Detailed Action
This communication is in response to the Application filed on 7/31/2023.
Claims 1-15 are pending and have been examined.
Claims 1-15 are rejected.
Claims 1 and 15 are independent and are parallel System and Method claims.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent Claim 1, the claim recites
“[Claim 1] A research viewpoint presentation system configured by an information processing apparatus, the system comprising:
a document information management unit that manages a document group that is an information extraction source;
a potential relevance level calculation unit that, for the document group, calculates a potential relevance level that is a value indicating a level of potential relevance between two words, using a co-occurrence rate determined based on a meaning of a word or a context in which the word appears, taking into consideration a potential relationship between the two words included in the document group;
an existing relevance level calculation unit that, for the document group, calculates an existing relevance level that is a value indicating a level of existing relevance between two words, based on a frequency of actual appearance of the two words;
a recommended research viewpoint extracting unit that, for pairs of two words extracted from the document group, selects a pair of two words, based on an index determined by comparing the potential relevance level with the existing relevance level, and extracts recommended research viewpoint information concerning the selected pair of two words from the document group;
and an information presentation unit that outputs the extracted recommended research viewpoint information.”
The limitations of “manage…”, “calculate…”, “calculate …”, “selects …” “extracts …” “outputs …” as drafted covers a human mental activity or process.
More specifically,
A human is capable of managing a document group using logic and reasoning in the human mind.
A human is capable of calculating a potential relevance level that is a value indicating a level of potential relevance between two words, using a co-occurrence rate determined based on a meaning of a word or a context in which the word appears, taking into consideration a potential relationship between the two words included in the document group using pen and paper and logic and reasoning and natural language understanding in the human mind.
A human is capable of calculating an existing relevance level that is a value indicating a level of existing relevance between two words, based on a frequency of actual appearance of the two words using pen and paper and logic and reasoning in the human mind.
A human is capable of selecting a pair of two words, based on an index determined by comparing the potential relevance level with the existing relevance level, using pen and paper and logic and reasoning in the human mind.
A human is capable of extracting recommended research viewpoint information concerning the selected pair of two words from the document group using pen and paper and logic and reasoning in the human mind.
A human is capable of outputting the extracted recommended research viewpoint information using pen and paper or vocal communication. No additional limitations present. The claims are not patent eligible.
Regarding independent Claim 15, Claim 15 is a method claim with limitations similar to that of claim 1 and is rejected under the same rationale. No additional limitations present. The claims are not patent eligible.
With respect to claim 2 the claim relates to selecting the pair of two words, using a size relationship between the potential relevance level and the existing relevance level as an index, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using logic and reasoning to select a pair of words and using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 3 the claim relates to selecting the pair of two words of which a difference or a ratio between the potential relevance level and the existing relevance level is larger than a preset threshold, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using logic and reasoning to select a pair of words and using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 4 the claim relates to selecting a pair of two words of which the potential relevance level is larger than a preset threshold and the existing relevance level is larger than a preset threshold, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using logic and reasoning to select a pair of words and using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 5 the claim relates to conducting a trend analysis of a word included in the document group, the trend analysis being based on the document, and thus determining word trend information indicating a trend in the word, wherein the recommended research viewpoint extracting unit selects the pair of two words based on a result of comparison of the potential relevance level, the existing relevance level, and the word trend information, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using pen and paper to conduct a trend analysis and using logic and reasoning in the human mind determine trend information and using natural language understanding in the human mind to extract information No additional limitations are present.
With respect to claim 6 the claim relates to selecting the pair of two words of which a difference or a ratio between the potential relevance level and the existing relevance level is larger than a preset threshold and of which a word trend growth rate obtained from the word trend information is high, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using logic and reasoning to select a pair of words and using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 7 the claim relates to conducting a trend analysis of a pair of two words included in the document group, the trend analysis being based on the document, and thus determining word pair trend information indicating a trend in the pair of two words, wherein the recommended research viewpoint extracting unit selects the pair of two words based on a result of comparison of the potential relevance level, the existing relevance level, and the word pair trend information, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using pen and paper to conduct a trend analysis and using logic and reasoning in the human mind determine trend information and using natural language understanding in the human mind to extract information No additional limitations are present.
