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 1-8 are presented for examination.
Claim Objections
Claim 8 is objected to because of the following informalities: “a processer coupled to the memory” should be “a processor coupled to the memory.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claim 7, the limitations “a third storage” [line 6] and “a second storage” [line 10] lack of antecedent basis/indefiniteness. The limitations “a third storage” and “a second storage” appear with no prior first storage introduced in the claim, because claim 7 is an independent claim. For the purposes of examination, Examiner will introduce a limitation of “a first storage” in the claim to cooperate with the limitations “a third storage” and/or “a second storage”, or Examiner will modify the limitations “a third storage” and “a second storage” to “a second storage” and “a first storage”, respectively.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claims
Step 1
Claim 1 is drawn to a non-transitory computer-readable recording medium, claim 7 is drawn to a non-transitory computer-readable recording medium, and claim 8 is drawn to an information processing device including a memory and a processor that performs a method of the non-transitory computer-readable medium of claim 1. Each of these claim groups falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Claims 1, 7 and 8 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claims 1, 7 and 8 recite a method of receiving a hypothesis to be interpreted that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to receive a proposition or a hypothesis to analyze. Therefore, the step of receiving a hypothesis to be interpreted is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1 and 8 recite further a method of acquiring the basis information and the rule identifier that correspond to the hypothesis to be interpreted that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to acquire or to retrieve a basis information and a rule. Therefore, the step of acquiring the basis information and the rule identifier is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claims 1 and 8 recite further a method of acquiring the probability of coinciding with the existing knowledge that corresponds to the acquired rule identifier that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to acquire or to evaluate a probability of a coincidence with the knowledge. Therefore, the step of acquiring the probability of coinciding with the existing knowledge is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claim 7 recites a method of determining whether or not knowledge that corresponds to the hypothesis to be interpreted exists in a third storage in which basis information that serves as a basis of the knowledge is connected with each piece of the knowledge that includes a subject, an object, and a predicate that indicates a relationship between the subject and the object that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to determine whether a knowledge corresponds to a hypothesis. Therefore, the step of determining whether or not knowledge that corresponds to the hypothesis to be interpreted is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Claim 7 recites further a method of acquiring the basis information that corresponds to the knowledge and the probability of coinciding with the existing knowledge that corresponds to the rule that relates to the hypothesis to be interpreted that under its broadest reasonable interpretation enumerates a mental concept. A human can mentally perform, with the physical aid such as pen and paper, to acquire or to retrieve a basis information and a probability of a coincidence. Therefore, the step of acquiring the basis information that corresponds to the knowledge and the probability of coinciding with existing knowledge is nothing more than a mental concept (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Claims 1 and 8 recite further by using a first storage that includes, for each piece of knowledge that indicates a plurality of resources and a relationship between the resources, basis information that serves as a basis of the knowledge and a rule identifier connected with a rule used to interpret the hypothesis that fail to integrate the abstract idea into a practical application. The step of using a first storage is a form of insignificant input and output solution activities, where using a first storage including a piece of knowledge indicating plurality of resources and a relationship is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claims 1 and 8 recite further by using a second storage that includes, for each rule identifier, a probability that the rule and the hypothesis coincide with existing knowledge that fail to integrate the abstract idea into a practical application. The step of using a second storage is a form of insignificant input and output solution activities, where using a second storage including a probability that a rule and a hypothesis coinciding with existing knowledge is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Claim 7 recites further by using a rule that relates to the hypothesis to be interpreted and is included in a second storage that includes, for each rule identifier, the rule used to interpret the hypothesis and a probability that the hypothesis coincide with existing knowledge that fail to integrate the abstract idea into a practical application. The step of using rule that relates to a hypothesis is a form of insignificant input and output solution activities, where using a rule relating to the hypothesis that coinciding with existing knowledge is necessary for all uses of the judicial exception. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Step 2B
The additional elements in step 2A-Prong 2 those are forms of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decision have determined that these additional elements of using a first storage including a piece of knowledge indicating plurality of resources and a relationship; using a second storage including a probability that a rule and a hypothesis coinciding with existing knowledge; and using a rule relating to the hypothesis that coinciding with existing knowledge to be well-understood, routine, and conventional when claimed in a merely generic manner (MPEP 2106.05(d)(II)).
