DETAILED ACTION
The following is a first office action upon examination of application number 18/802269. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
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 .
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-8 are directed to a method; thus these claims are directed to a process, which is one of the statutory categories of invention. Claims 10-17 are directed to a non-transitory computer-readable medium, which is a manufacture, and this a statutory category of invention. Claims 18-20 are directed to a system comprising a computing device; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to select a source location for delivery of a financial product, which is described by claim limitations reciting:
detecting location information associated with a plurality of source locations, and
receive … a natural language request for delivery of a payment card to a delivery address, wherein the payment card is to be delivered from one of the plurality of source locations,
receive … the location information comprising at least one of street traffic information or resource utilization information associated with the plurality of source locations,
calculate an optimal source location from the plurality of source locations for providing the payment card based on the natural language request and the location information, the optimal source location determined via a … model … using … data from a plurality of data sources, the plurality of data sources comprising at least one of a location database, a traffic database, a utilities database, or a security database to:
determine a score for each of a plurality of factors based on a respective weight for each of the plurality of factors, the plurality of factors comprising two or more of the following, at each of the plurality of source locations: internet connections associated with a plurality of internal customers, data bandwidth associated with a plurality of enterprise internet connections, consumption of one or more utility types, staff transactions, street traffic, and a security compliance measure,
determine an aggregate score for each of the plurality of source locations based on the score for each of a plurality of factors, and
designate one of the plurality of source locations as the optimal source location based on the score for the security compliance measure at the one of the plurality of source locations being over a threshold value and the aggregated score for the one of the plurality of source locations
providing the optimal source location determined by the … model to the user.
The identified limitations in the claims describing selecting a source location for delivery of a financial product (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practices or, alternatively, the “Mental Processes” grouping of abstract ideas since the identified limitations can be performed by a human, mentally or with pen and paper. Dependent claims 7, 12, 14, and 20 recite limitations that further narrow the abstract idea (i.e., selecting a source location for delivery of a financial product); therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the at least one processor of a computing device in claim 1; the non-transitory computer-readable storage medium storing computer-readable program code executable by a processor in claim 10; and the surveillance equipment; computing device communicatively coupled to the surveillance equipment, the computing device comprising a processor and memory comprising instructions; and computer network in claim 18, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer.
Additional elements such as receive, via a computer network, a natural language request… and receive, via the surveillance equipment, the location information… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering). Additional elements related to a machine learning (ML) model trained using training data and the ML model do not improve the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 2, 4, 6, 11, and 16, related to a ML model add do not yield an improvement and only generally link the abstract idea to a technological environment. Additional elements in claims 3, 5, 8, 13, 15, 17, and 19, reciting that data is obtained via surveillance equipment, resource meter, satellite system, and surveillance camera do not improve the computer or technology and only add insignificant extra-solution activities (data gathering). Additional elements in claim 9 reciting transmitting, via a communications network, instructions for impression machinery… do not provide an improvement and only add insignificant extra-solution activities (data transmission). Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as receive, via a computer network, a natural language request… and receive, via the surveillance equipment, the location information… do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements only add insignificant extra-solution activities (data gathering). Additional elements related to a machine learning (ML) model trained using training data and the ML model do not improve the computer or technology; these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Similarly, additional elements in claims 2, 4, 6, 11, and 16, related to a ML model add do not yield an improvement and only generally link the abstract idea to a technological environment. Additional elements in claims 3, 5, 8, 13, 15, 17, and 19, reciting that data is obtained via surveillance equipment, resource meter, satellite system, and surveillance camera do not improve the computer or technology and only add insignificant extra-solution activities (data gathering). Additional elements in claim 9 reciting transmitting, via a communications network, instructions for impression machinery… do not provide an improvement and only add insignificant extra-solution activities (data transmission). With respect to data gathering and transmission limitations, the courts have recognized the use of computers to receive and transmit data as a well-understood, routine, and conventional, 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). In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 103
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.
Claim(s) 1-7, 9-16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10163070 (Phillips); in view of US 2019/0130354 (Han).
