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 .
Detailed Action
In amendments dated 5/12/26, Applicant amended claims 1-2, 4-5, and 13-15, canceled claims 3, and 7-12, and added new claims 16-18. Claims 1-2, 4-6, and 13-18 are presented for examination.
Rejections under 35 U.S.C. 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 1-2, 4-6, and 13-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1 and 13 each recites evaluating each of the plurality of pieces of document data on the basis of the search query; inferring importance of each of a plurality of tags from the first classification; searching for a document with use of the tag received in the eighth step, wherein in the sixth step, learning of a classifier is performed with use of the first classification and a first feature vector as learning data to calculate the importance of each of the plurality of tags from the classifier, and wherein the classifier comprises a neural network, a decision tree, or a random forest. Evaluating document data is a mental process, inferring importance of a plurality of tags is also a mental process, and searching for a document is recited broadly and a mental process accomplishable in the human mind or on paper. Learning of a classifier is a known and conventional use of a classifier per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628), and using the classifier to calculate the importance of each of the plurality of tags is applying the classifier and is not significantly more than an abstract idea also per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Examiner notes specification paragraph 0087 describes the inferring of each of a plurality of tags is also performed by applying a classifier and is also not significantly more than an abstract idea also per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Each claim recites additional elements of receiving a plurality of pieces of document data, the plurality of pieces of document data is a plurality of machine-readable documents; receiving a search query input by an input device; receiving a first classification of at least the part of the plurality of pieces of document data; and receiving at least one of the tags whose importance is output in the seventh step, which are each data gathering steps and insignificant extra-solution activity; and outputting an evaluation result of at least a part of the plurality of pieces of document data as first evaluated document data together with information specifying the first evaluated document data to a graphical user interface; and outputting the importance of at least a part of the plurality of tags, which are each output steps and also insignificant extra-solution activity. Claim 13 recites a reception unit, a processing unit, and an output unit, which are generic modules and of a computer system. Examiner notes paragraph 0006 states “use of a PageRank system, like a web search, lacks the objectivity in a search for the contents of a document,” and “with respect to the meaning of one word, a plurality of expressions (e.g., Japanese phonetic scripts such as hiragana and katakana, kanji of Chinese characters, a representative word, a synonym, a broader term, and a narrower term) can be present, which makes it difficult to select a search keyword as appropriate,” and “patent documents are classified on the basis of technical matters … their classification codes have an enormous number of items, which makes it difficult to appropriately select a classification code.” Paragraph 0007 describes the invention as providing “a document search system, a document search method, or a method for outputting a document search result, which is of intuitiveness and efficient for a user, or … which can be operated easily by a user, or which enables a user to obtain needed information efficiently.” The specification recites details of how the invention addresses these objectives beginning in paragraph 0035 but those details are not claimed. Also, the claim steps do not recite a particular improvement in any technology or function of a computer per MPEP 2106.04(d) and do not recite any unconventional steps in the invention per MPEP 2106.05(a). Therefore, the recited mental processes are not integrated into a practical application. Taking the claims as a whole, the input and output steps are each recited broadly and amount to sending and receiving data across the network per specification paragraphs 0054, 0112, and figure 16 internet connection 5110, and are each routine and conventional activities per the list of such activities in MPEP 2106.05(d) part II. The reception unit, processing unit, and output unit are each still generic software modules and of a computer system. Thus the claims do not include additional elements that are sufficient to amount to significantly more than the recited mental processes.
Claims 2 and 14 each recites wherein each of the plurality of pieces of document data is given at least one tag, and a document given a tag is a mental process accomplishable in the human mind or on paper, wherein the search query comprises at least one tag, and a search query comprising a tag is a mental process accomplishable in the human mind or on paper, wherein the document search method further comprises a step of generating a feature vector for each of the plurality of pieces of document data with use of the tag given to the document data between the first step and the third step, and generating a vector is recited broadly and is a mental process accomplishable in the human mind or on paper, wherein the document search method further comprises a step of vectorizing the search query with use of the tag in the search query between the second step and the third step, and vectorizing a search query is recited broadly and is a mental process accomplishable in the human mind or on paper, and wherein in the third step, a similarity between the feature vector and the vectorized search query is calculated for each of the plurality of pieces of document data, and calculating a similarity is recited broadly and is a mental process accomplishable in the human mind or on paper.
