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
Claims 1-7, 9-19 are presented for examination.
Claim 8 was cancelled.
Claim 1 and 19 were amended.
This is a Final Action.
Response to Arguments
Applicant's arguments filed 11/21/2025 have been fully considered but they are not persuasive.
101 with respect to claim 19 has been obviated due to current amendments to the claim.
With respect to claim 17 applicant makes the following arguments:
1. Applicant argues, Chen does not teach determining frequency of occurrence of a POI category or updating the POI database based on such frequency.
Examiner respectfully disagrees with the applicant, Chen discloses a POI database containing POI entries associated with location information, which are analyzed by POI recognition component to identify candidate POIs corresponding to a query (Paragraph 53, Chen). A POI database that identifies POIs such as restaurants, hotels and other location types inherently associates POIs with categories of location, since POI entries corresponds to particular type of locations (e.g. restaurant, hotel, airport). The claim do not require any specific indexing mechanism or data structure for storing category labels. Under BRI, Chen’s POI entries corresponding to type of locations reasonably constitute POIs associated with categories and labels, Accordingly, Chen teaches accessing a POI database in which POIs are associated with categories.
2. Applicant argues, Chen does not teach determining frequency of occurrence of a POI category or updating the POI database based on such frequency.
Examiner respectfully disagrees with the applicant; applicant’s argument is not persuasive because the rejection does not rely solely on Chen for these limitations. While chen teaches accessing and retrieving POI entries from a POI database, Homma and Cerra are relied upon for updating database information based on observed usage and relationships between POI names and related terms.
Homma teaches updating POI-related structures by adding additional labels (confusion names) and updating associated scores based on observed usage patterns (Paragraph 166, Homma). Such updates are based on analysis of POI occurrences and relationships between POI names.
Cerra further teaches deriving related terms and adapting stored data based on correlations and usage history of terms (Paragraph 80, Cerra). These teachings demonstrate that information in the database may be updated based on observed usage statistics or relationships between terms.
Thus, the combination of Homma and Cerra teaches updating stored data structures associated with POIs based on observed occurrences or correlations between terms, which corresponds to adding labels or categories based on observed frequencies.
3. Applicant argues, Homma only relates to POI names and does not add new POI categories.
Examiner respectfully disagrees with the applicant, Homma teaches adding additional labels (confusion names) associated with POI names and updating corresponding information stored in the database (Paragraph 166, Homma). Adding additional labels associated with POIs corresponds to adding additional identifiers or category labels associated with POIs in the database. The claims do not require the categories to be created in any particular manner or structure. Under BRI, adding additional labels associated with POIs reasonably corresponds to adding category labels associated with POIs.
4. Applicant argues, Cerra does not teach adapting a POI database and is directed to speed recognition.
Examiner respectfully disagrees with the applicant, Applicants argument that Cerra fails to teach updating stored data structures I not persuasive. Cerra teaches adapting stored recognition data base on usage history and correlations between terms (Paragraph 80, Cerra). The derivation of related terms based on usage correlations necessarily involves updating stored data structures used for recognition or retrieval. A person of ordinary skill in the art would have recognized that such techniques for adapting stored data based on usage patterns can be applied to any system that stores terms or labels used for recognition or retrieval, including a POI database.
5. Applicant argues Cerra is non-analogous art.
Examiner respectfully disagrees with the applicant, Cerra and the claimed invention ar edirected to the same field of endeavor, namely processing and adapting stored data used for recognition of used input terms in computer systems. While Cerra discusses speech recognition, the reference addresses techniques for updating stored recognition data based on usage patterns and correlations between terms. Such techniques are reasonably pertinent to the problem addressed by the present invention, which involves updating labels or categories associated with POIs based on observed relationships between user input and stored information. A reference is analogous art if ti either from the same field of endeavor or reasonably pertinent to the problem faced by the inventor (MPEP 2141.01(a)). Cerra satisfies at least the second criterion because it teaches updating stored recognition data based on usage pattern, which is reasonably pertinent to updating POI labels or categories based on observed usage patterns.
For these reasons the combination of Chen, Homma and Cerra teach or suggest the limitations of claim 17.
