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 .
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.
Claim(s) 1-2, 4-5, 7-8, and 10 rejected under 35 U.S.C. 103 as being unpatentable over Takeda (US 20170358045) in view of Suzuki (the machine English translation of JP 2020098388).
Regarding claim 1:
Takeda discloses: An information processing device (“FIG. 1 is a diagram schematically illustrating a functional configuration of a data analysis system 1 according to an embodiment. The data analysis system 1 according to the embodiment includes a data analysis apparatus 100 and a memory unit 200.” (Takeda ¶ [0026]). Apparatus 100 comprises an acquisition unit 110, a relation evaluation unit 120, a data evaluation unit 150, an output unit 170, and a score calculation unit 180), comprising:
a model obtaining unit configured to obtain a learned model generated by machine learning that involves (Takeda teaches “data memory unit 210 for the memory unit 200 stores training data and a plurality of pieces of unknown data. The training data is a pair (combination) of “data” and “classification information” (Takeda ¶ [0032]); and that the data acquisition unit “acquires a data set including a plurality of combinations of the training data and the classification information” (Takeda ¶ [0034]); and that the relation evaluation unit “can learn patterns … included in the training data by evaluating the degree of contribution” of the data elements (Takeda ¶ [0036]).);
determining a weight of a morpheme in a model that is either a linear model or a generalized linear model, in accordance with a feature determined based on a result of morphological analysis of learning data that is learning document data (Takeda further teaches that the data elements are textual keywords and may be morphemes: “a “data element(s)” is a set of character strings which has a certain meaning in a certain language, that is, so-called a “keyword(s)” (for example, a morpheme(s)).” (Takeda ¶ [0024]). Takeda also expressly teaches a linear model based on weighted data elements. Takeda states that the score calculation unit “calculates score S of the data by calculating an inner product between the element vector … and a weight vector (column vector using the weight for each data element … as its element) according to the following formula. S = wT·s” (Takeda ¶ [0048]). Takeda further explains that “the relation evaluation unit 120 calculates the score indicating the strength of the relation between the data elements of data included in the training data and the classification information” (Takeda ¶ [0046]));
Takeda does not expressly teach: deleting, from input data of the model, the feature corresponding to the morpheme having the weight determined to be smaller than, or equal to, a given threshold value.
However, in a related field, Suzuki teaches: deleting, from input data of the model, the feature corresponding to the morpheme having the weight determined to be smaller than, or equal to, a given threshold value (Suzuki teaches that the word extraction unit 41 “executes morphological analysis … to extract K1a, K2a, K3a, K4a, etc. as keywords” (Suzuki ¶ [0034]); that the weight calculation unit 42 “calculates the weight of each keyword,” specifically “calculates the TFIDF … of each keyword” (Suzuki ¶ [0036]); and that the selection unit 43 “excludes, from among the keywords extracted by the word extraction unit 41, keywords having a weight less than a predetermined value” (Suzuki ¶ [0038]). Suzuki further teaches that, after this exclusion step, the selected keywords are output to later processing, “clustering unit 44 is a processing unit that performs clustering of products using the keyword selected by the selection unit 43” (Suzuki ¶¶ [0038]-[0040]));
an obtaining unit configured to obtain document data including an electronic mail transmitted and received by a monitored person (Takeda teaches that the unknown or training data may be document data including e-mails. Specifically, Takeda states that “the data (the training data and the unknown data) are not limited to the patent documents or technical papers and may be arbitrary text data (data at least partially including texts such as e-mails, presentation materials, spreadsheet materials, meeting materials, contracts, organization charts, and business plans)” (Takeda ¶ [0033]). Takeda also teaches acquisition of such data by the data acquisition unit: “The data acquisition unit 110 refers to the document data memory unit 210 and acquires a data set including a plurality of combinations of the training data and the classification information” (Takeda ¶ [0034]));
a feature determining unit configured to determine the feature to be input to the learned model, in accordance with the result of the morphological analysis of the document data obtained by the obtaining unit (Takeda teaches feature generation in the form of an element vector derived from textual data elements: “the score calculation unit 180 generates an element vector indicating whether a specified data element is included in data” (Takeda ¶ [0048]). Takeda also teaches that those data elements are keywords and morphemes (Takeda ¶ [0024], ¶ [0046]). Suzuki as well teaches the claimed morphological-analysis. Suzuki states that the word extraction unit “executes morphological analysis … to extract … keywords” (Suzuki ¶ [0034]); the weight calculation unit calculates weights for those extracted keywords (Suzuki ¶ [0036]); and the selection unit excludes low-weight keywords before outputting the result to subsequent processing (Suzuki ¶ [0038]). Suzuki then teaches that the clustering unit generates “clustering information” from the selected keywords (Suzuki ¶ [0040]). Accordingly, Takeda in view of Suzuki teaches determining the feature to be input to the learned model in accordance with the result of morphological analysis of the obtained document data.);
