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 05/22/2024, 11/18/2025, 03/04/2026, 07/06/2026 was filed. The submission is 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “the predicting using one or more of a machine learned (ML) model, the check-in data for the candidate venue, or the user location.” If the prediction only uses ML model, then it is unclear how it is predicting a score for each candidate venue and what exactly are the inputs to the model. If the prediction only uses check-in data and/or user location, then it is unclear how the information is used to predict a score, or specifically what predictor is used to input the information.
Claims 15 and 20 recites the same “the predicting using one or more of a machine learned (ML) model, the check-in data for the candidate venue, or the user location.” Therefore, they are rejected under the same reasoning as Claim 1.
Dependent Claims 2-14 and 16-19 have the same issue due to inherency.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, Claims 1-14 are directed to a method claim, Claims 15-19 are directed to a system claim, and Claim 20 is directed to a non-transitory computer-readable storage medium claim. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1
2A Prong 1:
predicting a venue score for each candidate venue, the predicting using (This step for predicting a venue score is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
identifying a candidate venue of the set of candidate venues with a highest predicted venue score as a predicted venue for the user location; (This step for identifying a candidate venue with a highest predicted venue score is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
determining that a user visit is associated with the predicted venue; (This step for determining a user visit is associated with the predicted venue is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving, at a computing device, a user location; retrieving a set of candidate venues based on the user location; retrieving check-in data associated with each candidate venue of the set of candidate venues, the check-in data comprising user check-ins for a plurality of users; (The step directed to receiving information, which is understood to be insignificant extra- solution activity. See MPEP 2106.05(g).)
the predicting using one or more of a machine learned (ML) model (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
and storing data indicating that the user visit is associated with the predicted venue. (The step directed to storing information, which is understood to be insignificant extra- solution activity. See MPEP 2106.05(g).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving, at a computing device, a user location; retrieving a set of candidate venues based on the user location; retrieving check-in data associated with each candidate venue of the set of candidate venues, the check-in data comprising user check-ins for a plurality of users; (This step is directed to receiving information, which is understood to be insignificant extra-solution activity such as mere data gathering as discussed in MPEP 2106.05(g) and is well understood, routine and conventional activity as storing and receiving information identified by the court (MPEP 2106.05(d)(ll)(IV)))))
the predicting using one or more of a machine learned (ML) model (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a model as a tool to perform the abstract idea (i.e., predicting) - see MPEP 2106.05(f).)
and storing data indicating that the user visit is associated with the predicted venue. (This step is directed to storing information, which is understood to be insignificant extra-solution activity such as mere data gathering as discussed in MPEP 2106.05(g) and is well understood, routine and conventional activity as storing and receiving information identified by the court (MPEP 2106.05(d)(ll)(IV)))))
The additional elements as disclosed above in combination of the abstract idea are not sufficient to amount to significantly more than the judicial exception as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 15: see the rejection of Claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “at least one processor; at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 20: see the rejection of Claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 2 and 16
2A Prong 1: None
2A Prong 2 & 2B: wherein the user location comprises a latitude and a longitude (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 3 and 17
2A Prong 1: None
2A Prong 2 & 2B: wherein the check-in data for each candidate venue comprises a plurality of latitude and longitude pairs. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 4
2A Prong 1: None
2A Prong 2 & 2B: wherein features of the ML model comprise conditional probabilities associated with the candidate venues, each conditional probability for a respective venue corresponding to a probability that the user location is generated by a probability distribution corresponding to check-in data associated with the venue. (The specification of model used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 5 and 18
2A Prong 1: wherein generating the conditional probability for the venue further comprises: determining parameters of the probability distribution based on the check-in data associated with the venue; (This step for determining parameters of a probability distribution is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
and computing the probability that the user location data is generated by the probability distribution with the determined parameters. (This step for computing probability by a probability distribution is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 6
2A Prong 1: None
2A Prong 2 & 2B: wherein the probability distribution is a bivariate Gaussian probability distribution. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 7
2A Prong 1: None
2A Prong 2 & 2B: wherein the ML model is a supervised multi-class classifier. (The specification of model used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Regarding Claim 8: see the rejection of Claim 1 above. Same rationale applies.
