DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09/02/2025 has been entered.
Status of Claims
Claims1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 15, and 19 have been amended by Applicant. Claims 2 and 16 are cancelled and claims 50-51 have been added. Claims 1, 3-15, 17-28, and 50-51 are pending. Claims 29-49 were withdrawn pursuant to Requirement for Restriction dated 2/22/2024.
Response to Arguments
Claim Rejections under 35 U.S.C. 101
The rejection of claims 7, 10, 13, and 15 under 35 U.S.C. 101 has been withdrawn in view of Applicant’s amendments to the claims.
Claim Rejections under 35 U.S.C. 103
The rejection of claims 1, 3-15, and 17-28, under 35 U.S.C. 103 have been withdrawn in view of Applicant’s amendment to independent claim 1. However, upon further consideration and in view of said amendments, a new grounds of rejection has been made herein.
Applicant’s arguments with respect to claim 1 and dependent claims 3-15, and 17-28 (as amended) 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 Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5, 12, 13-14,19, and 50-51 are rejected under 35 U.S.C. 103 as being unpatentable over Chin et al. (US 20170339235 A1) in view of Jia-Ching Ying et al., “Mining Geographic- Temporal-Semantic Patterns in Trajectories for Location Prediction”, (2013).
Regarding claim 1, Chin teaches a method for automatically assigning one or more semantic tags to store a point-of-interest (POI) using a processor, the stored POI being having a geographical location, the method comprising:
providing attribute data representing the stored POI as an input to a multilabel classifier comprising a neural network model, the attribute data comprising temporal attribute data and one or more of: spatial attribute data comprising geospatial data for a location of the stored POI or metadata comprising one or more of a unique identifier or a name of the stored POI (Chin, Paragraph [0049] further teaches for each POI category node, context data related to the user (e.g., user's activity) and/or the environment may be inputted. By way of example, user context data may be formatted as follows, user_context=[walking, duration; stationary, duration; . . . ], and the environmental context data may be formatted as follows, env_context=[street, probability, duration; restaurant, probability, duration; . . . ] [Note, “street” here corresponding to location of the stored POI and “duration” corresponding to temporal attribute data]. As shown in FIG. 6, the category classifier classifies a context to an associated category and may be trained based on the context feature of the category. For instance, a possible classifier may be based on a decision tree, a support vector machine (SVM), neural network, etc. Based on the POI classification model, such as classification model 600, probable POI categories for the POI may be ranked and a POI category ultimately determined.; Chin, Paragraph [0037] further teaches he probable POIs determined at block 410 may be based on information/data related to number of visits, ratings, and number of check-ins for all the POIs determined at block 406. Using this information a POI quality score for each of the POIs may be calculated to determine which POIs are “probable.” The POI quality score, for example, may be used to rank each POI based on the number of visits (e.g., a single visit may be considered a user being at the POI for a certain period of time defined by a threshold),; Chin, Fig. 5, 500 teaches named POIs such as “City Chathedral”, “City Train Station”, etc.).
receiving one or more predicted semantic tags for the stored POI from an output of the multilabel classifier, the received semantic tags being in addition to the attribute data provided to the multilabel classifier (Chin, Paragraph [0017] teaches the present disclosure is directed to determining a POI that accurately indicates the actual geographical location of a user based at least in part on various types of information; Chin, Paragraph [0055] teaches numerous advantages of the present disclosure, include but are not limited to, (1) automatically and accurately recording various POIs and other visited places without the user having to input POI or check into the POI, (2) searching and subscribing to related information, (3) providing various types of information to users based on accurate POI information, such as advertising, (4) creating labels for a given location [i.e., labels as in “tags”], (5) personalizing data and context associated with the user and relevant POIs [i.e., context associated as in “semantic”], (6) using crowd-sourced information from other users who have visited or checked into the user's POIs, (7) adding more data to POI analysis, such as vehicle sensor and condition data, and (8) using user activity and context (e.g., audio) data to improve accuracy and relevancy; Chin, Paragraph [0049] further teaches for each POI category node, context data related to the user (e.g., user's activity) and/or the environment may be inputted. By way of example, user context data may be formatted as follows, user_context=[walking, duration; stationary, duration; . . . ], and the environmental context data may be formatted as follows, env_context=[street, probability, duration; restaurant, probability, duration; . . . ]. As shown in FIG. 6, the category classifier classifies a context to an associated category and may be trained based on the context feature of the category. For instance, a possible classifier may be based on a decision tree, a support vector machine (SVM), neural network, etc. Based on the POI classification model, such as classification model 600, probable POI categories for the POI may be ranked and a POI category ultimately determined.);
However, Chin does not distinctly disclose:
storing the predicted semantic tags in a database as additional attribute data of the stored POI.
wherein the predicted semantic tags comprise category labels for the stored POI;
wherein the category labels are taken from a label set stored in the database.
Nevertheless, Jia-Ching Ying teaches:
storing the predicted semantic tags in a database as additional attribute data of the stored POI (Jia-Ching Ying, Abstract, teaches in this article we propose a novel mining-based location prediction approach which takes into account a user’s geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user visiting a location.; Jia-Ching Ying, Section 3.2.2, teaches we use a POI database and the activity label of the trajectory to calculate the probability of each semantic tag to each stay point…In our POI database, we store landmarks, their geographic scopes, and the associated semantic tags.).