With respect to claim 8 the claim relates to selecting a pair of two words of which a difference or a ratio between the potential relevance level and the existing relevance level is larger than a preset threshold and of which a word pair trend growth rate obtained from the word pair trend information is high, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using logic and reasoning to select a pair of words and using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 9 the claim relates to extracting the recommended research viewpoint information concerning a pair of two words including a word specified by a user, from the document group. This relates to a human using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 10 the claim relates to extracting the recommended research viewpoint information concerning a pair of two words including a word indicating a category specified by a user from the document group. This relates to a human using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 11 the claim relates to selecting a pair of two words, based on the index specified by the user, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. This relates to a human using logic and reasoning to select a pair of words and using natural language understanding in the human mind to extract information. No additional limitations are present.
With respect to claim 12 the claim relates to generating and outputting a graph in which the selected pair of two words are indicated by nodes representing the words, respectively, and an edge connecting the nodes to each other. This relates to a human using pen and paper to generate and output a graph. No additional limitations are present.
With respect to claim 13 the claim relates to generating and outputting the graph in which information based on the index determined for the pair of two words is attached to the edge. This relates to a human using pen and paper to generate and output a graph. No additional limitations are present.
With respect to claim 14 the claim relates to generating and outputting the graph in which a node representing the document that is an extraction source of the recommended research viewpoint information is connected to the node representing 65 each word, via an edge. This relates to a human using pen and paper to generate and output a graph. No additional limitations are present.
Claim Rejections - 35 USC § 103
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 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 1-11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Tsuzuki (US Patent Number US 8504357 B2), in view of Campbell (US Patent Number US 20180232215 A1).
Regarding independent Claim 1, Tsuzuki teaches [Claim 1] A research viewpoint presentation system configured by an information processing apparatus, the system comprising: a document information management unit that manages a document group that is an information extraction source; (see Tsuzuki (3:15-44) “(25) In order to achieve the above object, a related word presentation device according to an aspect of the present invention makes association dictionaries each including words and degrees of relevance between the words, and includes: a program information storage unit configured to store program information indicating, for each of programs, contents of the program using words; a classifying unit configured to make, for each of attributes of reference words each of which is a word included in the program information, at least one group as a unit including (i) a corresponding one of the reference words and (ii) a set of words which co-occur with the corresponding one of the reference words in a program including the corresponding one of the reference words; a first degree-of-relevance calculating unit configured to store the association dictionaries for the respective attributes ,the association dictionaries each including (i) a possible pair of words in a corresponding one of the attributes of the words and (ii) the degree of relevance between the words in the possible pair calculated based on the number of groups included in the corresponding one of the attributes of the words, the groups in the attributes of the words being classified based on presence or absence of the possible pair of words; an obtaining unit configured to obtain a search word and an attribute of the search word; a first related word selecting unit configured to select, as a first related word, a word related to the search word obtained by the obtaining unit from a corresponding association dictionary for the attribute obtained by the obtaining unit from among the association dictionaries for the respective attributes; and a presenting unit configured to present the first related word selected by the first related word selecting unit.”) a potential relevance level calculation unit that, for the document group, calculates a potential relevance level that is a value indicating a level of potential relevance between two words, using a co-occurrence rate determined based on a meaning of a word or a context in which the word appears, taking into consideration a potential relationship between the two words included in the document group; (see Tsuzuki (3:58-4:13) “(27) With this, in the related word presentation device according to the present invention, the words included in the program information are classified into groups based on their attributes such as Genre and Person's name, the degrees of relevance between the words are calculated based on the co-occurrence relationships in the to groups, and association dictionaries are made. Here, each of the groups corresponding to an attribute is composed for each reference word which is a word belonging to the attribute and present in the program information, and the group is a set of words which co-occur with the reference word in a program in the program information. Accordingly, each of the association dictionaries for the respective attributes made in this way indicates the degrees of relevance between the words calculated based on the co-occurrence relationships in the groups as described above, instead of the degrees of relevance between the words calculable based on the co-occurrence relationships in the programs as conventional. In other words, the degrees of relevance are calculated based on not co-occurrence relationships in a frame of a program but the co-occurrence