DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending and claims 1 and 12 are independent claims.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1 and 12 recite “receiving a document …; performing a tokenization …; receiving… an input text …; searching …; identifying …; searching, on a partial concept list, for a partial concept …; creating a partial concept record …; updating …; and generating output …; whereby the concept match corresponds to a completed concept…” as drafted cover an abstract idea of data analysis/retrieval and mental steps. More specifically, the “receiving a document in memory; performing a tokenization operation on the document to divide the document into a plurality of word tokens, to assign at least one heuristic segmentation index to each of the plurality of word tokens, and to form a tokenized text; receiving, through input into the system, an input text having an expression with at least one search parameter therein with the at least one search parameter being one of a plurality of partial concept search parameters; searching at least one of the tokenized text and the input text for a parameter match when the at least one search parameter is located in the input text; identifying at least one of the plurality of word tokens and the assigned at least one heuristic segmentation index that corresponds to the parameter match; searching, on a partial concept list, for a partial concept record for the expression; creating a partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when there is no existing partial concept for the expression; updating the partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when an existing partial concept record is found; and generating output indicating that a concept match has occurred when the partial concept list has a partial concept record for each of the plurality of partial concept search parameters; whereby the concept match corresponds to a completed concept” which requires just data analysis / retrieval step and mental process. For instance one can receive a document/paper or text and read the paper/text and may form tokens using a spreadsheet. One may look over the paper in different creative ways to search and find for some specific words, for example, whether those terms partially or completely match the terms we are searching for. Once a person finishes searching mentally and putting the results of searching a bunch of terminologies of interest, those can be put together in a spreadsheet to generate the search the completed search outputs. The claimed invention is, therefore, directed to an abstract idea and a mental process without significantly more and thus, claims 1 and 12 are rejected under 35 U.S.C. 101.
Similarly, the dependent claims 2-11 and 13-20 recite similar claim language as in claims 1 and 12.
Claims 2 and 13 recite “the at least one search parameter selected from the group consisting of a word, a number, and a phrase,” which requires just a mental step of picking/selecting a search criteria/parameter. Thus, these claims 2 and 13 are directed to an abstract idea.
Claims 3 and 14 which recite “identifying at least one nested concept within the partial concept list; and assigning at least one reference to the at least one nested concept,” which also requires just a mental step. From a paper or document one is able to read, one can mentally identify at least one nested concept for instance, finding a hierarchical structure where one item, data set, or process is placed inside another one. Thus, claims 3 and 14 are directed to an abstract idea.
Claims 4 and 15 which recite “the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the word token count and the cumulative heuristic segmentation weights,” which also requires just a simple mathematical procedure that can be easily performed by determining a segmentation index for each of the plurality of word tokens as the sum of the word token count and the cumulative heuristic segmentation weights. Thus, claims 4 and 15 are directed to an abstract idea.
Claim 5 which recites “determining a maximum cumulative concept size,” which also requires just a simple mathematical procedure that can be easily performed by calculating/comparing cumulative concept size. Thus, claim 5 is directed to an abstract idea.
Claims 6 and 16 which recite “the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the cumulative word count and the cumulative weight of the heuristic segmenters found prior to the word token,” which also requires just a simple mathematical procedure that can be easily performed by adding the cumulative word count and the cumulative weight of the heuristic segmenters found prior to the word token to determine the heuristic segmentation index. Thus, claims 6 and 16 are directed to an abstract idea.
Claims 7 and 17 which recite “wherein the completed concept is one of a plurality of completed concepts, further comprising: ranking each of the completed concepts within the plurality of completed concepts,” which also requires just a simple mathematical procedure or a metal step of that can be easily performed mentally or applying a mathematical step to rank or arrange each of the completed concepts within the plurality of completed concepts. Thus, claim 7 and 17 are directed to an abstract idea.
Claim 8, 9 and 18 which recites “pruning the plurality of completed concepts by deleting one of the plurality of completed concepts based upon a rule within a knowledge base,” which also requires just a simple mental process that can be easily performed by deleting/erasing the completed concepts from the set. For instance one can mentally search for a specific word or phrase on a paper and indicate that that searched word or phrase is completely found by erasing the term or phrase. Thus, claim 8, 9 and 18 are directed to an abstract idea.
Claims 10 and 19 recite “evaluating each partial concept record on the partial concept list; and deleting one of the partial concept records when the difference between the maximum heuristic segmentation index for the one of the partial concept records and the minimum heuristic segmentation index for the one of the partial concept records exceeds a predetermined threshold,” which is a mathematical abstract idea. This involves some mathematical formula to find the difference between the maximum segmentation index and the minimum segmentation index for the one of the partial concepts that can be performed using a conventional/generic (general-purpose) computer (the published Spec. para 0029) or using a simple calculator. Thus, claims 10 and 19 are directed to an abstract idea.
