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
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-10, 12, 13 remain rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1-12 of U.S. Patent No. 11727214 as the amendments do not overcome. Although the claims at issue are not identical, they are not patentably distinct from each other because the claim 1 of the ‘214 patent in conjunction with claims 7, 10 of the ‘214 patent form a species to which instant claim 1 can be considered generic. The newly recited matrix data structure of clustered condition sentences; the associations borne by the data structure is recited in the patented claims of the ‘214 patent as is the word vector data. The remaining claims are considered obvious variants or extensions of claims 2-10 of the 3’214 patent.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-10, 12, 13 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1, 12, 13 recite “wherein the case value creation unit…calculates a correlation coefficient for each cluster…” this conflicts with the previously claimed calculation by a coefficient creation unit in such a way as it cannot be determined which performs what calculation, particularly in consideration of the recited “and a relevant effect value,” which may be independently computed by the case value creation unit, or may be part of the correlation coefficient calculation possible additionally performed by the coefficient creation unit. Further, effect values are previously claimed and the newly claimed “relevant effect value,” is indefinite as apart from disclosure in the specification regarding a particular column in a data structure (see ¶ 63; Fig 13 of the specification) it cannot be discerned how relevance is determined nor an object to which the discernment applies. Additionally, in claim 1 the recited “parameter selection unit” is missing an article; claim 12 recites “parameter selection unit step,” which lacks an article and introduces the question of whether excess verbiage exists in the claim, or whether the step is intendent to select a parameter. selection unit—Examiner will presume the former. Claims 12, 13 additionally recite “the case value creation unit,” which lacks antecedent as the claims introduces “a case value creation step,” to which further unamended portions of the claims refer. Claims 2-10 do not remedy and are similarly rejected. Claim 10 additionally recites “that the case value creation unit “adds a numerical value indicating whether each cluster of the condition sentence…”. The adding of a numerical value is considered indefinite as it is unclear where such a value is added, further the “cluster of condition sentence,” lacks antecedent as the previous recitations resolve “clusters of the condition sentences.” Appropriate correction is required.
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 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-10, 12, 13 rejected under 35 U.S.C. 103 as being unpatentable over Burstein: 20100223051 hereinafter Bur further in view of Zhao: 20170278510 and further in view of Duan: 20050149518.
Regarding claim 1
Bur teaches:
A sentence classification apparatus for classifying sentences into classified groups (Bur: Abstract; ¶2, 6-9, 24, 67, 68: classifier model trained to predict relations and determine text coherence among sentences) comprising:
a case sentence obtention unit for obtaining a plurality of case sentences which are associated with effect values that are values obtained by evaluating effects (Bur: ¶ 9-12, 24, 43: such as by determining word by document values of singular value decomposition (SVD) with respect to sentences within a document wherein the sentences or other segments of the document are represented as vectors such as by performance of latent semantic analysis (LSA) which determines a vector space comprising vectors for each term, sentence, of a document and said space representative of effect values, vectors, etc. thereof and operable for ranking sentences, phrases, etc. along dimensions thereof along determined relevant dimensions);
a case value creation unit for creating case values obtained by numerizing the case sentences for each of a plurality of parameters with different values (Bur: Abstract; ¶ 9-12, 28-30, 52, 53, 59, 61-65: sparse random indexing vectors comprises a vocabulary reified with respect to text segments, sentences, etc. of an input document thereby generative of numerical values such as within a word by context co-occurrence matrix, and such as within a group of words in the form of a text segment vector; from such structures similarity scores are derived, rankings determined, etc.);
a correlation coefficient calculation unit for calculating a correlation coefficient between the case values and the effect values for the values of the parameters to provide a similarity score (Bur: ¶ 8, 28-30, 67-69, 72-77, 89: a cosine similarity measure used to determine relatedness of segments, sentences, etc. wherein parameters of case sentences determined in the document are compared with an effect, target, etc. sentence, parameters thereof); and
a parameter selection unit for selecting a parameter among the parameters with different values on the basis of a similarity score (Bur: ¶ 42-45: sentences are ranked with respect to plurality of dimensions; classified, labelled, etc. with respect to similarity; etc.);
wherein the case sentences include condition sentences showing the conditions for the cases and outcome sentences showing the outcomes of the cases respectively (Bur: ¶ 26, 37: such as by categorizing sentences, elements, etc. of the documents; such as by labelling of discourse elements such as: background, thesis, main idea, etc.),
wherein the case value creation unit clusters the condition sentences and the outcome sentences into a plurality of clusters of the condition sentences and the clusters of the outcome sentences, respectively (Bur: ¶ 8, 25, 37, 43, 66-77: Fig. 2: sentences annotated with labels such as by classification based labelling into defined categories, classes, etc., which are clustered with respect to relatedness by class based on vector values thereof),
and wherein the plurality of case sentences are classified using the parameter selected by the parameter selection unit (Bur: ¶ 66-77, 107, etc.: sentences classified as related based on training a classifier based on selected features, weights thereof, etc.) and
wherein the case value creation unit creates a word vector data structure comprising a vocabulary of words and at least a semantic vector for each word (Bur: 9-11, 54, 56, etc.: system generates a word vector vocabulary of input documents which functions similarly as a database of words, word vectors, which functions to track semantic relationships among words across documents)
in which words are associated with word vectors respectively for each of the parameters with different values on the basis of the case sentences obtained by the case sentence obtention unit (Bur: Abstract; ¶ 9-11, 60, 61: word vectors of the input document(s) used to generate contest vectors to generate semantic segments of input document(s) based thereon and using parameters derived therefrom such that a semantic vector is created for each word and increments same to include the label vectors of neighboring vectors within a certain distance).