wherein the predicted semantic tags comprise category labels for the stored POI (Jia-Ching Ying, Section 3.2.2 we use a POI database and the activity label of the trajectory to calculate the probability of each semantic tag to each stay point. The POI database is a customized spatial database that stores the semantic category of landmarks collected from Google Maps (alternatively, a gazetteer can be used as a general-purpose POI database for this operation.) In our POI database, we store landmarks, their geographic scopes, and the associated semantic tag(s).);
wherein the category labels are taken from a label set stored in the database (Jia-Ching Ying, Section 3.2.2, par.1, teaches We use a POI database and the activity label of the trajectory to calculate the probability of each semantic tag to each stay point. The POI database is a customized spatial database that stores the semantic category of landmarks collected from Google Maps (alternatively, a gazetteer can be used as a general-purpose POI database for this operation.) In our POI database, we store landmarks, their geographic scopes, and the associated semantic tag(s).; Jia-Ching Ying, Section 3.2.2, par. 2, further teaches as mentioned earlier, the semantic tag of a landmark for a user is always related to the activity (or purpose) of his/her trajectory. We utilize the co-occurrences of the activity label of the trajectory and the semantic tags of the landmarks to design the semantic vector for a stay point as follows.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin, to further include the POI database that stores semantic tags, as taught by Jia-Ching Ying, in order to calculate the probability of each semantic tag to each stay point and to provide excellent performance under various conditions outperforming state-of-the-art approaches such as collaborative location recommendation. (Jia-Ching Ying, Section 3.2.2 and Conclusions – Section 7).
Regarding claim 5, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, and the combination further teaches wherein the received attribute data comprises or is processed from stored metadata (Chin, Paragraph [0051] teaches FIG. 7A illustrates a table containing metadata for the POIs near the place visited by the user (City Cathedral parking lot as shown in FIG. 2) in accordance with one or more aspects of the disclosure. For example, the metadata may include POI category obtained from the context engine and/or third-party POI services. In that regard, as shown, the category for the City Cathedral is “Church, Sights, Museums,” the category for the City Train Station is “Transportation, Subway,” and the category for the ABC Convenient Store is “Supermarket, Food.” Although not shown, the metadata may include categories corresponding to the user's activity associated with the visited place, such as activity prior to arriving at the place (e.g., driving, walking, etc.), activity leaving that place to the user's next location (e.g., walking, taking the train, etc.).)
Motivation to combine same as stated for claim 1.
Regarding claim 12, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, and the combination further teaches wherein the received one or more predicted semantic tags … comprise a probability score for each of the category labels in the label set. (Jia-Ching Ying, Abstract, teaches in this article we propose a novel mining-based location prediction approach which takes into account a user’s geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user visiting a location.; Jia-Ching Ying, Section 3.2.2, teaches we use a POI database and the activity label of the trajectory to calculate the probability [i.e., reading on probability score] of each semantic tag to each stay point…In our POI database, we store landmarks, their geographic scopes, and the associated semantic tags.;)
…from the multilabel classifier… (Chin, Paragraph [0049] teaches “tags from the multi-label classifier”, as stated in the rejection of claim 1.)
Motivation to combine same as stated for claim 1.
Regarding claim 13, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 12, and the combination further teaches wherein the received one or more predicted semantic tags comprise category labels in the label set for which the probability score meets or exceeds a threshold (Chin, Paragraph [0049] teaches for each POI category node, context data related to the user (e.g., user's activity) and/or the environment may be inputted. By way of example, user context data may be formatted as follows, user_context=[walking, duration; stationary, duration; . . . ], and the environmental context data may be formatted as follows, env_context=[street, probability, duration; restaurant, probability, duration; . . . ]. As shown in FIG. 6, the category classifier classifies a context to an associated category and may be trained based on the context feature of the category. For instance, a possible classifier may be based on a decision tree, a support vector machine (SVM), neural network, etc. Based on the POI classification model, such as classification model 600, probable POI categories for the POI may be ranked and a POI category ultimately determined.; Chin Paragraph [0037] teaches as described above, the probable POIs determined at block 410 may be based on information/data related to number of visits, ratings, and number of check-ins for all the POIs determined at block 406. Using this information a POI quality score for each of the POIs may be calculated to determine which POIs are “probable.” The POI quality score, for example, may be used to rank each POI based on the number of visits (e.g., a single visit may be considered a user being at the POI for a certain period of time defined by a threshold),…).
Motivation to combine same as stated for claim 1.
Regarding claim 14, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, and the combination further teaches wherein the multilabel classifier comprises a neural network model (Chin, Paragraph [0049] teaches a possible classifier may be based on a decision tree, a support vector machine (SVM), neural network, etc.…;).
Motivation to combine same as stated for claim 1.
Regarding claim 19, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, and the combination further teaches wherein the temporal attribute data is solely acquired from publicly available information (Chin Paragraph [0055] teaches Numerous advantages of the present disclosure, include but are not limited to, (1) automatically and accurately recording various POIs and other visited places without the user having to input POI or check into the POI, (2) searching and subscribing to related information, (3) providing various types of information to users based on accurate POI information, such as advertising, (4) creating labels for a given location, (5) personalizing data and context associated with the user and relevant POIs, (6) using crowd-sourced information from other users who have visited or checked into the user's POIs, (7) adding more data to POI analysis, such as vehicle sensor and condition data, and (8) using user activity and context (e.g., audio) data to improve accuracy and relevancy.’; Chin, Paragraph [0002] teaches location-based social applications allow users to record and share the various places and physical locations that they visit in real-time. For example, these applications may be configured to record and/or track the various places that the users visit over a particular period of time, and allow the users to “check-in” to certain places that they want to share with friends and the public as their points or places of interest. Note: temporal attribute data from crowd-sourced information reading on acquired from publicly available information and because it does not rely on the user having to input POI or check into the POI it reads on “solely acquired” [Note: location check-ins are time-based at they are recorded according to visits over a period of time]. It is noted that this interpretation is consistent with Applicant’s own Specification at Paragraphs 0039, 0040, 0042, 0045, and 0201 where it states that user check-ins may be used, but are not always available] [Note: 0055 and 0002 teaches temporal based crowd-sourced check-ins, reading on the limitation as claimed]; Jia-Ching Ying, Section 2.2 concurrently teaches, existing studies on user location prediction could be classified into three categories: (1) those using only a user’s own data, (2) those using the data generated by crowds, and (3) hybrid methods using both kinds of data… The second category of studies consider only the datasets generated by crowds for next location prediction modeling, based on approaches such as some probability distribution models [Backstrom et al. 2010; Noulas et al. 2011] or location recommenders [Ge et al. 2010, 2011; Liu et al. 2010; Zhuang et al. 2011, 2012]..
