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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/7/2024 and 2/12/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 11-12, 13-16, and 19-21 are rejected under35 U.S.C. 101 because the claimed invention is directed to abstract idea not integrated into a practical application and without significantly more.
Claim 1 recites: A method for data matching, comprising: acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location; calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network; inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network; and determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network.
Analysis:
Step 1: The claim falls within the statutory categories of invention (i.e., process (STEP l=YES).
Step 2A Prong One which tests whether the claim recites a judicial exception. In particular, Claim 1 recites steps of "calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network”, “inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network” and “determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network”. These limitations are recited at a high level of generality such that the steps could be performed within the human mind. In essence, the steps are directed to calculation of data between target wireless network and point of interest, determining whether a target POI corresponds to the target wireless network, and determining one target wireless network corresponding to target point of interest fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). :. Moreover, MPEP 2106.04(a)(2)(III) reminds examiners that, “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.” While the claim limitations may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 1 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. The data collecting step for the calculating, “acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location”, and a preset link prediction model are the additional elements.
Acquiring location of each wireless network and the points of interest within a determined range of the location is a general data collecting step to be performed for calculation that uses the data. The step is the insignificant extra-solution activity to the judicial exception. (MPEP 2106.05(g))
The preset link prediction model is an additional element for determining a point of interest based on the feature date. Inputting the feature data into a link prediction model to determine a point of interest only generally links the judicial exception of determining a point of interest based on the feature date to a particular technological environment or field of use, without particular limiting use. Therefore, claim 1 does not recite additional elements that integrate the exception into a practical application (STEP 2A Prong Two =NO).
STEP 2B: The additional element/limitations are:
“acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location” and “a preset link prediction model”.
The limitation of acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location, is no more than well-understood, routine, conventional activities previously known to the industry, to collect data for the calculation performed on the collected data, which is recited at a high level of generality.
Similarly, inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network, is no more than well-understood, routine, conventional activities previously known to the industry.
The judicial exceptions with the additional elements thus when reconsidered individually and as an ordered combination do not amount to significantly more than the abstract idea. MPEP 2106.05(d)(II), bullet i, reminds examiners that, “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here
specify how interactions with the Internet are manipulated to yield a desired result--a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added))” The steps within the claim are well-understood and no inventive concept is recited, thus, claim 1 is ineligible (STEP 2B= NO).
Claim 2 recites “sorting the association probability between the target wireless network and each of the at least one point of interest to be matched to determine the target point of interest corresponding to the target wireless network from the at least one point of interest to be matched”. The limitation falls within "Mental Processes" (which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). Claim 2 does not recite additional element other than the preset link prediction model. Therefore, the claim does not amount to more than the abstract idea itself. Therefore, claim 2 is ineligible under 35 U.S.C. §101.
Claims 3 and 13 recite "performing clustering on the plurality of the target wireless networks according to the first identification information of each target wireless network to obtain a plurality of wireless network clusters". The limitation is recited at a high level of generality such that the step could be performed within the human mind. In essence, the step is directed to group the wireless networks based on identifications of the networks and falls within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). The claims also are also directed to determining an association probability between the target point of interest and each target wireless network and determining a target wireless network cluster of the target point of interest from the plurality of the wireless network clusters. These limitations are also recited at a high level of generality such that the steps could be performed within the human mind. Determining association probability between network and interest of point and determining a target cluster among multiple clusters fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment).
While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claims recite a mental process. Therefore, claims 3 and 13 recite the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. “a preset link prediction model” is an additional element for determining an association probability between the target point of interest and each target wireless network. Determining the association by a link prediction model only generally links the judicial exception of determining process to a particular technological environment or field of use, without particular limiting use. Therefore, claims 3 and 13 do not recite additional elements that integrate the exception into a practical application (STEP 2A Prong Two =NO).
STEP 2B: The additional element/limitation is “a preset link prediction model”.
Determining an association probability between the target point of interest and each target wireless network in the wireless network cluster by the preset link prediction model is no more than well-understood, routine, conventional activities previously known to the industry, to use a link prediction model to calculate probabilities based on input data, which is recited at a high level of generality. Thus, claims 3 and 13 are ineligible (STEP 2B= NO).
Claim 4 recites "according to the relationship matching feature data, calculating the association probability between the target wireless network and each of the at least one point of interest to be matched by the preset link prediction model” and “according to the association probability between the plurality of the target wireless networks and each of the at least one point of interest to be matched, determining the association probability between the target point of interest and each target wireless network in the wireless network cluster”. The limitations are recited at a high level of generality such that the steps could be performed within the human mind. Calculating the association probability the points of interest and networks and determining the association probability between a particular point of interest and networks fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 4 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. “a preset link prediction model” is an additional element for determining an association probability between the target points of interest and wireless networks. Calculating the association probability by a link prediction model only generally links the judicial exception of determining process to a particular technological environment or field of use, without particular limiting use. Therefore, claim 4 does not recite additional elements that integrate the exception into a practical application (STEP 2A Prong Two =NO).
STEP 2B: The additional element/limitation is “a preset link prediction model”.
According to the feature data, calculating the association probability between the target wireless network and each of the at least one point of interest by the preset link prediction model is no more than well-understood, routine, conventional activities previously known to the industry, to use a link prediction model to calculate probabilities based on input data, which is recited at a high level of generality. Thus, claim 4 is ineligible (STEP 2B= NO).
Claims 5 and 14 recite " according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target association probability between the wireless network cluster and the target point of interest” and “regarding a wireless network cluster that has a largest target association probability in the plurality of the wireless network clusters as the target wireless network cluster”. The limitations are recited at a high level of generality such that the steps could be performed within the human mind. Determining the association probability between a cluster and a point of interest based on the association probability of each network and the point of interest, and regarding the clusters of the largest association probability as the target cluster fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claims recite a mental process. Therefore, claims 5 and 14 recite the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. Claims 5 and 14 recite no additional element and are ineligible. (STEP 2A Prong Two =NO) and (STEP 2B= NO).
Claim 6 recites "acquiring a target mean value of the association probability between the target point of interest and the target wireless networks in the wireless network cluster” and “regarding the target mean value as the target association probability”. The limitations are recited at a high level of generality such that the step could be performed within the human mind. Calculating mean value of the point of interest and each network and regarding the mean value as the target association probability fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 6 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. Claim 6 recites no additional element and is ineligible. (STEP 2A Prong Two =NO) and (STEP 2B= NO).
Claims 7 and 15-16 recite "calculating distance feature data between the target wireless network and the point of interest to be matched” and “determining text feature data according to the first identification information of the target wireless network and second identification information of the point of interest to be matched, wherein the text feature data comprises at least one selected from the group consisting of character granularity feature data, word granularity feature data, and semantic feature data”. The limitations are recited at a high level of generality such that the step could be performed within the human mind. The elements such as “identification”, “character granularity feature data”, “word granularity feature data”, and “semantic feature data” are essentially abstract concepts. Calculating distance between point of interest and network and determining text feature data fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claims recite a mental process. Therefore, claims 7 and 15-16 recite the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. Claims 7 and 15-16 recite no additional element and are ineligible. (STEP 2A Prong Two =NO) and (STEP 2B= NO).
Claim 8 recites " wherein the first identification information comprises a first name of the target wireless network”, “the second identification information comprises a second name of the point of interest to be matched”, “the character granularity feature data comprises at least one selected from the group consisting of a proportion of identical characters, a character-level similarity coefficient, a longest common substring and a text editing distance between the first name and the second name”, “the word granularity feature data comprises at least one selected from the group consisting of a proportion of identical words between the first name and the second name, a word-level similarity coefficient and whether the first name is an alias of the second name” and “semantic feature data comprises a semantic similarity between the first name and the second name calculating distance feature data between the target wireless network and the point of interest to be matched”.
The limitations are recited at a high level of generality such that the step could be performed within the human mind. Names, character granularity feature data, word granularity feature data and semantic data are abstract concepts. Computing the character granularity feature data and the word granularity feature data fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 8 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. Claim 8 recites no additional element and is ineligible. (STEP 2A Prong Two =NO) and (STEP 2B= NO).
Claims 9, 17-18 recite: wherein the preset link prediction model is obtained through training by: acquiring a plurality of pieces of matching feature sample data, wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample; and training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model.
Analysis:
Step 1: The claim falls within the statutory categories of invention (i.e., process (STEP l=YES).