Claims 11 and 20 recite “negating a completed concept record for one of the plurality of segments when the concept falls inside of a predetermined range within a negative expression,” which require just applying a negation operation on the completed concept record that can in turn be performed using a conventional/generic (general-purpose) computer (the published Spec. para 0029) or a simple calculator. Thus, claims 11 and 20 are directed to an abstract idea.
Thus, claims 1-20 as drafted cover a mental process and abstract idea of data gathering/retrieval and analysis/processing steps, and they are mental processes directed to an abstract idea of implementing mathematical formulae for data processing and data analysis using a conventional/generic (general-purpose) computer as well and thus, all the claims are directed to an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claims 1 and 12 recite additional element of “processor” and “memory” as per the independent claims. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional general purpose computer implementation. Claims 1-20, are therefore not drawn to patent eligible subject matter as they are directed to an abstract idea without significantly more. Thus, the claimed invention is directed to an abstract idea and a mental process without significantly more and thus, claims 1-20 are rejected under 35 U.S.C. 101.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a computer is noted as a general computer as noted. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (Spec., para 0029). Further, the additional limitation in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Dependent claims 2-11 and 13-20 are also directed toward an abstract idea and do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. Therefore, claims 1-20 do not contain patent eligible subject matter that has been identified by the courts.
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 1-3, 7, 12-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Terui et al. Pat App No. US 8930372 B2 (Terui) in view of Lucas et al. Pat App No. US 20200126663 A1 (Lucas), and further in view of Simonyi Pat No. US 7756859 B2 (Simonyi).
Regarding Claim 1, Terui discloses a method performed by one or more processors of a system (Terui, col 6, ln 65-67, include one or more single-core processor or multi-core processor and other hardware/firmware/software typical of a computing device), the method comprising:
receiving a document in memory (Terui, col 2, ln 16-23, The search engine includes, for example, a relational database, and inquires information managed with the relational database to search for the received search words using an SQL statement or the like. It is assumed here that document data in the search-target information in this example includes metadata, a title, or headline information);
performing a tokenization operation on the document to divide the document into a plurality of word tokens, to assign at least one heuristic segmentation index to each of the plurality of word tokens, and to form a tokenized text (Terui, col 2, ln 33-45, In the conventional example of FIG. 12(a), document data in the information is segmented into tokens… As for the token "toriatsuka-i", a token ["to" "ri" "atsuka" "i"] that is different in declensional kana ending is indexed in association with the original token ["tori" "atsuka" "i"]. Under the above condition, if the referencing processing of FIG. 12(a) is performed, the search token ["to" "cho"] is not registered in the index list of the information, so the search engine indicates mishits. On the other hand, as for the search tokens ["to" "ri" "atsuka" "i"] and ["ji" "kan"], a corresponding token is registered in the index list);
searching at least one of the tokenized text and the input text for a parameter match when the at least one search parameter is located in the input text (Terui, col 1, ln 31-39, In most of the search systems, target information is divided into unit segments (hereinafter referred to as "tokens") such as characters, words, and sentences and then indexed. Further, an input search word or search string is also divided into predetermined unit segments (hereinafter referred to as "search tokens") such as characters, words, or sentences. Whether to extract targeted information as a search result is determined based on whether tokens registered for the targeted information match with search tokens);
identifying at least one of the plurality of word tokens and the assigned at least one heuristic segmentation index that corresponds to the parameter match (Terui, col 2, ln 33-45, In the conventional example of FIG. 12(a), document data in the information is segmented into tokens of ["to" "kyo" "to"], ["cho"], ["no"], ["go"], ["an" "nai"], ["tori" "atsuka" "i"], and ["ji" "kan"]. As for the token "toriatsuka-i", a token ["to" "ri" "atsuka" "i"] that is different in declensional kana ending is indexed in association with the original token ["tori" "atsuka" "i"]. Under the above condition, if the referencing processing of FIG. 12(a) is performed, the search token ["to" "cho"] is not registered in the index list of the information, so the search engine indicates mishits. On the other hand, as for the search tokens ["to" "ri" "atsuka" "i"] and ["ji" "kan"], a corresponding token is registered in the index list, so the search engine indicates hit counts);
searching, on a partial concept list, for a partial concept record for the expression (Terui, col 17, ln 1-6, In step S1004, it is determined whether N-gram tokens are also hit in a range of the morphological token based on a mapping result. If even a partial overlap is judged as an overlap between two tokens. In this case, a search result of the morphological token the meaning of which could be easily understood is set as "hit");
creating a partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when there is no existing partial concept for the expression (Terui, col 15, ln 31-41, FIG. 8 shows processing for generating a result set for displaying data for the clients 102 based on the intermediate result generated in accordance with the processing of FIGS. 6 and 7. An intermediate result set 800 includes a field 802 for registering an information identification value for designating information and a field 804 for generating the total scores associated with a search word (string) of information specified with the information identification value, which are generated as TEMP_TABLE. The information identification value and the total scores of the information are registered as a record in the files 802 and 804);
whereby the concept match corresponds to a completed concept (Terui, col 10, ln 12-22, the same scores can be assigned to the morphological token and the N-gram token. Alternatively, after counting the number of morphological tokens and the number of N-gram tokens, if the morphological tokens and the N-gram tokens are completely matched, the same scores in total may be assigned. In another example, in consideration of significance of the meaning or expression of each token, the sum of completely matched morphological tokens may be set s times larger than the sum of completely matched N-gram tokens (s is an arbitrary real number of 1 or more) in order to impart significance to the correspondence of the morphological tokens).