Bur discusses selection of parameters, clustering, etc. based on the calculation of correlation coefficients among sentences obtained from a document and projected values thereof and wherein the plurality of case sentences are classified using a parameter selected by a parameter selection unit. Bur teaches learning word vectors and generating sentence vectors therefrom such as by Latent Semantic Analysis such vectors used for generating a word vector database in the form of vectorized vocabulary for each of one or more documents (Bur: ¶ 9, 12, 25). Bur does not explicitly discuss the amended recited case value creation unit which creates a word vector database that creates …case values using the word vector database. Bur teaches determining correlations among sentences obtained from a document and projected into a vector space representative of effect values thereof however Bur does not discuss the explicit calculation and iteration of correlation coefficients for each of values of each of the parameters for the target and case values of the sentences in a manner suitable for parameter selection unit for selecting a parameter among the parameters with different values on the basis of the correlation coefficient, nor does Bur explicitly teach that the system configures a matrix data structure indicating, whether each cluster of condition sentences is associated with a cluster of the outcome sentences, calculates a correlation coefficient for each cluster based on the matrix data structure and a relevant effect value, wherein the similarly calculation unit calculates values for each cluster, and wherein the parameter selection unit selects parameters based on similarities for each cluster and wherein the plurality of case sentences are classified using the parameter selected by the parameter selection unit.
In a related field of endeavor Zhao teaches a system and method for training a language processor by using mappings, embeddings, etc. of word vectors derived from input sentences using a word vector database such as word2vec, glove, etc. and to thereby resolve attention parameters representing correlation between a word and each of one or more other words in each of the input sentences (Zhao: Abstract; ¶ 35, 39, 48: an input sentence segmented into words used to calculate word vectors, context vectors, etc.), such that for each of a plurality of parameters with different value (Zhao: ¶ 35, 39, 48, 79: system varies parameters to difference values based on a decay factor, window size, etc.); parameter selection unit for selecting a parameter among the parameters with different values on the basis of the correlation coefficient (Zhao: 57, 79, etc.: system selects parameters by iterating over parameter values thereby iteratively determining a performative metric by value setting such that the setting optimizes the metric), in this way the system operates to select parameter(s) on the basis of the determined correlation coefficient(s) which optimize the metric(s) (Zhao: ¶ 57, 79: system teaches a parameter selection framework) and that the plurality of case sentences are classified using the parameter selected by the parameter selection unit (Zhao: ¶ 57, 79, 63: sentences classified by a neural network based on words therein and attention parameter(s) corresponding to the word(s)); and a case value creation unit creates word vector data in which words are associated with word vectors respectively for each of the parameters with different values on the basis of the input sentences based on persisted mappings, embeddings, derived therefrom (Zhao: ¶ 35, 39, 48, 72: such as by maintaining a data structure mapping the word vectors, embeddings therefor upon a word vector database such as word2vec, etc.; said embeddings, mappings, etc. comprising a persistent data structure by which the word vector database learns to represent documents in a corpus and encodes relationships therebetween).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to create an explicit vocabulary related database to maintain the Bur taught or suggested encoded semantic relationships among words, word segment vectors, semantic vectors, etc. of the expanding Bur corpus specific vocabulary for persisting the Zhao taught mappings or embeddings to a word vector database such as word2vec etc. and for at least the purpose of maintaining the semantic structure(s) accreted by Bur with respect to emergent meaning thereof upon word2vec or similar word vector databases; one of ordinary skill in the art would have expected only predictable results therefrom.