Step 2A Prong One which tests whether the claim recites a judicial exception. In particular, Claims 9, 17-18 recite “acquiring a plurality of pieces of matching feature sample data, wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample”. The elements such as “distance feature data”, “character granularity feature data”, “word granularity feature data”, and “semantic feature data”. The limitation is recited at a high level of generality such that the steps could be performed within the human mind. The elements such as “distance feature data”, “character granularity feature data”, “word granularity feature data”, and “semantic feature data”. Acquiring the data falls within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). While the claim limitations may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claims 9 and 17-18 recite a mental process. Therefore, claims 9 and 17-18 recite the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. “training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model” is the additional element. Training a model for specific function is an improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a). Thus, the limitation is indicative of integration into a practical application (STEP 2A Prong Two =YES).. Therefore, claims 9, 17-18 qualifies as eligible subject matter under 35 U.S.C. 101.
Claim 11 is rejected under35 U.S.C. 101 because the claim does not fall within the statutory categories of invention.
Claim 11 recites: a non-transient computer-readable medium, wherein a computer program is stored on the non-transient computer-readable medium, when the computer program is executed by a processor, a method for data matching is implemented, and the method comprises: acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location; calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network; inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network; and determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network.
Analysis:
Step 1: The claim does not fall within the statutory categories of invention.
Claim 11 recites “A non-transient computer-readable medium” and the specification does not explicitly exclude transitory computer-readable medium. The specification discloses “It should be noted that the above computer-readable medium described in the present disclosure may be a computer-readable signal medium” [Para. 0141]. The claim term "A non-transient computer-readable medium" under the broadest reasonable interpretation thus covers signals. Therefore, claim 11 is directed to a signal per se. and does not fall within any statutory category ( (STEP l=NO).
Step 2A Prong One which tests whether the claim recites a judicial exception. In particular, Claim 11 recites steps of "calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network”, “inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network” and “determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network”. These limitations are recited at a high level of generality such that the steps could be performed within the human mind. In essence, the steps are directed to calculation of data between target wireless network and point of interest, determining whether a target POI corresponds to the target wireless network, and determining one target wireless network corresponding to target point of interest fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). :. Moreover, MPEP 2106.04(a)(2)(III) reminds examiners that, “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.” While the claim limitations may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 11 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. The data collecting step for the calculating, “acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location”, and a preset link prediction model are the additional elements.
Acquiring location of each wireless network and the points of interest within a determined range of the location is a general data collecting step to be performed for calculation that uses the data. The step is the insignificant extra-solution activity to the judicial exception. (MPEP 2106.05(g))
The preset link prediction model is an additional element for determining a point of interest based on the feature date. Inputting the feature data into a link prediction model to determine a point of interest only generally links the judicial exception of determining a point of interest based on the feature date to a particular technological environment or field of use, without particular limiting use. Therefore, claim 1 does not recite additional elements that integrate the exception into a practical application (STEP 2A Prong Two =NO).
STEP 2B: The additional element/limitations are:
“acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location” and “a preset link prediction model”.
The limitation of acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location, is no more than well-understood, routine, conventional activities previously known to the industry, to collect data for the calculation performed on the collected data, which is recited at a high level of generality.
Similarly, inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network, is no more than well-understood, routine, conventional activities previously known to the industry.
The judicial exceptions with the additional elements thus when reconsidered individually and as an ordered combination do not amount to significantly more than the abstract idea. MPEP 2106.05(d)(II), bullet i, reminds examiners that, “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here
specify how interactions with the Internet are manipulated to yield a desired result--a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added))” The steps within the claim are well-understood and no inventive concept is recited, thus, claim 11 is ineligible (STEP 2B= NO).
Claim 12 is rejected under35 U.S.C. 101 because the claimed invention is directed to abstract idea not integrated into a practical application and without significantly more.
Claim 12 recites: An electronic device, comprising: at least one memory, wherein a computer program is stored in the at least one memory; and at least one processor, configured to execute the computer program in the at least one memory to implement a method for data matching, wherein the method comprises: acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location; calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network; inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network; and determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network.
Analysis:
Step 1: The claim falls within the statutory categories of invention (i.e., manufacture (STEP l=YES).
Step 2A Prong One which tests whether the claim recites a judicial exception. In particular, Claim 12 recites steps of "calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network”, “inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network” and “determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network”. These limitations are recited at a high level of generality such that the steps could be performed within the human mind. In essence, the steps are directed to calculation of data between target wireless network and point of interest, determining whether a target POI corresponds to the target wireless network, and determining one target wireless network corresponding to target point of interest fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). :. Moreover, MPEP 2106.04(a)(2)(III) reminds examiners that, “Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer.” While the claim limitations may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 12 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. The data collecting step for the calculating, “acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location”, and a preset link prediction model are the additional elements.
Acquiring location of each wireless network and the points of interest within a determined range of the location is a general data collecting step to be performed for calculation that uses the data. The step is the insignificant extra-solution activity to the judicial exception. (MPEP 2106.05(g))
The preset link prediction model is an additional element for determining a point of interest based on the feature date. Inputting the feature data into a link prediction model to determine a point of interest only generally links the judicial exception of determining a point of interest based on the feature date to a particular technological environment or field of use, without particular limiting use. Therefore, claim 12 does not recite additional elements that integrate the exception into a practical application (STEP 2A Prong Two =NO).
STEP 2B: The additional element/limitations are:
“acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location” and “a preset link prediction model”.
The limitation of acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks, and at least one point of interest to be matched within a preset range of the target location, is no more than well-understood, routine, conventional activities previously known to the industry, to collect data for the calculation performed on the collected data, which is recited at a high level of generality.
Similarly, inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network, is no more than well-understood, routine, conventional activities previously known to the industry.
The judicial exceptions with the additional elements thus when reconsidered individually and as an ordered combination do not amount to significantly more than the abstract idea. MPEP 2106.05(d)(II), bullet i, reminds examiners that, “The courts have recognized the following computer functions as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here
specify how interactions with the Internet are manipulated to yield a desired result--a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added))” The steps within the claim are well-understood and no inventive concept is recited, thus, claim 12 is ineligible (STEP 2B= NO).
Claim 19 recites “sorting the association probability between the target wireless network and each of the at least one point of interest to be matched to determine the target point of interest corresponding to the target wireless network from the at least one point of interest to be matched”. The limitation falls within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). Claim 19 does not recite additional element other than the preset link prediction model. Therefore, the claim is non-statutory since the claim does not amount to more than the abstract idea itself. Claim is ineligible under 35 U.S.C. §101.
Claim 20 is rejected under35 U.S.C. 101 because the claimed invention is directed to abstract idea not integrated into a practical application and without significantly more.
Analysis:
Step 1: The claim falls within the statutory categories of invention (i.e., manufacture (STEP l=YES).
Step 2A Prong One which tests whether the claim recites a judicial exception. In particular, claim 20 recites "performing clustering on the plurality of the target wireless networks according to the first identification information of each target wireless network to obtain a plurality of wireless network clusters". The limitation is recited at a high level of generality such that the step could be performed within the human mind. In essence, the step is directed to group the wireless networks based on identifications of the networks and falls within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). The claims also are also directed to determining an association probability between the target point of interest and each target wireless network and determining a target wireless network cluster of the target point of interest from the plurality of the wireless network clusters. These limitations are also recited at a high level of generality such that the steps could be performed within the human mind. Determining association probability between network and interest of point and determining a target cluster among multiple clusters fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment).
While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 20 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. “a preset link prediction model” is an additional element for determining an association probability between the target point of interest and each target wireless network. Determining the association by a link prediction model only generally links the judicial exception of determining process to a particular technological environment or field of use, without particular limiting use. Therefore, claim 20 does not recite additional elements that integrate the exception into a practical application (STEP 2A Prong Two =NO).
STEP 2B: The additional element/limitation is “a preset link prediction model”.
Determining an association probability between the target point of interest and each target wireless network in the wireless network cluster by the preset link prediction model is no more than well-understood, routine, conventional activities previously known to the industry, to use a link prediction model to calculate probabilities based on input data, which is recited at a high level of generality. Thus, claim 20 is ineligible (STEP 2B= NO).
Claim 21 recites "according to the relationship matching feature data, calculating the association probability between the target wireless network and each of the at least one point of interest to be matched by the preset link prediction model” and “according to the association probability between the plurality of the target wireless networks and each of the at least one point of interest to be matched, determining the association probability between the target point of interest and each target wireless network in the wireless network cluster”. The limitations are recited at a high level of generality such that the steps could be performed within the human mind. Calculating the association probability the points of interest and networks and determining the association probability between a particular point of interest and networks fall within "Mental Processes" which is identified as "Abstract Idea (Judicial Exception)" since it recites concepts which can be performed in the human mind (including, observation, evaluation, judgment). While the claim limitation may recite structural elements, those elements merely perform them in a computer network environment. Reciting elements in such a field of use do not transform the claim itself beyond an abstract idea. See MPEP 2106.04(a)(2)(III)(C), bullet 2. Thus, the claim recites a mental process. Therefore, claim 21 recites the "Judicial Exception" in regard to "Step 2A Prong One" test (STEP 2A Prong One =YES).