Motivation to combine same as stated for claim 1.
Regarding claim 50, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, and the combination further teaches wherein the received one or more predicted semantic tags from the multilabel classifier comprise a plurality of predicted tags for the stored POI (Chin, Abstract, teaches determining [as in predicting] an actual point-of-interest (POI). For example, using at least one computing device, a first set of probable POIs corresponding to a first geographical location and first POI category information may be determined. The at least one computing device may also be used to determine a second set of probable POIs corresponding to a second geographical location and determine second POI category information. By analyzing the first and second POI category information, the actual POI from the first and second set of probable POIs may be determined; Chin, Paragraph [0017] teaches the present disclosure is directed to determining a POI that accurately indicates the actual geographical location of a user based at least in part on various types of information; Chin, Paragraph [0055] teaches numerous advantages of the present disclosure, include but are not limited to, (1) automatically and accurately recording various POIs and other visited places without the user having to input POI or check into the POI, (2) searching and subscribing to related information, (3) providing various types of information to users based on accurate POI information, such as advertising, (4) creating labels for a given location [i.e., labels as in “tags”], (5) personalizing data and context associated with the user and relevant POIs [i.e., context associated as in “semantic”], (6) using crowd-sourced information from other users who have visited or checked into the user's POIs, (7) adding more data to POI analysis, such as vehicle sensor and condition data, and (8) using user activity and context (e.g., audio) data to improve accuracy and relevancy; Chin, Paragraph [0049] further teaches for each POI category node, context data related to the user (e.g., user's activity) and/or the environment may be inputted. By way of example, user context data may be formatted as follows, user_context=[walking, duration; stationary, duration; . . . ], and the environmental context data may be formatted as follows, env_context=[street, probability, duration; restaurant, probability, duration; . . . ]. As shown in FIG. 6, the category classifier classifies a context to an associated category and may be trained based on the context feature of the category. For instance, a possible classifier may be based on a decision tree, a support vector machine (SVM), neural network, etc. Based on the POI classification model, such as classification model 600, probable POI categories for the POI may be ranked and a POI category ultimately determined [determined by the classifier as in predicted].).
Motivation to combine same as stated for claim 1.
Regarding claim 51, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, and the combination further teaches wherein the received one or more predicted semantic tags from the multilabel classifier are a complete label set of predicted semantic tags that supplements and/or corrects one or more existing semantic tags (Chin, Paragraph [0049] further teaches for each POI category node, context data related to the user (e.g., user's activity) and/or the environment may be inputted. By way of example, user context data may be formatted as follows, user_context=[walking, duration; stationary, duration; . . . ], and the environmental context data may be formatted as follows, env_context=[street, probability, duration; restaurant, probability, duration; . . . ]. As shown in FIG. 6, the category classifier classifies a context to an associated category and may be trained based on the context feature of the category.; Chin, Paragraph [0055], teaches numerous advantages of the present disclosure, include but are not limited to, (1) automatically and accurately recording various POIs and other visited places without the user having to input POI or check into the POI, (2) searching and subscribing to related information, (3) providing various types of information to users based on accurate POI information, such as advertising, (4) creating labels for a given location, (5) personalizing data and context associated with the user and relevant POIs, (6) using crowd-sourced information from other users who have visited or checked into the user's POIs, (7) adding more data to POI analysis, such as vehicle sensor and condition data, [i.e., adding as in supplementing as claimed] and (8) using user activity and context (e.g., audio) data to improve accuracy and relevancy.; Chin, Paragraph [0045] further teaches there are numerous sources of context that may be obtained from the user and the environment, which may be input into the context engine, such as (1) location of the user, (2) sensor data (e.g., data from sensors on the user's mobile computer such as audio, data from sensors of the user's vehicle to indicate when the user stops the ignition of the vehicle and leaves the vehicle), (3) user's software and service data (e.g., software running on the user's mobile computer indicating what the user may have been previously doing or where the user was previously located), and/or (4) user provided information (e.g., type of activity specified by the user). Based on this information, user and environment context data may be used to train a POI classification model, data from which feeds back into the context engine, as seen in FIG. 3, to allow POI category information associated with the POI and the user's activity near/at the POI to be determined.)
Motivation to combine same as stated for claim 1.
Claims 3 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, in further view of Wang et al., “LCE A Location Category Embedding Model for Predicting the Category Labels of POIs”, (2017)
Regarding claim 3, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, and the combination further teaches wherein the metadata comprises one or more observed semantic tags for the stored POI (Chin Paragraph [0015] teaches FIG. 7A illustrates another table containing metadata for POIs near a location in accordance with one or more aspects of the disclosure.; Chin, Paragraph [0016] FIG. 7B illustrates yet another table containing metadata for POIs near a next location in accordance with one or more aspects of the disclosure.; See also Chin Paragraphs [0050]-[0052] further teaching metadata for POIs);
However the combination does not distinctly disclose wherein the one or more semantic tags comprise category labels taken from the label set.