relationships in a frame of a set of words which co-occur with the reference words, that is, a set of words which has a common usage or meaning.”) an existing relevance level calculation unit that, for the document group, calculates an existing relevance level that is a value indicating a level of existing relevance between two words, based on a frequency of actual appearance of the two words; (see Tsuzuki (3:15-45) “(25) In order to achieve the above object, a related word presentation device according to an aspect of the present invention makes association dictionaries each including words and degrees of relevance between the words, and includes: a program information storage unit configured to store program information indicating, for each of programs, contents of the program using words; a classifying unit configured to make, for each of attributes of reference words each of which is a word included in the program information, at least one group as a unit including (i) a corresponding one of the reference words and (ii) a set of words which co-occur with the corresponding one of the reference words in a program including the corresponding one of the reference words; a first degree-of-relevance calculating unit configured to store the association dictionaries for the respective attributes ,the association dictionaries each including (i) a possible pair of words in a corresponding one of the attributes of the words and (ii) the degree of relevance between the words in the possible pair calculated based on the number of groups included in the corresponding one of the attributes of the words, the groups in the attributes of the words being classified based on presence or absence of the possible pair of words; an obtaining unit configured to obtain a search word and an attribute of the search word; a first related word selecting unit configured to select, as a first related word, a word related to the search word obtained by the obtaining unit from a corresponding association dictionary for the attribute obtained by the obtaining unit from among the association dictionaries for the respective attributes; and a presenting unit configured to present the first related word selected by the first related word selecting unit.”) (see Tsuzuki (3:45-57) “(26) In addition, the first degree-of-relevance calculating unit may be configured to calculate the degree of relevance between the words in the possible pair according to the frequency of co-occurrence of the words in the possible pair in each of the groups generated by the classifying unit, and make the association dictionary which indicates the possible pair of words and the degree of relevance between the words in the possible pair in an associated manner. In addition, the first related word selecting unit may be configured to preferentially select a word having the greatest degree of relevance with the search word obtained by the obtaining unit as the first related word from the association dictionary for the attribute obtained by the obtaining unit.”)
Tsuzuki does not specifically teach a recommended research viewpoint extracting unit that, for pairs of two words extracted from the document group, selects a pair of two words, based on an index determined by comparing the potential relevance level with the existing relevance level, and extracts recommended research viewpoint information concerning the selected pair of two words from the document group; However, Campbell does teach this limitation (see Campbell [0031-0033] “It should now be appreciated, this disclosure describes a system and method illustrated in FIGS. 1 and 2. The disclosure begins by accessing open source research databases to collect papers of interest to the user, starting with the target paper. Automating this step eliminates the need for the researcher to repeatedly type title and author queries into, for example, Google Scholar, download each document individually, and manually look for both previous research and subsequent citations of each paper. Iterative downloads leveraging citation co-reference techniques such as those explored in IARPA FUSE can result in a suitable set of papers to form bibliometric clusters. [0032] This disclosure teaches the use of techniques such as vector-based word similarity to then find and relate concepts from the collected set of published research. A semantic vector defines the probability with which each word in the vocabulary is likely to appear in the context of a given word. This does not create a dictionary definition of the word, but allows us to plot terms in vector space to understand their relationships. The specificity of scientific terms will require that we use small contexts, such as individual paragraphs, to calculate the word embeddings. The disclosure then clusters the semantic vectors to create a concise representation of the field. Summing the vectors for all words in a publication provides a view of the paper's relation to the various concept clusters. This disclosure includes the use of other techniques for identifying significant words to find and relate concepts from the collected research works. [0033] …The system learns the concepts that exist within a field, the specific terms associated with those concepts, the association of each publication to each concept, and the terms in the target paper that differ from the other similar publications. Multiple terms with the same meaning, such as the terms “adjacency matrix” and “connection matrix”, will both be added to the corpus of search terms. A visual and editable representation of the word clusters, similar to the ubiquitous “word cloud,” can be used to describe the identified terms to a researcher attempting to implement the paper's methodology.”) and an information presentation unit that outputs the extracted recommended research viewpoint information. (see Campbell [0023] “Referring now to FIG. 1, a system 10 is shown where an electronic copy of a paper, here referred to as a target paper 2, is provided to a text and bibliography topic analyzer 12, where the analyzer 12 will capture the bibliography information from the paper as well as review the text to capture topics discussed in the paper into identify similar clusters of topics. Now that the paper has been analyzed, the analyzer 12 provides a bibliography cluster as well as topic clusters as an output to a text and bibliography topic comparator 14.”)