Claim Objections
Claims 9 and 10 are objected to because of the following informalities: claims 9 and 10 are dependent on cancelled claim 8. In view of compact prosecution examiner has interpreted that claims 9 and 10 are dependent on claim 1. 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.
Claims 1-16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US2018/0349380 – IDS) in view of Mondlock et al. (US 2025/0131247) further in view of Li et al. (US 8,843,470)
1. Chen teaches, A method for identifying at least one geospatial location based on user input (Paragraph 1 - teaches a point of interest may be any location to which a user may wish to navigate. Examples of points of interest includes… restaurants, hotels, retail stores, airports…, Chen), the method comprising:
using the string for identifying at least one point-of-interest (POI), wherein the string comprises (Paragraph 4 - teaches match a first text segment to a first point-of-interest segment index… match a second text segment… and use the … indices to identify one or more candidate POI entries, Chen):
identifying, as the at least one geospatial location, a geospatial location corresponding to the at least one POI (Abstract – teaches identify one or more candidate POI entities matching both the first and second text segments – thus disclosing identifying candidate POIs corresponding to user queries, which inherently correspond to physical locations, Chen);
Chen does not explicitly teach,
receiving, as the user input, a string from a user;
determining whether at least part of the string satisfies a matching criterion with at least one a predetermined plurality of POI categories;
in response to determining that the string does not satisfy the matching criterion, querying, based on at least part of the string and a predetermined plurality of POI categories, a large language model (LLM) for output relating to identification of at least one POI category label.
However, Mondlock teaches,
receiving, as the user input, a string from a user (Fig 7A: 710 – receive a user query, Mondlock).
querying, based on at least part of the string and a predetermined plurality of POI categories, a large language model (LLM) for output relating to identification of at least one POI category label (Fig 7C: 736- teaches send augmented user query to LLM to generate an answer – thus disclosing sending a user query to LLM to obtain output generated from the query input, Mondlock)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to incorporate the LLM-based query processing of Mondlock in the query classification framework of Li within the POI search system of Chen in order to improve interpretation of user queries that do no satisfy predefined category matching criteria and thereby improve the identification of relevant POI and associated geospatial locations. The combination merely applies known natural-language interpretation techniques using a LLM to a conventional POI query processing system, yielding predictable improvement in interpreting ambiguous or unmatched user queries.
However, Li teaches,
determining whether at least part of the string satisfies a matching criterion with at least one a predetermined plurality of POI categories (Col 18: lines8-20 - teaches generate category scores for meta-classifier categories… at least one category score being greater than a threshold value – thus disclosing determining whether a query satisfies a classification criterion by comparing category scores against a threshold, corresponding to determining whether the query matches one of a plurality of predefined categories, Li);
in response to determining that the string does not satisfy the matching criterion (Col 10: lines 14-24- teaches if none of the category scores is above a threshold value, then the query is not associated with any of the meta-classifier categories – thus disclosing determining when a query does not satisfy the classification criterion (i.e. no category score exceeds the threshold, Li).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to modify Chen’s POI identification method to incorporate the query classification techniques of Li to determine whether a query matches predefined categories and, where the query does not satisfy the matching criteria, to utilize the LLM-based query processing of Mondlock to interpret the query and determine an appropriate category or response. Such modification represents the predictable use of kown query-processing techniques to improve the interpretation of user queries and thereby improve retrieval of relevant POIs and associated geospatial locations.
2. The combination of Chen, Mondlock and Li teaches, The method of claim 1, wherein using the string further comprises:
extracting, from the output of the LLM, at least one candidate POI category label and identifying the at least one POI based on the at least one candidate POI category label (Fig 7C:736 - teaches send augmented user query to LLM to generate an answer, Mondlock).
3. The combination of Chen, Mondlock and Li teaches, The method of claim 2, wherein identifying the at least one POI based on the at least one candidate POI category label comprises:
in response to determining that the at least one candidate POI category label corresponds to at least one corresponding POI category from among a predetermined plurality of POI categories (Paragraphs 53 and 57 - teaches comparing candidate labels against a fixed taxonomy of POI categories, Chen), identifying the at least one POI based on the at least one corresponding POI category (Paragraphs 53-59 - teaches identifying POIs based on the matched categories, Chen).