Claim 4 recites wherein the search query comprises at least one word, and a search query comprising a word is a mental process accomplishable in the human mind or on paper, wherein the document search method further comprises a step of generating a first feature vector for each of the plurality of pieces of document data with use of a word extracted from the document data between the first step and the third step, and generating a vector is recited broadly and is a mental process accomplishable in the human mind or on paper, wherein the document search method further comprises a step of vectorizing the search query with use of the word in the search query between the second step and the third step, and vectorizing a search query is recited broadly and is a mental process accomplishable in the human mind or on paper, wherein in the third step, a similarity between the first feature vector and the vectorized search query is calculated for each of the plurality of pieces of document data, and calculating a similarity is recited broadly and is a mental process accomplishable in the human mind or on paper. Claim 5 recites wherein each of the plurality of pieces of document data is given at least one tag, and a document given a tag is a mental process accomplishable in the human mind or on paper, wherein in the sixth step, learning of a classifier is performed with use of the classification and a second feature vector as learning data to calculate the importance of each of the plurality of tags from the classifier, and applying a classifier with a classification and vector as input data is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628), and wherein the second feature vector of the document data is generated with use of the tag given to the document data, and generating a vector of document data is recited broadly and is a mental process accomplishable in the human mind or on paper. Examiner further notes per specification 0058 a tag can be a word.
Claim 6 recites wherein the inference in the sixth step comprises a calculation of a probability of determining the document data, and calculating a probability is recited broadly and is a mental process accomplishable in the human mind or on paper, and wherein in the seventh step, the probability of determining the document data is further output, which is recited broadly and amounts to sending data across a network per specification paragraphs 0054, 0112, and figure 16 internet connection 5110, and are each routine and conventional activities per the list of such activities in MPEP 2106.05(d) part II. Claim 15 recites wherein in the sixth step, learning of a classifier is performed with use of the classification and the feature vector as learning data to calculate the importance of each of the plurality of tags from the classifier, and applying a classifier with a classification and vector as input data is not significantly more than a mental process per Recentive Analytics v. Fox Broadcasting Corp. (134 F.4th 1205, 2025 U.S.P.Q.2d 628). Claim 16 recites wherein data is extracted from a database provided outside the data processing device, and extracting data from a database is retrieving data from a memory and is routine and conventional per the list of such activities in MPEP 2106.05(d) part II, and wherein the database comprises text data and image data, and storing data is routine and conventional per the list of such activities in MPEP 2106.05(d) part II.
Claim 17 recites wherein, in a second classification, the first evaluated document data is selected by a user in accordance with the closeness of the first evaluated document data to a desired document using the information specifying the first evaluated document data, and selecting a document involves evaluating and is a mental process. Claim 18 recites wherein the graphical user interface is configured to display data so that a user is capable of selecting data among the first evaluated document data determined to be close to a document to be searched, and displaying data which is recited broadly and amounts to sending data across a network per specification paragraphs 0054, 0112, and figure 16 internet connection 5110, and are each routine and conventional activities per the list of such activities in MPEP 2106.05(d) part II.
Rejections under 35 U.S.C. 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 1-2, 4, 6, and 13-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa et al (US 20210382881), hereafter Ishikawa, in view of Miracolo et al (US 20170270197), hereafter Miracolo, and in further view of Duchin et al (US 11,074,591), hereafter Duchin.
With respect to claims 1 and 13, Ishikawa teaches:
a first step of receiving a plurality of pieces of document data, the plurality of pieces od document data is a plurality of machine-readable documents (paragraphs 0014-0015, also 0032 retrieving document data, asemantic content of document data, paragraph 0059 figure 5, documents searched are machine-readable by search system 10);
a second step of receiving a search query input by an input device (paragraphs 0013, 0029 input search query);
a third step of evaluating each of the plurality of pieces of document data on the basis of the search query (paragraphs 0015, 0032 interpreting semantic content of a document relevant to a search query);
a fourth step of outputting an evaluation result of at least a part of the plurality of pieces of document data as first evaluated document data together with information specifying the first evaluated document data to a graphical user interface (paragraphs 0016, 0032-0033 evaluation result as location information in a document determined relevant to search query);
a fifth step of receiving classification of at least the part of the plurality of pieces of document data (paragraphs 0018, 0049 categorizing locations in a document);
a sixth step of inferring importance of each of a plurality of tags from the classification (paragraphs 0053, 0056 tags for categories in portions of a sentence, the tags’ importance inferred as related to a category); and
a seventh step of outputting the importance of at least a part of the plurality of tags (paragraphs 0053, 0056 tags outputted).