Tsuzuki in view of Campbell are in the same field of endeavor of speech processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of combination of Tsuzuki to incorporate the teachings of Campbell to include a recommended research viewpoint extracting unit that, for pairs of two words extracted from the document group, selects a pair of two words, based on an index determined by comparing the potential relevance level with the existing relevance level, and extracts recommended research viewpoint information concerning the selected pair of two words from the document group; and an information presentation unit that outputs the extracted recommended research viewpoint information. Doing so allows for more rapid analyzing and evaluating of a significant number of published works in a subfield to reduce uncertainty as recognized by Campbell in [0005].
Regarding independent Claim 15, Claim 15 is a parallel Method claim with limitations similar to that of Claim 1 and is rejected under the same rationale.
Regarding claim 2, Tsuzuki in view of Campbell teaches [Claim 2] The research viewpoint presentation system according to claim 1,
Furthermore, Tsuzuki teaches, wherein the recommended research viewpoint extracting unit selects the pair of two words, using a size relationship between the potential relevance level and the existing relevance level as an index, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (26:29-43) “(194) For example, in the above Embodiment and Variations, when the search condition obtaining unit 104 obtains the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the words each having a degree of relevance with the search word not less than a threshold value from among the attribute-based association dictionary for the attribute of the search word. However, the substitute word obtaining unit 105 may obtain, as substitute words (the third related words) each having a degree of relevance not less than the threshold value with the search word from the attribute-based association dictionary for the attribute, for each of search words and their attributes obtained in a predetermined past period in which the search words and their attributes were obtained.”)
Regarding claim 3, Tsuzuki in view of Campbell teaches [Claim 3] The research viewpoint presentation system according to claim 2,
Furthermore, Tsuzuki teaches, wherein the recommended research viewpoint extracting unit selects the pair of two words of which a difference or a ratio between the potential relevance level and the existing relevance level is larger than a preset threshold, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (25:46-67) “(190) For example, in the above Embodiment and the Variations thereof, when the search condition obtaining unit 104 obtains the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the words each having a degree of relevance not less than a threshold value with the search word from among the attribute-based association dictionary for the attribute of the search word. However, the substitute word obtaining unit 105 may obtain, as substitute words, only the words each having a degree of relevance not less than the threshold value with the search word and having the same attribute as that of the search word from the attribute-based association dictionary. (191) For example, when the search condition obtaining unit 104 obtains "Hanako Matsushita/Person's name" as the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the word "Shiro Matsushita" having a degree of relevance not less than the threshold value (for example, 0.6) with the "Hanako Matsushita" and having the same attribute "Person's name" which is also the attribute of the search word "Hanako Matsushita" from the attribute-based association dictionary for an attribute B 102b for the attribute "Person's name" shown in FIG. 8..”)