an inference processing unit configured to input the feature, determined by the feature determining unit, to the learned model, in order to calculate a score indicating a degree of relevance between the document data and a given event (Takeda expressly teaches calculating a score representing relation strength between document data and classification information. Takeda states that “The data evaluation unit 150 calculates a score indicating the relation between each piece of the partial unknown data … and the classification information” (Takeda ¶ [0042]), and that “the relation evaluation unit 120 calculates the score indicating the strength of the relation between the data elements of data included in the training data and the classification information” (Takeda ¶ [0046]). Takeda further teaches the score formula based on the feature vector and weight vector: “the score calculation unit 180 calculates score S of the data by calculating an inner product between the element vector … and a weight vector … S = wT·s” (Takeda ¶ [0048])); and
a display control unit configured to perform display control based on the score of the document data (Takeda teaches score-based output. Takeda states that “The output unit 170 outputs the score calculated by the data evaluation unit 150 to the user” (Takeda ¶ [0043]) and “may output the score … together with the corresponding partial unknown data or an identifier” (Takeda ¶ [0044])).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takeda by incorporating Suzuki’s taught step of excluding low-weight morpheme and keyword features after morphological analysis into Takeda’s weighted text-scoring framework, so that Takeda’s score is calculated after deleting data elements whose weights are less than or equal to a threshold. Both references process textual document data by extracting textual elements, assigning weights to those elements, and using the weighted elements in subsequent scoring processing. Applying Suzuki’s weight-based feature deleting to Takeda would have been a predictable use of known feature selection techniques to reduce noise, improve efficiency, and preserve more informative textual features for the score calculation.
Takeda recognizes the problem of finding data related to specified ideas or event from large amounts of unknown document data and solves it by weighting textual data elements and scoring relation strength (Takeda ¶¶ [0006]-[0008], [0036], [0042], [0048]). Suzuki recognizes that not all extracted keywords are equally useful and solves that problem by excluding keywords “having a weight less than a predetermined value” after morphological analysis and weight calculation (Suzuki ¶¶ [0034], [0036], [0038]). A PHOSITA would have been motivated to apply Suzuki’s known low-weight exclusion step to Takeda’s weighted textual scoring framework to remove weak or noisy textual features before the score computation, yielding to predictable result of more efficient and potentially more accurate weighted document scoring system.
Regarding claim 2:
Takeda in view of Suzuki teaches claim 1 as applied above.
a learning processing unit configured to perform the machine learning (Suzuki teaches a learning processing unit that performs the claimed machine learning. Specifically, Suzuki teaches that “The control unit 30 has a learning processing unit 40 and a prediction processing unit 50” and “The learning processing unit 40 includes a word extraction unit 41, a weight calculation unit 42, a selection unit 43, a clustering unit 44, a learning data generation unit 45, and a learning unit 46, and is a processing unit that learns a prediction model” (Suzuki ¶¶ [0032] – [0033])); that involves
determining the weight of the morpheme in either the linear model or the generalized linear model (Takeda teaches “a “data element(s)” is a set of character strings which has a certain meaning in a certain language, that is, so-called a “keyword(s)” (for example, a morpheme(s)).” (Takeda ¶ [0024]). And further teaches “calculates score S of the data by calculating an inner product between the element vector … and a weight vector (column vector using the weight for each data element … as its element) according to the following formula. S = wT·s” (Takeda ¶ [0048]).);
in accordance with the feature determined based on the result of the morphological analysis of the learning data (Suzuki states that the learning processing unit includes a word extraction unit and that “the word extraction unit 41 executes morphological analysis or the like on the document 1a described in item a of the plan document of the product 1 to extract K1a, K2a, K3a, K4a, etc. as keywords.” (Suzuki ¶ [0034]). Suzuki further teaches that “The weight calculation unit 42 is a processing unit that calculates the weight of each keyword” (Suzuki ¶ [0036]); and
deleting, from input data of the model, the feature corresponding to the morpheme having the weight determined to be smaller than, or equal to, a given threshold value (Suzuki further teaches that “the selection unit 43 excludes, from among the keywords extracted by the word extraction unit 41, keywords having a weight less than a predetermined value, keywords that correspond to the stop word list, and keywords that correspond to the part of speech to be excluded” (Suzuki ¶ [0038]));
wherein the model obtaining unit obtains the learned model generated by the learning processing unit (Suzuki states that “The learning result DB 20 is a database that stores the learning results of each prediction model for each month. For example, the learning result DB 20 stores the determination result (classification result) of the learning data by the control unit 30, and various parameters learned by multiple regression analysis, machine learning, or the like” (Suzuki ¶ [0030]) and that “The learning unit 46 is a processing unit that executes learning of a prediction model” (Suzuki ¶ [0047]); “the learning unit 46 stores the learning result in the learning result DB 20” (Suzuki ¶ [0048]); and “the prediction processing unit 50 reads various parameters from the learning result DB 20 and constructs a prediction model” (Suzuki ¶ [0049]).