STEP 2A Prong Two which Identifies whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluates those additional elements to determine whether they integrate the exception into a practical application of the exception. “a preset link prediction model” is an additional element for determining an association probability between the target points of interest and wireless networks. Calculating the association probability by a link prediction model only generally links the judicial exception of determining process to a particular technological environment or field of use, without particular limiting use. Therefore, claim 21 does not recite additional elements that integrate the exception into a practical application (STEP 2A Prong Two =NO).
STEP 2B: The additional element/limitation is “a preset link prediction model”.
According to the feature data, calculating the association probability between the target wireless network and each of the at least one point of interest by the preset link prediction model is no more than well-understood, routine, conventional activities previously known to the industry, to use a link prediction model to calculate probabilities based on input data, which is recited at a high level of generality. Thus, claim 21 is ineligible (STEP 2B= NO).
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 nonobviousness.
Claims 1-2, 7-8, 11-12, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN111881377A, hereinafter Zhang) in view of Jiang et al. (CN112182427A, hereinafter Jiang).
For claim 1, Zhang teaches a method for data matching ([Para. 11 and 12], a method for processing location interest points including: Acquiring first key information of at least two wireless local area networks, and second key information of location points of interest within the target area covered by each of the wireless local area networks), comprising: acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks ([Para. 163], Step 601: The server obtains the names of at least two wireless local area networks and the names of points of interest in locations within the target area covered by each wireless local area network. [Para. 164], Step 602: Use partial information associated with geographic information in the names of the at least two wireless local area networks as the first key information of the wireless local area network. [Para. 167], Step 605: Obtain the latitude and longitude of at least two wireless local area networks corresponding to each first key information), and at least one point of interest to be matched within a preset range of the target location ([Para. 165], Step 603: Use the pinyin and English translation corresponding to the name of the location interest point as the keyword of the location interest point to obtain the second key information of the location interest point within the target area covered by each wireless local area network. [Para. 104], the target area covered by the wireless local area network can be a preset size of the area, such as a circular range with a radius of 250 meters, where the center of the circular range is the location of the wireless local area network. [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name. [Para. 183] and [FIG. 8], when matching Wi-Fi and POI around Peking University, we will extract all POIs 250 meters around each Wi-Fi); calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network ([Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 127], the similarity between the first key information of the wireless local area network and the first key information of the location interest point is calculated to indicate the similarity between the wireless local area network and the location interest point. [Para. 129], The ratio of the number of characters to the number of characters of the first key information of the wireless local area network is used as the similarity between the wireless local area network and the points of interest in the corresponding target area. [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name [Examiner’s Note: Distance and similarity constitute the relationship matching feature data]); inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network ([Para. [171], Step 608: Match the first key information of each wireless local area network with the second key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 172], Step 609: for each cluster, when the cluster comprises at least two wireless local area networks, sequencing candidate position interest points corresponding to the at least two wireless local area networks in the cluster according to the similarity between the at least two wireless local area networks in the cluster and the corresponding candidate position interest points. [Para. 173], Step 610: According to the sorting result, the candidate location interest point corresponding to the maximum similarity is used as the target location interest point of the corresponding cluster); and determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network ([Para. 181], Step 702: cluster Wi-Fis with the same name in the same city to obtain multiple clusters. [Para. 171], Step 608: Match the first key information of each wireless local area network with the second key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 172], Step 609: for each cluster, when the cluster comprises at least two wireless local area networks, sequencing candidate position interest points corresponding to the at least two wireless local area networks in the cluster according to the similarity between the at least two wireless local area networks in the cluster and the corresponding candidate position interest points. [Para. 173], Step 610: According to the sorting result, the candidate location interest point corresponding to the maximum similarity is used as the target location interest point of the corresponding cluster [Examiner’s Note: The target POI is determined]. [Para. 174], Step 611: Associate each wireless local area network with the target location interest point corresponding to the cluster to which the wireless local area network belongs [Examiner’s Note: Associating each network in a particular cluster with the determined target POI is determining the networks]. [Para. 163-165], the name of the wireless local area network correspond to the first key information of the wireless local area network and the name of the location interest point correspond to the second key information of the location interest point [Examiner’s Note: All networks in the cluster have the same name and the name of the networks in the cluster is the first identification of each target network in the cluster]).
Although teaching determining target point of interest based on distance and similarity, Zhang does not explicitly disclose inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network.
Jiang is directed to providing data processing method, device, electronic device and storage medium. More specifically, Jiang teaches inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network ([Para. 36], The number of candidate POIs can be one or more. [Para. 39], the number of candidate WiFi connected to a candidate POI can be one or more. [Para. 40], for candidate POIs, candidate WiFis whose positioning positions are located around the coordinates of the candidate POI and whose WiFi name is similar to the POI name of the candidate POI can be identified. [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined [Examiner’s Note: The Wi-Fis and POIs are matched based on distance and similarity first]. [Para. 45], the candidate POI has its own POI characteristics, such as name, coordinates, etc., then the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model, which can use algorithm strategies (including But not limited to at least one of the following: marginal distance, clustering, sorting) to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi, Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: The pre-trained prediction model that predicts association between POI and WiFI is a preset link prediction model. The POI characteristics of the candidate POI and the WiFi characteristics of the WiFi in the input are feature data matched based on distance and similarity. There are multiple candidate POIs. The candidate POI that has high association probability with Wi-Fis is determined for use by the prediction model based on the matched feature data. According to Zhang, this determined POI by the prediction model can be the determined target POI in Zhang in Step 610. The networks in Zhang are determined to be the networks in a cluster with the determined target POI in Step 611]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 2, Zhang and Jiang teach the method according to claim 1. The references further teach wherein the inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network, comprises: inputting the relationship matching feature data into the preset link prediction model (Jiang [Para. 40], for candidate POIs, candidate WiFis whose positioning positions are located around the coordinates of the candidate POI and whose WiFi name is similar to the POI name of the candidate POI can be identified. Jiang [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined. Jiang [Para. 45], the candidate POI has its own POI characteristics, such as name, coordinates, etc., then the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model, which can use algorithm strategies (including But not limited to at least one of the following: marginal distance, clustering, sorting) to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi); calculating an association probability between the target wireless network and each of the at least one point of interest to be matched based on the preset link prediction model (Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name. Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: The POI and WIFI in the input are matched]), and sorting the association probability between the target wireless network and each of the at least one point of interest to be matched to determine the target point of interest corresponding to the target wireless network from the at least one point of interest to be matched (Jiang [Para. 36], The number of candidate POIs can be one or more. Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: To determine the high association probability indicates sorting the association probabilities. There are multiple candidate POIs. The candidate POI that has high association probability with Wi-Fis is determined for use by the prediction model based on the matched feature data. According to Zhang, this determined POI by the prediction model can be the determined target POI in Zhang in Step 610]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi and the prediction model predicts the association probabilities, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 7, Zhang and Jiang teach the method according to claim 1. The references further teach wherein the calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network, comprises: calculating distance feature data between the target wireless network and the point of interest to be matched (Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name); and/or determining text feature data according to the first identification information of the target wireless network and second identification information of the point of interest to be matched (Zhang [Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. Zhang [Para. 102], the first key information of the wireless local area network may be all or part of the information in the name of the wireless local area network, and the first key information of the location interest point may be all or part of the information of the location interest point. Zhang [Para. 107], the similarity between the first key information of the wireless local area network and the first key information of the location interest point is calculated to indicate the similarity between the wireless local area network and the location interest point [Examiner’s Note: The names of the network and interest point are the first and second identification information respectively and the similarity is the text feature data]), wherein the text feature data comprises at least one selected from the group consisting of character granularity feature data (Zhang [Para. 128], the similarity between the wireless local area network and the points of interest in the corresponding target area can be obtained in the following manner: Zhang [Para. 130], the longest common string matching method can be used to match the first key information of the wireless local area network with the second key information of the location interest point. Zhang [Para. 138], the similarity between the wireless local area network and the point of interest in the corresponding target area can also be obtained through the edit distance of the string. Here, the string edit distance refers to the minimum operations required to convert two strings. The less operations required, the more similar the two strings are. String operations include: insert a character, delete a character, and replace a character [Examiner’s Note: Longest common string matching and edit distance are character granularity feature data]), word granularity feature data (Zhang [Para. 179], Step 701: Clean the names of Wi-Fi and POI, and obtain the pinyin and English translation of the POI name. Zhang [Para. 180], because Wi-Fi has different naming rules, it is very noisy and needs to be cleaned, such as PK U-teacher, PKU-student, PKU-staff, etc. We only take the part associated with geographic information, namely PKU . For POI, such as Peking University, you will English "peking university". Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name [Examiner’s Note: PKU is alias of Peking University. Name similarity is calculated based on alias in network and the name in POI]), and semantic feature data (Jiang [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined. Candidate WiFi whose locating position is around the coordinates of the candidate POI and whose SSID is similar to the name of the candidate POI (semantic similarity is greater than threshold 2) may be mined from the business data to attach to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the name of network and name of POI are matched on semantic similarity, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 8, Zhang and Jiang teach the method according to claim 7. The references further teach wherein the first identification information comprises a first name of the target wireless network (Zhang Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. Zhang [Para. 102], the first key information of the wireless local area network may be all or part of the information in the name of the wireless local area network), the second identification information comprises a second name of the point of interest to be matched (Zhang [Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. Zhang [Para. 102], and the first key information of the location interest point may be all or part of the information of the location interest point), and the character granularity feature data comprises at least one selected from the group consisting of a proportion of identical characters, a character-level similarity coefficient (Zhang [Para. 134], the similarity between the wireless local area network and the points of interest in the corresponding target area can be obtained in the following manner: Zhang [Para. 135], taking intersection of the first key information of the wireless local area network and the second key information of the position interest points in the corresponding target area range, and acquiring the character number of the intersection; merging the second key information of the wireless local area network with the first key information of the position interest points in the corresponding target area range, and acquiring the number of characters of the union; and taking the ratio of the number of the intersected characters to the number of the characters of the union as the similarity of the wireless local area network and the interest points at the positions in the corresponding target area range [Examiner’s Note: This operation corresponds to both proportion of identical characters, a character-level similarity coefficient]), a longest common substring and a text editing distance between the first name and the second name (Zhang [Para. 128], the similarity between the wireless local area network and the points of interest in the corresponding target area can be obtained in the following manner: Zhang [Para. 130], the longest common string matching method can be used to match the first key information of the wireless local area network with the second key information of the location interest point. Zhang [Para. 138], the similarity between the wireless local area network and the point of interest in the corresponding target area can also be obtained through the edit distance of the string. Here, the string edit distance refers to the minimum operations required to convert two strings. The less operations required, the more similar the two strings are. String operations include: insert a character, delete a character, and replace a character); the word granularity feature data comprises at least one selected from the group consisting of a proportion of identical words between the first name and the second name, a word-level similarity coefficient and whether the first name is an alias of the second name (Zhang [Para. 179], Step 701: Clean the names of Wi-Fi and POI, and obtain the pinyin and English translation of the POI name. Zhang [Para. 180], because Wi-Fi has different naming rules, it is very noisy and needs to be cleaned, such as PK U-teacher, PKU-student, PKU-staff, etc. We only take the part associated with geographic information, namely PKU . For POI, such as Peking University, you will English "peking university". Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name [Examiner’s Note: PKU is alias of Peking University. Name similarity is calculated based on alias in network and the name in POI]); and the semantic feature data comprises a semantic similarity between the first name and the second name (Jiang [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined. Candidate WiFi whose locating position is around the coordinates of the candidate POI and whose SSID is similar to the name of the candidate POI (semantic similarity is greater than threshold 2) may be mined from the business data to attach to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the name of network and name of POI are matched on semantic similarity, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 11, Zhang teaches a non-transient computer-readable medium ([Para. 51], The embodiment of the present application provides a computer-readable storage medium that stores executable instructions), wherein a computer program is stored on the non-transient computer-readable medium ([Para. 51], The embodiment of the present application provides a computer-readable storage medium that stores executable instructions), when the computer program is executed by a processor, a method for data matching is implemented ([Para. 51], The embodiment of the present application provides a computer-readable storage medium that stores executable instructions, which are used to cause a processor to execute the method for processing a point of interest. [Para. 11 and 12], a method for processing location interest points including: Acquiring first key information of wireless networks, and second key information of location points of interest within the target area covered by each of the wireless networks), and the method comprises: acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks ([Para. 163], Step 601: The server obtains the names of at least two wireless local area networks and the names of points of interest in locations within the target area covered by each wireless local area network. [Para. 164], Step 602: Use partial information associated with geographic information in the names of the at least two wireless local area networks as the first key information of the wireless local area network. [Para. 167], Step 605: Obtain the latitude and longitude of at least two wireless local area networks corresponding to each first key information), and at least one point of interest to be matched within a preset range of the target location ([Para. 165], Step 603: Use the pinyin and English translation corresponding to the name of the location interest point as the keyword of the location interest point to obtain the second key information of the location interest point within the target area covered by each wireless local area network. [Para. 104], the target area covered by the wireless local area network can be a preset size of the area, such as a circular range with a radius of 250 meters, where the center of the circular range is the location of the wireless local area network. [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name. [Para. 183] and [FIG. 8], when matching Wi-Fi and POI around Peking University, we will extract all POIs 250 meters around each Wi-Fi); calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network ([Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 127], the similarity between the first key information of the wireless local area network and the first key information of the location interest point is calculated to indicate the similarity between the wireless local area network and the location interest point. [Para. 129], The ratio of the number of characters to the number of characters of the first key information of the wireless local area network is used as the similarity between the wireless local area network and the points of interest in the corresponding target area. [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name [Examiner’s Note: Distance and similarity constitute the relationship matching feature data]); inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network ([Para. [171], Step 608: Match the first key information of each wireless local area network with the second key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 172], Step 609: for each cluster, when the cluster comprises at least two wireless local area networks, sequencing candidate position interest points corresponding to the at least two wireless local area networks in the cluster according to the similarity between the at least two wireless local area networks in the cluster and the corresponding candidate position interest points. [Para. 173], Step 610: According to the sorting result, the candidate location interest point corresponding to the maximum similarity is used as the target location interest point of the corresponding cluster); and determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network ([Para. 181], Step 702: cluster Wi-Fis with the same name in the same city to obtain multiple clusters. [Para. 171], Step 608: Match the first key information of each wireless local area network with the second key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 172], Step 609: For each cluster, when the cluster contains at least two wireless local area networks, according to the similarity between the at least two wireless local area networks in the cluster and the interest points of the corresponding candidate locations, determine the candidate locations corresponding to the at least two wireless local area networks in the cluster Points of interest are sorted. [Para. 173], Step 610: According to the sorting result, the candidate location interest point corresponding to the maximum similarity is used as the target location interest point of the corresponding cluster [Examiner’s Note: The target POI is determined]. [Para. 174], Step 611: Associate each wireless local area network with the target location interest point corresponding to the cluster to which the wireless local area network belongs [Examiner’s Note: Associating each network in a particular cluster with the determined target POI is determining the networks]. [Para. 163-165], the name of the wireless local area network correspond to the first key information of the wireless local area network and the name of the location interest point correspond to the second key information of the location interest point [Examiner’s Note: All networks in the cluster have the same name and the name of the networks in the cluster is the first identification of each target network in the cluster]).
Although teaching association between POI and cluster of networks, Zhang does not explicitly disclose inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network.