As such, claims 1, 7 and 8 are not patent eligible.
Dependent claims
Claims 2-6 merely narrow the previously recited abstract idea limitations. For the reasons described above with respect to claim 1, this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mental and mathematical processes that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 2-6 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Step 1
Claims 2-6 are drawn to a non-transitory computer-readable recording medium. This claim group falls under one of four categories of statutory subject matter (process/method, machines/product/apparatus, manufactures, and composition of matter).
Step 2A – Prong 1
Dependent claim 3 recites further the mental process by a set of the resources is further received, for the set of the resources that is based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Dependent claim 5 recites further the mental process by a list of sets of the resources and the relationship to be extracted are further received those are based on one or more features of the ML project (MPEP 2106.04(a)(2)(III)).
Step 2A – Prong 2
Dependent claim 2 recites further the insignificant extra solution activities by wherein the basis information and the probability of coinciding with the existing knowledge that have been acquired are presented for the hypothesis to be interpreted. This additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 3 recites further the insignificant extra solution activities by for the set of the resources, the basis information is acquired from a third storage in which the basis information that serves as the basis of the knowledge is connected with each piece of the knowledge that includes a subject, an object, and a predicate that indicates the relationship between the subject and the object, based on the rule included in the second storage, and for the set of the resources, information that includes the relationship between the resources obtained from the rule in the second storage, the basis information, and the rule identifier connected with the rule are added to the first storage. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 4 recites further the insignificant extra solution activities by the third storage that treats a target document as the basis information is generated by using a predetermined relationship extraction process from a natural language, and a plurality of target databases is integrated by using a predetermined integration process configured to integrate a plurality of existing databases, and the third storage that treats the integrated databases as the basis information is generated. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 5 recites further the insignificant extra solution activities by a feature that corresponds to the sets of the resources included in the list and the relationship to be extracted is extracted from a third storage in which the basis information that serves as the basis of the knowledge is connected with each piece of the knowledge that includes a subject, an object, and a predicate that indicates the relationship between the subject and the object, the extracted feature is input, and confidence of the feature is output by applying an explainable artificial intelligence (AI) that treats a variable that indicates presence or absence of the relationship to be extracted, as an objective variable, and information in which the extracted feature is treated as the rule, which is the information in which the output confidence of the feature is treated as the probability of coinciding with the existing knowledge, and which is the information with which the relationship to be extracted is associated, is added to the second storage. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
Dependent claim 6 recites further the insignificant extra solution activities by the basis information and the probability of coinciding with the existing knowledge that have been acquired are further stored in a fourth storage for the hypothesis to be interpreted, and the basis information and the probability of coinciding with the existing knowledge for a target hypothesis are extracted and presented from the fourth storage, based on a user interface. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (MPEP 2106.05(g)).
As such, dependent claims 2-6 are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Stumpe et al (US 20220261668 A1) hereafter Stumpe, and further in view of Li et al (US 20190244122 A1) hereafter Li.
With respect to claim 1, Stumpe teaches a non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute a process (a non-transitory computer-readable medium including a program that is stored for causing a computer to perform a method for directed hypothesis generation and ranking [par. 0137]) comprising:
receiving a hypothesis to be interpreted (a method including an artificial intelligence (AI) engine for directed hypothesis generation and ranking. A moderation engine may receive a hypothesis query generated for filtering and ranking [par. 0012-0014, 0127]);
by using a first storage that includes, for each piece of knowledge that indicates a plurality of resources and a relationship between the resources (a heterogeneous knowledge graph is presented integrating multi-omic data and representing a relevant bio-medical subspace. The knowledge graph is constructed by using jointed entity and relationship extraction from text and normalization of entities and relations from structured annotation data sources [par. 0012-0014, 0048, 0049, 0195]), basis information that serves as a basis of the knowledge and (a background bio-medical knowledge graph used to classify generated hypothesis into a number of classes, or known fact with a specific degree of evidence (basis information). Other hypothesis may be unknown knowledge or potential facts, false statements or statements that have contradictory evidence. A moderator is used to rank and filter hypotheses. The moderator performs filtering via decision rule based on examining embedding-based ranking and the scoring function with specific threshold [par. 0048-0053, 0094-0099]); and
by using a second storage that includes, for each rule identifier, a probability that the rule and the hypothesis coincide with existing knowledge, acquiring the probability of coinciding with the existing knowledge that corresponds to the acquired rule identifier (a directed correlation exploration may be implemented based on the evaluation of available features of feature set and/or generated correlation matrices to identify new feature sets and AI engines. A correlation matrix may reveal the predictive value of a first feature with any second feature of the feature set. A predictive value may be a percentage where 100% represents that the first feature always coincides with the second feature, 0% represents that the first feature never coincides with the second feature. A hypothesis generator may identify correlations from the matrices for further analysis [par. 0118-121]).