Regarding Claim 9 and 19
2A Prong 1: wherein determining the user visit associated with the predicted venue further comprises determining that the predicted venue matches the predicted updated venue. (This step for determining matches is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 10
2A Prong 1: wherein determining the user visit associated with the predicted venue further comprises determining that a time period between receiving the user location and receiving the updated user location transgresses a predefined duration threshold. (This step for determining that a time period transgresses a threshold is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 11
2A Prong 1: wherein determining the user visit associated with the predicted venue further comprises determining that: a predicted venue score associated with the predicted venue transgresses a first confidence threshold; and a predicted updated venue score associated with the predicted updated venue transgresses a second confidence threshold. (This step for determining if a score transgresses a threshold is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claim 12: see the rejection of Claim 1 above. Same rationale applies.
2A Prong 1: The claim recites another additional exception “and determining that the predicted additional venue differs from the predicted venue.” (This step for determining if predicted additional venue differs from the predicted venue is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation).)
Regarding Claim 13
2A Prong 1: None
2A Prong 2: displaying, on a user interface (UI) of a second computing device, a map comprising a visual indicator associated with the user in a vicinity of a visual indicator associated with the predicted venue. (The step directed to presenting information, is understood to be insignificant extra- solution activity. See MPEP 2106.05(g).)
2B: displaying, on a user interface (UI) of a second computing device, a map comprising a visual indicator associated with the user in a vicinity of a visual indicator associated with the predicted venue. (This step is directed to present information, which is understood to be insignificant extra-solution activity such as mere data gathering as discussed in MPEP 2106.05(g) and is well understood, routine and conventional activity of as presenting offer and gathering statistics as identified by the court (MPEP 2106.05(d)(ll)(iv)))))
Regarding Claim 14
2A Prong 1: None
2A Prong 2 & 2B: wherein the second computing device is associated with a connection of the user. (The specification of data used is understood to be a field of use limitation – See MPEP 2106.05(h).)
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter 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 pre-AIA 35 U.S.C. 103(a) 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.
Claim(s) 1-6, 8-9, 13-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Priness et al. (US 20160300263 A1, hereinafter "Priness") in view of Kalis et al. (US 20170134508 A1, hereinafter “Kalis”).
Regarding Claim 1
Priness discloses: A method comprising:
receiving, at a computing device, a user location; ([Priness, Fig. 3, 0142] discloses receiving user location based on sensor data at venue visit engine (i.e computing device))
retrieving a set of candidate venues based on the user location; ([Priness, Fig. 3, 0143] discloses retrieving a set of candidate venue based on user location)
retrieving ([Priness, Fig. 3, 0144] discloses retrieving semantic information (i.e data) associated with each candidate venues of a set of candidate venues.)
predicting a venue score for each candidate venue, the predicting using one or more of a machine learned (ML) model, the ([Priness, 0086; Fig. 3, 0142, 0144, 0145] discloses predicting a confidence score (i.e venue score) for each candidate venue by inputting semantic information (i.e data) for the candidate venue and user location into the venue visit engine (i.e ML model). [0023] discloses venue visit engine uses a probabilistic model, a ML model, to generate confidence scores)
identifying a candidate venue of the set of candidate venues with a highest predicted venue score as a predicted venue for the user location; ([Priness, 0106; Fig. 3, 0146] selects a candidate venue of the set of candidate venues with a highest confidence score (i.e predicted venue score) as the visited venue (i.e predicted venue for the user location))
determining that a user visit is associated with the predicted venue; ([Priness, 0081] discloses the venue visit engine 212 determining if the selected candidate venue (i.e predicted venue) has been visited or not by the user (i.e user visit is associated with the predicted venue): “However, if at least one candidate venue is selected, venue visit engine 212 may proceed with further analysis. For example, venue visit engine 212 may perform an analysis capable of distinguish between whether a venue had been visited or merely passed or walked by.”)
and storing data indicating that the user visit is associated with the predicted venue. ([Priness, 0068] discloses storing data in the routine tracker from user visits inferred or discovered from the venue visit engine )
Priness does not explicitly disclose: retrieving check-in data associated with each candidate venue of the set of candidate venues, the check-in data comprising user check-ins for a plurality of users;
However, Kalis discloses: receiving, at a computing device, a user location; ([Kalis, 0047] discloses receiving at social-networking system (i.e computing device) user’s coordinates (i.e location): “…a user's smartphone may send its latitude-longitude coordinates to social-networking system 160…”)
retrieving a set of candidate venues based on the user location; ([Kalis, 0047-0048] discloses identifying (i.e retrieving) two or more candidate place-entities (i.e set of candidate venues) based on user’s coordinates (i.e location))
retrieving check-in data associated with each candidate venue of the set of candidate venues, the check-in data comprising user check-ins for a plurality of users; ([Kalis, 0048] discloses retrieving check-in data associated with each candidate place-entity (i.e candidate venue of the set of candidate venues). [0040] describes that check-in data collected by many users (i.e user check-ins for a plurality of users): “…the client application may support geo-social networking functionality that allows users to “check-in” at various locations or places and communicate this location or place to other users. A check-in to a given location or place may occur when a user is physically located at a location ...”)
