Source: http://www.google.com/patents/US6195657?dq=6,977,809&ei=-AObT5vAOoSgiQL_5qznDg
Timestamp: 2016-05-30 09:53:59
Document Index: 613213456

Matched Legal Cases: ['art 700', 'art 700', 'art 700', 'art 700', 'art 700', 'art 800', 'art 800', 'art 800', 'art 700']

Patent US6195657 - Software, method and apparatus for efficient categorization and ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsA system for determining recommendations which are likely to be relevant to a user's current tasks. The determination is made based on the user's grouping behavior as applied to collected references, documents and information objects. The recommendations are with respect to one or more of the “target”...http://www.google.com/patents/US6195657?utm_source=gb-gplus-sharePatent US6195657 - Software, method and apparatus for efficient categorization and recommendation of subjects according to multidimensional semanticsAdvanced Patent SearchPublication numberUS6195657 B1Publication typeGrantApplication numberUS 08/936,726Publication dateFeb 27, 2001Filing dateSep 25, 1997Priority dateSep 26, 1996Fee statusLapsedPublication number08936726, 936726, US 6195657 B1, US 6195657B1, US-B1-6195657, US6195657 B1, US6195657B1InventorsJames L. Rucker, Marcos J. PolancoOriginal AssigneeImana, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (18), Non-Patent Citations (15), Referenced by (314), Classifications (12), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetSoftware, method and apparatus for efficient categorization and recommendation of subjects according to multidimensional semantics
A system for determining recommendations which are likely to be relevant to a user's current tasks. The determination is made based on the user's grouping behavior as applied to collected references, documents and information objects. The recommendations are with respect to one or more of the “target” user's existing groupings or categories, and can be of information objects, categories of information objects, or other users who have created similar groupings of objects to the target user's groupings. The basis for recommendations is the discovery of “matching” categories for a “target” category, where a matching category is one that includes at least one object also included in the target category. Objects which are in matching categories but not in the target category are candidates for recommendation. In this way the recommendation system aids the target user by taking advantage of objects gathered and grouped by other users over time.
Filtering systems exist which attempt to keep users informed by delivering relevant documents (e.g., Tak W. Yan and Hector Garcia-Molina, “SIFT—A Tool for Wide-Area Information Dissemination” Proc. of the 1995 USENIX Tech. Conf., pp 177-86, 1995). Unfortunately, these systems are based on preferences which need to be explicitly submitted by users. This is an onerous task. It is not always easy for users to clearly define their preferences nor to formulate them in a way that allows the computer system to make sense of them. Commonly used techniques require the user to specify a list of keywords denoting their interests, or to select from among a predetermined set of categories. Both of these requirements impose an additional workload upon the user.
Alternative systems exist which perform “collaborative filtering,” for instance systems described in U.S. Pat. Nos. 4,996,642 (issued Feb. 26, 1991) and 5,583,763 (issued Dec. 10, 1996). In these systems, the user is required to denote a single set of favorite objects exemplifying their interests, or to supply preference scores for a number of objects. Once again, these are onerous tasks outside of the normal workload of a user. Furthermore, in many settings a user will have several different contexts for which they might require entirely different sets of recommendations. For instance, a user might be working on a number of projects simultaneously. The collaborative filtering systems referenced represent the user as having a monolithic set of interests and do not make different recommendations for different contexts.
There are also many varieties of “push” systems which do not attempt to target individual users or the specific interests of each user, but broadcast the same information objects to large segments of the user population. With this non-personalized approach, these systems end up adding to the problem of information overload rather than alleviating it.
In an embodiment, in order to provide recommendations to a “target” user for a particular “target” category they have created, the recommendation system of the present invention discovers categories created by users which are similar to the target category. Similarity between a discovered category and the target category is determined in part by the number of information objects which are in both the discovered and target categories. “Matching” categories have at least one information object which is also in the target category. From the set of discovered matching categories, recommendations can be made of information objects which are not already in the target category, as well as of the users who originally submitted the matching categories, and of the matching categories themselves. These recommendations can be delivered to the target user in the context of their target category. Thus if the user has a number of target categories, the grouping of the resulting recommendations will match the user's own intuitive grouping as exemplified by their target categories, rather than some predetermined categorization created by an administrator or editor.