4. The combination of Chen, Mondlock and Li teaches, The method of claim 3, further comprising, in response to determining that the at least one candidate POI category label does not correspond to at least one POI category from among the predetermined plurality of POI categories (Paragraph 52 – teaches where a candidate string/label has no correspondence in the known POI categories/entries, Chen), identifying the at least one POI by searching a POI database and/or map database based on the candidate POI category label (Paragraph 143 – teaches …no corresponding index may be found… which may indicate that the segment does not appear in any known point-of-interest entry (process then continues with retrieval techniques) - describes a fallback handling when no matching index/category exists, leading the system to search for candidate POIs in the database, Chen).
5. The combination of Chen, Mondlock and Li teaches, The method of claim 4, further comprising providing data for displaying the at least one POI on a display (Paragraph 1 - teaches navigation system allow a user to search for POI and present the results to the user though navigation interface – thus disclosing returning POI search results and presenting them to the user though the navigation interface, which necessarily involves providing data for displaying POIs on a display device, Chen), wherein each POI displayed on the display is selectable by the user (Paragraph 2 – the navigation system may prompt the user to confirm that the identified POI is indeed what the user intended, Chen).
6. The combination of Chen, Mondlock and Li teaches, The method of claim 5, further comprising, in response to receiving a selection of a POI from among the POI displayed on the display (Paragraph 2 – the navigation system may prompt the user to confirm that the identified POI is indeed what the user intended, Chen) that was identified by searching a POI database and/or map database (Paragraph 4 - teaches match a first text segment to a first POI segment index stored in the database… and use the indices to identify one or more candidate POI interest entries, Chen )based on the candidate POI category label (Col 18: lines8-20 - teaches generate category scores for meta-classifier categories… select meta-classifier category based on the category score greater than the threshold value, Li), adding, to the POI database, data indicative of an association between the selected POI and the corresponding candidate POI category label (Paragraph 95, Fig 3:340 - teaches the system maintains POI data structures and updates or stores indexed POI entries associated with query processing).
7. The combination of Chen, Mondlock and Li teaches, The method of claim 3, further comprising, in response to determining that the at least one candidate POI category label does not correspond to at least one POI category from among the predetermined plurality of POI categories (Paragraph 143 – teaches the system cannot find a correspondence between the input (candidate label) and existing POI categories, Chen), providing data for displaying, on a display, the at least one candidate POI category label, each POI category label displayed on the display being selectable by the user (Paragraphs 109 and 137 – teaches providing candidate output to the user for selection, Chen).
9. The combination of Chen, Mondlock and Li teaches, The method of claim 8, further comprising, in response to determining that at least part of the string satisfies the matching criterion for at least one POI category, identifying the at least one POI based on the at least one POI category (Paragraphs 53 and 58 – teaches matching part of user’s string to a stored category index and once categories/indices are matched, the system identifies POIs associated with them and returns the entries, Chen).
10. The combination of Chen, Mondlock and Li teaches, The method of claim 8, wherein determining whether at least part of the string satisfies a matching criterion with at least one of the predetermined plurality of POI categories comprises at least one of:
identifying, for each POI category label, whether there is a correspondence between the POI category label and at least part of the string; calculating, for each POI category among the predetermined plurality of POI categories, a similarity parameter between the string and the POI category (Paragraphs 53 & 109 – teaches testing between user input segment and POI category indices (labels) and calculating of similarity parameters between the input string and stored POI categories/entries, Chen); and
converting at least part of the received string into a search vector, calculating distances between the search vector and vector representations of each POI category among the predetermined plurality of POI categories (Paragraph 38 – teaches generating vector embeddings of textual input using techniques such as Word2Vec. Word2Vec represents words as vectors in a semantic vector space such that semantic similarity between textual input can be determined based on the distance between their corresponding vector representations. Thus teaching converting a received string into a search vector and comparing the vector to vector representation of textual categories, Mondlock)
11. The combination of Chen, Mondlock and Li teaches, The method of claim 1, wherein using the string further comprises, in response to the output from the LLM indicating a failure of the identification of at least one candidate POI category label (Paragraph 42 – teaches recognition/identification failure analogous to an LLM being unable to identify a candidate label, Chen), identifying the at least one POI by searching a POI database and/or map database based on at least part of the string (Paragraph 143 – teaches fallback, if no label/index is matched, the system searches in the POI/map database using the input string segment, Chen).