predicting a venue score for each candidate venue, the predicting using one or more of a machine learned (ML) model, the check-in data for the candidate venue, or the user location; ([Kalis, 0006, 0050, 0080] discloses predicting confidence scores (i.e venue score) for each candidate place-entities (i.e candidate venue), the predicting using a place-classifier (machine learned ML model), check-in data for each place-entities, and user location)
Priness and Kalis are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method to find a predicted venue for a user visit disclosed by Priness with using check-in data by Kalis. One of ordinary skill in the art would have been motivated to make this modification in order to determine one or more places where a user is most likely located. ([Kalis, 0006])
Regarding Claim 2
Priness in view of Kalis discloses: wherein the user location comprises a latitude and a longitude ([Priness, 0020] discloses user location comprises latitude and longitude)
Regarding Claim 3
Kalis discloses: wherein the check-in data for each candidate venue comprises a plurality of latitude and longitude pairs. ([Kalis, 0045] discloses check-in data may include location coordinates that are latitude-longitude pair)
Regarding Claim 4
Priness in view of Kalis discloses: wherein features of the ML model comprise conditional probabilities associated with the candidate venues, each conditional probability for a respective venue corresponding to a probability that the user location is generated by a probability distribution corresponding to check-in data associated with the venue. ([Kalis, 006, 0050-0053]. Specs [0078-0082] applicant describes the conditional probability as a point on a distribution by inputting the user location in order to find the probability under the distribution)
Regarding Claim 5
Priness in view of Kalis discloses: wherein generating the conditional probability for the venue further comprises: ([Kalis, 0057] discloses generating values for place-entities (i.e venue) on a probability distribution based on user’s location)
determining parameters of the probability distribution based on the check-in data associated with the venue; ([Kalis, Fig. 4, 0053] discloses determining parameters for a power-law distribution (i.e probability distribution). The power-law distribution has multiple parameters, such as different dimensional functions, geographic-location center point, and P(r) equation. [0052] states that power-law distribution is a location-probability distribution based on check-in data associated with the place-entity (i.e venue))
and computing the probability that the user location data is generated by the probability distribution with the determined parameters. ([Kalis, 0057] states computing a probability using the user location data: “...a location-probability distribution for a place-entity has a value of 0.7 at the user's location…” The value 0.7 represents a conditional probability for the entity venue corresponding to a user location is under (i.e generated by) the location-probability distribution, where given a user’s location, the probability value is 0.7 at the place-entity. Specs [0078-0082] applicant describes the conditional probability as a point on a distribution by inputting the user location in order to find the probability under the distribution. [Fig. 4, 0053] describes a probability distribution with determined parameters using an Eiffel Tower example)
Regarding Claim 6
Priness in view of Kalis discloses: wherein the probability distribution is a bivariate Gaussian probability distribution. ([Kalis, 0053] discloses a location-probability as a power-law distribution that can be a two-dimensional function (i.e bivariate) and can have a Gaussian shape)
Regarding Claim 8
Priness discloses: receiving an updated user location; ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, updated user location is received to calculate new rankings. [Fig. 3, 0142] discloses receiving user location based on sensor data at venue visit engine (i.e computing device))
retrieving an updated set of candidate venues based on the updated user location;
([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, updated set of candidate venues to calculate new rankings. [Fig. 3, 0143] discloses retrieving a set of candidate venue based on user location)
retrieving updated ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, updated candidate venues with their associated data are retrieved. [Fig. 3, 0144] discloses retrieving semantic information (i.e data) associated with each candidate venues of a set of candidate venues.)