Jiang is directed to providing data processing method, device, electronic device and storage medium. More specifically, Jiang teaches inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network ([Para. 36], The number of candidate POIs can be one or more. [Para. 39], the number of candidate WiFi connected to a candidate POI can be one or more. [Para. 40], for candidate POIs, candidate WiFis whose positioning positions are located around the coordinates of the candidate POI and whose WiFi name is similar to the POI name of the candidate POI can be identified. [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined [Examiner’s Note: The Wi-Fis and POIs are matched based on distance and similarity first]. [Para. 45], the candidate POI has its own POI characteristics, such as name, coordinates, etc., then the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model, which can use algorithm strategies (including But not limited to at least one of the following: marginal distance, clustering, sorting) to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi, Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: The pre-trained prediction model that predicts association between POI and WiFI is a preset link prediction model. The POI characteristics of the candidate POI and the WiFi characteristics of the WiFi in the input are feature data matched based on distance and similarity. There are multiple candidate POIs. The candidate POI that has high association probability with Wi-Fis is determined for use by the prediction model based on the matched feature data. Based on Zhang, this determined POI by the prediction model can be the determined target POI in Zhang in Step 610. The networks in Zhang are determined to be the networks in a cluster with the determined target POI in Step 611]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 12, Zhang teaches an electronic device ([Para. 48], An embodiment of the present application provides a computer device), comprising: at least one memory ([para. 48], the computer device, including: [para. 49], Memory), wherein a computer program is stored in the at least one memory ([para. 49], Memory, used to store executable instructions); and at least one processor ([para. 50], The processor is configured to execute the executable instructions), configured to execute the computer program in the at least one memory to implement a method for data matching ([para. 50], The processor is configured to execute the executable instructions stored in the memory to implement the method for processing points of interest. [Para. 11 and 12], a method for processing location interest points including: Acquiring first key information of wireless networks, and second key information of location points of interest within the target area covered by each of the wireless networks), wherein the method comprises: acquiring a target location corresponding to each target wireless network of a plurality of target wireless networks ([Para. 163], Step 601: The server obtains the names of at least two wireless local area networks and the names of points of interest in locations within the target area covered by each wireless local area network. [Para. 164], Step 602: Use partial information associated with geographic information in the names of the at least two wireless local area networks as the first key information of the wireless local area network. [Para. 167], Step 605: Obtain the latitude and longitude of at least two wireless local area networks corresponding to each first key information), and at least one point of interest to be matched within a preset range of the target location ([Para. 165], Step 603: Use the pinyin and English translation corresponding to the name of the location interest point as the keyword of the location interest point to obtain the second key information of the location interest point within the target area covered by each wireless local area network. [Para. 104], the target area covered by the wireless local area network can be a preset size of the area, such as a circular range with a radius of 250 meters, where the center of the circular range is the location of the wireless local area network. [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name. [Para. 183] and [FIG. 8], when matching Wi-Fi and POI around Peking University, we will extract all POIs 250 meters around each Wi-Fi); calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network ([Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 127], the similarity between the first key information of the wireless local area network and the first key information of the location interest point is calculated to indicate the similarity between the wireless local area network and the location interest point. [Para. 129], The ratio of the number of characters to the number of characters of the first key information of the wireless local area network is used as the similarity between the wireless local area network and the points of interest in the corresponding target area. [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name [Examiner’s Note: Distance and similarity constitute the relationship matching feature data]); inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network ([Para. [171], Step 608: Match the first key information of each wireless local area network with the second key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 172], Step 609: for each cluster, when the cluster comprises at least two wireless local area networks, sequencing candidate position interest points corresponding to the at least two wireless local area networks in the cluster according to the similarity between the at least two wireless local area networks in the cluster and the corresponding candidate position interest points. [Para. 173], Step 610: According to the sorting result, the candidate location interest point corresponding to the maximum similarity is used as the target location interest point of the corresponding cluster); and determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network ([Para. 181], Step 702: cluster Wi-Fis with the same name in the same city to obtain multiple clusters. [Para. 171], Step 608: Match the first key information of each wireless local area network with the second key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. [Para. 172], Step 609: For each cluster, when the cluster contains at least two wireless local area networks, according to the similarity between the at least two wireless local area networks in the cluster and the interest points of the corresponding candidate locations, determine the candidate locations corresponding to the at least two wireless local area networks in the cluster Points of interest are sorted. [Para. 173], Step 610: According to the sorting result, the candidate location interest point corresponding to the maximum similarity is used as the target location interest point of the corresponding cluster [Examiner’s Note: The target POI is determined]. [Para. 174], Step 611: Associate each wireless local area network with the target location interest point corresponding to the cluster to which the wireless local area network belongs [Examiner’s Note: Associating each network in a particular cluster with the determined target POI is determining the networks]. [Para. 163-165], the name of the wireless local area network correspond to the first key information of the wireless local area network and the name of the location interest point correspond to the second key information of the location interest point [Examiner’s Note: All networks in the cluster have the same name and the name of the networks in the cluster is the first identification of each target network in the cluster]).
Although teaching association between POI and cluster of networks, Zhang does not explicitly disclose inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network.
Jiang is directed to providing data processing method, device, electronic device and storage medium. More specifically, Jiang teaches inputting the relationship matching feature data into a preset link prediction model to determine a target point of interest corresponding to the target wireless network ([Para. 36], The number of candidate POIs can be one or more. [Para. 39], the number of candidate WiFi connected to a candidate POI can be one or more. [Para. 40], for candidate POIs, candidate WiFis whose positioning positions are located around the coordinates of the candidate POI and whose WiFi name is similar to the POI name of the candidate POI can be identified. [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined [Examiner’s Note: The Wi-Fis and POIs are matched based on distance and similarity first]. [Para. 45], the candidate POI has its own POI characteristics, such as name, coordinates, etc., then the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model, which can use algorithm strategies (including But not limited to at least one of the following: marginal distance, clustering, sorting) to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi, Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: The pre-trained prediction model that predicts association between POI and WiFI is a preset link prediction model. The POI characteristics of the candidate POI and the WiFi characteristics of the WiFi in the input are feature data matched based on distance and similarity. There are multiple candidate POIs. The candidate POI that has high association probability with Wi-Fis is determined for use by the prediction model based on the matched feature data. Based on Zhang, this determined POI by the prediction model can be the determined target POI in Zhang in Step 610. The networks in Zhang are determined to be the networks in a cluster with the determined target POI in Step 611]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 15, Zhang and Jiang teach the method according to claim 2. The references further teach wherein the calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network, comprises: calculating distance feature data between the target wireless network and the point of interest to be matched (Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name); and/or determining text feature data according to the first identification information of the target wireless network and second identification information of the point of interest to be matched (Zhang [Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. Zhang [Para. 102], the first key information of the wireless local area network may be all or part of the information in the name of the wireless local area network, and the first key information of the location interest point may be all or part of the information of the location interest point. Zhang [Para. 107], the similarity between the first key information of the wireless local area network and the first key information of the location interest point is calculated to indicate the similarity between the wireless local area network and the location interest point [Examiner’s Note: The names of the network and interest point are the first and second identification information respectively and the similarity is the text feature data]), wherein the text feature data comprises at least one selected from the group consisting of character granularity feature data (Zhang [Para. 128], the similarity between the wireless local area network and the points of interest in the corresponding target area can be obtained in the following manner: Zhang [Para. 130], the longest common string matching method can be used to match the first key information of the wireless local area network with the second key information of the location interest point. Zhang [Para. 138], the similarity between the wireless local area network and the point of interest in the corresponding target area can also be obtained through the edit distance of the string. Here, the string edit distance refers to the minimum operations required to convert two strings. The less operations required, the more similar the two strings are. String operations include: insert a character, delete a character, and replace a character [Examiner’s Note: Longest common string matching and edit distance are character granularity feature data]), word granularity feature data (Zhang [Para. 179], Step 701: Clean the names of Wi-Fi and POI, and obtain the pinyin and English translation of the POI name. Zhang [Para. 180], because Wi-Fi has different naming rules, it is very noisy and needs to be cleaned, such as PK U-teacher, PKU-student, PKU-staff, etc. We only take the part associated with geographic information, namely PKU. For POI, such as Peking University, you will English "peking university". Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name [Examiner’s Note: PKU is alias of Peking University. Name similarity is calculated based on alias in network and the name in POI]), and semantic feature data (Jiang [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined. Candidate WiFi whose locating position is around the coordinates of the candidate POI and whose SSID is similar to the name of the candidate POI (semantic similarity is greater than threshold 2) may be mined from the business data to attach to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the name of network and name of POI are matched on semantic similarity, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Claim 19 is apparatus claim and it does not teach or further define over the limitations recited in claim 2. Therefore, claim 19 is also rejected for similar reasons set forth in claim 2.
Claims 3-5, 13-14, 16 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN111881377A, hereinafter Zhang) in view of Jiang et al. (CN112182427A, hereinafter Jiang), and further in view of Zhu (CN113420781A, hereinafter Zhu).
For claim 3, Zhang and Jiang teach the method according to claim 1. The references further teach wherein the determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network, comprises: performing clustering on the plurality of the target wireless networks according to the first identification information of each target wireless network to obtain a plurality of wireless network clusters (Zhang [Para. 181], Step 702: cluster Wi-Fis with the same name in the same city to obtain multiple clusters. [Examiner’s Note: All Wi-Fis in a cluster have the same name and the Wi-FI name of the Wi-Fis in the cluster is the first identification of each target network in the cluster]); according to the relationship matching feature data, determining an association probability between the target point of interest and each target wireless network in the wireless network cluster by the preset link prediction model (Jiang [Para. 45], the candidate POI has its own POI characteristics, such as name, coordinates, etc., then the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi, Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: To determine the high association probability indicates determining the association probability between POI and each WiFi]. Zhang [Para. 143], Step 404: According to the similarity between the wireless local area network in each cluster and the location interest points in the corresponding target area, select the target location interest points corresponding to each cluster. Zhang [Para. 187], Step 705: for each cluster, acquiring candidate POIs corresponding to all Wi-Fi in the cluster, and taking the candidate POI corresponding to the maximum similarity as a target POI of the whole cluster according to the similarity between the Wi-Fi name and the corresponding candidate POI name. Zhang [189], Step 706: Associate the target POI with all Wi-Fis in the cluster corresponding to the target POI [Examiner’s Note: The Wi-Fis in the cluster are determined to correspond to the selected POI. Jiang teaches determining association probability between POI and WiFi. Zhang teaches determining POI for WiFis in a cluster. Jiang and Zhang in combination teach association probability for WiFis in a cluster]), wherein the target wireless network cluster comprises at least one of the target wireless networks (Zhang [Para. 181], Step 702: Use the DBSCAN algorithm to cluster Wi-Fis with the same name in the same city to obtain multiple clusters).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi and the prediction model predicts the association probabilities, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Although teaching determining association probability between a cluster and the target POI, Zhang and Jiang do not explicitly disclose and according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target wireless network cluster of the target point of interest from the plurality of the wireless network clusters.