Terui does not specifically disclose receiving, through input into the system, an input text having an expression with at least one search parameter therein with the at least one search parameter being one of a plurality of partial concept search parameters, and generating output indicating that a concept match has occurred when the partial concept list has a partial concept record for each of the plurality of partial concept search parameters.
However, Lucas, in the same field of endeavor, discloses:
receiving, through input into the system, an input text having an expression with at least one search parameter therein with the at least one search parameter being one of a plurality of partial concept search parameters (Lucas, para 0046, In the event that a desired patient is not displayed on the patient interface 18, the home screen 14 may also include a search indicator 16 that, upon selection by the user, receives text input such as a patient's name, unique identifier, or diagnosis, that permits the user to filter the patients by the search criteria of the text input to search for a specific patient );
generating output indicating that a concept match has occurred when the partial concept list has a partial concept record for each of the plurality of partial concept search parameters (Lucas, para 0183, Due to the large volume of concept candidates that may exist from the previous pipeline stage, merely searching for a match and terminating the search upon finding a single match may provide a substantial benefit in reducing the processing time spent crawling the relevant databases/dictionaries. However, the best matches may not be the first matches, and if there are multiple matches within a group (such as synonyms which are off by a single word to the concept candidate), it may be necessary to pick the match which has the lowest fuzzy “score” (the value that counts the number of errors corrected to generate the fuzzy match). If there are still ties (such as there are two matches of equal fuzzy “score”), then the tied matches may be sorted based on length of characters or length of words (such as shorter matches with less words/characters score higher than longer matches with more words/characters); [i.e., “If there are still ties (such as there are two matches of equal fuzzy “score”), then the tied matches may be sorted…” as “generating output indicating that a concept match has occurred when the partial concept list has a partial concept record …”]; OR, Lucas, para 0258, The initiated query may be generated by extracting the data from each field of the application and generating a query with the extracted data. For example, each field may include both the text that has been confirmed by the physician and the entity linking data identifying the match to a concept in the medical dictionary as described above. The fields may be added to a container, object model used by the underlying databases, a nested database/dictionary, or other format before encoding and transmission. Transmission may be received and processed at an endpoint of a cohort repository and engine for processing patients and generating the cohort report; [i.e., “…the generated cohort report” includes “partial or fuzzy concept matches” ] );
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Lucas in the method of Terui because this would enable mobile device application to be configured to capture a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient, with at least some of the information extracted from the document using an entity linking engine, and the extracted information provided into a structured data repository where it is accessible to provide more information regarding the patient specifically as well as collectively as part of a cohort of patients with similar genetic variants, medical histories, or other commonalities (Lucas, Abstract).
Terui in view of Lucas do not specifically disclose updating the partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when an existing partial concept record is found.
However, Simonyi, in the same field of endeavor, discloses updating the partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when an existing partial concept record is found (Simonyi, col 6, ln 58 - col 7, ln 23, In block 840, the component scores the selected entry based on the level of matching and the similarity of order of segments in the entry and query segments in the query string. In block 850, the component creates a score/identifier pair and adds it to the list. The component then loops to block 820 to select the next search result… In block 905, the component uses the segment extractor to obtain lists of segments from the query string and the matching string… In block 930, the component initializes the score for the currently selected pair of segments. In decision block 935, if the prefixes of the segments match, the component continues to block 940, else the component continues at block 945. In block 940, the component increments the score based on the length of match. In decision block 945, if the segments exactly match, the component continues at block 950, else the component continues at block 955. In block 950, the component increments the score based on the exact match. In block 955, the component increments the score based on the position of the segment in the query string relative to the position of the segment in the matching string. In block 960, the component adds the score for the current segments to the total score for the matching string ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Simonyi in the method of Terui in view of Lucas because this would enable any matching segments that are found from the query strings (both partial and complete matches) to be added to the search results when a user performs a search and the search system divides the query string into query segments and matches each segment with the segments in the index (Simonyi, col 3, ln 15-20).
