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 preliminary amendments dated 3/6/25, Applicant amended claims 3, 6, 8-9, and 12-15, canceled no claims, and added no new claims. Claims 1-15 are presented for examination.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because each of the claim limitations uses a generic placeholder that is coupled with functional language (“is configured to”) without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: the second through fourth limitations in claim 13, the third limitation in claim 14, and the second limitation in claim 15.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Examiner found such structure in specification paragraph 0107 for a reception unit rephrased as a communications unit that may include a hub, a router, and a modem; paragraph 0093 for a processing unit that can be an arithmetic circuit or a CPU; and paragraph 0107 for an output unit that also may include a hub, a router, and a modem.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental processes without significantly more. Independent claims 1, 7, 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 classification; and searching for a document with use of the tag received in the eighth step. 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. Each claim recites additional elements of receiving a plurality of pieces of document data, receiving a search query, receiving 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, 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 software 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, 9, ad 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. Claims 3, 9, and 15 each 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).
Claims 4 and 8 each 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. Claims 5 and 11 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 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.
Claims 6 and 12 each 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.
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, 6-8, 10, and 12-14 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.
With respect to claims 1, 7, and 13, Ishikawa teaches:
a first step of receiving a plurality of pieces of document data (paragraphs 0014-0015, also 0032 retrieving document data, asemantic content of document data);
a second step of receiving a search query (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 (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.
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 (paragraph 0019 system with software modules for implementing the invention).
With respect to claims 2, 10, and 14, all the limitations in claims 1, 7, and 13 are addressed by Ishikawa 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 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 (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 to conduct a more accurate search using a relevant tag.
With respect to claims 4 and 8, all the limitations in claims 1 and 7 are addressed by Ishikawa 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 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 (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 claims 6 and 12, all the limitations in claims 1 and 7 are addressed by Ishikawa 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).
Claims 3, 5, 9, 11, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa and Miracolo and further in view of Unsal et al (US 20240119057), hereafter Unsal.
With respect to clams 3, 9, and 15, all the limitations in claims 1, 2, and 7, 8, 13, and 14 are addressed by the combination of Ishikawa and Maricolo above. The combination of Ishikawa and Maricolo does not teach 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. Unsal teaches this by inputting patient visit vectors into a machine language/BERT model (classification) to generate relevant vectors, wherein tags in visit vectors are prescription codes for a document (paragraphs 0022, 0024). With respect to claim 15, Unsal also teaches wherein a classifier is stored in the storage unit (paragraph 0080 figure 1, machine learning model stored in predictive data analysis computing entity 106). It would have been obvious to have combined this function of inputting classification and a vector into a classifier to calculate importance of the tags in the vector with the document search techniques in Ishikawa and Maricolo as using a machine language model like BERT is more accurate for classification and importance/relevance for documents.
With respect to claims 5 and 11, all the limitations in claims 1, 4, 7, and 10 are addressed by the combination of Ishikawa 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).
The combination of Ishikawa and Maricolo does not teach:
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
wherein the second feature vector of the document data is generated with use of the tag given to the document data.
Unsal teaches these things:
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 (paragraphs 0022, 0024 inputting patient visit vectors into a machine language/BERT model (classification) to generate relevant vectors, wherein tags in visit vectors are prescription codes for a document), and
wherein the second feature vector of the document data is generated with use of the tag given to the document data (paragraphs 0022, 0024 generating vectors in machine learning/BERT model having visit vectors with tags as input).
It would have been obvious to have combined this function of inputting classification and a vector into a classifier to calculate importance of the tags in the vector with the document search techniques in Ishikawa and Maricolo as using a machine language model like BERT is more accurate for classification and importance/relevance for documents.
Inquiry
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/BRUCE M MOSER/Primary Examiner, Art Unit 2154 2/7/26