Zhu is directed to providing brand identification method, apparatus, device, storage medium and program product. More specifically, Zhu teaches and according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target wireless network cluster of the target point of interest from the plurality of the wireless network clusters (Zhu [Para. 56], S310: Perform clustering on the location information of the wireless network in the target area where the POI is to be identified, to obtain at least two candidate clusters. Zhu [Para. 57], S320: Determine the network location information of the candidate cluster according to the network location information of the wireless network in the candidate cluster, and select the network name information of the candidate cluster from the network name information of the wireless network in the candidate cluster. Zhu [Para. 61], S340. Input the name association information and location association information between the candidate cluster and the POI to be identified, the network location information of the candidate cluster, the network name information of the candidate cluster, and the attribute information of the POI to be identified into the gradient descent tree model, and obtain the matching scores between the candidate clusters output by the gradient descent tree model and the POI to be identified. Zhu [Para. 63], S350: Use the candidate cluster with the highest matching score with the POI to be identified as the target cluster. Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Jiang, so that the cluster that has the highest matching score with the target POI is selected to be the target cluster, as taught by Zhu. The modification would have accurately identified the brands corresponding to the points of interest (Zhu [Para. 6]).
For claim 4, Zhang, Jiang and Zhu teach the method according to claim 3. The references further teach wherein the according to the relationship matching feature data, determining an association probability between the target point of interest and each target wireless network in the wireless network cluster by the preset link prediction model, comprises: according to the relationship matching feature data, calculating the association probability between the target wireless network and each of the at least one point of interest to be matched by the preset link prediction model (Jiang [Para. 40], for candidate POIs, candidate WiFis whose positioning positions are located around the coordinates of the candidate POI and whose WiFi name is similar to the POI name of the candidate POI can be identified. Jiang [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined. [Examiner’s Note: POI and WiFi are matched based on distance and similarity]. Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: To determine the high association probability indicates determining the association probability between each POI and each WiFi]); and according to the association probability between the plurality of the target wireless networks and each of the at least one point of interest to be matched, determining the association probability between the target point of interest and each target wireless network in the wireless network cluster (Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Zhu, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi and the prediction model predicts the association probabilities, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 5, Zhang, Jiang and Zhu teach the method according to claim 3. The references further teach wherein the according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target wireless network cluster of the target point of interest from the plurality of the wireless network clusters, comprises: according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target association probability between the wireless network cluster and the target point of interest (Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI. (Zhu [Para. 56], S310: Perform clustering on the location information of the wireless network in the target area where the POI is to be identified, to obtain at least two candidate clusters. Zhu [Para. 57], S320: Determine the network location information of the candidate cluster according to the network location information of the wireless network in the candidate cluster, and select the network name information of the candidate cluster from the network name information of the wireless network in the candidate cluster. Zhu [Para. 61], S340. Input the name association information and location association information between the candidate cluster and the POI to be identified, the network location information of the candidate cluster, the network name information of the candidate cluster, and the attribute information of the POI to be identified into the gradient descent tree model, and obtain the matching scores between the candidate clusters output by the gradient descent tree model and the POI to be identified. Zhu [Para. 63], S350: Use the candidate cluster with the highest matching score with the POI to be identified as the target cluster [Examiner’s Note: Based on Jiang, the highest matching score in Zhu indicates the high association probability in Jiang. The association probability as the highest matching score for the target cluster selected in Zhu is the determined target association probability between the wireless network cluster and the target point of interest]),
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Zhu, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi and the prediction model predicts the association probabilities, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
The references further teach and regarding a wireless network cluster that has a largest target association probability in the plurality of the wireless network clusters as the target wireless network cluster (Zhu [Para. 61], S340. Input the name association information and location association information between the candidate cluster and the POI to be identified, the network location information of the candidate cluster, the network name information of the candidate cluster, and the attribute information of the POI to be identified into the gradient descent tree model, and obtain The matching scores between the candidate clusters output by the gradient descent tree model and the POI to be identified. Zhu [Para. 63], S350: Use the candidate cluster with the highest matching score with the POI to be identified as the target cluster. Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Jiang, so that the cluster that has the highest matching score with the target POI is selected to be the target cluster, as taught by Zhu. The modification would have accurately identified the brands corresponding to the points of interest (Zhu [Para. 6]).
Claim 13 is method claim and it does not teach or further define over the limitations recited in claim 3. Therefore, claim 13 is also rejected for similar reasons set forth in claim 3.
Claim 14 is method claim and it does not teach or further define over the limitations recited in claim 5. Therefore, claim 14 is also rejected for similar reasons set forth in claim 5.
For claim 16, Zhang, Jiang and Zhu teach the method according to claim 3. The references further teach wherein the calculating relationship matching feature data between each target wireless network and the at least one point of interest to be matched that corresponds to the target wireless network, comprises: calculating distance feature data between the target wireless network and the point of interest to be matched (Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name); and/or determining text feature data according to the first identification information of the target wireless network and second identification information of the point of interest to be matched (Zhang [Para. 126], Step 403: Match the first key information of each wireless local area network with the first key information of the location interest points in the corresponding target area to obtain the similarity between each wireless local area network and the location interest points in the corresponding target area. Zhang [Para. 102], the first key information of the wireless local area network may be all or part of the information in the name of the wireless local area network, and the first key information of the location interest point may be all or part of the information of the location interest point. Zhang [Para. 107], the similarity between the first key information of the wireless local area network and the first key information of the location interest point is calculated to indicate the similarity between the wireless local area network and the location interest point [Examiner’s Note: The names of the network and interest point are the first and second identification information respectively and the similarity is the text feature data]), wherein the text feature data comprises at least one selected from the group consisting of character granularity feature data (Zhang [Para. 128], the similarity between the wireless local area network and the points of interest in the corresponding target area can be obtained in the following manner: Zhang [Para. 130], the longest common string matching method can be used to match the first key information of the wireless local area network with the second key information of the location interest point. Zhang [Para. 138], the similarity between the wireless local area network and the point of interest in the corresponding target area can also be obtained through the edit distance of the string. Here, the string edit distance refers to the minimum operations required to convert two strings. The less operations required, the more similar the two strings are. String operations include: insert a character, delete a character, and replace a character [Examiner’s Note: Longest common string matching and edit distance are character granularity feature data]), word granularity feature data (Zhang [Para. 179], Step 701: Clean the names of Wi-Fi and POI, and obtain the pinyin and English translation of the POI name. Zhang [Para. 180], because Wi-Fi has different naming rules, it is very noisy and needs to be cleaned, such as PK U-teacher, PKU-student, PKU-staff, etc. We only take the part associated with geographic information, namely PKU . For POI, such as Peking University, you will English "peking university". Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi, and calculate the similarity between the Wi-Fi name and the corresponding POI name [Examiner’s Note: PKU is alias of Peking University. Name similarity is calculated based on alias in network and the name in POI]), and semantic feature data (Jiang [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined. Candidate WiFi whose locating position is around the coordinates of the candidate POI and whose SSID is similar to the name of the candidate POI (semantic similarity is greater than threshold 2) may be mined from the business data to attach to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Zhu, so that the name of network and name of POI are matched on semantic similarity, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
For claim 20, Zhang and Jiang teach the electronic device according to claim 12. The references further teach wherein the determining at least one of the target wireless networks corresponding to the target point of interest according to the relationship matching feature data and first identification information of each target wireless network, comprises: performing clustering on the plurality of the target wireless networks according to the first identification information of each target wireless network to obtain a plurality of wireless network clusters (Zhang [Para. 181], Step 702: cluster Wi-Fis with the same name in the same city to obtain multiple clusters. [Examiner’s Note: All Wi-Fis in a cluster have the same name and the Wi-FI name of the Wi-Fis in the cluster is the first identification of each target network in the cluster]); according to the relationship matching feature data, determining an association probability between the target point of interest and each target wireless network in the wireless network cluster by the preset link prediction model (Jiang [Para. 45], the candidate POI has its own POI characteristics, such as name, coordinates, etc., then the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi, Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: To determine the high association probability indicates determining the association probability between POI and each WiFi]. Zhang [Para. 143], Step 404: According to the similarity between the wireless local area network in each cluster and the location interest points in the corresponding target area, select the target location interest points corresponding to each cluster. Zhang [Para. 187], Step 705: for each cluster, acquiring candidate POIs corresponding to all Wi-Fi in the cluster, and taking the candidate POI corresponding to the maximum similarity as a target POI of the whole cluster according to the similarity between the Wi-Fi name and the corresponding candidate POI name. Zhang [189], Step 706: Associate the target POI with all Wi-Fis in the cluster corresponding to the target POI [Examiner’s Note: The Wi-Fis in the cluster are determined to correspond to the selected POI. Jiang teaches determining association probability between POI and WiFi. Zhang teaches determining POI for WiFis in a cluster. Jiang and Zhang in combination teach association probability for WiFis in a cluster]), wherein the target wireless network cluster comprises at least one of the target wireless networks (Zhang [Para. 181], Step 702: Use the DBSCAN algorithm to cluster Wi-Fis with the same name in the same city to obtain multiple clusters).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Zhang, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi and the prediction model predicts the association probabilities, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Although teaching determining association probability between a cluster and the target POI, Zhang and Jiang do not explicitly disclose and according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target wireless network cluster of the target point of interest from the plurality of the wireless network clusters.