Regarding Claim 4, Tsuzuki in view of Campbell teaches [Claim 4] The research viewpoint presentation system according to claim 2,
Furthermore, Tsuzuki teaches, wherein the recommended research viewpoint extracting unit 61 selects a pair of two words of which the potential relevance level is larger than a preset threshold and the existing relevance level is larger than a preset threshold, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (25:46-67) “(190) For example, in the above Embodiment and the Variations thereof, when the search condition obtaining unit 104 obtains the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the words each having a degree of relevance not less than a threshold value with the search word from among the attribute-based association dictionary for the attribute of the search word. However, the substitute word obtaining unit 105 may obtain, as substitute words, only the words each having a degree of relevance not less than the threshold value with the search word and having the same attribute as that of the search word from the attribute-based association dictionary. (191) For example, when the search condition obtaining unit 104 obtains "Hanako Matsushita/Person's name" as the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the word "Shiro Matsushita" having a degree of relevance not less than the threshold value (for example, 0.6) with the "Hanako Matsushita" and having the same attribute "Person's name" which is also the attribute of the search word "Hanako Matsushita" from the attribute-based association dictionary for an attribute B 102b for the attribute "Person's name" shown in FIG. 8..”)
Regarding Claim 5, Tsuzuki in view of Campbell teaches [Claim 5] The research viewpoint presentation system according to claim 2,
Furthermore, Tsuzuki teaches, further comprising a trend information generating unit that conducts a trend analysis of a word included in the document group, the trend analysis being based on the document, and thus determining word trend information indicating a trend in the word, wherein the recommended research viewpoint extracting unit selects the pair of two words based on a result of comparison of the potential relevance level, the existing relevance level, and the word trend information, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (25:46-67) “(190) For example, in the above Embodiment and the Variations thereof, when the search condition obtaining unit 104 obtains the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the words each having a degree of relevance not less than a threshold value with the search word from among the attribute-based association dictionary for the attribute of the search word. However, the substitute word obtaining unit 105 may obtain, as substitute words, only the words each having a degree of relevance not less than the threshold value with the search word and having the same attribute as that of the search word from the attribute-based association dictionary. (191) For example, when the search condition obtaining unit 104 obtains "Hanako Matsushita/Person's name" as the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the word "Shiro Matsushita" having a degree of relevance not less than the threshold value (for example, 0.6) with the "Hanako Matsushita" and having the same attribute "Person's name" which is also the attribute of the search word "Hanako Matsushita" from the attribute-based association dictionary for an attribute B 102b for the attribute "Person's name" shown in FIG. 8..”)
Regarding Claim 6, Tsuzuki in view of Campbell teaches [Claim 6] The research viewpoint presentation system according to claim 5,
Furthermore, Tsuzuki teaches, wherein the recommended research viewpoint extracting unit selects the pair of two words of which a difference or a ratio between the potential relevance level and the existing relevance level (see Tsuzuki (23:24-30) “(165) Likewise the related word presentation device 100a in Variation 1, the related word presentation device according to this Variation presents narrow-down words and substitute words, and changes the ratio (word ratio) of the narrow-down words and substitute words to be presented, depending on the number of programs searched out based on a search condition.”) is larger than a preset threshold and of which a word trend growth rate obtained from the word trend information is high, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (25:46-67) “(190) For example, in the above Embodiment and the Variations thereof, when the search condition obtaining unit 104 obtains the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the words each having a degree of relevance not less than a threshold value with the search word from among the attribute-based association dictionary for the attribute of the search word. However, the substitute word obtaining unit 105 may obtain, as substitute words, only the words each having a degree of relevance not less than the threshold value with the search word and having the same attribute as that of the search word from the attribute-based association dictionary. (191) For example, when the search condition obtaining unit 104 obtains "Hanako Matsushita/Person's name" as the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the word "Shiro Matsushita" having a degree of relevance not less than the threshold value (for example, 0.6) with the "Hanako Matsushita" and having the same attribute "Person's name" which is also the attribute of the search word "Hanako Matsushita" from the attribute-based association dictionary for an attribute B 102b for the attribute "Person's name" shown in FIG. 8..”)