predicting an updated venue score for each updated candidate venue, of the set of updated candidate venues, using at least one of the ML model, the updated ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, updated information is used to predict updated scores for ranking. [0086; Fig. 3, 0142, 0144, 0145] discloses predicting a confidence score (i.e venue score) for each candidate venue using the venue visit engine (i.e ML model), semantic information (i.e data) for the candidate venue, and user location as input to the venue visit engine. [0023] discloses venue visit engine uses a probabilistic model, a ML model, to generate confidence scores)
and identifying an updated candidate venue of the set of updated candidate venues with a highest predicted updated venue score as the predicted updated venue. ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, an updated candidate venue (i.e predicted updated venue) is chosen based on the highest updated venue score. [0106; Fig. 3, 0146] selects a candidate venue of the set of candidate venues with a highest confidence score (i.e predicted venue score) as the visited venue (i.e predicted venue for the user location))
Priness does not explicitly disclose: retrieving updated
However, Kalis discloses: receiving an updated user location; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location. Therefore, updated user location is received in real-time. [0047] discloses receiving at social-networking system (i.e computing device) user’s coordinates (i.e location): “…a user's smartphone may send its latitude-longitude coordinates to social-networking system 160…”)
retrieving an updated set of candidate venues based on the updated user location; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location (i.e updated user location). Therefore, updated set of candidate place-entities (i.e venues) is retrieved based on user’s current location. [0047-0048] discloses identifying (i.e retrieving) two or more candidate place-entities (i.e set of candidate venues) based on user’s coordinates (i.e location))
retrieving updated check-in data associated with each updated candidate venue of the set of updated candidate venues; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location and user’s check-in data (i.e updated check-in data): “The social-networking system 160 may automatically check-in a user to a location or place based on the user's current location and past location data.” [Kalis, 0048] discloses retrieving check-in data associated with each candidate place-entity (i.e candidate venue of the set of candidate venues). [0040] describes that check-in data collected by many users (i.e user check-ins for a plurality of users): “…the client application may support geo-social networking functionality that allows users to “check-in” at various locations or places and communicate this location or place to other users. A check-in to a given location or place may occur when a user is physically located at a location ...”)
predicting an updated venue score for each updated candidate venue, of the set of updated candidate venues, using at least one of the ML model, the updated check-in data for each updated candidate venue, or the updated user location; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location and user’s check-in data. Therefore, updated candidate place-entity (i.e venue) is retrieved based on user’s current location. Updated information is used for updated venue scores. [0006, 0050, 0080] discloses predicting confidence scores (i.e venue score) for each candidate place-entities (i.e candidate venue), the predicting using a place-classifier (machine learned ML model), check-in data for each place-entities, and user location)
Priness and Kalis are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method to find a predicted venue for a user visit disclosed by Priness with using check-in data by Kalis. One of ordinary skill in the art would have been motivated to make this modification in order to determine one or more places where a user is most likely located automatically. ([Kalis, 0006, 0040])
Regarding Claim 9
Priness in view of Kalis discloses: wherein determining the user visit associated with the predicted venue further comprises determining that the predicted venue matches the predicted updated venue. ([Priness, 0021, 0081-0083] discloses determining a user visit associated with the predicted venue by tracking and clustering (i.e matches) the user’s previous visits (i.e predicted venue) and subsequent visits (i.e predicted updated venue) in order to determine the venue the user visited. Clustering analysis involves finding which of the predicted venues is matches most closely to other venue visits)
Regarding Claim 13
Priness in view of Kalis discloses: the predicted venue displaying, on a user interface (UI) of a second computing device, a map comprising a visual indicator associated with the user in a vicinity of a visual indicator associated with the predicted venue. ([Kalis, Fig. 1, 0038-0041, 0066] discloses displaying graphical user interface in a web browser 132, at the user’s mobile-client system 130 (i.e second computing device), a map of current user location (i.e visual indicator associated with user) and nearby points of interest (i.e visual indicator associated with predicted venue). The first computing device is a social-networking system 160 that determines the user’s location associated to a place (i.e predicted venue))
Regarding Claim 14
Priness in view of Kalis discloses: wherein the second computing device is associated with a connection of the user. ([Kalis, Fig. 1, 0038-0040, 0066] discloses mobile-client system 130 (i.e second computing device) has positioning signals (i.e connection) of the user)
Regarding Claim 15
Claim 15 is a system claim having similar limitations of method of Claim 1, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 15 includes additional limitations below that rejected under Priness.
Priness teaches: at least one processor; at least one memory component storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: ([Priness, Fig. 6, 0159-0164] discloses a system with processors and memory storing instructions.)
Regarding Claim 16
(Claim 16 recites analogous limitations to Claim 2 and therefore is rejected on the same ground as Claim 2.)