Zhu is directed to providing brand identification method, apparatus, device, storage medium and program product. More specifically, Zhu teaches and according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target wireless network cluster of the target point of interest from the plurality of the wireless network clusters (Zhu [Para. 56], S310: Perform clustering on the location information of the wireless network in the target area where the POI is to be identified, to obtain at least two candidate clusters. Zhu [Para. 57], S320: Determine the network location information of the candidate cluster according to the network location information of the wireless network in the candidate cluster, and select the network name information of the candidate cluster from the network name information of the wireless network in the candidate cluster. Zhu [Para. 61], S340. Input the name association information and location association information between the candidate cluster and the POI to be identified, the network location information of the candidate cluster, the network name information of the candidate cluster, and the attribute information of the POI to be identified into the gradient descent tree model, and obtain the matching scores between the candidate clusters output by the gradient descent tree model and the POI to be identified. Zhu [Para. 63], S350: Use the candidate cluster with the highest matching score with the POI to be identified as the target cluster. Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the apparatus of Zhang and Jiang, so that the cluster that has the highest matching score with the target POI is selected to be the target cluster, as taught by Zhu. The modification would have accurately identified the brands corresponding to the points of interest (Zhu [Para. 6]).
For claim 21, Zhang, Jiang and Zhu teach the electronic device according to claim 20. The references further teach wherein the according to the relationship matching feature data, determining an association probability between the target point of interest and each target wireless network in the wireless network cluster by the preset link prediction model, comprises: according to the relationship matching feature data, calculating the association probability between the target wireless network and each of the at least one point of interest to be matched by the preset link prediction model (Jiang [Para. 40], for candidate POIs, candidate WiFis whose positioning positions are located around the coordinates of the candidate POI and whose WiFi name is similar to the POI name of the candidate POI can be identified. Jiang [Para. 41], When mining candidate WiFi hooked with a candidate POI, not only the distance between the POI and WiFi may be referred to, but also the similarity between the name of the POI and the name of WiFi may be combined. [Examiner’s Note: POI and WiFi are matched based on distance and similarity]. Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: To determine the high association probability indicates determining the association probability between each POI and each WiFi]); and according to the association probability between the plurality of the target wireless networks and each of the at least one point of interest to be matched, determining the association probability between the target point of interest and each target wireless network in the wireless network cluster (Jiang [Para. 45], the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected. Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI.).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Zhu, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi and the prediction model predicts the association probabilities, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN111881377A, hereinafter Zhang) in view of Jiang et al. (CN112182427A, hereinafter Jiang) and Zhu (CN113420781A, hereinafter Zhu), and further in view of Kleinbeck (US20220262261A1, hereinafter Kleinbeck).
For claim 6, Zhang, Jiang and Zhu teach the method according to claim 5. The references further teach wherein the according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target association probability between the wireless network cluster and the target point of interest, comprises: acquiring a target mean value of the association probability between the target point of interest and the target wireless networks in the wireless network cluster (Jiang [Para. 45], the candidate POI has its own POI characteristics, such as name, coordinates, etc., then the POI characteristics of the candidate POI and the WiFi characteristics of the WiFi to be detected can be input into the pre-trained prediction model to predict the candidate WiFi that can be connected to the candidate POI from the WiFi to be detected, so as to output the connection relationship between the candidate POI and the candidate WiFi, Among them, in the attachment relationship, the candidate WiFi is a WiFi that has a high probability of belonging to the address of the candidate POI [Examiner’s Note: To determine the high association probability indicates determining the association probability between POI and each WiFi]. Zhang [Para. 143], Step 404: According to the similarity between the wireless local area network in each cluster and the location interest points in the corresponding target area, select the target location interest points corresponding to each cluster. Zhang [Para. 187], Step 705: for each cluster, acquiring candidate POIs corresponding to all Wi-Fi in the cluster, and taking the candidate POI corresponding to the maximum similarity as a target POI of the whole cluster according to the similarity between the Wi-Fi name and the corresponding candidate POI name. Zhang [Para. 189], Step 706: Associate the target POI with all Wi-Fis in the cluster corresponding to the target POI [Examiner’s Note: The Wi-Fis in the cluster are determined to correspond to the selected POI. Jiang teaches determining association probability between POI and WiFi. Zhang teaches determining POI for WiFis in a cluster. Jiang and Zhang in combination teach association probability for WiFis in a cluster]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the matched feature data is input to the prediction model to determine the POI corresponding to the Wi-Fi and the prediction model predicts the association probabilities, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Although teaching acquiring association probability between the target POI and each network in the target cluster, Zhang, Jiang and Zhu do not explicitly disclose wherein the according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target association probability between the wireless network cluster and the target point of interest, comprises: acquiring a target mean value of the association probability between the target point of interest and the target wireless networks in the wireless network cluster; and regarding the target mean value as the target association probability, and regarding the target mean value as the target association probability.
Kleinbeck is directed to providing unmanned vehicle recognition and threat management. More specifically, wherein the according to the association probability between the target point of interest and each target wireless network in the wireless network cluster, determining a target association probability between the wireless network cluster and the target point of interest, comprises: acquiring a target mean value of the association probability between the target point of interest and the target wireless networks in the wireless network cluster ([Para. 0054], The learning system of the present invention is highly accurate and capable of assessing detected UAV signals and/or controller signals for classification with a high confidence level. [Para. 0069], Tiles from different frequency spans and center frequencies are identified as a tile group. [Para. 0071], The YOLO AI engine generates an output for each tile to identify drones and their controllers with a probability. An average probability is calculated based on outputs for multiple tiles in the tile group [Examiner’s Note: The AI engine is a prediction model. The AI engine computes the association probability between drones and a tile in a group and then computes the average probability of the probabilities of all tiles in the group]), and regarding the target mean value as the target association probability ([Para. 0071], An average probability is calculated based on outputs for multiple tiles in the tile group [Examiner’s Note: That the average probability is for multiple tiles in the group indicates that averaged association probability is the association probability of the group]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, Jiang and Zhu, so that the averaged association probability is provided by a prediction model as the association probability of a group, as taught by Kleinbeck. The modification would have provided learning system for classification with a high confidence level (Kleinbeck [Para. 0054]).
Claims 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN111881377A, hereinafter Zhang) in view of Jiang et al. (CN112182427A, hereinafter Jiang), and further in view of Cao et al. (CN110781256A, hereinafter Cao).
For claim 9, Zhang and Jiang teach the method according to claim 1. The references further teach wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data (Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi), character granularity feature data (Zhang [Para. 135], taking the ratio of the number of the intersected characters to the number of the characters of the union as the similarity of the wireless local area network and the interest points at the positions in the corresponding target area range), word granularity feature data (Zhang [Para. 179 and 180], Alias “PKU” in the name of wireless network is matched with "peking university" in the name of POI for name similarity), and semantic feature data that correspond to a point of interest sample and a wireless network sample (Jiang [Para. 41], Candidate WiFi whose locating position is around the coordinates of the candidate POI and whose SSID is similar to the name of the candidate POI (semantic similarity is greater than threshold 2) may be mined from the business data to attach to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the name of network and name of POI are matched on semantic similarity, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Although teaching distance and similarity data, Zhang and Jiang do not explicitly disclose wherein the preset link prediction model is obtained through training by: acquiring a plurality of pieces of matching feature sample data, wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample; and training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model.