Regarding Claim 2, Terui in view of Lucas, and further in view of Simonyi discloses the method of claim 1, wherein the at least one search parameter selected from the group consisting of a word, a number, and a phrase (Terui, col 1, ln 34-36, an input search word or search string is also divided into predetermined unit segments (hereinafter referred to as "search tokens") such as characters, words, or sentences; [“search tokens” as “search parameters”]).
Regarding Claim 3, Terui in view of Lucas, and further in view of Simonyi discloses the method of claim 1, further comprising:
Furthermore, Lucas teaches:
identifying at least one nested concept within the partial concept list; and assigning at least one reference to the at least one nested concept (Lucas, para 0258, …the match to a concept in the medical dictionary as described above. The fields may be added to a container, object model used by the underlying databases, a nested database/dictionary).
Regarding Claim 7, Terui in view of Lucas, and further in view of Simonyi discloses the method of claim 1.
Furthermore, Simonyi teaches:
wherein the completed concept is one of a plurality of completed concepts, further comprising: ranking each of the completed concepts within the plurality of completed concepts (Simonyi, col 1, ln 60-63, The search system may rank search results based on the closeness of the match to make it easier for a user to identify the best matching string).
Regarding Claim 12, Terui discloses a system, comprising:
one or more processors (Terui, col 6, ln 65-67, include one or more single-core processor or multi-core processor and other hardware/firmware/software typical of a computing device); and
at least one memory coupled to the one or more processors and storing instructions that, when executed by the one or more processors, cause the system to perform operations (Terui, col 8, ln 4-8, Each functional unit of FIG. 2 is executable on the server 104 by opening a problem on a memory of the server 104 and executing the program to control hardware resources) comprising:
receiving a document in memory (Terui, col 2, ln 16-23, The search engine includes, for example, a relational database, and inquires information managed with the relational database to search for the received search words using an SQL statement or the like. It is assumed here that document data in the search-target information in this example includes metadata, a title, or headline information);
performing a tokenization operation on the document to divide the document into a plurality of word tokens, to assign at least one heuristic segmentation index to each of the plurality of word tokens, and to form a tokenized text (Terui, col 2, ln 33-45, In the conventional example of FIG. 12(a), document data in the information is segmented into tokens… As for the token "toriatsuka-i", a token ["to" "ri" "atsuka" "i"] that is different in declensional kana ending is indexed in association with the original token ["tori" "atsuka" "i"]. Under the above condition, if the referencing processing of FIG. 12(a) is performed, the search token ["to" "cho"] is not registered in the index list of the information, so the search engine indicates mishits. On the other hand, as for the search tokens ["to" "ri" "atsuka" "i"] and ["ji" "kan"], a corresponding token is registered in the index list);
searching at least one of the tokenized text and the input text for a parameter match when the at least one search parameter is located in the input text (Terui, col 1, ln 31-39, In most of the search systems, target information is divided into unit segments (hereinafter referred to as "tokens") such as characters, words, and sentences and then indexed. Further, an input search word or search string is also divided into predetermined unit segments (hereinafter referred to as "search tokens") such as characters, words, or sentences. Whether to extract targeted information as a search result is determined based on whether tokens registered for the targeted information match with search tokens);
identifying at least one of the plurality of word tokens and the assigned at least one heuristic segmentation index that corresponds to the parameter match (Terui, col 2, ln 33-45, In the conventional example of FIG. 12(a), document data in the information is segmented into tokens of ["to" "kyo" "to"], ["cho"], ["no"], ["go"], ["an" "nai"], ["tori" "atsuka" "i"], and ["ji" "kan"]. As for the token "toriatsuka-i", a token ["to" "ri" "atsuka" "i"] that is different in declensional kana ending is indexed in association with the original token ["tori" "atsuka" "i"]. Under the above condition, if the referencing processing of FIG. 12(a) is performed, the search token ["to" "cho"] is not registered in the index list of the information, so the search engine indicates mishits. On the other hand, as for the search tokens ["to" "ri" "atsuka" "i"] and ["ji" "kan"], a corresponding token is registered in the index list, so the search engine indicates hit counts);
searching, on a partial concept list, for a partial concept record for the expression (Terui, col 17, ln 1-6, In step S1004, it is determined whether N-gram tokens are also hit in a range of the morphological token based on a mapping result. If even a partial overlap is judged as an overlap between two tokens. In this case, a search result of the morphological token the meaning of which could be easily understood is set as "hit");
creating a partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when there is no existing partial concept for the expression (Terui, col 15, ln 31-41, FIG. 8 shows processing for generating a result set for displaying data for the clients 102 based on the intermediate result generated in accordance with the processing of FIGS. 6 and 7. An intermediate result set 800 includes a field 802 for registering an information identification value for designating information and a field 804 for generating the total scores associated with a search word (string) of information specified with the information identification value, which are generated as TEMP_TABLE. The information identification value and the total scores of the information are registered as a record in the files 802 and 804);
whereby the concept match corresponds to a completed concept (Terui, col 10, ln 12-22, the same scores can be assigned to the morphological token and the N-gram token. Alternatively, after counting the number of morphological tokens and the number of N-gram tokens, if the morphological tokens and the N-gram tokens are completely matched, the same scores in total may be assigned. In another example, in consideration of significance of the meaning or expression of each token, the sum of completely matched morphological tokens may be set s times larger than the sum of completely matched N-gram tokens (s is an arbitrary real number of 1 or more) in order to impart significance to the correspondence of the morphological tokens).