Regarding claim 4:
Takeda in view of Suzuki teaches claim 2 as applied above.
wherein the feature determining unit determines a metadata feature in accordance with metadata assigned to the document data, the metadata feature being a feature corresponding to the metadata (Takeda teaches “…acquires information about a right holder(s); acquires updated digital information on the basis of that information at regular time intervals; organizes a plurality of files, which constitute the acquired digital information, in a specified storage place on the basis of recording place information, file names, and metadata regarding the acquired digital information; and creates a status distribution by visualizing the status of the plurality of organized files so that the status of the right holder who has accessed the digital information can be recognized.” (Takeda ¶ [0103]) and “acquires metadata related to digital information; updates a weighted parameter set based on the relation between first digital information, which has a relation with a specific matter, and the metadata; and updates the relation between morphemes and the digital information by using the weighted parameter set.” (Takeda ¶ [0104]), and
the learning processing unit performs the machine learning in accordance with the feature corresponding to the morpheme and the metadata feature (Takeda teaches “acquires metadata related to digital information; updates a weighted parameter set based on the relation between first digital information, which has a relation with a specific matter, and the metadata; and updates the relation between morphemes and the digital information by using the weighted parameter set.” (Takeda ¶ [0104]); and Takeda teaches that the learning side determines weighted relations for morpheme elements in ¶ ¶ [0024], [0036], [0048], [0073] – [0075]).
Regarding claim 5:
Takeda in view of Suzuki teaches claim 2 as applied above.
wherein the inference processing unit performs processing of: dividing the document data into a plurality of blocks in any given length (Takeda teaches “…The partial data generation unit 140 divides each of the plurality of pieces of the acquired unknown data into partial unknown data which constitute part of each piece of the unknown data.” (Takeda ¶ [0038]); “some items may be further divided into subitems. Each item or each subitem includes a group of texts, diagrams, charts, and so on. For example, in a case of a description of a patent document, the description is divided by numbers representing paragraph numbers into a plurality of paragraphs and each paragraph includes texts. Furthermore, documents illustrating diagrams are divided by numbers representing figure numbers into some items and each item includes a diagram” (Takeda ¶ [0039]); “the partial data generation unit 140 breaks down the unknown data (for example, a new e-mail) into the partial unknown data” (Takeda ¶ [0125]));
and outputting probability data for each of the plurality of blocks (Takeda teaches “…the data evaluation unit 150 calculates a score indicating the relation between each piece of the partial unknown data generated by the partial data generation unit 140 and the classification information” (Takeda ¶ [0042]); “…the output unit 170 may output the score calculated by the data evaluation unit 150 together with the corresponding partial unknown data or an identifier for identifying the partial unknown data” (Takeda ¶ [0044]));
the probability data being provided as the score and indicating a probability relevant to the given event (Takeda teaches “…the data evaluation unit 150 calculates a score indicating the relation between each piece of the partial unknown data generated by the partial data generation unit 140 and the classification information” (Takeda ¶ [0042])).
