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 1, 9, 10 and 19 were amended.
This is a Non-Final Action.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/22/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-7 and 9-19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim objection in view of claims 9 and 10 were obviated due to current amendment to the claims.
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 1-7, 9-16 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) and of Li et al. (US 8,843,470) further in view of Chen et al (US 2023/0419049 – “Chen2”)
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; and
…wherein the querying comprises providing the predetermined plurality of POI categories to the LLM.
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.
However, Chen2 teaches,
…wherein the querying comprises providing the predetermined plurality of POI categories to the LLM (Abstract, Claim 1 – teaches a set of labels of interest is generated by sampling from a set of possible labels…. Prompting data that comprises a first plurality of class labels is generated and a pretrained language model generates a text output is response to each of the generated prompts – thus disclosing providing a predetermined labels/categories to a language model as part of the model prompt/input so that the model output related to identification of a label/class, Chen2. In modified POI system of Chen, the predetermined labels/classes correspond to the predetermined plurality of POI categories provided to Mondlock’s LLM).
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 the combination of Chen/Li/Mondlock with Chen2 because Chen2 teaches providing a plurality of possible class labels to a language model as part of prompt/input for text classification. Since Chen/Li/Mondlock combination uses an LLm to interpret a user query when category matching fails, a POSITA would have been motivated to provide Chen’s predetermined POI categories to the LLM using Chen2’s label-prompting technique so that the LLM output is directed to identifying one of the predetermined POI category labels. This would improve classification accuracy and constrain the LLM output to the known POI category taxonomy, yielding predictable results.
2. The combination of Chen, Mondlock, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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, Li and Chen2 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).
Claim 19 is similar to the combination of claims 1 hence rejected similarly.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US2018/0349380 – IDS) in view of Mansour et al. (US 10,242,114) and Greco et al. (US 2018/0225355)
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 (Paragraphs 53, 78-79 – teaches a POI database with POIs indexed by categories and labels, and one or more point-of-interest names may be retrieved from an unsegmented point-of-interest database and segmented point-of-interest names may be stored in a segmented point-of-interest database, 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, Mansour teaches,
for a given candidate POI category label (Col 3: lines 23-25 – teaches co-occurring terms are used as tags (also referred to as ‘keywords’) to label the point of interest in the local index): 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 (Fig 2 and 3, Col 7:lines 40-47– teaches the remaining terms are ranked according to a score which is based on a user frequency inverse user frequency (UF-IUF) metric (or a term frequency inverse document frequency (TF-IDF), further teaching the number of users within the neighborhood of the point of interest that mentioned the term in their messages is divided by the number of users that posted a message within the neighborhood of the point of interest, Mansour);
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 (Fig3, Col 8: lines 10-14– teaches terms having a score higher than a threshold will be added as POI metadata tags under the attribute ‘description’ in the entry 100 of the entity, if that term is not yet stored under ‘description’, Mansour).
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 with Mansour because both references are directed to improving retrieval of point of interest information in local/navigation search systems. Chen teaches identifying POIs from user input using a POI database, and Mansour teaches enriching POI entries in a local index with tags/keywords based on frequency based analysis so that POIs are better characterized and more easily found in future searches. A POSITA would have been motivated to apply Mansour’s POI tagging technique to Chen’s POI database to improve POI search accuracy and retrieval of relevant POIs, yielding predictable results.
Greco teaches,
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 (Fig 2, Paragraphs 30-33 – teaches responsive to a determination to reclassify the sensor data, determining whether to define a new category; responsive to a determination to define a new category, define a new category; and classification program 114 may define a ‘Faulty Gear’ category, responsive to receiving three maintenance reports describing repairing a gear based on threshold rule to define a new category only after three similar maintenance reports are received, Greco – thus teaching second branch when repeated/frequency like evidence satisfies a threshold rule, define a new category and associate the new category label with the relevant data, applied to Mansour’s POI local index environment, this teaches adding the candidate POI label as a new POI category and associating it with the POIs having that candidate label).
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 the combination of Chen and Mansour with Greco because Chen teaches POI database used for POI recognition, Mansour teaches frequency based enrichment of POI entries in a local index using tags/keywords to improve future local searches, and Greco teaches a self-improving classification system that determines whether received label evidence matches an existing category or should define a new category based on threshold evidence. A POSITA would have been motivated to apply Greco’s category creation technique to the Chen/Mansour POI index so that candidate POI labels derived from frequency based POI tagging can either be added to an existing POI category or used to create a new POI category when threshold criteria are satisfied, thereby improving completeness and accuracy of the POI database and yielding predictable results.
Allowable Subject Matter
Claim 18 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
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/AMRESH SINGH/Primary Examiner, Art Unit 2159
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