Bur in view of Zhao thus teaches determining correlations among sentences obtained from a document and projected into a vector space representative of effect values thereof however Bur in view of Zhao does not discuss the explicit calculation of correlation coefficients and selection of parameters, clustering, etc. based on the calculation of correlation coefficients among sentences obtained from a document and projected values thereof; wherein the case value creation unit: clusters the condition sentences and the outcome sentences into a plurality of clusters of the condition sentences and the clusters of the outcome sentences, respectively, to thereby configure a matrix data structure indicating, whether each cluster of condition sentences is associated with a cluster of the outcome sentences, and calculates a correlation coefficient for each cluster based on the matrix data structure and a relevant effect value, wherein the parameter selection unit selects the parameter on the basis of the correlation coefficient for each cluster.
In a related field of endeavor Tse teaches a system and method for mining and analyzing text (Tse: Abstract) wherein the system, method, etc. wherein the system clusters the condition sentences and the outcome sentences into a plurality of clusters of the condition sentences and the clusters of the outcome sentences, respectively (Tse: § 1 pp 2-3; 2 pp 7, 4.2.2 pp 17, Table 2: system clusters documents using automated clustering algorithms, said clustering with respect to first and second types representative of a condition side such as technology and an outcome side, such as the effect, respectively), to thereby
configure a matrix data structure indicating, whether each cluster of condition sentences is associated with a cluster of the outcome sentences (Tse: § 1 pp 2-3; Table 1, 2: the shown technology effect matrix comprises rows indexed by clusters of a particular type and columns indexed by clusters of a second type and the cell entries indicate whether instances of input documents are associated with each/any of the particular pairings), and
calculates a correlation coefficient for each cluster based on the matrix data structure and a relevant effect value (Tse: § 3.6.4 pp 17, 4.2.1 pp 22-23; 4.2.2 pp 23-24; Table 6-8: system computes a correlation coefficient for each cluster between a term per cluster(s) presence or absences across the collection of documents and the external category-membership indicator such as reified by effect values or parameters which show the distribution in each set per category; such that “the correlation coefficient selects exactly those terms that are highly indicative of membership in a category,”),
wherein the parameter selection unit selects the parameter on the basis of the correlation coefficient for each cluster (Tse: § 3.6.1 pp 15:-16, 4.4 pp 28-29: a per cluster correlation coefficient determines, compares, etc. parameter alternatives based on optimizing cluster quality based on parameter choice which allows evaluating parameters as they amount to correlation based clustering quality).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to utilize the technology-effect clustering matrix and per cluster correlation coefficient as taught or suggested by Tse to organize condition side and effect side sentences and thereby evaluate cluster quality against effect values within the sentence classification system of Bur in view of Zhao and to utilize the per-cluster correlation of Tse as the metric driving the parameter selection framework of Bur in view of Zhao for at least the purpose of revealing case level relations among the matrix parameters and thereby improve classification accuracy through parameter optimization based on a cluster quality metric; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 2
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 1 wherein the parameter selection unit selects the parameter on the basis of the number of correlation coefficients that exceeds a certain threshold for each parameter (Bur: ¶ 61, 70, Fig 2: word vector reified based on threshold semantic distance); (Zhao: ¶ 37, 53-55, 69, 70: a decay parameter functions as a threshold limiting the number of correlations by limiting the allowable distance between words generative of an attention parameter thereby enabling threshold based parameter selection); (Tse: § 3.6.4 pp 17-18, 3.6.5 pp 18, 4.4 pp 28-29: threshold based filtering of correlation coefficients per cluster based on document frequency and quality of clusters exceeding a quality metric). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 3
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 1 wherein the parameter selection unit selects the parameter on the basis of the average value of the correlation coefficients calculated for each parameter (Bur: ¶ 8: adjacency among vectors, etc. based on average values therebetween; based on an average distance, etc.); (Zhao: ¶ 78, 79: system plots, compares, etc. an averaged accuracy with respect to parameter values); (Tse: § § 4.2.2 pp23-25: system averages term coverage rates on correlation based quality across categories; “term-covering percentage (of the best terms and the relevant terms) of each set was accumulated and averaged over all categories,”). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 4
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 1, further comprising: a classification outcome display unit for displaying the outcome obtained by classifying the plurality of case sentences using the parameter selected by the parameter selection unit (Bur: ¶ 8, 25, 43-45, 70-77: Fig. 2: such as the display of classification data); (Tse: § 5.3 pp 32-35, 5.4 pp 36; Fig 1, 8-10, etc.: system generates a topic map for investigating clustering). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 5