As to Claim 7, Tsuzuki in view of Campbell teaches [Claim 7] The research viewpoint presentation system according to claim 2,
Furthermore, Tsuzuki teaches further comprising a trend information generating unit that conducts a trend analysis of a pair of two words included in the document group, the trend analysis being based on the document, and thus determining word pair trend information indicating a trend in the pair of two words, (see Tsuzuki (25:46-67) “(190) For example, in the above Embodiment and the Variations thereof, when the search condition obtaining unit 104 obtains the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the words each having a degree of relevance not less than a threshold value with the search word from among the attribute-based association dictionary for the attribute of the search word. However, the substitute word obtaining unit 105 may obtain, as substitute words, only the words each having a degree of relevance not less than the threshold value with the search word and having the same attribute as that of the search word from the attribute-based association dictionary. (191) For example, when the search condition obtaining unit 104 obtains "Hanako Matsushita/Person's name" as the search word and the attribute thereof, the substitute word obtaining unit 105 obtains, as substitute words, the word "Shiro Matsushita" having a degree of relevance not less than the threshold value (for example, 0.6) with the "Hanako Matsushita" and having the same attribute "Person's name" which is also the attribute of the search word "Hanako Matsushita" from the attribute-based association dictionary for an attribute B 102b for the attribute "Person's name" shown in FIG. 8..”) wherein the recommended research viewpoint extracting unit selects the pair of two words based on a result of comparison of the potential relevance level, the existing relevance level, and the word pair trend information, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (23:24-30) “(165) Likewise the related word presentation device 100a in Variation 1, the related word presentation device according to this Variation presents narrow-down words and substitute words, and changes the ratio (word ratio) of the narrow-down words and substitute words to be presented, depending on the number of programs searched out based on a search condition.”)
As to Claim 8, Tsuzuki in view of Campbell teaches [Claim 8] The research viewpoint presentation system according to claim 7,
Furthermore, Tsuzuki teaches wherein the recommended research viewpoint extracting unit selects a pair of two words of which a difference or a ratio between the potential relevance level and the existing 63 relevance level is larger than a preset threshold and of which a word pair trend growth rate obtained from the word pair trend information is high, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (23:24-30) “(165) Likewise the related word presentation device 100a in Variation 1, the related word presentation device according to this Variation presents narrow-down words and substitute words, and changes the ratio (word ratio) of the narrow-down words and substitute words to be presented, depending on the number of programs searched out based on a search condition.”)
As to Claim 9, Tsuzuki in view of Campbell teaches [Claim 9] The research viewpoint presentation system according to claim 1,
Furthermore, Tsuzuki teaches wherein the recommended research viewpoint extracting unit extracts the recommended research viewpoint information concerning a pair of two words including a word specified by a user, from the document group. (see Tsuzuki (3:45-57) “(26) In addition, the first degree-of-relevance calculating unit may be configured to calculate the degree of relevance between the words in the possible pair according to the frequency of co-occurrence of the words in the possible pair in each of the groups generated by the classifying unit, and make the association dictionary which indicates the possible pair of words and the degree of relevance between the words in the possible pair in an associated manner. In addition, the first related word selecting unit may be configured to preferentially select a word having the greatest degree of relevance with the search word obtained by the obtaining unit as the first related word from the association dictionary for the attribute obtained by the obtaining unit.”) (see Tsuzuki (5:4-19) “(32) In addition, the related word presentation device may further include a program selecting unit configured to identify programs selected by the user from among the programs indicated in the program information, wherein the obtaining unit may be configured to obtain a single word as the search word from among the words according to the frequency of appearance of the words used in the program information to show the contents of the programs identified by the program selecting unit. (33) When programs are selected by the user in this way, a search word is predicted from the programs, the related word presentation device can present the first related words without receiving a direct input of the search word from the user. In other words, the user can cause it to present the first related words by selecting an attractive program even when the user does not have a specific search word.”)