Referring next to FIG. 3 there is a simplified illustration of one embodiment of the present invention that is adapted to perform an information object and colleague recommendation service. Such a recommendation service has a number of users, i.e., Barney 302, Wilma 304 and Fred 306, each of which would be using a different one of client terminals 104 x of FIG. 1. Each of Barney, Wilma and Fred in turn have personally defined categories of items, or objects, that they wish to keep track of using their client terminal 104 x. In FIG. 3 it is shown that Barney has created a “New Technologies” category (308); Wilma has created two separate categories, namely “Pagers” (310) and “Modems” (312); while Fred has created the category “Communication Companies” (314).
In general any type of uniquely identifiable object can be recommended by the present invention. In the preferred embodiment, the “domain” is information objects accessible via a public data communications network, for instance World-Wide Web pages. However, other domains would be equally feasible, for instance books, movies, music compact discs, etc.
In this scenario, user Wilma has a task of “investigating the pager market” with the following example demonstrating how the recommendation service of the present invention helps Wilma with this information seeking task. In the following discussion, Wilma is the “target user” that has requested the recommendation system of the present invention to search for additional objects that fit within the specified category that Wilma has created, and “pagers” (310) is the target category.
On her client terminal 104 x (e.g.,a personal computer), Wilma has created an electronic folder, or category 310, to contain references to discovered documents pertaining to the task “investigating the pager market”. Step 402 of FIG. 4 illustrates Wilma's submission of category 310 to the recommendation system, giving it the title “Pagers”. Category 310 is the representation of this category within the database accessible by the recommendation system. In the normal course of work, Wilma has discovered two relevant documents, or information objects, “B” (318) and “C” (320) related to “pagers”. Correspondingly, category 310 is linked to record 318 for information object “B” and record 320 for information object “C”.
Also, user Barney had previously submitted a single category 308, named “New Technologies”, containing information objects “A” (316), “B” (318) and “C” (320). Correspondingly, category 308 is linked to three information object records: record 316 for information object “A,” record 318 for information object “B,” and record 320 for information object “C”.
At step 402 (FIG. 4), the target user invokes the recommendation service by specifying a particular category for which recommendations are required. In this example user Wilma has specified the “Pagers” category, category 310, as the “target” category .
Matching categories are identified by correlating the linked information object records of the target category to the linked object records of other users' categories. That correlation is performed at host processing station 102 of FIG. 1. In this example, category 308 of FIG. 3, Barney's “New Technologies” category, is the only matching category to target category 304 since it is the only category in this example that contains either of the objects “B” and “C” that are linked to the target category “pagers” (310).
At step 406, recommendations are selected by the recommendation system from the union of linked information objects in the matching categories, considering only those which are not already linked to the target category. In this example the only recommendation is information object “A”, represented by record 316 of FIG. 3. Then at step 408, such recommendations will be delivered to the target user in the context of the target category. In this example, Wilma receives a recommendation of information object “A” in the context of her “Pagers” category as illustrated by broken line 317 in FIG. 3. This recommendation might be delivered by the addition of an iconic or textual representation of object “A” to a representation of the target category “Pagers” on a graphical user interface on user Wilma's personal computer.
The reasoning behind this recommendation is as follows. User Barney considers information objects “A”, “B” and “C” to be related, since all of them are part of his “New Technologies” category. User Wilma has already discovered information objects “B” and “C” and grouped them in her “Pagers” category, presumably because she considers “B” and “C” relevant to her “Pagers” category. Therefore, it is likely that user Wilma would be interested in also seeing information object “A”, since in user Barney's opinion it is related to the information objects she has already grouped together. In this way user Barney's experience can help user Wilma via an automatic recommendation system, without either user even needing to know of the other's existence. Additionally, this allows the recommendation system of the present invention to not require a built-in filtering criterion by which to judge whether or not an object identified by one user as being relevant to at least one object identified by a target user as being relevant to the target category. The recommendation system of the present invention thus relies on the expertise of the individual users in their grouping of objects as a filter of what is related and what is not.
Each recommended information object delivered to the target user was submitted to the recommendation system by one or more “originating users”. For each originating user the information object was submitted in the context of a particular category, referred to as the “originating category”. As well as delivering recommended information objects, at step 408 the recommendation system will additionally deliver identifiers of the originating categories and originating users. In some scenarios the identities of the originating user(s) is/are more relevant to the target user than the recommended information objects themselves, for example if the target user is attempting to locate colleagues who may be able to help with a particular task related to the contents of the target category.