Cao is directed to providing method and device for determining POI (Point of interest) matched with Wi-Fi (Wireless Fidelity) based on transmitted position data. More specifically, Cao teaches wherein the preset link prediction model is obtained through training by: acquiring a plurality of pieces of matching feature sample data ([Para. 218], the machine classification model is a classification model trained by using a training sample obtained by sending second position data in a historical time period, where the training sample includes a co-occurrence relationship pair that matches Wi-Fi and POI, and the co-occurrence relationship pair that Wi-Fi and POI do not match. [Para. 220], obtain relevant statistical information of Wi-Fi and POI in the training sample. [Para. 221], mapping the relevant statistical information into an input feature vector, and taking the association degree corresponding to matching/mismatching of Wi-Fi and POI in the training sample as an output feature to train a machine classification model. [Para. 226], mapping the co-current behavior related statistical information to the first input feature vector includes: [Para. 230], Among different terminals connected to the Wi-Fi and selecting the POI, the average distance between the measured terminal location and the POI is mapped to a first input feature vector. [Examiner’s Note: The model is trained with the similarity data and distance]. [Para. 216], According to the association degree of Wi-Fi and POI in different co-occurrence relationship pairs, for different Wi-Fi, determine the POI corresponding to the maximum association degree among different POIs that co-occur with the Wi-Fi [Examiner’s Note: The classification model that outputs association degree between Wi-Fi and POI is a link prediction model]), wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample ([Para. 218], where the training sample includes a co-occurrence relationship pair that matches Wi-Fi and POI, and the co-occurrence relationship pair that Wi-Fi and POI do not match. [Para. 230], Among different terminals connected to the Wi-Fi and selecting the POI, the average distance between the measured terminal location and the POI is mapped to a first input feature vector), and training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model ([Para. 221], mapping the relevant statistical information into an input feature vector, and taking the association degree corresponding to matching/mismatching of Wi-Fi and POI in the training sample as an output feature to train a machine classification model).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Jiang, so that the classification model is trained with distance and similarity data samples to predict the association degree between Wi-Fi and POI, as taught by Cao. The modification would have input the input feature vector into a machine classification model to obtain the association degree of the Wi-Fi and the POI (Cao, [Abstract]).
For claim 17, Zhang and Jiang teach the method according to claim 2. The references further teach wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data (Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi), character granularity feature data (Zhang [Para. 135], taking the ratio of the number of the intersected characters to the number of the characters of the union as the similarity of the wireless local area network and the interest points at the positions in the corresponding target area range), word granularity feature data (Zhang [Para. 179 and 180], Alias “PKU” in the name of wireless network is matched with "peking university" in the name of POI for name similarity), and semantic feature data that correspond to a point of interest sample and a wireless network sample (Jiang [Para. 41], Candidate WiFi whose locating position is around the coordinates of the candidate POI and whose SSID is similar to the name of the candidate POI (semantic similarity is greater than threshold 2) may be mined from the business data to attach to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, so that the name of network and name of POI are matched on semantic similarity, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Although teaching distance and similarity data, Zhang and Jiang do not explicitly disclose wherein the preset link prediction model is obtained through training by: acquiring a plurality of pieces of matching feature sample data, wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample; and training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model.
Cao is directed to providing method and device for determining POI (Point of interest) matched with Wi-Fi (Wireless Fidelity) based on transmitted position data. More specifically, Cao teaches wherein the preset link prediction model is obtained through training by: acquiring a plurality of pieces of matching feature sample data ([Para. 218], the machine classification model is a classification model trained by using a training sample obtained by sending second position data in a historical time period, where the training sample includes a co-occurrence relationship pair that matches Wi-Fi and POI, and the co-occurrence relationship pair that Wi-Fi and POI do not match. [Para. 220], obtain relevant statistical information of Wi-Fi and POI in the training sample. [Para. 221], mapping the relevant statistical information into an input feature vector, and taking the association degree corresponding to matching/mismatching of Wi-Fi and POI in the training sample as an output feature to train a machine classification model. [Para. 226], mapping the co-current behavior related statistical information to the first input feature vector includes: [Para. 230], Among different terminals connected to the Wi-Fi and selecting the POI, the average distance between the measured terminal location and the POI is mapped to a first input feature vector. [Examiner’s Note: The model is trained with the similarity data and distance]. [Para. 216], According to the association degree of Wi-Fi and POI in different co-occurrence relationship pairs, for different Wi-Fi, determine the POI corresponding to the maximum association degree among different POIs that co-occur with the Wi-Fi [Examiner’s Note: The classification model that outputs association degree between Wi-Fi and POI is a link prediction model]), wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample ([Para. 218], where the training sample includes a co-occurrence relationship pair that matches Wi-Fi and POI, and the co-occurrence relationship pair that Wi-Fi and POI do not match. [Para. 230], Among different terminals connected to the Wi-Fi and selecting the POI, the average distance between the measured terminal location and the POI is mapped to a first input feature vector), and training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model ([Para. 221], mapping the relevant statistical information into an input feature vector, and taking the association degree corresponding to matching/mismatching of Wi-Fi and POI in the training sample as an output feature to train a machine classification model).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Jiang, so that the classification model is trained with distance and similarity data samples to predict the association degree between Wi-Fi and POI, as taught by Cao. The modification would have input the input feature vector into a machine classification model to obtain the association degree of the Wi-Fi and the POI (Cao, [Abstract]).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN111881377A, hereinafter Zhang) in view of Jiang et al. (CN112182427A, hereinafter Jiang) and Zhu (CN113420781A, hereinafter Zhu), and further in view of Cao et al. (CN110781256A, hereinafter Cao).
For claim 18, Zhang, Jiang and Zhu teach the method according to claim 3. The references further teach wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data (Zhang [Para. 182], Step 703: For each Wi-Fi, extract all POIs within 250 meters of the Wi-Fi), character granularity feature data (Zhang [Para. 135], taking the ratio of the number of the intersected characters to the number of the characters of the union as the similarity of the wireless local area network and the interest points at the positions in the corresponding target area range), word granularity feature data (Zhang [Para. 179 and 180], Alias “PKU” in the name of wireless network is matched with "peking university" in the name of POI for name similarity), and semantic feature data that correspond to a point of interest sample and a wireless network sample (Jiang [Para. 41], Candidate WiFi whose locating position is around the coordinates of the candidate POI and whose SSID is similar to the name of the candidate POI (semantic similarity is greater than threshold 2) may be mined from the business data to attach to the candidate POI).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang and Zhu, so that the name of network and name of POI are matched on semantic similarity, as taught by Jiang. The modification would have realized the timely judgment and acquisition of the state of the candidate POI (Jiang [Para. 20]).
Although teaching distance and similarity data, Zhang, Jiang and Zhu do not explicitly disclose wherein the preset link prediction model is obtained through training by: acquiring a plurality of pieces of matching feature sample data, wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample; and training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model.
Cao is directed to providing method and device for determining POI (Point of interest) matched with Wi-Fi (Wireless Fidelity) based on transmitted position data. More specifically, Cao teaches wherein the preset link prediction model is obtained through training by: acquiring a plurality of pieces of matching feature sample data ([Para. 218], the machine classification model is a classification model trained by using a training sample obtained by sending second position data in a historical time period, where the training sample includes a co-occurrence relationship pair that matches Wi-Fi and POI, and the co-occurrence relationship pair that Wi-Fi and POI do not match. [Para. 220], obtain relevant statistical information of Wi-Fi and POI in the training sample. [Para. 221], mapping the relevant statistical information into an input feature vector, and taking the association degree corresponding to matching/mismatching of Wi-Fi and POI in the training sample as an output feature to train a machine classification model. [Para. 226], mapping the co-current behavior related statistical information to the first input feature vector includes : [Para. 230], Among different terminals connected to the Wi-Fi and selecting the POI, the average distance between the measured terminal location and the POI is mapped to a first input feature vector. [Examiner’s Note: The model is trained with the similarity data and distance]. [Para. 216], According to the association degree of Wi-Fi and POI in different co-occurrence relationship pairs, for different Wi-Fi, determine the POI corresponding to the maximum association degree among different POIs that co-occur with the Wi-Fi [Examiner’s Note: The classification model that outputs association degree between Wi-Fi and POI is a link prediction model]), wherein the matching feature sample data comprises at least one selected from the group consisting of distance feature data, character granularity feature data, word granularity feature data, and semantic feature data that correspond to a point of interest sample and a wireless network sample ([Para. 218], where the training sample includes a co-occurrence relationship pair that matches Wi-Fi and POI, and the co-occurrence relationship pair that Wi-Fi and POI do not match. [Para. 230], Among different terminals connected to the Wi-Fi and selecting the POI, the average distance between the measured terminal location and the POI is mapped to a first input feature vector), and training a preset initial model according to the plurality of the pieces of the matching feature sample data to obtain the preset link prediction model ([Para. 221], mapping the relevant statistical information into an input feature vector, and taking the association degree corresponding to matching/mismatching of Wi-Fi and POI in the training sample as an output feature to train a machine classification model).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Zhang, Jiang and Zhu, so that the classification model is trained with distance and similarity data samples to predict the association degree between Wi-Fi and POI, as taught by Cao. The modification would have input the input feature vector into a machine classification model to obtain the association degree of the Wi-Fi and the POI (Cao, [Abstract]).
Conclusion
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/S.L./Examiner, Art Unit 2417 /REBECCA E SONG/Supervisory Patent Examiner, Art Unit 2417
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