Terui does not specifically disclose receiving, through input into the system, an input text having an expression with at least one search parameter therein with the at least one search parameter being one of a plurality of partial concept search parameters, and generating output indicating that a concept match has occurred when the partial concept list has a partial concept record for each of the plurality of partial concept search parameters.
However, Lucas, in the same field of endeavor, discloses:
receiving, through input into the system, an input text having an expression with at least one search parameter therein with the at least one search parameter being one of a plurality of partial concept search parameters (Lucas, para 0046, In the event that a desired patient is not displayed on the patient interface 18, the home screen 14 may also include a search indicator 16 that, upon selection by the user, receives text input such as a patient's name, unique identifier, or diagnosis, that permits the user to filter the patients by the search criteria of the text input to search for a specific patient );
generating output indicating that a concept match has occurred when the partial concept list has a partial concept record for each of the plurality of partial concept search parameters (Lucas, para 0183, Due to the large volume of concept candidates that may exist from the previous pipeline stage, merely searching for a match and terminating the search upon finding a single match may provide a substantial benefit in reducing the processing time spent crawling the relevant databases/dictionaries. However, the best matches may not be the first matches, and if there are multiple matches within a group (such as synonyms which are off by a single word to the concept candidate), it may be necessary to pick the match which has the lowest fuzzy “score” (the value that counts the number of errors corrected to generate the fuzzy match). If there are still ties (such as there are two matches of equal fuzzy “score”), then the tied matches may be sorted based on length of characters or length of words (such as shorter matches with less words/characters score higher than longer matches with more words/characters); [i.e., “If there are still ties (such as there are two matches of equal fuzzy “score”), then the tied matches may be sorted…” as “generating output indicating that a concept match has occurred when the partial concept list has a partial concept record …”]; OR, Lucas, para 0258, The initiated query may be generated by extracting the data from each field of the application and generating a query with the extracted data. For example, each field may include both the text that has been confirmed by the physician and the entity linking data identifying the match to a concept in the medical dictionary as described above. The fields may be added to a container, object model used by the underlying databases, a nested database/dictionary, or other format before encoding and transmission. Transmission may be received and processed at an endpoint of a cohort repository and engine for processing patients and generating the cohort report; [i.e., “…the generated cohort report” includes “partial or fuzzy concept matches” ] );
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Lucas in the method of Terui because this would enable mobile device application to be configured to capture a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient, with at least some of the information extracted from the document using an entity linking engine, and the extracted information provided into a structured data repository where it is accessible to provide more information regarding the patient specifically as well as collectively as part of a cohort of patients with similar genetic variants, medical histories, or other commonalities (Lucas, Abstract).
Terui in view of Lucas do not specifically disclose updating the partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when an existing partial concept record is found.
However, Simonyi, in the same field of endeavor, discloses updating the partial concept record on the partial concept list for the parameter match, the at least one of the plurality of word tokens, and the associated at least one heuristic segmentation index when an existing partial concept record is found (Simonyi, col 6, ln 58 - col 7, ln 23, In block 840, the component scores the selected entry based on the level of matching and the similarity of order of segments in the entry and query segments in the query string. In block 850, the component creates a score/identifier pair and adds it to the list. The component then loops to block 820 to select the next search result… In block 905, the component uses the segment extractor to obtain lists of segments from the query string and the matching string… In block 930, the component initializes the score for the currently selected pair of segments. In decision block 935, if the prefixes of the segments match, the component continues to block 940, else the component continues at block 945. In block 940, the component increments the score based on the length of match. In decision block 945, if the segments exactly match, the component continues at block 950, else the component continues at block 955. In block 950, the component increments the score based on the exact match. In block 955, the component increments the score based on the position of the segment in the query string relative to the position of the segment in the matching string. In block 960, the component adds the score for the current segments to the total score for the matching string ).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Simonyi in the method of Terui in view of Lucas because this would enable any matching segments that are found from the query strings (both partial and complete matches) to be added to the search results when a user performs a search and the search system divides the query string into query segments and matches each segment with the segments in the index (Simonyi, col 3, ln 15-20).