Nevertheless, Wang teaches wherein the one or more semantic tags comprise category labels taken from the label set (Wang, pg. 710, Abstract, teaches this paper aims at predicting the category labels which will provide a succinct summarization of POIs.; Wang, pg. 710, Section 1, teaches some POIs have been labeled with semantic categories…with these semantic categories, we are able to better understand human’s mobility patterns and further improve the performance of future navigation recommendations.;).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the category labels and semantic categories, as taught by Wang, in order to better understand human’s mobility patterns and further improve the performance of future navigation recommendations. (Wang, pg. 710, Section 1).
Claims 4 and 17 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, and further in view of Ruta et al. “A semantic-Enhanced Augmented Reality Tool for OpenStreetMap POI Discovery”, (2014),
Regarding claim 4, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however the combination does not distinctly disclose wherein the metadata comprises a unique identifier for the stored POI.
Nevertheless, Ruta teaches wherein the metadata comprises a unique identifier for the stored POI (Ruta, pg. 482, section 3.1, teaches the semantic prefix is used to distinguish semantic annotations from other tags. The index n identifies different annotations – possibly referring to different ontologies…<tag k=”semantic:n:ontology” v=”URI” /. denotes the ontology the semantic node annotation refers to.; Note: Ruta, pg. 480 refers to metadata as “annotations”; Ruta, pg. 484, teaches semantic description concerning each POI is stored as an attribute of its marker.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the use of metadata, as taught by Ruta, in order to enable more advanced location-based resource discovery through proper inferences. (Ruta, pg. 480, Section 1).
Regarding claim 17, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however, the combination does not distinctly disclose wherein the database comprises a global crowdsourced database.
Nevertheless, Ruta teaches wherein the database comprises a global crowdsourced database (Ruta, pg. 488, par. 3, teaches MAR application supported by a cloud database and integrated with social networks…the collaborative and social features are part of the proposed MAR framework by leveraging the OpenStreetMap crowd-sourcing.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the crowd-sourcing, as taught by Ruta, as it allows semantic annotations and allows a content-based POI discovery and in order to enable more advanced location-based resource discovery through proper inferences. (Ruta, pg. 480, Section 1 and pg. 488, par. 3)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, in further view of Quan, “Exploiting spatial, temporal, and semantic information for point of interest recommendation”, (2014)
Regarding claim 6, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however the combination does not distinctly disclose wherein the temporal attribute data comprises one or more of opening times, closing times, or access times for the stored POI.
Nevertheless, Quan teaches wherein the temporal attribute data comprises one or more of opening times, closing times, or access times for the stored POI. (Quan, pg. 23, par. 1, teaches time-aware POI recommendations is defined as recommending POIs for a given user at a specified time in a day…time has been considered in several studies on POI recommendation. Some researchers develop models that can recommend POIs for users at the time close to the user’s last check-ins…different from these papers we aim to design a model that can recommend POIs for any target time.).
[EXAMINER NOTE: Jia-Ching Ying teaches Temporal Tagging in Section 3.2.3. To this effect, it teaches the stay location in addition to the arrival and departure times to represent the geographic and semantic properties of the POI. However, Examiner believes that Quan more clearly teaches the limitation, as drafted].
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the exploiting spatial, temporal, and semantic information for point-of-interest recommendation, as taught by Quan, as merchants can benefit from it to deliver location-based advertisements and attract more customers. (Quan, Summary).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, and further in view of Giannopoulos et al., “Learning Domain Driven and Semantically Enriched Embeddings for POI Classification”, (2019)
Regarding claim 7, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however the combination does not distinctly disclose wherein the attribute data provided to the multilabel classifier comprises vectorized and concatenated attribute data.
Nevertheless, Giannopoulos teaches wherein the attribute data provided to the multilabel classifier comprises vectorized and concatenated attribute data (Giannopoulos, Abstract, teaches State of the art works for Point-Of-Interest (POI) classification use either traditional feature extraction methods, or, more recently, deep learning (DL) methods, in order to train classification models on historical data, i.e. POIs already annotated with categories, and then deploy these models to classify new, unannotated POIs; Giannopoulos, Section 1, teaches POI classification consists in the automatic selection of a set of categories for annotating new, unannotated POIs, or for enriching already annotated POIs. The classification task usually comprises of an initial feature extraction step, where attributes and relations of POIs are quantified, and a second step of feeding the feature vectors of pre-annotated POIs into machine learning (ML) flows in order to train classification models; Giannopoulos, Introduction, col. 2, teaches the classification task usually comprises of an initial feature extraction step, where attributes and relations of POIs are quantified, and a second step of feeding the feature vectors of pre-annotated POIs into machine learning (ML) flows in order to train classification models; Giannopoulos, Section 3.2.3 teaches techniques and findings of traditional feature extraction methods can assist and further improve current embedding-based methods in training more meaningful and rich POI representations. Ranging from the approach of concatenating traditional feature vectors and embeddings to a more elaborate approach of utilizing traditional features for augmenting the embedding learning process, we aim to research, devise and evaluate several approaches for the effective combination of the two worlds; Giannopoulos, Section 3.2.3, teaches having an initial set of POI representations “learned” from traditional feature extractions techniques, we could use them as is, or via proper normalization transformations as input of the context embedding learning process).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the vectorizing, concatenating, as taught by Giannopoulos. The advantage of these methods is that they incorporate the feature extraction process within the learning process, relieving the domain experts from manually performing it. However, we believe that techniques and findings of traditional feature extraction methods can assist and further improve current embedding-based methods in training more meaningful and rich POI representations.(Giannopoulos, Section 3.2.3)
Claim 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, and Giannopoulos, as applied to claim 7, and further in view of Bo Wang et al., “Reveal the hidden layer via entity embedding in traffic prediction”, (2019)
Regarding claim 8, the combination of Chin in view of Jia-Ching Ying, and Giannopoulos teaches all of the limitations of claim 7, however the combination does not distinctly disclose wherein the provided attribute data comprises at least one categorical variable; and wherein the at least one categorical variable is represented by one-hot encoding.