Regarding claim 7:
Takeda in view of Suzuki teaches claim 1 as applied above.
wherein if a plurality of inference target data items are obtained as the document data to be inferred, the inference processing unit calculates the score for each of the plurality of inference target data items (Takeda teaches “the document data memory unit 210 for the memory unit 200 stores training data and a plurality of pieces of unknown data.” (Takeda ¶ [0032]); “The data acquisition unit 110 acquires the plurality of pieces of unknown data stored in the document data memory unit 210 (S210).” (Takeda [0077]); Takeda further teaches that the system ranks unknown data by scores generated for the respective data items (Takeda ¶¶ [0062] – [0070]). Takeda also teaches that the system calculates a score for each category (Takeda ¶ [0111]), and “displays data to the user; accepts identification information (tag) which is assigned to review object data on the basis of a judgment on whether or not the data is related to the technique which is the purpose of the search by the user; compares the feature quantity of the object data regarding which the tag is accepted, with the feature quantity of the data; updates a score of the data corresponding to a specified tag on the basis of the comparison result; and controls the order to display the data to be displayed on the basis of the updated score” (Takeda ¶ [0091]).
and the display control unit controls a display mode of each block in accordance with a result of the comparison performed by the inference processing unit (Takeda teaches ““displays data to the user; accepts identification information (tag) which is assigned to review object data on the basis of a judgment on whether or not the data is related to the technique which is the purpose of the search by the user; compares the feature quantity of the object data regarding which the tag is accepted, with the feature quantity of the data; updates a score of the data corresponding to a specified tag on the basis of the comparison result; and controls the order to display the data to be displayed on the basis of the updated score” (Takeda ¶ [0091])).
Regarding claim 8:
Takeda in view of Suzuki teaches claim 1 as applied above.
wherein the display control unit performs control to display the list in which the inference target data items included in the plurality of inference target data items and having the relatively high scores are sorted in descending order of the scores (Takeda states that the system “displays data to the user,” “updates a score of the data,” and “controls the order to display the data to be displayed on the basis of the updated score” (Takeda ¶ [0091]). That is similar to using the score to determine the display order of multiple data items. Takeda explains that the unknown data are sorted according to a normalized rank derived from score, where “a smaller value of this normalized rank represents a stronger relation (that is, a higher score)” (Takeda ¶ [0062]); that all invalid materials are found when the normalized rank falls within a particular top portion of the ranked data (Takeda ¶¶ [0063]-[0064]); and similarly for prior-art documents in the prior-art-search context (Takeda ¶¶ [0068]-[0070]). Therefore, Takeda already uses score-based prioritization of multiple data items. Therefore, a person having ordinary skill in the art would have understood that once Takeda teaches controlling display order based on score in order to prioritize more relevant items, the routine implementation is to sort the displayed list in descending order of score. That is also consistent with Takeda’s own ranking discussion and with the purpose of the system, which is to help a user efficiently find data related to specified events from a large amount of unknown data).
Regarding claim 10: the claim limitations are similar to those of claim 1; therefore, rejected in the same manner as applied above.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Takeda (US 20170358045) in view of Suzuki (the machine English translation of JP 2020098388) and Jin (US 6651057).
Regarding claim 6:
Takeda in view of Suzuki teaches claim 5 as applied above.
Takeda teaches that, for each block, a score is obtained and output (Takeda states that “The partial data generation unit 140 divides each of the plurality of pieces of the acquired unknown data into partial unknown data which constitute part of each piece of the unknown data” (Takeda ¶ [0038]) and that “The data evaluation unit 150 calculates a score indicating the relation between each piece of the partial unknown data … and the classification information” (Takeda ¶ [0042]). Takeda further teaches that the output unit “outputs the score calculated by the data evaluation unit 150 to the user” (Takeda ¶ [0043]) and may output the score together with the corresponding partial unit or identifier (Takeda ¶ [0044]).
Takeda does not expressly teach comparing each block score to a threshold value.
However, in a related field, Jin teaches that “scores represent a measure of relevance to the topic. After the various statistics have been collected, a score assigned to a testing document is normalized based on those statistics. The normalized score is then compared to a threshold score. Subsequently, the testing document is designated as relevant or not relevant to the topic based on the comparison” (Jin: abstract). Jin also teaches “normalizes a score assigned to a testing document based on the statistics; compares the normalized score to a threshold score; and designates the testing document as relevant or not relevant to the topic based on the comparison” (Jin col. 2, lines 26-30).
the score and a threshold value independent of a genre of the document data (Jin teaches “because the scores assigned documents for one query do not generally relate to the scores assigned documents for a different query. This results in a degradation of system performance for the task. The alternative is to set the threshold for each query, but this is impracticable. Accordingly, there is presently a need for a system that normalizes scores so that a decision threshold is consistent across different queries” (Jin col. 1, lines 50-57); “The newly generated normalized score is compared to a threshold score. For example, suppose that the threshold score has been Is set to 6.5, and the normalized score for a given story and topic is 7.0. The normalized score and threshold score are compared to each other to determine whether the story is on-topic or off-topic. When the normalized score is above the threshold, the testing story is considered on-topic. Normalizing scores in this manner makes it possible to use the same threshold across many topics and makes it possible for applications to handle multiple queries by taking the intersection of selected relevant documents.” (Jin col. 3, lines 14-24).