Regarding Claim 13, Terui in view of Lucas and Simonyi the system of claim 12, wherein the at least one search parameter selected from the group consisting of a word, a number, and a phrase (Terui, col 1, ln 34-36, an input search word or search string is also divided into predetermined unit segments (hereinafter referred to as "search tokens") such as characters, words, or sentences; [“search tokens” as “search parameters”]).
Regarding Claim 14, Terui in view of Lucas and Simonyi the system of claim 12.
Furthermore, Lucas teaches:
identifying at least one nested concept within the partial concept list; and assigning at least one reference to the at least one nested concept (Lucas, para 0258, …the match to a concept in the medical dictionary as described above. The fields may be added to a container, object model used by the underlying databases, a nested database/dictionary).
Regarding Claim 17, Terui in view of Lucas and Simonyi the system of claim 12.
Furthermore, Simonyi teaches:
wherein the completed concept is one of a plurality of completed concepts, further comprising: ranking each of the completed concepts within the plurality of completed concepts (Simonyi, col 1, ln 60-63, The search system may rank search results based on the closeness of the match to make it easier for a user to identify the best matching string).
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Terui in view of Lucas, further in view of Simonyi, further in view of Pat No. US 7756859 B2 (Simonyi), and further in view of Yerebakan et al. Pat No US 11645447 B2 (Yerebakan).
Regarding Claim 4, Terui in view of Lucas, and further in view of Simonyi discloses the method of claim 1.
Terui in view of Lucas, and further in view of Simonyi do not specifically disclose wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the word token count and the cumulative heuristic segmentation weights.
However, Yerebakan, in the same field of endeavor discloses wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the word token count and the cumulative heuristic segmentation weights (Yerebakan, col 8, ln 40-44, The above example formula provides a scoring value for a given segmentation of the first word based on a weighted sum of a term depending on the number of sub-words in the segmentation and a term depending on the frequencies associated with the sub-words).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Yerebakan in the method of Terui in view of Lucas and Simonyi because this would introduce an effective and efficient method of processing textual information, for example to extract from text information which is relevant to a specific task, which is essential for the ever increasingly generated and stored volume of textual information (Yerebakan, col 1, ln 18-23).
Regarding Claim 15, Terui in view of Lucas and Simonyi the system of claim 12.
Terui in view of Lucas, and further in view of Simonyi do not specifically disclose wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the word token count and the cumulative heuristic segmentation weights.
However, Yerebakan, in the same field of endeavor discloses wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the word token count and the cumulative heuristic segmentation weights (Yerebakan, col 8, ln 40-44, The above example formula provides a scoring value for a given segmentation of the first word based on a weighted sum of a term depending on the number of sub-words in the segmentation and a term depending on the frequencies associated with the sub-words).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Yerebakan in the method of Terui in view of Lucas and Simonyi because this would introduce an effective and efficient method of processing textual information, for example to extract from text information which is relevant to a specific task, which is essential for the ever increasingly generated and stored volume of textual information (Yerebakan, col 1, ln 18-23).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Terui in view of Lucas, further in view of Simonyi, further in view of Pat No. US 7756859 B2 (Simonyi), further in view of Yerebakan, and further in view of Tang et al. Pat App. No US 20080077570 A1 (Tang).
Regarding Claim 5, Terui in view of Lucas, further in view of Simonyi, and further in view of Yerebakan disclose the method of claim 4.
Terui in view of Lucas, Simonyi and Yerebakan do not specifically disclose determining a maximum cumulative concept size
However, Tang, in the same field of endeavor discloses determining a maximum cumulative concept size (Tang, para 0799, For those entries that made into the final hit candidate set, we can reconstruct each entry based on the positional information retrieved so far for words in all levels (level 1, 2 & 3). We will perform both global search and segmented search based on the re-constructed entries. In global search, an overall score for the entire entry is generated, based on the cumulative matching between query itoms and itoms within the entry. For segmented search, a gap penalty is applied for each of the non-matching word within a segment. The lower and upper boundaries of segments are determined so that the overall segment score can be maximized. There is a minimum threshold requirement for segments. If the score for the candidate segment is above this threshold, it is kept. Otherwise, it is ignored).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Tang in the method of Terui in view of Lucas, Simonyi and Yerebakan because this would introduce an automatic method of generating key phrases as well as a method of eliminating common words (stopping words) in a query to reduce query complexities Tang, para 0012-0013).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Terui in view of Lucas, further in view of Simonyi, further in view of Pat No. US 7756859 B2 (Simonyi), and further in view of Wang et al. Pat App. No CN 114692630 A (Wang).