In the example of FIGS. 3 and 4, as well as recommending a reference to information object “A” at step 408, the recommendation system could further deliver an identifier for the originating user, “Barney”, represented by user record 302, and an identifier for Barney's “New Technologies” category, represented by category record 308.
Referring again to FIG. 3, Wilma had previously also submitted category 312, named “Modems,” containing information object “D” (322). Correspondingly, category 312 is linked to one information object record, record 322 for information object “D”. User Fred had also previously submitted a single category 314 named “Communications Companies”, containing information objects “D” and “E” with category 314 linked to the two information object records, record 322 for information object “D” and record 324 for information object “E”. Here, if Wilma's category “modems” had been designated as the “target category”, then, using the same rationale discussed above, Fred's object E (324) would be recommended to Wilma's “modems” category 312 as indicated by broken line 325 in FIG. 3.
Having presented the recommended information objects, the recommendation system can now allow the user to optionally select one or more information objects from those recommended as being of particular interest. By creating a special new target category linked to just the selected information objects, the recommendation process of the present invention can be repeated, to provide a new set of recommendations of relevance to the selected information objects. In the present example, the system could construct a new target category linked to a single information object record, namely record 316 of FIG. 3 representing information object “A”. The system could then recommend further information objects or colleagues relevant to this new target category. However the small size of the example database means that no further recommendations would be possible in this particular case.
Having received identities of originating users, one option for the target user is to select a number of these originating users and initiate some form of synchronous or asynchronous communication. In such an embodiment, the user can optionally initiate an electronic discussion among the selected originating users by clicking on a button on a computer screen (e.g., hypertext, a web browser, or an icon when the user is using a graphical interface on the client terminal). This discussion could be transmitted over a number of media, for instance it might consist of a series of electronic mail messages. The purpose of this feature is to allow the target user to contact colleagues who might have knowledge or experience pertinent to the task at hand. For instance, in the current example, user Wilma could elect to send an electronic mail message to user Barney, since he may have experience which would be relevant to her task of “investigating the pager market”. In such an embodiment, such electronic mail messages would be routed through host processing station 102 by means of an electronic mailing list hosted on that same host processing station. The recipients of the mailing list are defined to be the originating users selected by the target user, as well as the target user.
Further, the full text of the ensuing electronic discussion is also archived on the host processing station, in the form of a “discussion object”, an information object accessible via a public data communications network, e.g., a World-Wide Web page. Optionally, access restrictions would be imposed on such an information object, for instance only allowing the recipients of the electronic mailing list to access or be recommended the object. Further, the information object representing the electronic discussion can automatically be appended to the target category, and henceforth be available for recommendation to future target users. For instance, in the current example, the ensuing series of electronic mail messages between users Wilma and Barney can be stored on a World-Wide Web page, which can be linked in the system's database to user Wilma's “Pagers” category. In the future, a user with a category which correlates with Wilma's “Pagers” category might have that discussion object recommended to them, in the same way as any other information object is recommended by the present invention.
In a more complex example there could be several originating users and originating categories. For instance, many users could have submitted categories which linked to information objects “A,” “B” and “C”.
In another configuration of the present invention, a central administrator, via host processing station 102, could define a set, or hierarchy, of categories to be used by all of the users. In that case, user Wilma would need to choose an existing category from that set in which to collect her information on “investigating the pager market”.
An extension of the present invention is to permit the recommendation system to automatically scan the memory of the user's personal computer for folders or categories, rather than having all of the information stored in host 102. For instance, the user might be using well-known software packages which generate such categories or electronic folders, e.g., a Web browser which allows the creation of a hierarchy of “bookmarks”, “favorites” or “short-cuts” to Web pages. A further option is for the recommendation system itself to provide means for defining and storing such categories or electronic folders. For example: (1) if the user uses a web browser to create a set of bookmarks, hierarchically organized, then the software running on the client termanl can copy those bookmarks, submit them to the host processing station, receive recommendations, perhaps just into bookmark folders which have changed since the last time this process happened, and then present those recommendations in the context of the user's own bookmark hierarchy; and (2) part of the recommendation system software can be software running on the client terminal which provides means for organizing location pointers for web pages into categorties, locally storing the categorties, and communicating with the host processing station to initiate recommendation delivery.