However Bo Wang teaches wherein the provided attribute data comprises at least one categorical variable; (Bo Wang, Abstract, since neural network models treat independent variables as continuous variables, there are few studies on the use of categorical variables…The research results show that 1. Entity embedding can effectively increase the continuity of categorical variables and therefore, improve the prediction efficiency for the neural network models. 2. The relationship between variables can be identified through visual analysis, and the trained embedding vectors can also be used to supervise clustering.); and wherein the at least one categorical variable is represented by one-hot encoding. (Bo Wang, pg. 164, par. 2-3 teach preprocessing discrete variables is an effective way to improve neural networks…Another common way to solve this problem is to use one-hot encoding…One-hot encoding is a method for handling and extending categorical variables).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, and Giannopoulos, to further include the categorical variables and one-hot encoding, as taught by Bo Wang, in order to improve the prediction efficiency for the neural network models. (Bo Wang, Abstract).
Regarding claim 11, the combination of Chin in view of Jia-Ching Ying, and Giannopoulos teaches all of the limitations of claim 7, however, the combination does not distinctly disclose wherein said vectorizing comprises one or more of: representing the temporal attribute data as a categorical variable using one-hot encoding; representing the temporal attribute data as a periodic variable by transforming the temporal attribute data into one or more vectors respectively representing dimensions of the temporal attribute data; or representing the temporal attribute data as a formatted string using an n-gram character based long short-term memory (LSTM) model.
Nevertheless Bo Wang teaches wherein said vectorizing comprises one or more of: representing the temporal attribute data as a categorical variable using one-hot encoding (Bo Wang, pg. 164, par. 2-3 teach preprocessing discrete variables is an effective way to improve neural networks…Another common way to solve this problem is to use one-hot encoding…One-hot encoding is a method for handling and extending categorical variables; Bo Wang, Abstract, further teaches the model describes the spatiotemporal relationship from the data with several different historical periods to make the best predictions at the time); representing the temporal attribute data as a periodic variable by transforming the temporal attribute data into one or more vectors respectively representing dimensions of the temporal data; or representing the temporal attribute data as a formatted string using an n-gram character based long short-term memory (LSTM) model.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, and Giannopoulos, to further include the categorical variables and one-hot encoding, as taught by Bo Wang, in order to improve the prediction efficiency for the neural network models. (Bo Wang, Abstract).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, and Giannopoulos, as applied to claim 7, and further in view of Lee (US 20140279053 A1) and Mai et al. (US 20200218722 A1)
Regarding claim 9, the combination of Chin in view of Jia-Ching Ying, and Giannopoulos teach all of the limitations of claim 7, however, the combination does not distinctly disclose wherein the provided attribute data comprises at least one sequential variable;
Nevertheless, Lee teaches wherein the received attribute data comprises at least one sequential variable (Lee, Paragraph [0050] teaches In an exemplary embodiment of the present invention, the term "data map" may refer to a collection of indexed data that has three data subsets associated with each datum. The first part is a collection of informational data that is used for modeling purposes, both descriptive and predictive; the second part is a geospatial data set that describes the location of the datum within a physical or logical space; the third part is a temporal data set that describes a period descriptive of predictive validity within calendar time or some other sequential indexing variable. The latter two parts, the geospatial data and the temporal data, comprise the indexing system for the map.; Lee, Abstract, teaches a data map made up of data representing spatially sorted information related to attributes of a plurality of users of electronic devices each located at a respective location within a space and used to access electronic media, receiving from a user computer a bid amount on an opportunity to display an advertisement through the electronic media, and automatically adjusting by the one or more computers the bid amount for each location so that display of the advertisement through the electronic media is spatially optimized based on the spatially sorted information provided by the data map);
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, and Giannopoulos, to further include the data map comprising a sequential indexing variable, as taught by Lee, as geo-targeted campaigns can provide powerful advantages for advertisers seeking to run cost-efficient campaigns. (Lee, Paragraph [0044])
However the combination in view of Lee does not distinctly disclose wherein the at least one sequential variable is processed using an n-gram character-based long short-term memory (LSTM) model.
Nevertheless Mai teaches wherein the at least one sequential variable is processed using an n-gram character-based long short-term memory (LSTM) model (Mai, Abstract, teaches receiving a query, wherein the query includes a first sequence of words…; Mai, Paragraph [0037] teaches in some embodiments, the answerer 334 may be trained alone based on randomly sampled n-grams from sentences and to be able to generate the answer given a concatenation of a question and an n-gram. The answerer 334 may be one of a Long short-term Memory (LSTM) network [Note: reading on n-gram based LSTM model]).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, Giannopoulos, and Lee, to further include n-gram based LSTM model. Many services that perform information retrieval for Points of Interest (POI) utilize a Lucene-based setup for their semi-structured and unstructured data such as user reviews. While this type of system is easy to implement, it does not make use of semantics, but relies on direct word matches between a query and reviews, leading to a loss in both precision and recall. A semantically enriched information retrieval from semi-structured and unstructured data is needed to support better results for open domain search and question answering. (Mai, Paragraph [0002]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, and Giannopoulos, as applied to claim 7, and further in view of Lee (US 20140279053 A1)
Regarding claim 10, the combination of Chin in view of Jia-Ching Ying, and Giannopoulos teaches all of the limitations of claim 7, however, the combination does not distinctly disclose wherein the provided attribute data comprises at least one spatial variable; wherein the at least one spatial variable is modeled using a discretized input space or; mapped to a geographical region represented by a categorical variable or a sequential variable.