Regarding Claim 6, Terui in view of Lucas, and further in view of Simonyi disclose the method of claim 1.
Terui in view of Lucas, and further in view of Simonyi do not specifically disclose wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the cumulative word count and the cumulative weight of the heuristic segmenters found prior to the word token.
However, Wang, in the same field of endeavor discloses wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the cumulative word count and the cumulative weight of the heuristic segmenters found prior to the word token (Wang, 9th page, 3rd para, aiming at each of the candidate word segmentation result, searching the word frequency of the preset word segmentation same as each candidate word segmentation in the candidate word segmentation result from the preset word stock, and accumulating the obtained word frequency, so as to obtain the word frequency total number corresponding to the candidate word segmentation result. under the condition of the word frequency total number obtain to each candidate word segmentation result, can be compared, determining the maximum word frequency total number, and the candidate word segmentation result corresponding to the maximum word frequency total number as the target word segmentation result).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Wang in the method of Terui in view of Lucas, Simonyi because this would provide convenience to obtain word segmentation result with conventional word segmentation habit, with a computing of the frequency total number to be small which is convenient to ensure the efficiency (Wang, 9th page, 3rd para).
Regarding Claim 16, Terui in view of Lucas and Simonyi the system of claim 12.
Terui in view of Lucas, and further in view of Simonyi do not specifically disclose wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the cumulative word count and the cumulative weight of the heuristic segmenters found prior to the word token.
However, Wang, in the same field of endeavor discloses wherein the at least one heuristic segmentation index for each of the plurality of word tokens is the sum of the cumulative word count and the cumulative weight of the heuristic segmenters found prior to the word token (Wang, 9th page, 3rd para, aiming at each of the candidate word segmentation result, searching the word frequency of the preset word segmentation same as each candidate word segmentation in the candidate word segmentation result from the preset word stock, and accumulating the obtained word frequency, so as to obtain the word frequency total number corresponding to the candidate word segmentation result. under the condition of the word frequency total number obtain to each candidate word segmentation result, can be compared, determining the maximum word frequency total number, and the candidate word segmentation result corresponding to the maximum word frequency total number as the target word segmentation result).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Wang in the method of Terui in view of Lucas, Simonyi because this would provide convenience to obtain word segmentation result with conventional word segmentation habit, with a computing of the frequency total number to be small which is convenient to ensure the efficiency (Wang, 9th page, 3rd para).
Claims 8-9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Terui in view of Lucas, further in view of Pat No. US 7756859 B2 (Simonyi), and further in view of Xu Pat App No. CN 112308122 A (Xu).
Regarding Claim 8, Terui in view of Lucas, and further in view of Simonyi discloses the method of claim 7.
Terui in view of Lucas, and further in view of Simonyi do not disclose pruning the plurality of completed concepts.
However, Xu, in the same field of endeavor, discloses pruning the plurality of completed concepts (Xu, 6th page, 2nd para, the nearest neighbour point of the point to be searched in the pruning point set is very close to the K nearest neighbour point of the point to be searched in the complete set. using the pruning point set to form pruning tree… because the number of the data points in the pruning point set is small, so it can quickly locate the nearest neighbour point in the pruning tree; Xu, 2nd page, 7th para - 3rd page, 2nd para, the invention claims a high-dimensional vector space sample fast searching method and device based on double-tree, furthest keeping the original data point set in the distribution state of the high-dimensional space, fast locating the nearest neighbour point, effectively reducing the pruning radius, improving the pruning effect, improving the K neighbour query efficiency…step 2, filtering each sub-tree with the number of data points greater than or equal to the maximum number of pruning points; keeping the data point closest to the centroid in each sub-tree…; removing the remaining elements from all sample point set to form pruning point set).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Tang in the method of Terui in view of Lucas and Simonyi because this would enable searching K neighbour in the deleted tree and the complete tree because when the initial near point position is not fixed, but located near the point to be checked, that effectively reduces the pruning radius that effectively improves the pruning effect and improves the K neighbour query efficiency (Xu, 6th page, 2nd para & 2nd page, 7th para).
Regarding Claim 9, Terui in view of Lucas, and further in view of Simonyi discloses the method of claim 7.
Terui in view of Lucas, and further in view of Simonyi do not disclose deleting one of the plurality of completed concepts based upon a rule within a knowledge base.