However, Stumpe does not particularly disclose a rule identifier connected with a rule used to interpret the hypothesis.
In the same field of endeavor, Li teaches a rule identifier connected with a rule used to interpret the hypothesis (the system includes a knowledge model constructor and an executor, wherein the executor includes an explainer that includes a rule identifier and an inference & hypothesis generator. The rule identifier performs the role of identifying the inferencing rule that will be applicable in a given problem, on the basis of which the system provides the decision and the reasoning. A problem may be processed and a hypothesis may be provided for the problem and an explanation to accompany the hypothesis may be generated [par. 0037, 0051-0069, 0140-0141]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of generating inferencing rules and a knowledge model for resolving a real-world problem as suggested by Li into the concept of using multiple heterogeneous knowledge graphs to rank hypotheses that integrates disease-specific multi-omic data specific to a patient as suggested by Stumpe because both of these systems addressing the process of applying a certain rule with a specific ID (identifier) to a given hypothesis. Doing so would be desirable because the system of Stumpe would be more efficient by implementing an explainable AI system to construct inferencing rules used for developing a reasoning process and for developing a framework of “confidence” or “trust” levels to indicate the strength of the reasoning process (Li, [par. 0030]).
With respect to claim 2, the combination of Stumpe and Li teaches wherein the basis information and the probability of coinciding with the existing knowledge that have been acquired are presented for the hypothesis to be interpreted (Stumpe, hypotheses are verified using sources of evidence: clinical trials, external literature sources, etc. and other types of data indexed in background knowledge graph. Probabilistic graphical models and factor graphs are used to estimate the probability of each hypothesis based on various sources. Hypotheses are ranked using evidence scoring based on embedding likelihoods [par. 0097-0099]).
With respect to claim 3, the combination of Stumpe and Li teaches wherein
a set of the resources is further received (Stumpe, additional multi-omic data is integrated in a method for converting clinic-multi-omic patient and cohort data into knowledge graph integrating multi-omic data [par. 0013]),
for the set of the resources, the basis information is acquired from a third storage in which the basis information that serves as the basis of the knowledge is connected with each piece of the knowledge that includes a subject, an object, and a predicate that indicates the relationship between the subject and the object, based on the rule included in the second storage (Stumpe, the method comprises disease-specific data that is specific to a patient. The method ingesting the additional data, such that the multi-omic genomic and clinical information is triplified into (subject, predicate, object), wherein predicate indicates the relationship between head and tail [par. 0013]), and
for the set of the resources, information that includes the relationship between the resources obtained from the rule in the second storage, the basis information, and the rule identifier connected with the rule are added to the first storage (Stumpe, the hypothesis generator module evaluates the knowledge graph’s coverage including relationship summarization and sub-graph summary of a particular domain/topic. The knowledge graph defines a semantic data model: entities, relationships, properties. A correlation matrix may reveal the predictive value of a first feature with any second feature of the feature set. Queries received from hypothesis generator may be sent to the knowledge matching database to confirm whether the query hypothesis is known or unknown [par. 0059-0065, 0121, 0132]).
With respect to claim 4, the combination of Stumpe and Li teaches wherein
the third storage that treats a target document as the basis information is generated by using a predetermined relationship extraction process from a natural language (Stumpe, the method includes continuously updated background biomedical knowledge graph. This knowledge graph is constructed using joined entity and relationship extraction from text and normalization of entities and relations from structured annotation data sources [par. 0048-0053]), and
a plurality of target databases is integrated by using a predetermined integration process configured to integrate a plurality of existing databases, and the third storage that treats the integrated databases as the basis information is generated (Stumpe, the background knowledge graph captures disease state understanding to create or to form a complete picture of disease biology from real-world knowledge literature, clinical trials, patients, and from public and private multi-omic data sets [par. 0080]).