Ishikawa does not teach:
an eighth step of receiving at least one of the tags whose importance is output in the seventh step; and
a ninth step of searching for a document with use of the tag received in the eighth step,
wherein in the sixth step, learning of a classifier is performed with use of the first classification and a first feature vector as learning data to calculate the importance of each of the plurality of tags from the classifier,
wherein the classifier is stored in a storage unit, and
wherein the classifier comprises a neural network, a decision tree, or a random forest.
Duchin teaches:
wherein in the sixth step, learning of a classifier is performed with use of the first classification and a first feature vector as learning data to calculate the importance of each of the plurality of tags from the classifier (column 6 lines 39-49 figure 1, vector representations as learning data used to train the tag classifier 123),
wherein the classifier is stored in a storage unit (figure 1 tag classifier 123 stored in a compliance management platform 110), and
wherein the classifier comprises a neural network, a decision tree, or a random forest (column 6 lines 29-39 tag classifier 123 includes a random forest algorithm as classifier).
It would have been obvious to have combined the searching and use of a classifier in Duchin with the document searching techniques in Ishikawa to improve searching for large datasets such as patent documents.
Maricolo teaches these things:
an eighth step of receiving at least one of the tags whose importance is output in the seventh step (paragraph 0078 receiving a tag as input for search); and
a ninth step of searching for a document with use of the tag received in the eighth step (paragraph 0078 searching for document data with a tag).
It would have been obvious to have combined the function of searching with a tag in Maricolo with the document content searching techniques in Ishikawa to conduct a more accurate search using a relevant tag.
With respect to claim 13, Ishikawa teaches a reception unit, a processing unit, and an output unit, and a graphical user interface (paragraph 0019 system with software modules for implementing the invention, also paragraph 0030 output unit 5 for displaying a search result as a graphical user interface).
With respect to claims 2 and 14, all the limitations in claims 1 and 13 are addressed by Ishikawa, Duchin, and Maricolo above. Ishikawa also teaches:
wherein each of the plurality of pieces of document data is given at least one tag (paragraphs 0053, 0057 portion of document has a tag),
wherein the document search method further comprises a step of generating the first feature vector for each of the plurality of pieces of document data with use of the tag given to the document data between the first step and the third step (paragraph 0018 vectorizing sentences, words in a document),
wherein the document search method further comprises a step of vectorizing the search query with use of the tag in the search query between the second step and the third step (paragraph 0032 search is vectorized to search document data), and
wherein in the third step, a similarity between the feature vector and the vectorized search query is calculated for each of the plurality of pieces of document data (paragraph 0032 determine degree of similarity between document vectors and search query vectors).
Ishikawa does not teach wherein the search query comprises at least one tag. Maricolo teaches this in searching with a tag received as input (paragraph 0078). It would have been obvious to have combined the function of searching with a tag in Maricolo with the document content searching techniques in Ishikawa and Duchin to conduct a more accurate search using a relevant tag.
With respect to claim 4, all the limitations in claim 1 are addressed by Ishikawa, Duchin, and Maricolo above. Ishikawa also teaches:
wherein the search query comprises at least one word (paragraphs 0002, 0011 search has at least a keyword),
wherein the document search method further comprises a step of generating a second feature vector for each of the plurality of pieces of document data with use of a word extracted from the document data between the first step and the third step (paragraph 0018 generating vector of words in a document),
wherein the document search method further comprises a step of vectorizing the search query with use of the word in the search query between the second step and the third step (paragraph 0032 vectorize a search query), and
wherein in the third step, a similarity between the first feature vector and the vectorized search query is calculated for each of the plurality of pieces of document data (paragraph 0032 determine similarity between search vectors and document).
With respect to claim 6, all the limitations in claim 1 are addressed by Ishikawa, Duchin, and Maricolo above. Ishikawa also teaches:
wherein the inference in the sixth step comprises a calculation of a probability of determining the document data (paragraphs 0014, 0015 document data is input so it is determined, calculated probability is 1), and
wherein in the seventh step, the probability of determining the document data is further output (paragraph 0007 documents output as search results so probability is also output).