As to Claim 10, Tsuzuki in view of Campbell teaches [Claim 10] The research viewpoint presentation system according to claim 1,
Furthermore, Tsuzuki teaches wherein one word making up the pair of two words is a word indicating a category of the other word of the pair of two words, and the recommended research viewpoint extracting unit extracts the recommended research viewpoint information concerning a pair of two words including a word indicating a category specified by a user from the document group. (see Tsuzuki (3:45-57) “(26) In addition, the first degree-of-relevance calculating unit may be configured to calculate the degree of relevance between the words in the possible pair according to the frequency of co-occurrence of the words in the possible pair in each of the groups generated by the classifying unit, and make the association dictionary which indicates the possible pair of words and the degree of relevance between the words in the possible pair in an associated manner. In addition, the first related word selecting unit may be configured to preferentially select a word having the greatest degree of relevance with the search word obtained by the obtaining unit as the first related word from the association dictionary for the attribute obtained by the obtaining unit.”) (see Tsuzuki (5:4-19) “(32) In addition, the related word presentation device may further include a program selecting unit configured to identify programs selected by the user from among the programs indicated in the program information, wherein the obtaining unit may be configured to obtain a single word as the search word from among the words according to the frequency of appearance of the words used in the program information to show the contents of the programs identified by the program selecting unit. (33) When programs are selected by the user in this way, a search word is predicted from the programs, the related word presentation device can present the first related words without receiving a direct input of the search word from the user. In other words, the user can cause it to present the first related words by selecting an attractive program even when the user does not have a specific search word.”)
As to Claim 11, Tsuzuki in view of Campbell teaches [Claim 11] The research viewpoint presentation system according to claim 1,
Furthermore, Tsuzuki teaches wherein the recommended research viewpoint extracting unit selects a pair of two words, based on the index specified by the user, and extracts the recommended research viewpoint information concerning the selected pair of two words from the document group. (see Tsuzuki (3:45-57) “(26) In addition, the first degree-of-relevance calculating unit may be configured to calculate the degree of relevance between the words in the possible pair according to the frequency of co-occurrence of the words in the possible pair in each of the groups generated by the classifying unit, and make the association dictionary which indicates the possible pair of words and the degree of relevance between the words in the possible pair in an associated manner. In addition, the first related word selecting unit may be configured to preferentially select a word having the greatest degree of relevance with the search word obtained by the obtaining unit as the first related word from the association dictionary for the attribute obtained by the obtaining unit.”) (see Tsuzuki (5:4-19) “(32) In addition, the related word presentation device may further include a program selecting unit configured to identify programs selected by the user from among the programs indicated in the program information, wherein the obtaining unit may be configured to obtain a single word as the search word from among the words according to the frequency of appearance of the words used in the program information to show the contents of the programs identified by the program selecting unit. (33) When programs are selected by the user in this way, a search word is predicted from the programs, the related word presentation device can present the first related words without receiving a direct input of the search word from the user. In other words, the user can cause it to present the first related words by selecting an attractive program even when the user does not have a specific search word.”)
Claims 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Tsuzuki (US Patent Number US 8504357 B2), in view of Campbell (US Patent Number US 20180232215 A1), and further in view of Crouse (US Patent Number US 20130212060 A1).
As to Claim 12, Tsuzuki in view of Campbell teaches [Claim 12] The research viewpoint presentation system according to claim 1,
Tsuzuki in view of Campbell do not specifically teach wherein the information presentation unit generates and outputs a graph in which the selected pair of two words are indicated by nodes representing the words, respectively, and an edge connecting the nodes to each other. However, Crouse does teach this limitation (see Crouse [0007] “Types of relationships among concept line items addressed by this patent application include prerequisites and dependencies (arrayed as nodes in a directed graph), levels in an architecture of the Ontology (arrayed as an undirected graph), distance relationships among nodes (stored in a distance matrix),”)
Tsuzuki in view of Campbell and Crouse are in the same field of endeavor of speech processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of Tsuzuki and Campbell to incorporate the teachings of Crouse to include the information presentation unit generates and outputs a graph in which the selected pair of two words are indicated by nodes representing the words, respectively, and an edge connecting the nodes to each other. Doing so allows for improved visualization of the interrelationships between concepts as recognized by Crouse in [0052].