Nevertheless, Lee teaches wherein the provided attribute data comprises at least one spatial variable; wherein the at least one spatial variable is modeled using a discretized input space or; mapped to a geographical region represented by a categorical variable or a sequential variable (Lee, Paragraph [0050] teaches In an exemplary embodiment of the present invention, the term "data map" may refer to a collection of indexed data that has three data subsets associated with each datum. The first part is a collection of informational data that is used for modeling purposes, both descriptive and predictive; the second part is a geospatial data set that describes the location of the datum within a physical or logical space; the third part is a temporal data set that describes a period descriptive of predictive validity within calendar time or some other sequential indexing variable. The latter two parts, the geospatial data and the temporal data, comprise the indexing system for the map.).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, and Giannopoulos, to further include the data map comprising a sequential indexing variable, as taught by Lee, as geo-targeted campaigns can provide powerful advantages for advertisers seeking to run cost-efficient campaigns. (Lee, Paragraph [0044])
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, and further in view of Giannopoulos and Ruta, and Ivanov et al. (US 20200314584 A1)
Regarding claim 15, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however the combination does not distinctly disclose vectorizing a remainder of the filtered attribute data to provide training data for the training set and training the multilabel classifier using the provided training set; providing a training set, wherein said providing comprises: filtering attribute data for each of a plurality of POls, wherein the filtered attribute data comprises the temporal attribute data and metadata, the metadata further comprising at least one semantic tag; for each of the plurality of POls, selecting the at least one semantic tag to be completed as a target; wherein the filtered attribute data for each of the plurality of POls comprises POI names; wherein the POI names among the plurality of POls are represented by a plurality of languages and/or scripts.
Nevertheless, Giannopoulos teaches:
vectorizing a remainder of the filtered attribute data to provide training data for the training set (Giannopoulos, Introduction, col. 2, teaches the classification task usually comprises of an initial feature extraction step, where attributes and relations of POIs are quantified, and a second step of feeding the feature vectors of pre-annotated POIs into machine learning (ML) flows in order to train classification models.);
Giannopoulos teaches training the multilabel classifier using the provided training set. (Giannopoulos, Introduction, col. 2, teaches the classification task usually comprises of an initial feature extraction step, where attributes and relations of POIs are quantified, and a second step of feeding the feature vectors of pre-annotated POIs into machine learning (ML) flows in order to train classification models.)
Motivation to combine same as stated for claim 7.
However, the combination does not distinctly disclose providing a training set, wherein said providing comprises: filtering attribute data for each of a plurality of POls, wherein the filtered attribute data comprises the temporal attribute data and metadata, the metadata further comprising at least one semantic tag; for each of the plurality of POls, selecting the at least one semantic tag to be completed as a target; wherein the filtered attribute data for each of the plurality of POls comprises POI names; wherein the POI names among the plurality of POls are represented by a plurality of languages and/or scripts.
Nevertheless, Ruta teaches providing a training set, wherein said providing comprises: filtering attribute data for each of a plurality of POls, wherein the filtered attribute data comprises … metadata, the metadata further comprising at least one semantic tag; for each of the plurality of POls, selecting the at least one semantic tag to be completed as a target; (Ruta, pg. 482, section 3.1, teaches the semantic prefix is used to distinguish semantic annotations from other tags. The index n identifies different annotations – possibly referring to different ontologies…<tag k=”semantic:n:ontology” v=”URI” /. denotes the ontology the semantic node annotation refers to.; Note: Ruta, pg. 480 refers to metadata as “annotations”; Ruta, pg. 484, teaches semantic description concerning each POI is stored as an attribute of its marker.; Ruta, pg. 480, Section 1, teaches semantic-based technologies can allow more articulated and meaningful descriptions of locations and POIs. The use of metadata (annotations) endowed with formal machine-understandable meaning can enable more advanced location-based resource discovery through proper interferences; Ruta, Fig. 1 – Location-based pre-filter; Ruta, pg. 484 par. 3 teaches once the tool starts, the OSM Data Parser module extracts from the OSM map file the list of points of interest, filtering only those endowed with semantic annotation [i.e., semantic annotations reading on semantic tags, as claimed ).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, Giannopoulos, to further include the use of metadata, as taught by Ruta, in order to enable more advanced location-based resource discovery through proper inferences. (Ruta, pg. 480, Section 1).
However the combination does not distinctly disclose wherein the filtered attribute data comprises…wherein the filtered attribute data for each of the plurality of POls comprises POI names; wherein the POI names among the plurality of POls are represented by a plurality of languages and/or scripts.
Nevertheless, Ivanov teaches
…wherein the filtered attribute data comprises the temporal attribute data… (Ivanov, Paragraph [0074] teaches Venue type information may represent the type of the venue. Examples of such a type of the venue are “restaurant”, “coffee shop”, “hotel”, “museum”, “school”, “university”, “shop”, “shopping mall”, etc. Opening hours information may represent the opening hours of the venue. Visit frequency information may represent a visit frequency profile of the venue over a day. [note: both opening hours of a location [i.e., POI] and visit frequency reading on the temporal attribute data)
wherein the filtered attribute data for each of the plurality of POls comprises POI names (Ivanov, Paragraph [0104] teaches according to an exemplary embodiment of the invention, the plurality of venue information items is part of a point of interest (POI) database. Such a POI database may for example comprise a plurality of POI data items (e.g. corresponding to the plurality of venue information items), wherein each POI data item of the plurality of POI data items may be associated with a respective POI and represent information about the respective POI. For example the information about the POI may represent one or more of a name, a geographic position, a type, opening hours, a visit frequency, contact details (e.g. address, phone number, website address, email address, etc.), a layout of the POI.); wherein the POI names among the plurality of POls are represented by a plurality of languages and/or scripts (Ivanov, Paragraph [0104] further teaches an example data format for such a POI data item is the JavaScript Object Notation (JSON) data format or the Extensible Markup Language (XML) data format. [reading on language or script as claimed]).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, Giannopoulos, and Ruta, to further include the POI name and POls represented by a plurality of languages and/or scripts, as taught by Ivanov, in order to apply a simple matching algorithm, such as a match for each name represented by the plurality of venue items. (Ivanov, Paragraph [0021]).