With respect to claim 15, all the limitations in claim 13 are addressed by Ishikawa, Duchin, and Maricolo above. Duchin also teaches:
wherein a classifier is stored in the storage unit (figure 1 tag classifier 123 stored in a compliance management platform 110),
wherein the classifier comprises a neural network, a decision tree, or a random forest (column 6 lines 29-39 tag classifier 123 includes a random forest algorithm as classifier), and
wherein the processing unit is configured to perform learning of the classifier with use of the classification and the feature vector as learning data and configured to calculate the importance of the plurality of tags from the classifier (column 6 lines 39-49 figure 1, vector representations as learning data used to train the tag classifier 123).
With respect to claim 16, all the limitations in claim 1 are addressed by Ishikawa, Duchin, and Maricolo above. Ishikawa also teaches:
wherein data is extracted from a database provided outside the data processing device (paragraph 0034 data extracted from corpus 27, paragraph 0038 corpus 27 may be external), and
wherein the database comprises text data and image data (paragraph 0034 corpus 27 has words (text data)).
With respect to claim 17, all the limitations in claim 1 are addressed by Ishikawa, Duchin, and Maricolo above. Ishikawa also teaches wherein, in a second classification, the first evaluated document data is selected by a user in accordance with the closeness of the first evaluated document data to a desired document using the information specifying the first evaluated document data (paragraph 0018 second category (classification) selected by a user as relevant location in document).
With respect to claim 18, all the limitations in claim 13 are addressed by Ishikawa, Duchin, and Maricolo above. Ishikawa also teaches wherein the graphical user interface is configured to display data so that a user is capable of selecting data among the first evaluated document data determined to be close to a document to be searched (paragraph 0018 displaying data in a GUI so user can select it).
Relevant Prior Art
During his search for prior art, Examiner found the following reference to be relevant to Applicant's claimed invention. Said reference is listed on the Notice of References form included in this office action:
Kuksta et al (US 2023018387) teaches collecting web documents and determining categories from web pages, teaches a classifier but does not teach tags from a classification or learning of a classifier with classifications and a feature vector (paragraph 0035, 0056, 0059, 0067-0072 figure 2).
Responses to Applicant’s Remarks
Regarding rejections of claims 1-15 under 35 U.S.C. 101 for reciting mental processes without significantly more, Applicant’s arguments have been considered but are not persuasive. On page 8 of his Remarks Applicant asserts the amended claims recite more than mere mental processes and transform the claims into a practical application. Examiner disagrees as the mental process steps identified in the rejections above are broad and invoke a generic computer or computer module as a tool per MPEP 2106.05(f). For example, the BRI of “evaluating each of the plurality of pieces of document data on the basis o the search query” might involve mentally rating each of said pieces of data; and the BRI of “searching for a document with use of a tag” might involve looking for a document using the tag from a folder of documents. That the mental processes are performed by a data processing device in claim 1 does not make them any less abstract per MPEP 2106.05(f) (“adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more."). Examiner notes the claim steps do not specifically address the problems described in specification paragraph 0006 (the difficulty of selecting a search keyword as appropriate and, the enormous number of classification codes making it difficult to appropriately select a classification code). Amending the claims to recite “a classifier” still just recites a classifier, and reciting that said classifier comprises a neural network, a decision tree, or a random forest still recites a general type of classifier. On page 9 of his Remarks, Applicant states “Examiner’s citation to Recentive Analytics asserting that use of a classifier is a mental process.” Examiner did cite the Recentive Analytics case but to show that conventional use of a classifier is not significantly more than a mental process and is invoking computer software as a tool to complete the recited activity, here inferring and calculating importance of tags. Thus Examiner does not believe the claims are integrated into a practical application.
Regarding rejections under 35 U.S.C. 103 of claims 1-2, 4, 6-8, 10, and 12-14 by Ishikawa and Miracolo, and claims 3, 5, 9, 11, and 15 by Ishikawa and Miracolo and further in view of Unsal, Applicant’s argument on page 11, that neither Ishikawa nor Miracalo teach a classifier which can include a neural network, is persuasive. Examiner conducted another search of the prior art and found Duchin, which Examiner believes teaches the classifier functions as recited in the new grounds of rejection above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE M MOSER whose telephone number is (571)270-1718. The examiner can normally be reached M-F 9a-5p.
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 9/2/26
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