A further enhancement of the present invention is to allow users to specify a privacy setting when submitting a new category. The user could specify that a category should be “private,” in which case information from that category would not be used to make recommendations to other users. Alternatively, a user could specify that a category be marked “anonymous” resulting in the identity of the originating user being withheld when delivering recommendations from an “anonymous” category. Another option is to mark a category “hidden”, in which case only the identity of the originating user could be delivered, whereas the constituent information objects would be withheld. Finally, the user could specify that a category be marked “public” with recommendations originating from a “public” category freely being made that also identify the originating user.
Each user record 502 comprises several fields. A first of those fields is an identifier 506, which may be a sequence of characters, that is assigned uniquely to this record. User record 502 further comprises a field for user personal information 508, which may comprise such items as the user's name, telephone number, electronic mail address, position within a company, World-Wide Web address of the user's personal “home page”, etc. User record 502 also includes a field 504 for identifiers of categories submitted by the user identified in field 508. In turn, field 504 contains at least one sub-field with a unique identifier of each submitted category 510 . . . 512 designated by the user identified in field 508.
Looking at the user record for Barney, user record 602 (502), comprises: an identifier 604 (506), in this case “USER1”; user personal information 606 (508), in this case just the user's name “Barney”; and an identifier of a submitted category 608 (510), in this case the category whose identifier is “CATEGORY1”.
Similarly, the user record for Wilma, user record 610 (502) comprises: an identifier 612 (506), in this case “USER2”; user personal information 614 (508), in this case just the user's name “Wilma”; an identifier of a first submitted category 616 (510), in this case “CATEGORY2”; and an identifier of a second submitted category 618 (512), in this case “CATEGORY3”. The category record with identifier “CATEGORY3” is not illustrated in FIG. 6 since it was not part of the above discussed example of the recommendation between Barney and Wilma.
The two category records included in FIG. 6 are category records 620 and 660 the composition of which is discussed as follows. Category record 620 (526) is the record for “CATEGORY 1” identified by “USER 1”, namely user Barney's “New Technologies” category, corresponding to category record 308 of FIG. 3. Category record 620 comprises: an identifier 622 (530), in this case “CATEGORY1”; an identifier of originating user 624 (532), in this case user “Barney” whose unique identifier is “USER1”; a title 626 (534) assigned by the originating user, in this case “New Technologies”; identifiers of constituent information object records 628, 630 and 632 (536 . . . 538) with identifiers “OBJECT A”, “OBJECT B” and “OBJECT C”, respectively.
Category record 660 (526) is the record for user Wilma's “Pagers” category, corresponding to category record 310 of FIG. 3. Category record 660 comprises: an identifier 662 (530), in this case “CATEGORY2”; an identifier of originating user 664 (532), in this case user “Wilma” whose unique identifier is “USER2”; a title 666 (534) assigned by the originating user, in this case “Pagers”; identifiers of two constituent information object records 668 and 670 (536 . . . 538) with identifiers “OBJECT B” and “OBJECT C” respectively.
Additionally, there are three individual information object records illustrated, one for each of objects “A”, “B” and “C”. Looking first at the record for object “A”, information object record 640 (514) is the record representing information object “A”, corresponding to information object record 316 of FIG. 3. Record 640 comprises: an identifier 642 (518), in this case “OBJECT A”; a domain 643 (519) (e.g., “Web Page”); a location pointer 644 (520) sufficient to locate information object “A” (e.g., on a public data communications network); only one identifier of the containing category 646 (522), in this case the category with identifier “CATEGORY 1” (since the corresponding object was only identified by Barney in the example of FIG. 3).
A second information object record 650 (514) is the record representing information object “B”, corresponding to information object record 318 of FIG. 3. Record 650 comprises: an identifier 652 (518), in this case “OBJECT B”; a domain 653 (519) (e.g., “Web Page”); a location pointer 654 (520) sufficient to locate information object “B” (e.g., on a public data communications network); two identifiers of containing categories 656 and 658 (522 . . . 524), in this case the categories with identifiers “CATEGORY 2” and “CATEGORY 3” (since the corresponding object was identified by both Barney and Wilma in the example of FIG. 3).