With respect to claim 5, the combination of Stumpe and Li teaches wherein
a list of sets of the resources and the relationship to be extracted are further received (Stumpe, additional multi-omic data is integrated in a method for converting clinic-multi-omic patient and cohort data into knowledge graph integrating multi-omic data [par. 0013]),
a feature that corresponds to the sets of the resources included in the list and the relationship to be extracted is extracted from a third storage in which the basis information that serves as the basis of the knowledge is connected with each piece of the knowledge that includes a subject, an object, and a predicate that indicates the relationship between the subject and the object (Stumpe, a feature corresponds to the sets of resources may be considered as a gene. The proposed method is provided for ingressing domain-specific entities into a domain specific knowledge graph. The method ingesting the additional data, such that the multi-omic genomic and clinical information is triplified into (subject, predicate, object), wherein predicate indicates the relationship between head and tail [par. 0013, 0014]),
the extracted feature is input, and confidence of the feature is output by applying an explainable artificial intelligence (AI) that treats a variable that indicates presence or absence of the relationship to be extracted, as an objective variable (Li, the techniques used in the explainable AI system use a feature vector extracted from the input data. An explainable AI system configured to construct inferencing rules used for developing a reasoning process and for developing a framework of “confidence” or “trust” levels to indicate the strength of the reasoning process. A knowledge model detector may identify co-occurrence of entities and relationships that results in the extraction of a knowledge graph [par. 0006, 0030, 0049]), and
information in which the extracted feature is treated as the rule, which is the information in which the output confidence of the feature is treated as the probability of coinciding with the existing knowledge, and which is the information with which the relationship to be extracted is associated, is added to the second storage (Stumpe, a directed correlation exploration may be implemented based on the evaluation of available features of feature set and/or generated correlation matrices to identify new feature sets and AI engines. A correlation matrix may reveal the predictive value of a first feature with any second feature of the feature set. A predictive value may be a percentage where 100% represents that the first feature always coincides with the second feature, 0% represents that the first feature never coincides with the second feature. A hypothesis generator may identify correlations from the matrices for further analysis [par. 0118-121]).
With respect to claim 6, the combination of Stumpe and Li teaches wherein
the basis information and the probability of coinciding with the existing knowledge that have been acquired are further stored in a fourth storage for the hypothesis to be interpreted (Stumpe, hypotheses are verified using sources of evidence: clinical trials, external literature sources, etc. and other types of data indexed in background knowledge graph. Probabilistic graphical models and factor graphs are used to estimate the probability of each hypothesis based on various sources. Hypotheses are ranked using evidence scoring based on embedding likelihoods [par. 0097-0099]), and
the basis information and the probability of coinciding with the existing knowledge for a target hypothesis are extracted and presented from the fourth storage, based on a user interface (Stumpe, the ranking of hypothesis could be iterative. After providing the initial ranking of hypothesis, the user may manually exclude certain variables, and the outputs can be refined upon that [par. 0136]).
With respect to claim 7, Stumpe teaches receiving a hypothesis to be interpreted (a method including an artificial intelligence (AI) engine for directed hypothesis generation and ranking. A moderation engine may receive a hypothesis query generated for filtering and ranking [par. 0012-0014, 0127]);
determining whether or not knowledge that corresponds to the hypothesis to be interpreted exists in a third storage in which basis information that serves as a basis of the knowledge is connected with each piece of the knowledge that includes a subject, an object, and a predicate that indicates a relationship between the subject and the object (the method comprises disease-specific data that is specific to a patient. The method ingesting the additional data, such that the multi-omic genomic and clinical information is triplified into (subject, predicate, object), wherein predicate indicates the relationship between head and tail [par. 0013]), by using a rule that relates to the hypothesis to be interpreted and is included in a second storage that includes, (a background bio-medical knowledge graph used to classify generated hypothesis into a number of classes, or known fact with a specific degree of evidence (basis information). Other hypothesis may be unknown knowledge or potential facts, false statements or statements that have contradictory evidence. A moderator is used to rank and filter hypotheses. The moderator performs filtering via decision rule based on examining embedding-based ranking and the scoring function with specific threshold [par. 0048-0053, 0094-0099]); and
in a case where it is determined that the knowledge that corresponds to the hypothesis to be interpreted exists in the third storage, acquiring the basis information that corresponds to the knowledge and the probability of coinciding with the existing knowledge that corresponds to the rule that relates to the hypothesis to be interpreted (a directed correlation exploration may be implemented based on the evaluation of available features of feature set and/or generated correlation matrices to identify new feature sets and AI engines. A correlation matrix may reveal the predictive value of a first feature with any second feature of the feature set. A predictive value may be a percentage where 100% represents that the first feature always coincides with the second feature, 0% represents that the first feature never coincides with the second feature. A hypothesis generator may identify correlations from the matrices for further analysis [par. 0118-121]).