As per claim 1, Phillips teaches: a computer implemented method comprising, via at least one processor of a computing device: executing a machine learning (ML) model trained to calculate output of an optimal source location, from a plurality of source locations, for providing a tangible financial services product based on input of a natural language request for the sensitive item tangible financial services product from a user, (Col 3 ln 64 – Col 4 ln 4 user device may send, to the scheduling platform, a request for a product. For example, a user of the user device may send a request for a cashier's check to the scheduling platform (e.g., using a banking application operating on the user device). In some implementations, data associated with the request may indicate the product, the location to which the product should be delivered (e.g., an address at which the user is or will be located)… Col 15 64-67 – Col 16 ln 1-2 …information obtained from user device 210, product location device(s) 220, courier device(s) 240, and/or third party device(s) 250 to an artificial intelligence model (e.g., a machine learning model) trained to select a product location and/or a courier based on the obtained information Col 10 ln 55-57 the product may be a financial product, such as a credit card, a debit card, cash, a cashier's check, or the like.)
the ML model trained using training data from a plurality of data sources, the plurality of data sources comprising at least one of a location database, a traffic database, a utilities database, or a security database to: (Col 2 ln 58-63 data from product location devices (e.g., devices located at area locations that house a product), from courier devices (e.g., devices associated with independent contractors, drones, carriers, or the like), and/or from third party devices (e.g., devices that provide traffic data, weather data, or the like). Col 4 ln 25-31 platform may receive, from the third party devices, the information that may affect timing of a delivery, such as traffic data associated with traffic conditions (e.g., current or projected traffic conditions), and/or weather data associated with weather conditions (e.g., current or projected weather conditions), or the like Col 15 ln 64 – Col 16 ln 4 providing information obtained from user device 210, product location device(s) 220, courier device(s) 240, and/or third party device(s) 250 to an artificial intelligence model (e.g., a machine learning model) trained to select a product location and/or a courier based on the obtained information (e.g., delivery location, delivery time, product location, fulfillment time, product location characteristics, courier characteristics, or the like) Col 5 ln 12-13 data may include, for example, the product location, delivery location, and delivery time Col 17 ln 23-24 data may include, for example, the product location, delivery location, and delivery time)
determine a score for each of a plurality of factors …, the plurality of factors comprising two or more of the following, at each of the plurality of source locations: internet connections associated with a plurality of internal customers, data bandwidth associated with a plurality of enterprise internet connections, consumption of one or more utility types, staff transactions, street traffic, (Col 16 ln 20-24 the scheduling model may produce a score for a combination of product location and potential courier, the score being based on input data indicating courier cost, courier ability to deliver on time, fulfillment time associated with the product location Col 4 ln 39-47 As shown by reference number 135, the scheduling platform may receive courier characteristics from the courier devices. For example, the courier characteristics may include a cost associated with the courier, a range associated with the courier, availability for the courier, traffic conditions associated with the courier, insurance characteristics associated with the courier, a language spoken by the courier, information relating to the quantity of on-time deliveries that the courier has made, and/or the like; resource availability (consumption resources). Col 18 ln 60 traffic (e.g., a measurement of traffic… Col 15 ln 1-5 scheduling platform 225 may identify a potential courier based on data indicating availability for the potential courier (e.g., a courier may not be a potential courier if the courier is otherwise engaged or otherwise unable to deliver a product) and a security compliance measure (Col 15 ln 6-11 scheduling platform 225 may identify a potential courier based on an insurance characteristic, or insurance policy, associated with the potential courier (e.g., for some products, scheduling platform 225 may require a particular type of insurance for a courier to be eligible to be a potential courier)).
determine an aggregate score for each of the plurality of source locations based on the score for each of a plurality of factors, and (Col 16 ln 20-30 …the scheduling model may produce a score for a combination of product location and potential courier, the score being based on input data indicating courier cost, courier ability to deliver on time, fulfillment time associated with the product location…Scores output from the scheduling model may be compared and/or ranked to determine the particular product location from which the requested product will be prepared and to determine the particular courier that will pick up the requested product from the product location and deliver it to the delivery location).