Regarding Claim 17
(Claim 17 recites analogous limitations to Claim 3 and therefore is rejected on the same ground as Claim 3.)
Regarding Claim 18
(Claim 18 recites analogous limitations to Claim 4 and therefore is rejected on the same ground as Claim 4.)
Regarding Claim 19
(Claim 19 recites analogous limitations to Claim 5 and therefore is rejected on the same ground as Claim 5.)
Regarding Claim 20
Claim 20 is a non-transitory computer-readable storage medium claim having similar limitations of method of Claim 1, therefore it is rejected under the same rational as of Claim 1. Additionally, Claim 20 includes additional limitations below that rejected under Kalis.
Kalis teaches: A non-transitory computer-readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising ([Kalis, 0123] discloses non-transitory computer-readable storage medium.)
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Priness in view of Kalis and in view of Weinberg et al (US 20170034666 A1, hereinafter “Weinberg”)
Regarding Claim 7
Priness in view of Kalis discloses: wherein the ML model is a ([Kalis, 0080-0081] discloses using a multi-class place-classifier (machine learned ML model) to classify different place-entities)
Priness in view of Kalis does not explicitly discloses: wherein the ML model is a supervised multi-class classifier.
However, Weinberg discloses: wherein the ML model is a supervised multi-class classifier. ([Weinberg, 0066, 0070] discloses a logical hub classifier (i.e ML model) as supervised and can determine one or more logical hubs associated with a user device (i.e multi-class))
Priness, Kalis, and Weinberg are analogous art to the present invention because they are from the same field of endeavor directed to machine learning.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the method to find a predicted venue for a user visit disclosed by Priness in view of Kalis with using a supervised multi-class classifier by Weinberg. One of ordinary skill in the art would have been motivated to make this modification in order to detect and infer user’s location. ([Weinberg, Abstract, 0070])
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Priness in view of Kalis, and Dotan-Cohen et al (US 20180232764 A1, hereinafter “Dotan”)
Regarding Claim 10
Priness in view of Kalis discloses: wherein determining the user visit associated with the predicted venue ([Priness, 0081] discloses the venue visit engine 212 determining if the selected candidate venue (i.e predicted venue) has been visited or not by the user (i.e user visit is associated with the predicted venue))
Priness in view of Kalis does not explicitly discloses: further comprises determining that a time period between receiving the user location and receiving the updated user location transgresses a predefined duration threshold.
However, Dotan discloses: wherein determining the user visit associated with the ([Dotan, 0049] discloses determining an extended user visit associated with a place of interest (i.e venue) by determining the duration using a location history detected from signals. The duration is between the first detected set of signals and ending at a time of a last detected set of similar signals from a user device (i.e time period between receiving user location and receiving the updated user location). A user visit for the place of interest is extended if the duration is greater than (i.e transgresses) a default threshold (i.e predefined duration threshold))
Priness, Kalis, and Dotan are analogous art to the present invention because they are from the same field of endeavor directed to user location.
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have combined determining a user visit disclosed by Priness in view of Kalis with using a predefined duration threshold by Dotan. One of ordinary skill in the art would have been motivated to make this modification in order to determine how long the user visited at a place of interest and categorize the visit. ([Dotan, 0049])
Regarding Claim 11
Priness in view of Kalis and Dotan disclose: wherein determining the user visit associated with the predicted venue further comprises determining that: ([Priness, 0081] discloses the venue visit engine 212 determining if the selected candidate venue (i.e predicted venue) has been visited or not by the user (i.e user visit is associated with the predicted venue))
a predicted venue score associated with the predicted venue transgresses a first confidence threshold; ([Priness, 0086, 0106] discloses confidence score (i.e predicted venue score) associated with the selected candidate venue (i.e predicted venue) exceeds (i.e transgresses) a threshold value (i.e first confidence threshold))
and a predicted updated venue score associated with the predicted updated venue transgresses a second confidence threshold. ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, an updated candidate venue (i.e predicted updated venue) is chosen based on the highest updated venue score. [0086, 0106] discloses confidence score (i.e predicted venue score) associated with the selected candidate venue (i.e predicted venue) exceeds (i.e transgresses) a threshold value (i.e second confidence threshold))
Regarding Claim 12
Priness in view of Dotan dislcoses: further comprising determining an end to the user visit associated with the predicted venue, the determining of the end to the user visit comprising: ([Priness, 0021, 0024-0025, 0082] discloses tracking subsequent venue visits after the current user visit, which indicates determining end of user visits)
receiving an additional user location; ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, additional user location is received to calculate new rankings. [Fig. 3, 0142] discloses receiving user location based on sensor data at venue visit engine (i.e computing device))
retrieving an additional set of candidate venues based on the additional user location; ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, there is an additional set of candidate venues to calculate new rankings. [Fig. 3, 0143] discloses retrieving a set of candidate venue based on user location)
retrieving additional check-in data associated with each additional candidate venue of the set of additional candidate venues; ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, additional candidate venues with their associated data are retrieved. [Fig. 3, 0144] discloses retrieving semantic information (i.e data) associated with each candidate venues of a set of candidate venues.)