The third information object record 680 (514) is the record representing information object “C”, corresponding to information object record 320 of FIG. 3. Record 680 comprises: an identifier 682 (518), in this case “OBJECT C”; a domain 683 (519) (e.g., “Web Page”); a location pointer 684 (520) sufficient to locate information object “C” (e.g., on a public data communications network); two identifiers of the containing categories 686 and 688 (522 . . . 524), in this case the categories with identifiers “CATEGORY 2” and “CATEGORY 3” (since the corresponding object was identified by both Barney and Wilma in the example of FIG. 3).
Referring next to FIG. 7 there is shown a more detailed flowchart 700 of a routine for recommending references of the present invention than that shown in FIG. 4. The decision flow represented by flowchart 700 can be implemented with one or more software routines on one or more computers. Further, flowchart 700 illustrates the broad logical flow of the routine of the present invention to accomplish the recommendation procedure in a more general case than the simplified example given above with respect to FIGS. 3, 4 and 6. Other logical flows to implement the present invention will also become apparent from the full details of the present invention and they will each be within the scope of the present invention. A software routine, or routines, implementing flowchart 700 may be written in any suitable computer language, such as “C”, “Java”, assembly language, or others. For ease of discussion we will refer to the routine of flowchart 700 as a single routine.
At step 704 a working list of target categories is initialized, while a “recommendations list” is cleared for use by the routine as described below.
At step 706, a first target category from the list created in step 704 is made the “current target category”. Then, at step 708 a category record (526) for the “current target category” is added to the database, comprising the fields shown in category record 526 of FIG. 5. If a user record (502) for the “target user” does not already exist in the database, a new user record is also added, comprising the fields shown in user record 502 of FIG. 5, otherwise, the existing user record for the “target user” is located. A bidirectional link between the target user record and the new category record is created by means of fields 510 and 532, respectively, of FIG. 5.
Then at step 710, categories that match the “current target category” are identified in the database. As explained previously, matching categories are defined to be those categories which link to an information object record which is also linked to by the target category. For each matching category a “match count” is calculated as explained in relation to flowchart 800 of FIG. 8.
The routine of step 710, as illustrated in flowchart 800 in FIG. 8, is entered at step 802 where it is assumed there is a single target user and a single target category. In step 804 the database category record (526) for the target category is retrieved with the a list of the “identifiers of constituent information objects” extracted from the target category record. This list is compiled from the sub-fields of field 528 of the target category record (see FIG. 5). Additionally, the “matching category list” is cleared to be used by the routine to define a new “matching categories list” as described below. In an alternative embodiment, the list of identifiers of constituent information objects can be restricted to just those objects belonging to specified domains. For instance, certain domains might provide more consistent matching performance.
Next, at step 806, the identifier of constituent information object corresponding to the first information object identifier in the information object identifier list created in step 804 is made the “current information object”. In step 808, the information object record for the “current information object” is retrieved from the database (the data structure of that record corresponds to information object record 514 of FIG. 5). From the information object record of the “current information object” a list of identifiers of containing categories from field 516 of FIG. 5 is created. Then at step 810, the category corresponding to the identifier of the first category from the list created at step 808 is selected as the “current category”.
Continuing at step 812, a test is performed to determine if the “current category” has already been encountered in this process. If the response to step 812 is “no”, execution proceeds to step 814 where the “current category” is added to the “matching category list” (a list of categories that include an identifier to at least the “current information object”) with a “match count” for the “current category” initialized to 0.
Then, from step 814, or if the result of step 812 is “yes”, execution proceeds to step 816 where the “match count” for the “current category” is incremented by 1. The “match count” for a category denotes how many matching information objects there are between that category and the “target category”.
An alternative to step 816 is to use the ratings for matching information objects, if supplied. In that case, the current information object will have a “target rating” as supplied by the target user, and it will also have an “other rating” as supplied by the originating user of the current category. The match count of the current category is then incremented by an amount proportional to the similarity between the target rating and the other rating. For instance, the absolute value of the difference between the target rating and the other rating integers could be calculated. Then the match count of the current category could be incremented by the result of subtracting that difference from 100.