designate one of the plurality of source locations as the optimal source location based on the score for the security compliance measure at the one of the plurality of source locations being over a threshold value and (Col 15 ln 6-11 scheduling platform 225 may identify a potential courier based on an insurance characteristic, or insurance policy, associated with the potential courier (e.g., for some products, scheduling platform 225 may require a particular type of insurance for a courier to be eligible to be a potential courier))
the aggregated score for the one of the plurality of source locations; and (Col 16 ln 25-35 Scores output from the scheduling model may be compared and/or ranked to determine the particular product location from which the requested product will be prepared and to determine the particular courier that will pick up the requested product from the product location and deliver it to the delivery location. Other types of machine learning models may be used by scheduling platform 225 to facilitate selection of a particular product location and/or a particular courier. In some implementations, selection may be automatic (e.g., based on highest ranked machine learning scores or the like)
providing provide the optimal source location determined by the ML model to the user. (Col 11 ln 44-46 Notifications may be sent to user device 210, for example, to confirm a delivery location (e.g., for every delivery, or based on particular events Col 18 ln 66 – Col 19 ln 7 Upon selecting a particular product location (e.g., bank location), scheduling platform 225 may perform an action by sending a notification to a courier regarding … and sending confirmation data regarding the product deliver to user device 210 associated with the request).
Although not explicitly taught by Phillips, Han teaches: determine a score for each of a plurality of factors based on a respective weight for each of the plurality of factors, … determine an aggregate score for each of the plurality of source locations based on the score for each of a plurality of factors, ([0101] At 608, the identified merchants are scored based on predetermined parameters. Such parameters may be weighted … to generate a score for the target merchants. The target merchants that score above a predetermined minimum threshold may be assigned runners at the corresponding depot).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Phillips with the aforementioned teachings of Han with the motivation of using scores to make assignments/selections (Han [0101]). Further, one of ordinary skill in the art would have recognized that applying the teachings of Han to the system of Phillips would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for scores to be weighted.
As per claim 2, Phillips teaches: wherein the ML model is continuously trained using traffic training data comprising dynamically updated street traffic information of transportation routes associated with the plurality of source locations (Col 14 ln 26-27 … model may be trained, using previous product orders, fulfillment times, and product location characteristics Col 17 ln 23-27 various pieces of information may be provided to a machine learning model training device (e.g., in a manner designed to update an existing model and/or generate a new model to facilitate scheduling product preparation and delivery). Col 8 ln 27-46 Third party device(s) 250 include one or more one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with scheduling preparation and delivery of products… third party device 250 may be associated with a traffic server, which may provide traffic-related information (e.g., automobile traffic, air traffic, or the like) for particular geographic areas and/or routes Col 14 ln 46-52 …scheduling platform 225 may identify at least one potential courier based on obtaining courier characteristics. For example, courier characteristics may include data indicating a cost associated with a courier, data indicating a range associated with a courier, data indicating availability for a courier, data indicating traffic conditions associated with a courier).
As per claim 3, Phillips teaches: wherein at least a portion of the traffic training data is obtained via one of satellite data or surveillance equipment associated with the plurality of source locations (Col 2 ln 58-63 … that gathers data from product location devices (e.g., devices located at area locations that house a product), from courier devices (e.g., devices associated with independent contractors, drones, carriers, or the like), and/or from third party devices (e.g., devices that provide traffic data Col 4 ln 25-28 scheduling platform may receive, from the third party devices, the information that may affect timing of a delivery, such as traffic data associated with traffic conditions; the claim does not require a specific type of surveillance equipment. The third party device provides traffic data (surveillance device). Col 8 ln 43-47 third party device 250 may be associated with a traffic server, which may provide traffic-related information (e.g., automobile traffic, air traffic, or the like) for particular geographic areas and/or routes. Col 15 ln 64-67 providing information obtained from user device 210, product location device(s) 220, courier device(s) 240, and/or third party device(s) 250 to an artificial intelligence model (e.g., a machine learning model)).
As per claim 4, Phillips teaches: wherein the ML model is continuously trained using utilities training data comprising dynamically updated resource utilization information of the plurality of source locations (Col 17 ln 23-27 various pieces of information may be provided to a machine learning model training device (e.g., in a manner designed to update an existing model and/or generate a new model to facilitate scheduling product preparation and delivery) Col 4 ln 41-43 the courier characteristics may include a cost associated with the courier, a range associated with the courier, availability for the courier Col 6 17-18 providing information associated with products, including product availability Col 12 ln 13-14 locations that have the product based on inventory data available to scheduling platform 225 Col 13 ln 22-28 location characteristics may specify a variety of information for a particular product location, such as data regarding entities capable of preparing products for delivery, data regarding the availability of the entities, data indicating the products available at the particular product location, or the like).