As to Claim 13, Tsuzuki in view of Campbell teaches [Claim 13] The research viewpoint presentation system according to claim 12,
Tsuzuki in view of Campbell do not specifically teach wherein the information presentation unit generates and outputs the graph in which information based on the index determined for the pair of two words is attached to the edge. However, Crouse does teach this limitation (see Crouse [0006] “From extraction of a single problem written for Kindergarten, an analyst distilled 549 unique concept line items. In experimentations with problems of trigonometry, the typical extraction produced some 3,000 concept line items. To manually array the CLI data from the Kindergarten problem into a directed graph (a directed graph comprised of a subset of the 549 CLIs appears in FIGS. 26A to 26E of this application), and to create a node-arc incidence matrix to store those data relationships, squares the number of cells to be filled with data. Storage of concept line items extracted from the Kindergarten problem calls for a node-arc incidence matrix with 549.sup.2=301,401 cells. By the time a student reaches Algebra 1, we estimate that support of his math skill set can require 20,000 or more concept line items; 20,0002=400,000,000. That is 400 million cells in a node-arc incidence matrix populated with data that store some numeric description of an attribute of the relationship between pairs of concept line items. Clearly, a computer system comprising a processor and extensive database storage and analysis capabilities is essential to accomplishing the goals of the present disclosure.”) (see Crouse [0007] “Types of relationships among concept line items addressed by this patent application include prerequisites and dependencies (arrayed as nodes in a directed graph), levels in an architecture of the Ontology (arrayed as an undirected graph), distance relationships among nodes (stored in a distance matrix),”)
Tsuzuki in view of Campbell and Crouse are in the same field of endeavor of speech processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of Tsuzuki and Campbell to incorporate the teachings of Crouse to include the information presentation unit generates and outputs the graph in which information based on the index determined for the pair of two words is attached to the edge. Doing so allows for improved visualization of the interrelationships between concepts as recognized by Crouse in [0052].
As to Claim 14, Tsuzuki in view of Campbell teaches [Claim 14] The research viewpoint presentation system according to claim 12,
Tsuzuki in view of Campbell do not specifically teach wherein the information presentation unit generates and outputs the graph in which a node representing the document that is an extraction source of the recommended research viewpoint information is connected to the node representing each word, via an edge. However, Crouse does teach this limitation (see Crouse [0006] “From extraction of a single problem written for Kindergarten, an analyst distilled 549 unique concept line items. In experimentations with problems of trigonometry, the typical extraction produced some 3,000 concept line items. To manually array the CLI data from the Kindergarten problem into a directed graph (a directed graph comprised of a subset of the 549 CLIs appears in FIGS. 26A to 26E of this application), and to create a node-arc incidence matrix to store those data relationships, squares the number of cells to be filled with data. Storage of concept line items extracted from the Kindergarten problem calls for a node-arc incidence matrix with 549.sup.2=301,401 cells. By the time a student reaches Algebra 1, we estimate that support of his math skill set can require 20,000 or more concept line items; 20,0002=400,000,000. That is 400 million cells in a node-arc incidence matrix populated with data that store some numeric description of an attribute of the relationship between pairs of concept line items. Clearly, a computer system comprising a processor and extensive database storage and analysis capabilities is essential to accomplishing the goals of the present disclosure.”) (see Crouse [0007] “Types of relationships among concept line items addressed by this patent application include prerequisites and dependencies (arrayed as nodes in a directed graph), levels in an architecture of the Ontology (arrayed as an undirected graph), distance relationships among nodes (stored in a distance matrix),”)
Tsuzuki in view of Campbell and Crouse are in the same field of endeavor of speech processing, therefore It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of combination of Tsuzuki and Campbell to incorporate the teachings of Crouse to include the information presentation unit generates and outputs the graph in which a node representing the document that is an extraction source of the recommended research viewpoint information is connected to the node representing each word, via an edge. D