12. The combination of Chen, Mondlock and Li teaches, The method of claim 1, further comprising:
providing data for displaying the at least one geospatial location to a user (Paragraph 1 - teaches a point of interest may be any location to which a user may wish to navigate. Examples of points of interest includes… restaurants, hotels, retail stores, airports…, Chen), wherein:
providing data for displaying the at least one geospatial location to a user comprises providing data for visually indicating, for at least one geospatial location, that the geospatial location was identified based on a POI category (Paragraph 109 – teaches presenting candidate POIs grouped/ranked by category information, which serves as a visual indication to the user that the POI was identified via a category, Chen).
13. The combination of Chen, Mondlock and Li teaches, The method of claim 1, further comprising:
providing data for displaying the at least one geospatial location to a user, wherein: when a plurality of geospatial locations are identified (Paragraph 1 - teaches a point of interest may be any location to which a user may wish to navigate. Examples of points of interest includes… restaurants, hotels, retail stores, airports…, Chen), providing data for displaying the plurality of geospatial locations to a user comprises providing data for grouping and/or ranking the plurality of geospatial locations that are identified in accordance with a corresponding POI category (Paragraph 109 – teaches groupping/ranking candidate POIs for presentation with the ranking tired to the recognition/category scoring, Chen ).
14. The combination of Chen, Mondlock and Li teaches, The method of claim 1, further comprising:
providing data for displaying the at least one geospatial location to a user, the providing including providing data for displaying, on a display, an indication of at least one POI category identified using the string (Paragraph 109 – teaches presenting recognized categories/etries to the user, which serves as indications of categories identified from the string, Chen), each indication of a POI category displayed on the display being selectable by the user (Paragraph 137 – teaches presenting candidate categories/entries in a user selectable format, Chen); and in response to receiving a selection of a given indication of a POI category, identifying at least one geospatial location based on a corresponding POI category and providing data for displaying the at least one geospatial location to the user (Paragraphs 58-59 – teaches once a user has selected, the system identifies POIs associated with the corresponding category and returns/displays then, Chen).
15. The combination of Chen, Mondlock and Li teaches, The method of claim 1, further comprising:
providing data for displaying the at least one geospatial location to a user, wherein: when a plurality of geospatial locations are identified, providing data for displaying the plurality of geospatial locations to a user (Paragraph 1 - teaches a point of interest may be any location to which a user may wish to navigate. Examples of points of interest includes… restaurants, hotels, retail stores, airports…, Chen) comprises providing data for displaying, on a display, an indication of at least one POI category corresponding to the at least one geospatial location (Paragraph 109 – teaches presenting candidate POIs grouped/ranked with category indications tied to their recognition source, Chen), each indication of a POI category displayed on the display being selectable by the user (Paragraph 137 – teaches presenting candidate POIs/categories in a user-selectable format, Chen).
16. The combination of Chen, Mondlock and Li teaches, The method of claim 1, further comprising:
providing data for displaying, on a display, the at least one geospatial location to the user, each geospatial location displayed on the display being selectable by the user (Paragraph 1 - teaches a point of interest may be any location to which a user may wish to navigate. Examples of points of interest includes… restaurants, hotels, retail stores, airports…, Chen); and
responsive to receiving the selection of at least one geospatial location, providing data indicative of instructions for navigating to the selected geospatial location (Paragraph 1 - teaches a point of interest may be any location to which a user may wish to navigate. Examples of points of interest includes… restaurants, hotels, retail stores, airports…, Chen).
Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US2018/0349380 – IDS) in view of Homma et al. (US 2010/0185446 -IDS) further in view of Cerra et al. (US 2009/0030685 - IDS)
17. Chen teaches, A method for updating point-of-interest (POI) information, the method comprising:
accessing a POI database in which each POI from among a plurality of POIs is associated with at least one POI category from among a plurality of POI categories, wherein at least one POI category label is associated with each POI category among the plurality of POI categories (Paragraph 53 – teaches a POI database with POIs indexed by categories and labels, Chen).