Proceeding to step 818, a test is performed to determine whether there are any categories left on the list created in step 808 which have not been considered. If there are more categories on the list from step 808, the test result at step 818 is “yes”, and execution proceeds to step 820. At step 820, the next category on the list from step 808 is set equal to the “current category”, and execution returns to step 812. The loop of steps 812, 814, 816, 818 and 820 continues until the list of categories from step 808 is exhausted. Each new category is added to the “matching categories list” in step 814 exactly once, however, on each subsequent pass through step 816 the corresponding “match count” for such a category is merely incremented by one each time through.
Once the final category from the list of step 808 has been considered, the result of the test of step 818 will be “no” with execution then proceeding to step 822. At step 822, a test is performed to determine if there are any more information objects left on the list created at step 804 to consider. If the result of the test of step 822 is “yes”, execution proceeds to step 826 where the next information object in the list of step 804 is set equal to the “current object”, and execution returns to step 808, repeating the loop of steps 808, 810, 812, 814, 816, 818, 820 and 826 for another information object. When the final information object from the list of step 804 has been considered, the test result of step 822 will be “no”, with execution proceeding to exit at step 824. At step 824, the flowchart 800 of FIG. 8 is complete, and assembly of the “matching categories list” together with a “match count” for each matching category has been accomplished.
Returning to flowchart 700 of FIG. 7, execution of step 710 is thus completed and the decision flow proceeds to step 712. In step 712, a decision loop is begun to examine all of the “matching categories” from the list created in step 814, together with the associated “match count”. Optionally, a cache could be maintained at the host processing station comprising a list of the strongest matching categories for each category. When speed is of the essence, instead of following the routine of step 710 as described, this cache could be consulted to quickly provide a list of matching categories for a given category. However such a list would not necessarily contain the most recent information.
At step 712 the first category from the list of “Matching categories” is set equal to the “current matching category”. Then at step 714, another decision loop is begun to calculate a score for each unmatched information object identified by each category identified in the list created in step 814 as it cycles through as the “current matching category”. Note, an unmatched information object is an information object which is linked with at least one matching category in the list from step 814, but is not also linked with the target category. Also note that an unmatched information object may occur in more than one matching category. Thus, in a preferred embodiment, a score for an unmatched information object is calculated by summing the match counts for each matching category in which it occurs. Then, all of the unmatched information objects and their associated scores are added to a “recommendations list”.
In an alternative embodiment, before the addition of “match counts” in step 714, the “match count” for each “matching category” can be divided by the number of information objects linked to by that “matching category,” in order to normalize for categories which may have large differences in their cardinality.
Proceeding to step 718, a test is performed to determine whether there are any more “matching categories” in the list found of step 814 which have not yet been addressed. If the test result of step 718 is “yes”, execution continues at step 716 where the “current matching category” is set equal to the next unaddressed “matching category” in the list from step 814, and execution continues at step 714. The loop of steps 714, 716 and 718 continues until the list of matching categories is exhausted, at which point the test result at step 718 will be “no” and execution continues with step 720.
At step 720, the “recommendations list” of information objects is sorted by the score assigned to each in step 714. Then, at step 722, information objects from the “recommendations list” are provided to the “target user” in the context of the “current target category”. These recommendations, at least initially, will consist of the top scoring information objects from the “recommendations list”. In addition, for each recommended information object, the corresponding score, and a list of all of the originating categories and users who submitted them are also provided to the “target user”. In the preferred embodiment of the present invention, the location pointers or identifiers of these object recommendations will be downloaded to the target user's client terminal 104 x (FIG. 1). Software running on the target user's client terminal could also allow the user to select whether to receive recommendations of information objects, originating users, originating categories or any combination thereof. Furthermore, the target user could exercise an option as to whether to receive a specified number of recommendations for a “target category”, or alternatively just those recommendations whose score exceeds a specified threshold score. Finally, the target user could elect to receive recommendations from particular specified domains, e.g., only documents, or only books.
At step 726, a test is performed to determine whether there are any additional “target categories” in the list created in step 704. If the test result of step 726 is “yes” execution proceeds to step 728 where the next target category in the list is set equal to the “current target category,” and execution proceeds to step 708 to repeat steps 708 through 726 for the new target category. If the test result of step 726 is “no” then the routine exits at 724, having completed all loops and having made recommendations for each specified “target category”.
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