predicting an additional venue score for each additional candidate venue of the set of additional candidate venues, using at least one of the ML model, the additional check-in data for each additional candidate venue, or the additional user location; ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, additional information is used to predict additional scores for ranking. [0086; Fig. 3, 0142, 0144, 0145] discloses predicting a confidence score (i.e venue score) for each candidate venue using the venue visit engine (i.e ML model), semantic information (i.e data) for the candidate venue, and user location as input to the venue visit engine. [0023] discloses venue visit engine uses a probabilistic model, a ML model, to generate confidence scores)
identifying an additional candidate venue of the set of additional candidate venues with a highest predicted additional venue score as the predicted additional venue; ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. Therefore, an additional candidate venue (i.e predicted additional venue) is chosen based on the highest additional venue score. [0106; Fig. 3, 0146] selects a candidate venue of the set of candidate venues with a highest confidence score (i.e predicted venue score) as the visited venue (i.e predicted venue for the user location))
and determining that the predicted additional venue differs from the predicted venue. ([Priness, 0021, 0024-0025] discloses tracking subsequent venue visits and reranking candidate venues. [0026] discloses determining a contradiction (i.e differs) with a subsequent venue visit (i.e predicted additional venue) and selected venue visit (i.e predicted venue visit): “…select one of the candidate venues as a venue that the user is currently visiting, and subsequently select a different one of the candidate venues after further analysis when more information is available to the system (e.g., one or more subsequent venue visits are detected, a contradiction with another venue visit…”)
Priness do not explicitly disclose: retrieving additional check-in data associated with each additional candidate venue of the set of additional candidate venues;
Kalis discloses: receiving an additional user location; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location. Therefore, additional user location is received in real-time. [0047] discloses receiving at social-networking system (i.e computing device) user’s coordinates (i.e location): “…a user's smartphone may send its latitude-longitude coordinates to social-networking system 160…”)
retrieving an additional set of candidate venues based on the additional user location; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location (i.e additional user location). Therefore, additional set of candidate place-entities (i.e venues) is retrieved based on user’s current location. [0047-0048] discloses identifying (i.e retrieving) two or more candidate place-entities (i.e set of candidate venues) based on user’s coordinates (i.e location))
retrieving additional check-in data associated with each additional candidate venue of the set of additional candidate venues; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location and user’s check-in data (i.e additional check-in data): “The social-networking system 160 may automatically check-in a user to a location or place based on the user's current location and past location data.” [Kalis, 0048] discloses retrieving check-in data associated with each candidate place-entity (i.e candidate venue of the set of candidate venues). [0040] describes that check-in data collected by many users (i.e user check-ins for a plurality of users): “…the client application may support geo-social networking functionality that allows users to “check-in” at various locations or places and communicate this location or place to other users. A check-in to a given location or place may occur when a user is physically located at a location ...”)
predicting an additional venue score for each additional candidate venue of the set of additional candidate venues, using at least one of the ML model, the additional check-in data for each additional candidate venue, or the additional user location; ([Kalis, 0040, 0064] discloses automatically tracking user’s current location and user’s check-in data. Therefore, additional candidate place-entity (i.e venue) is retrieved based on user’s additional current location. Additional information is used for additional venue scores. [0006, 0050, 0080] discloses predicting confidence scores (i.e venue score) for each candidate place-entities (i.e candidate venue), the predicting using a place-classifier (machine learned ML model), check-in data for each place-entities, and user location)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Amanda D. Nguyen whose telephone number is (571)270-1854. The examiner can normally be reached M-F, 7:30am to 5:00 pm ET First Fridays off, 2nd Friday 7:30 am - 4:00 pm ET.
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/AMANDA D NGUYEN/Examiner, Art Unit 2127
/JEREMY L STANLEY/Examiner, Art Unit 2127
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