels, just as summarization does…”
The section on the Removal of Ambiguities in question-answer systems also provides species identification key data, and its collection, with the example of a particular individual, and the associated names, ID numbers, and other information beginning at [1249, 1274].
Key indices are used in the join operations for joining linked nodes or pathways as set forth beginning at [1513]. These indices contain tables, indexed as sorted lists, with column/attribute identifications.
With respect to Claim 1 and:
“an identification key database configured for hierarchically managing a set of questions of the collected species identification key set;”
Zadeh provides a database structure, with hierarchical structure and sets of questions for the topic, beginning at [1226], Deduction (Extension) Principle:
“[1226] Underlying almost all examples involving computation of an answer to a question, is a basic principle which may be referred to as the Deduction Principle. This principle is closely related to the extension principle of fuzzy logic.”
Zadeh provides Query processing, and elements meeting the BRI of recommendation systems:
“[1236] This question can be scanned or parsed, to extract its components, as (for example) in the following shorthand notation or format: “Mary/Age?” The parsing is done using many templates for recognition of the pattern or grammar for a specific language (e.g. American English), dialect, topic (e.g. political topic), or method and type of speech (e.g. written, as opposed to spoken information or question). The templates are stored and designed by linguists or experts, in special databases beforehand, to be able to dissect the sentences into its components automatically later on, and extract the relevant and important words and information. The degree of matching to a specific template (e.g. for English grammar), to find (for example) the subject and the verb in the sentence, is done by fuzzy membership function and other fuzzy concepts described elsewhere in this disclosure…
[1248] Obviously, many other choices of templates and grammar also work here, as long as there is consistency and brevity in the definitions and templates, to reduce the size and get the common features for batch processing, faster search, faster data extraction, better data presentation, and more efficient data storage. The good thing about templates is that it makes the translation between different human languages (or translation between speech and computer commands) much easier, as they tend to carry only pure necessary (bare bone) information, without extra words, in a predetermined order or format, for fast and efficient access, search, and comparison.”
Identifying or generating relevant questions for the method is discussed beginning at [1253].
“[1260] … In addition, using a relevance scoring system, one can filter and find all or most relevant questions. Each relevant question can in turn refer to another relevant question or information, as a cascade and chain, bringing or suggesting more questions and information for the user. The history of the user or history of the users or history of similar or same question(s) can be stored in some relational databases with relevance scoring, for future filtering and usage, based on a threshold. The system is adaptive and dynamic, as well as having learning/training mode, because as the time passes, with more experience and history, the database gets more accurate and larger in size, to fit or find the questions or relevant information better and faster.
Hierarchical storage is provided at [1291]:
“[1291] Furthermore, some repositories are assigned as intermediary repository, as a hierarchical structure or tree configuration, to access certain data faster.”
“[1302] In one embodiment, the same information may have various representations with different levels of details: L.sub.1, L.sub.2, . . . L.sub.N, where L.sub.1<L.sub.2< . . . <L.sub.N, in term of “level of details”. So, we can store them in different repositories, available for different searches. Search and access to L.sub.1 is much faster than those of L.sub.N (which carries more details). Based on the application, if it is determined that there is no need for details of L.sub.N, one can choose a version with lower amount of details, such as L.sub.1 or L.sub.2”
Zadeh provides reasoning systems for reasoning additional characteristics in the examples for the individual Mary Jones at [1249+].
With respect to claim 7, the method of using the program/system of Zadeh is also described at the same places.
“A biological information reasoning method being performed by a biological information reasoning apparatus utilizing a species identification, comprising:
transmitting a user question from a user terminal to a recommendation system;
analyzing, by the recommendation system, the user question;
driving a reasoning system when a relevant biological system information exists as a result of the analysis exists;
searching for, by the reasoning system, a similar identification key from an identification key database; and linking to a biological system information database and
recommending a related biological system.”
“[1326] In one embodiment, the search engine and corresponding natural language parsing and processing are tailored toward the specific application or industry, e.g. telecommunication, stock trading, economy, medical diagnosis, IP (intellectual property), patent, or claim analysis or valuation, company valuation, medical knowledge, and the like.”