However, Xu, in the same field of endeavor, discloses deleting one of the plurality of completed concepts based upon a rule within a knowledge base (Xu, 6th page, 2nd para, The invention filters little data points from the original data point set to form the pruning point set; filtering the residual data points to form the deleted point set; … searching K neighbour in the deleted tree and the complete tree; Xu, 2nd page, 7th para - 3rd page, 2nd para, the invention claims a high-dimensional vector space sample fast searching method and device based on double-tree, furthest keeping the original data point set in the distribution state of the high-dimensional space, fast locating the nearest neighbour point, effectively reducing the pruning radius, improving the pruning effect, improving the K neighbour query efficiency…step 2, filtering each sub-tree with the number of data points greater than or equal to the maximum number of pruning points; keeping the data point closest to the centroid in each sub-tree…; removing the remaining elements from all sample point set to form pruning point set).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Tang in the method of Terui in view of Lucas and Simonyi because this would enable improving the pruning effect and improving the whole query efficiency that effectively improves the pruning effect and improves the K neighbour query efficiency (Xu, 6th page, 2nd para & 2nd page, 7th para).
Regarding Claim 18, Terui in view of Lucas and Simonyi the system of claim 17.
Terui in view of Lucas, and further in view of Simonyi do not disclose pruning the plurality of completed concepts by deleting one of the plurality of completed concepts based upon a rule within a knowledge base.
However, Xu, in the same field of endeavor, discloses pruning the plurality of completed concepts by deleting one of the plurality of completed concepts based upon a rule within a knowledge base (Xu, 6th page, 2nd para, The invention filters little data points from the original data point set to form the pruning point set; filtering the residual data points to form the deleted point set; … searching K neighbour in the deleted tree and the complete tree; Xu, 2nd page, 7th para - 3rd page, 2nd para, the invention claims a high-dimensional vector space sample fast searching method and device based on double-tree, furthest keeping the original data point set in the distribution state of the high-dimensional space, fast locating the nearest neighbour point, effectively reducing the pruning radius, improving the pruning effect, improving the K neighbour query efficiency…step 2, filtering each sub-tree with the number of data points greater than or equal to the maximum number of pruning points; keeping the data point closest to the centroid in each sub-tree…; removing the remaining elements from all sample point set to form pruning point set).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Tang in the method of Terui in view of Lucas and Simonyi because this would enable improving the pruning effect and improving the whole query efficiency that effectively improves the pruning effect and improves the K neighbour query efficiency (Xu, 6th page, 2nd para & 2nd page, 7th para).
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Terui in view of Lucas, further in view of Simonyi, and further in view of Wood Pat App No. US 20130232172 A1 (Wood).
Regarding Claim 11, Terui in view of Lucas, and further in view of Simonyi discloses the method of claim 1.
Terui in view of Lucas, and further in view of Simonyi does not specifically disclose negating a completed concept record for one of the plurality of segments when the concept falls inside of a predetermined range within a negative expression.
However, Wood, in the same field of endeavor discloses negating a completed concept record for one of the plurality of segments when the concept falls inside of a predetermined range within a negative expression (Wood, para 0018, any expression that includes excluded or negated terms is converted to a purely positive expression by removing those terms joined to the remainder of the expression by an excluding operator (e.g., `NOT` or the like)).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Wood in the method of Terui in view of Lucas and Simonyi because this would enable, after the positive expression set is obtained, the expression matching process to continue by identifying, for each respective data item subset, a subset of that expression set that matches that respective data item subset, each expression of the expression set (neglecting any excluded terms) collectively compared to an entire data item subset to determine whether there might be a data item that matches that expression within the data item subset, thereby effectively partitioning the expression set by removing or otherwise disregarding expressions that do not have a potential match within the data item subset from unnecessary further comparisons (Wood, para 0019).
Regarding Claim 20, Terui in view of Lucas and Simonyi the system of claim 12.
Terui in view of Lucas, and further in view of Simonyi does not specifically disclose negating a completed concept record for one of the plurality of segments when the concept falls inside of a predetermined range within a negative expression.
However, Wood, in the same field of endeavor discloses negating a completed concept record for one of the plurality of segments when the concept falls inside of a predetermined range within a negative expression (Wood, para 0018, any expression that includes excluded or negated terms is converted to a purely positive expression by removing those terms joined to the remainder of the expression by an excluding operator (e.g., `NOT` or the like)).
Therefore, it would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to incorporate the method of Wood in the method of Terui in view of Lucas and Simonyi because this would enable, after the positive expression set is obtained, the expression matching process to continue by identifying, for each respective data item subset, a subset of that expression set that matches that respective data item subset, each expression of the expression set (neglecting any excluded terms) collectively compared to an entire data item subset to determine whether there might be a data item that matches that expression within the data item subset, thereby effectively partitioning the expression set by removing or otherwise disregarding expressions that do not have a potential match within the data item subset from unnecessary further comparisons (Wood, para 0019).
Allowable Subject Matter
Claims 10 and 19 are objected to as being dependent upon rejected base claims, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and also if all these claims overcome the 101 rejections. The reasons for allowance are that the prior art of record do not specifically teach the limitations as recited in the mentioned claims.
Conclusion
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/MULUGETA TUJI DUGDA/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
02/28/2026