Claim 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, and further in view of Xi et al., “Modelling of Bi-directional Spatio-Temporal Dependence and User’s Dynamic Preferences for Missing POI Check-in Identification”, (2019)
Regarding claim 18, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however the combination does not distinctly disclose wherein the attribute data provided to the multilabel classifier does not include attribute data derived from user check-ins.
Nevertheless, Xi teaches wherein the attribute data provided to the multilabel classifier does not include attribute data derived from user check-ins (Xi, pg. 5458, col. 2, teaches in this paper we focus on missing POI check-in identification, which is to identify where a user has visited at a specific time in the past; Xi, pg. 5465, Conclusion, teaches to address this task, we proposed a novel neural network model called Bi-STDDP; Xi, pg. 5460, col. 2, teaches we utilize multiple hidden layers of feed-forward neural networks to transform POI popularity, user preference, and target temporal pattern into the same space and add them up to model user’s dynamic preferences.).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the missing POI check-in identification which is able to alleviate the sparsity problem for user understanding. (Xi, pg. 5458, col. 2)
Claim 20-25 are rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, and further in view of Braun et al., “Collaborative Creation of Semantic Points of Interest as Linked Data on the Mobile Phone, (2010)
Regarding claim 20, the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however the combination does not distinctly disclose wherein said automatically assigning one or more semantic tags is in response to a user input via a user terminal; wherein the method further comprises: providing one or more of the predicted semantic tags to the user via the user terminal.
Nevertheless, Braun teaches wherein said automatically assigning one or more semantic tags is in response to a user input via a user terminal; wherein the method further comprises: providing one or more of the predicted semantic tags to the user via the user terminal (Braun, Abstract, teaches mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) that enables its users to collaboratively create, share, and modify semantic points of interest (POI). Users can easily create, delete, and modify their POIs and those shared by others.; Braun, Section 4.2, further teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map.).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the mobile application that enables its users to collaboratively create, share, and modify semantic POIs, as taught by Braun, in order to improve the quality of the collaborative POI dataset. (Braun, Conclusion).
Regarding claim 21, the combination of the combination of Chin in view of Jia-Ching Ying, and Braun, teaches all of the limitations of claim 20, and the combination further teaches wherein the user input comprises the POI or a search request for a POI (Braun, Abstract, teaches mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) that enables its users to collaboratively create, share, and modify semantic points of interest (POI). Users can easily create, delete, and modify their POIs and those shared by others.; Braun, Section 4.2, further teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map.).
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, and Braun, to further include the mobile application that enables its users to collaboratively create, share, and modify semantic POIs, as taught by Braun, in order to improve the quality of the collaborative POI dataset. (Braun, Conclusion).
Regarding claim 22, the combination of the combination of Chin in view of Jia-Ching Ying, and Braun, teaches all of the limitations of claim 20, and the combination further teaches wherein the user input comprises a proposed semantic tag; and wherein said providing one or more semantic tags to the user comprises providing additional or alternative semantic tags to the proposed semantic tag (Braun, Abstract, teaches mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) that enables its users to collaboratively create, share, and modify semantic points of interest (POI). Users can easily create, delete, and modify their POIs and those shared by others.; Braun, Section 4.2, further teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map.).
Motivation to combine same as stated for claim 20.
Regarding claim 23, the combination of the combination of Chin in view of Jia-Ching Ying, and Braun, teaches all of the limitations of claim 22, and the combination further teaches wherein the user input further comprises the POI or a search request for a POI (Braun, Abstract, teaches mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) that enables its users to collaboratively create, share, and modify semantic points of interest (POI). Users can easily create, delete, and modify their POIs and those shared by others.; Braun, Section 4.2, further teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map.).
Motivation to combine same as stated for claim 20.
Regarding claim 24, the combination of the combination of Chin in view of Jia-Ching Ying, and Braun, teaches all of the limitations of claim 22, and the combination further teaches wherein said provided one or more semantic tags comprise one or more tags related to the proposed semantic tag in a hierarchy (Braun, Abstract, teaches mobile application csxPOI (short for: collaborative, semantic, and context-aware points-of-interest) that enables its users to collaboratively create, share, and modify semantic points of interest (POI). Users can easily create, delete, and modify their POIs and those shared by others.; Braun, Section 4.2, further teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map.; Braun, pg. 8, par. 1, teaches, in the csxPOI ontology, POIs are instances of categories which themselves have subclass relations among each other. This taxonomic structure is utilized to assess a semantic similarity between POIs by comparing their relative position in the hierarchy).
Motivation to combine same as stated for claim 20.
Regarding claim 25, the combination of the combination of Chin in view of Jia-Ching Ying, and Braun, teaches all of the limitations of claim 24, and the combination further teaches wherein said providing the one or more semantic tags comprises generating for display on the user terminal a visualization of the one or more related semantic tags (Braun, section 4.2 teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map. Here, two options are possible: semantic search and presenting all POIs associated with the current user.).
Motivation to combine same as stated for claim 20.
Claim 26, 27, and 28 is rejected under 35 U.S.C. 103 as being unpatentable over Chin in view of Jia-Ching Ying, as applied to claim 1, and further in view of Braun and Quan.
Regarding claim 26, the combination of the combination of Chin in view of Jia-Ching Ying teaches all of the limitations of claim 1, however the combination does not distinctly disclose receiving a search request for a POI from a user via a user terminal, the search request comprising a category and a geographic location; in response to said request, searching the database; retrieving one or more POIs having spatial attribute data corresponding to the received geographic location and having one or more of the predicted semantic tags corresponding to the received category; and generating for displaying on the user terminal the retrieved one or more POIs.