However, Stumpe does not particularly disclose for each rule identifier, the rule used to interpret the hypothesis.
In the same field of endeavor, Li teaches for each rule identifier, the rule used to interpret the hypothesis (the system includes a knowledge model constructor and an executor, wherein the executor includes an explainer that includes a rule identifier and an inference & hypothesis generator. The rule identifier performs the role of identifying the inferencing rule that will be applicable in a given problem, on the basis of which the system provides the decision and the reasoning. A problem may be processed and a hypothesis may be provided for the problem and an explanation to accompany the hypothesis may be generated [par. 0037, 0051-0069, 0140-0141]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the concept of generating inferencing rules and a knowledge model for resolving a real-world problem as suggested by Li into the concept of using multiple heterogeneous knowledge graphs to rank hypotheses that integrates disease-specific multi-omic data specific to a patient as suggested by Stumpe because both of these systems addressing the process of applying a certain rule with a specific ID (identifier) to a given hypothesis. Doing so would be desirable because the system of Stumpe would be more efficient by implementing an explainable AI system to construct inferencing rules used for developing a reasoning process and for developing a framework of “confidence” or “trust” levels to indicate the strength of the reasoning process (Li, [par. 0030]).
With respect to claim 8, it is an information processing device that corresponding to the non-transitory computer-readable recording medium of claim 1. Therefore, it is rejected for the same reason as claimed in claim 1 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yamamoto (US 20210326735 A1) disclosed an abduction apparatus 1 includes: a probability calculation unit 2 configured to, with respect to each of candidate hypotheses generated using observation information and knowledge information, calculate a probability that the candidate hypothesis holds true as an explanation of the observation information; and a reward selection unit 3 configured to, when the candidate hypothesis holds true, select a reward value regarding the candidate hypothesis that has held true by referring to reward definition information in which a condition that the candidate hypothesis holds true is associated with the reward value.
Adamo et al (US 11113175 B1) disclosed system for discovering semantic relationships in computer programs is disclosed. In particular, the system may synergistically identify and validate semantic relationships, concepts, and groupings associated with data elements within a static or dynamic, time varying, source input. The system may utilize feature extractors to extract features from the input and reasoners to develop associations using data from multiple feature set types, and, can thus generate reliable, robust, and complete sets of semantic relationships from the input. The system may generate hypotheses associated with the relationships, concepts, and groupings, and validate the hypotheses by testing an application under evaluation by the system and observing the outputs generated from the testing.
Ul Ain et al (US 20210174906 A1) disclosed a method that enables the discovery of new relationships between diseases and genes by prioritizing the selection of gene targets for a disease using an embedding space generated from a knowledge graph by mapping datasets collected from various data sources using a graph schema, modeling disease and gene associations with link weightings, analyzing the data with several machine learning models, and scoring predictions.
Brake et al (US 20190279104 A1) disclosed a question and answer (QA) system, computer program product, and computer-implemented method configured to determine an answer to a question that includes a measurement value. In one example, the QA system receives a question and analyzes the question to identify a measurement value specified in the question. The QA system determines relevant passages to the question. The QA system assigns a measurement value confidence score to a relevant passage based on a comparison of the measurement value specified in the question and a second measurement value specified in the relevant passage.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quoc Phung whose telephone number is (703) 756 1330. The examiner can normally be reached on Monday through Friday from 9am to 5pm PT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached on 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Q.L.P./Examiner, Art Unit 2143
/JENNIFER N WELCH/Supervisory Patent Examiner, Art Unit 2143
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