“[1327] In one embodiment, using common rules for grammar and syntax for a specific language for sentence structure (and corresponding exceptions to those rules), the search engine parses and dissects the sentence (as explained elsewhere in this disclosure) and applies dictionaries (in different categories, such as medical dictionaries) and thesaurus (or phrase books or glossaries or idiom or phrase or dialect listings) to find or interpret the meaning of the words, phrases, and sentences, e.g. to convert them into codes, templates, abbreviations, machine codes, instructions, text, printout, voice, sound, translation, script, or computer commands, to process further, if needed. See FIG. 72 for a diagram of such system.
[1328] In one embodiment, the synonyms module, spell check module, antonyms module, and variation or equivalent word module are all part of a search engine, to help find similar words and concepts, or parse the sentences. In one embodiment, for analytics, the search engine includes summarization module and clustering module, to group the data in sets for systematic analysis, such as based on N-dimensional feature space for components of a word or phrase, based on all the possibilities for basic components, partial words, or letters in a given language (as a dictionary for all possible basic word components in a given language, with all connecting possibilities with other neighboring components, which is held in a database(s) or relational databases, and can be updated and improved by users periodically as feedback, or by machine or processor, automatically, with a training module, such as a neural network). FIG. 111 is an example of a system described above.”
“[1372] To analyze a phrase or sentence, in one embodiment, the system looks at adjectives or related words, e.g. “water tank”. For example, for “tank”, when used as a word equivalent to a “container” (which can be extracted from the context, from neighboring words or paragraphs), it logically can hold some objects, especially fluids, e.g. gas, liquid, water, nitrogen, and liquid nitrogen. Thus, one can combine them this way, as a template: [1373] [FLUID+tank] [1374] Or: [1375] [tank of +FLUID]
[1376] One can store these templates (and any exception to the templates) in multiple databases, which can be categorized and separated based on their topics and usages, in a hierarchical or tree or pyramid structure, with inherency property, e.g. parent nodes and children nodes.
[1377] This can be done with adjectives, as well, for example, “big” in the phrase “big tank”, which is expressed as a template…”
“[1380] Once a template is found (to match the pattern of a given sentence or phrase), the system can understand the meaning of that section of the text, phrase, or sentence. Then, it can understand the meaning of the whole sentence or phrase through the combinations or series of templates that construct those phrases and sentences (for a given language, based on the collection of the grammar templates (along with their exceptions or special usages)).”
“[1387] Again, the above templates are categorized and stored accordingly, in various (e.g. tagged) hierarchical storages, files, and databases, for future use by the search engine, to dissect, recognize the patterns and templates, and understand the meaning of the sentence or phrase.
[1388] In one embodiment, the range of numbers or values or approximate values or measurement accuracies (e.g. length of the table=(5 meter±2 centimeter)) are expressed based on fuzzy values. In one embodiment, the dimensions in the image (for recognition purposes) are based on approximation, based on fuzzy values.
[1389] In one embodiment, the relationships and templates are based on fuzzy terms, with membership values. In one embodiment, the relationships and templates (or grammar) are based on Z-numbers, with terms such as “USUALLY”, expressing concepts such as certainty for the relationships, templates, and grammar.”
“[1446] In one embodiment, a query (e.g., an iterative query) is made to expand the facts and related rules from the knowledge base.”
As such, the independent claims are anticipated by Zadeh.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12,585,701 B2, published 24 March 2026, has an author of the same name, and appears to be directed to a narrow embodiment of a platform which comprises an aspect the claimed invention. The claims set forth “concept cards” which appear to be “species identification keys” for biomimetic information, which is applied to a knowledge space, with a database that manages the data, and questions, and concepts, which use similarity assessment, mapping, and priority of connections. This patent may be the subject of future nsdp rejections, depending on the direction of examination.
Lally, A. et al. (2017) WatsonPaths: Scenario based question answering and inference over unstructured information. Assoc for the Advancement of artificial intelligence. Summer, 2017, p59-76.
Hassanzadeh, O. et al. (2019) Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), p5003-5010.
Sarrouti, M et al. (2020) SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions. Artificial Intelligence in Medicine, vol 102, e101767, 16 pages.
Sazal, M. R. et al. (2020, March 3) Causal Inference in Microbiomes Using Intervention Calculus. bioRxiv, e970624, 12 pages.
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/MARY K ZEMAN/ Primary Examiner, Art Unit 1686
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