and the display control unit controls a display mode of each block in accordance with a result of the comparison performed by the inference processing unit (Takeda teaches changing the display mode of textual units based on the corresponding score. Takeda states that the system “extracts sentences … calculates a score indicating the degree of relevance … and changes the emphasis degree of the sentences according to the score” (Takeda ¶ [0088]). Takeda also teaches that the output unit outputs the score and the corresponding partial data or identifier (Takeda ¶¶ [0043]-[0044]. Takeda teaches changing the display mode of each block according to score, and Jin teaches threshold-based comparison of normalized relevance scores; together they teach or render obvious controlling the display mode of each block in accordance with the result of the comparison).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Takeda in view of Suzuki and further in view of Jin so that Takeda’s inference processing compares, for each block, the block score to a normalized threshold that is consistent across different topics (genres), and the display control changes the display mode of each block based on the comparison result. The result would have been predictable: a document-review system that both scores each block for relevance and uses normalized score thresholds to present blocks differently depending on their relative relevance.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Takeda (US 20170358045) in view of Suzuki (the machine English translation of JP 2020098388) and Gross (20040133564).
Regarding claim 9:
Takeda in view of Suzuki teaches claim 5 as applied above.
Takeda teaches a scored set of displayed data items for user review (Takeda states “displays data to the user”; “updates a score of the data”; and “controls the order to display the data to be displayed on the basis of the updated score” (Takeda ¶ [0091]). Takeda also teaches that the output unit may output the score together with the corresponding data or identifying information (Takeda ¶ [0044]));
Takeda does not expressly teach the limitation wherein, when any one or more of document data items included in the document data are selected from the list.
However, in a related field, Gross teaches: wherein, when any one or more of document data items included in the document data are selected from the list (“a view area or pane 326A is provided that displays the content of the first item or user-selected item from the list pane or area 324A” (Gross ¶ [0105]); “when the user clicks on a list pane entry, the corresponding contents are displayed in the view pane 326A” (Gross ¶ [0106]);
the display control unit performs control to display details of the any one or more selected document data items in a window separate from a window displaying the list (“a view area or pane 326A is provided that displays the content of the first item or user-selected item from the list pane or area 324A” (Gross ¶ [0105]); “when the user clicks on a list pane entry, the corresponding contents are displayed in the view pane 326A” (Gross ¶ [0106]); and “the list and view areas 324A, 326A can optionally float separately from one another, and the user can separately drag the areas as desired on the user's display device” (Gross ¶ [0108]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Takeda in view of Suzuki and further in view of Gross so that when one or more scored document data items are selected from the displayed list, the system displays the details of the selected items in a window separate from the list window. The combination would enable the to select one or more of those items and view their details in a separate pane. This would have predictably improved usability by allowing simultaneous review of the summary list and the selected item’s details without losing the ranked context.
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 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 6-9, and 12 of copending Application No. 18605073 in view of cited prior art above in the 103 rejection section. Motivations to combine are similar to those found throughout the office action.
Although the claims at issue are not identical to the co-pending application’s claim, they are not patentably distinct in view of the cited prior art above. For example, current claim 1 is provisionally rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over copending claim 6 of the reference application in view of Takeda. Copending claim 6 in its independent form (1+6) already recites obtaining document data, performing morphological analysis, determining a feature from the analysis result, performing machine learning to determine morpheme weights in a linear or generalized linear model, deleting low-weight features, and then performing inference in accordance with the learned model to output, as a score, probability data indicating probability that inference target data is related to a given event. Current claim 1 adds that the document data includes monitored-person e-mail and that display control is performed based on the score. Takeda teaches application of the scored document-analysis system to a mail monitoring system using emails distributed daily over the network as data, and further teaches score-based display and control of display order based on score. It would have been obvious to apply the scored-inference framework of copending claim 6 to Takeda’s monitored e-mail review and score-based display environment, rendering current claim 1 an obvious variant of the copending claim.
This is a provisional nonstatutory double patenting rejection.
Allowable Subject Matter
Claim 3 would be allowable once the double patenting rejection set forth in this Office action is overcome, and the claim is rewritten to include all of the limitations of the base claim and any intervening claims.
Conclusion
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/WASSIM MAHROUKA/Primary Examiner, Art Unit 2665
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