Nevertheless, Braun teaches:
receiving a search request for a POI from a user via a user terminal, the search request comprising a category and a geographic location (Braun, section 4.2 teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map. Here, two options are possible: semantic search and presenting all POIs associated with the current user…The details of a POI displayed on the map can be shown by tapping on it. It shows the name at the top, and below a list of all categories the POI belongs to.; Braun, Abstract, teaches semantic POIs describe geographic places with explicit semantic properties of a collaboratively created ontology); in response to said request, searching the database (Braun, Section 3, teaches searching the triplestore; [Note: a triplestore is a purpose-built database]); and generating for displaying on the user terminal the retrieved one or more POls (Braun, section 4.2 teaches users can find POIs, i.e., search for POIs in the vicinity and display them on the map. Here, two options are possible: semantic search and presenting all POIs associated with the current user).
[EXAMINER NOTE: Jia-Ching Ying concurrently teaches searching a POI database].
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, to further include the mobile application that enables its users to collaboratively create, share, and modify semantic POIs, as taught by Braun, in order to improve the quality of the collaborative POI dataset. (Braun, Conclusion).
However, the combination does not distinctly disclose retrieving one or more POls having spatial attribute data corresponding to the received geographic location and having one or more of the predicted semantic tags corresponding to the received category.
Nevertheless, Quan teaches retrieving one or more POls having spatial attribute data corresponding to the received geographic location and having one or more of the predicted semantic tags corresponding to the received category (Quan, Chapter 1, Section 1.2, teaches the rich spatial-temporal information of geo-annotated UGC, along with the semantics embedded in the text content, provides opportunities for a number of recommendation tasks, one representative of which is POI recommendation.; Quan, pg. 4, par. 3, teaches to exploit time as additional information for recommendation, we define a new task, namely, time-aware POI recommendation, which aims to return a set of POIs for a user to visit at a specific time in a day.; Quan, pg. 7, par. 2, teaches for the graph-based propagation approach, the spatial influence is used to estimate the relativeness between POIs: if two POIs are close to each other, a user is more likely to visit a POI from the other one.; Note: Braun teaches in Section 2 POI category)
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to have modified the method and system for determining an actual point-of-interest based on user activity and environment contexts, as taught by Chin in view of Jia-Ching Ying, and Braun, to further include the exploiting spatial, temporal, and semantic information for point of interest recommendation, as taught by Quan, as merchants can benefit from it to deliver location-based advertisements and attract more customers. (Quan, Summary).
Regarding claim 27, the combination of Chin in view of Jia-Ching Ying, Braun, and Quan teaches all of the limitations of claim 26, and the combination further teaches wherein the retrieved one or more predicted semantic tags match the received category (Braun, pg. 6, par. 1, teaches the user can either select one of the suggested categories [i.e., because the user has found a match] and annotate the POI by pressing the plus button or enter a new name, i.e., a category that is not included in the ontology [i.e., because the user has not found a match].).
Motivation to combine same as stated for claim 26.
Regarding claim 28, the combination of Chin in view of Jia-Ching Ying, Braun, and Quan teaches all of the limitations of claim 26, and the combination further teaches wherein the retrieved one or more predicted semantic tags are related to the received category in a hierarchy of categories stored in the database (Braun, pg. 6, par. 1, teaches each entry corresponds to a category from the collaborative POI ontology introduced in Section 2 with all its relations and interlinks.; Braun, pg. 8, par. 1 further teaches in the csxPOI ontology, POIs are instances of categories which themselves have subclass relations among each other. This taxonomic structure is utilized to assess the semantic similarity between POIs by comparing their relative position in the hierarchy.; Braun, Section 3, teaches the csxPOI data such as the semantic POIs, categories, and users are stored and retrieved by the triplestore. [Note: a triplestore is a purpose-built database].).
Motivation to combine same as stated for claim 26.
Conclusion
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Baig et al. (US 20210364311 A1) – disclosing [0003] Today, digital maps of geographic areas are displayed on computing devices, such as computers, tablets, and mobile phones via mapping applications, web browsers, etc. Many mapping applications display points of interest (POIs), such as businesses or other organizations on the map. Each POI may be displayed using an icon or other indicator of the type of POI (e.g., a restaurant symbol for restaurants, a shopping symbol for department stores, etc.). Furthermore, the mapping applications, web browsers, etc. provide geographic search results (e.g., POIs) in response to geographic search queries and present one or several of the geographic search results within the digital map using an icon or other indicator; [0008] When the user selects a location or POI presented by the mapping application, the personalized map data generation system identifies landmarks in the user's location history that are near the selected location. One of the landmarks then is selected based on the frequency and/or recency with which the user visited the landmark. Then the mapping application presents the selected landmark with the selected location on a map display to provide the user with a frame of reference for the selected location.
CAO et al. (US 20190342698 A1) – disclosing [0029] When planning to visit a certain place, users typically select a corresponding POI or address in order to identify the place in terms of navigation and destination management. Therefore, it can be assumed that users have or can easily accumulate a collection of POItags and/or addresses from navigation and destination management (e.g. from a navigation system in a vehicle or a corresponding app running on a mobile device) which can be used to reveal the users' interests over time. Simultaneously, locations can be provided with POItags, for example by a map provider or from different users (e.g. crowdsourcing). Such POItags can be used as input for a location's profile; [0030] Therefore, there are several data sets available for processing: user (interest) profiles, location profiles, and the modeling of respective relationships. An exemplary use case benefits from such data sets in that user-POItag and location-POItag relationships (i.e. matrixes) can be employed in a recommendation system. For example, a user may define a type of place they intend to visit and the methods and systems disclosed herein can be used in order to provide the user with a corresponding recommendation.
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/B.R.B./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146