Chen does not explicitly teach,
for a given candidate POI category label: determining, for POIs having a specified association with the given candidate POI category label, a frequency of occurrence in the POI database of at least one POI category from among the plurality of POI categories;
when the frequency of occurrence for a POI category satisfies a first criterion, adding, to the POI database, the given candidate POI category label as a POI category label for that POI category; and
when the frequency of occurrence for a POI category satisfies a second criterion, adding, to the POI database, the given candidate POI category label as a new POI category and associating, in the POI database, the new POI category to the POIs having the specified association with the given candidate POI category label.
However, Homma and Cerra teach,
for a given candidate POI category label: determining, for POIs having a specified association with the given candidate POI category label, a frequency of occurrence in the POI database of at least one POI category from among the plurality of POI categories (Paragraph 109-113 – teaches the server extracts information on deletions, additional, and changes of POIs. The information includes which POIs occur in both versions, and which do not, Homma);
when the frequency of occurrence for a POI category satisfies a first criterion, adding, to the POI database, the given candidate POI category label as a POI category label for that POI category (Paragraph 166 – to the confusion information of the existing POI names, the added confusion POI names are added as confusion POI names and their corresponding confusion scores, are updated – describes adding frequently encountered “confusion” labels (synonyms) to existing POI names/categories, Homma); and
when the frequency of occurrence for a POI category satisfies a second criterion, adding, to the POI database, the given candidate POI category label as a new POI category and associating, in the POI database, the new POI category to the POIs having the specified association with the given candidate POI category label (Paragraph 80 – teaches usage history 158 may be used… altering the probabilities of terms used in the past, and… related terms may be derived based on correlations of usage of term observed in the past – discloses the frequent/correlated usage can generate (add) new recognition terms/categories in the DB, Cerra).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains to allow to combine Chen with Homma and Cerra because all the prior arts are in the same field of endeavor of point-of-interest recognition for navigation/local search systems, and by applying Cerra’s frequency-based adaptation to Homma’s POI database schema, to improve accuracy of label/category assignment
All the limitations of claim 17 are taught above.
18. The combination of Chen, Homma and Cerra teach, The method of claim 17, further comprising:
generating the POI database, the generating including:
using an input string for identifying at least one POI, the using comprising:
querying, based on at least part of the string and a plurality of POI categories, a large language model (LLM) for output relating to identification of at least one POI category label;
extracting, from the output of the LLM, at least one candidate POI category label; and
identifying at least one identified POI based on the at least one candidate POI category label, the identifying including:
when the at least one candidate POI category label corresponds to at least one corresponding POI category from among the plurality of POI categories, identifying the at least one identified POI based on the at least one corresponding POI category (Paragraph 53 – teaches the component 120 may search the indices… to identify at least one matching index for each input segment obtained – describes identifying POIs when labels match known categories, Chen); and
when the at least one candidate POI category label does not correspond to a POI category, identifying the at least one identified POI by searching one or more of a POI database and a map database based on the at least one candidate POI category label (Paragraph 143 – teaches .. no corresponding index may be found… which may indicate that the segment does not appear in any known point-of-interest entry – describes fallback searching when no category correspondence is found, Chen);
displaying, on a display, the at least one identified POI, each identified POI displayed on the display being selectable by the user (Paragraphs 149-150 – teaches … the terminal output a response ‘there are three candidates… ‘in response… when a sound ‘Two’ is input… the terminal selects the second… -describes displaying candidate POIs and user selection, Homma) and
in response to receiving, from the user, a selection of a particular identified POI from among the at least one identified POI displayed on the display, storing, in the POI database, data indicative of an association between the particular identified POI and a corresponding candidate POI category label (Paragraph 166 – teaches added confusion POI names are added as confusion POI names and their corresponding confusion scores are added or the confusion scores… are updated – describes updating the DB to store associations between user-selected POIs and labels (confusion info), Homma).
Claim 19 is similar to the combination of claims 1 hence rejected similarly.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/AMRESH SINGH/Primary Examiner, Art Unit 2159