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 4 wherein the classification outcome display unit displays representative sentences that are sentences representing classified groups respectively (Bur: ¶ 8, 25, 43-45, 70-77: Fig. 2, 3: data points representative of sentence vector represented as classified groups for display); (Tse: § 3.6.4 pp 17-18 Tables 11-13: system allows for selection and display of representative terms, sentences, etc. per cluster in the form of a summary title based on term frequency per cluster). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 6
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 5 The sentence classification apparatus according to wherein the classification outcome display unit includes a selection unit for selecting a classified group among the classified groups and displays a case sentence included in the classified group selected by the selection unit (Bur: ¶ 8, 25, 43-45, 70-77: Fig. 2, 3: such as selection for human annotation, labelling etc. of particular groupwise data); (Tse: § 4.3 pp 26-27: system allows for interactive selection of clusters and subsequent display of cases, members, instances therein). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 7
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 1 wherein the case sentence obtention unit obtains a case sentence that meets a condition set on a condition setting screen where a condition for selecting the parameter is set (Bur: ¶ 8, 25, 43-45, 70-77, 106: Fig. 2, 3: labelling of sentences that have met conditions, threshold, distance, etc. necessary to each/any particular cluster); (Zhao: ¶ 69-72: user configurable parameters configure input processing); (Tse: § 5.1 pp 31: system enables limiting of results such as based on search terms). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 8
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 1 wherein the parameter selection unit selects a parameter that makes the absolute value of the correlation coefficient maximum (Bur: ¶ 8, 25, 43-45, 70-77: Fig. 2: dimensions, parameters, etc. selected as part of labelling process and with respect to a maximum similarity score); (Zhao: ¶ 79: such as based on selecting a parameter to maximize a performance metric; (Tse: § 4.4: system selects value of CC x TFC as a maximizing method to rank parameter alternatives based on per cluster correlation quality). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 9
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 1, wherein the case sentences include condition sentences showing the conditions for the cases and outcome sentences showing the outcomes of the cases respectively; and wherein the case value creation unit creates condition values obtained by numerizing the condition sentences and outcome values obtained by numerizing the outcome sentences (Bur: ¶12, 25, 37, 43-45, 74-75; Claim 1: sentences, segments, etc. labelled such as a thesis or other conditional values wherein each/any sentence, segment, etc. vector comprises a numerical value applied to the sentence, segment, etc.); (Zhao: ¶ 35, 361: per sentence word vector numerization); (Tse: § 3.6.1: per-document per cluster vector numerization). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claim 10
Bur in view of Zhao in view of Tse teaches or suggests:
The sentence classification apparatus according to claim 8 wherein the case value creation unit extracts the clusters of the condition sentences associated with the clusters of the outcome sentences respectively (Bur: ¶ 9, 43-45, 76, etc.: such as by labelling of particular portions of a particular document, such as a thesis, etc. and with respect to extracted values of each/any of the additional words, segments, sentences, etc.); (Tse: § 5.4; Fig 8-11; Fable 2: columnar extraction of row clusters , such that for each effect column technology row clusters to thereby determine associated case IDs per outcome cluster and associated condition cluster distributions);
and adds a numerical value indicating whether each cluster of the condition sentence is associated with the clusters of the outcome sentences (Tse: § 3.6.4 pp 17-18; Table 1, 4, 6: such as by recording a numerical association for row-cluster / column-cluster pairings as well as association strength values based thereon),
wherein the correlation coefficient calculation unit calculates the correlation coefficient for each of the extracted clusters of the condition sentences (Bur: ¶ 8, 9, 29, 43-45, 76, etc.: such as by determination of similarities between a target sentence and a values of the plurality of additional sentences, such as a cosine similarity ); (Zhao: Abstract; ¶ 35, 39, 48); (Tse: 3.6.4 pp 17-18; Table 1, 4, 6: relatedness of terms determined by correlation coefficient calculation with respect to a concept or cluster); and
wherein the parameter selection unit selects the parameter that makes the correlation coefficients between effect values associated with the clusters of the outcome sentences and the clusters of the condition sentences maximum (Bur: ¶ 9, 42-45, 76, etc.: sentences are ranked with respect to plurality of dimensions; classified, labelled, etc. with respect to similarities therebetween, etc.); (Zhao: ¶ 79; Fig 11: system selects parameter maximizing a metric); (Tse: § 4.4 pp 28-29: alternatives for correlation quality analyzed, selected to generate a highest quality). The claim is considered obvious over Bur as modified by Zhao, and Tse as addressed in the base claim as it would have been obvious to apply the further teaching of Bur, Zhao, and/or Tse to the modified device of Bur, Zhao, and Tse; one of ordinary skill in the art would have expected only predictable results therefrom.
Regarding claims 12, 13 – the claims are considered to recite substantially similar subject matter to that of claim 1 as rejected supra and are similarly rejected.
Response to Arguments
Applicant’s arguments and amendments, see Remarks and Claims, filed 5/21/26, with respect to the rejection(s) of claim(s) 1-13 under 35 USC 103 over Burstein, Sun, and Duan have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Burstein, Zhao, and Tseng.
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.
Any
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/PAUL C MCCORD/ Primary Examiner, Art Unit 2692
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