As per claim 5, Phillips teaches: wherein at least a portion of the utilities training data is obtained via one of a resource meter or surveillance equipment associated with the plurality of source locations (Col 13 ln 6467 providing information obtained from user device 210, product location device(s) 220, courier device(s) 240, and/or third party device(s) 250 to an artificial intelligence model (e.g., a machine learning model)Col 14 ln 1-9 Scheduling platform 225 may use one or more of the product location characteristics to determine the fulfillment time in a variety of ways. Using the cashier's check example, scheduling platform 225 may obtain, from product location device 220 associated with a bank branch, the schedule or availability of one or more employees capable of authorizing a cashier's check, data indicating the availability of blank cashier's checks, and data indicating the availability of a printing device for printing the cashier's check; claim does not require a specific type of surveillance equipment. The product location device (surveillance equipment) provides resource utilization data. Col 17 ln 23-27 various pieces of information may be provided to a machine learning model training device (e.g., in a manner designed to update an existing model and/or generate a new model to facilitate scheduling product preparation and delivery)).
As per claim 6, Phillips teaches: wherein the ML model is trained to require a minimum value for a threshold score for the security compliance measure in order to select one of the plurality of source locations as the optimal source location, the minimum value configured to indicate that a resource at the plurality of source locations is certified to handle the tangible financial services product (Col 15 ln 6-11 scheduling platform 225 may identify a potential courier based on an insurance characteristic, or insurance policy, associated with the potential courier (e.g., for some products, scheduling platform 225 may require a particular type of insurance for a courier to be eligible to be a potential courier). Col 10 ln 55-56 …a financial product, such as a credit card, a debit card… Col 3 ln 46-47 quick delivery of banking products (e.g., credit cards, debit cards).
As per claim 7, Phillips teaches: wherein each of the plurality of source locations is a bank branch, (Col 18 ln 29-36 scheduling platform 225 may identify product locations (e.g., bank branches, ATMs, warehouse locations, storage lockers, or the like) that might have the requested product. In this situation, scheduling platform 225 may narrow down the product locations to product locations that have cashier's checks in stock, and that have the banking equipment and/or personnel to prepare a cashier's check. Col 18 ln 55-56 scheduling platform 225 may select one of the product locations (e.g., bank locations))
and the minimum value for the threshold score for the security compliance corresponds to at least one security measure associated with transporting the payment card (Col 15 ln 6-11 scheduling platform 225 may identify a potential courier based on an insurance characteristic, or insurance policy, associated with the potential courier (e.g., for some products, scheduling platform 225 may require a particular type of insurance for a courier to be eligible to be a potential courier) Col 15 ln 27-32 characteristics may vary based on the product location from which the courier will pick up the product (e.g., the cost associated with courier delivery, traffic associated with courier delivery, and/or insurance characteristics might change based on the product location)).
As per claim 9, Phillips teaches: transmitting, via a communications network, instructions for impression machinery at the optimal source location, determined by the ML model, to generate the tangible financial services product (Col 6 ln 38-40 connected to a device for preparing products, such as a cashier's check printer capable of preparing cashier's checks for delivery. Col 12 ln 57-62 A product preparing location may be, for example, a retail location having equipment and/or people that are capable of preparing a product. For example, a bank branch location may include equipment for printing a cashier's check and personnel capable of authorizing the issuance of a cashier's check Col 19 ln 3-5 sending instructions to a product location device at a bank location to initiate preparation of the cashier's check).
As per claim 10, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies.
As per claim 11, Phillips teaches: train the ML model using training data, wherein the training data comprises at least one of traffic training data or utilities training data (Col 14 ln 26-27 … model may be trained, using previous product orders, fulfillment times, and product location characteristics Col 17 ln 23-27 various pieces of information may be provided to a machine learning model training device (e.g., in a manner designed to update an existing model and/or generate a new model to facilitate scheduling product preparation and delivery). Col 8 ln 27-46 Third party device(s) 250 include one or more one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with scheduling preparation and delivery of products… third party device 250 may be associated with a traffic server, which may provide traffic-related information (e.g., automobile traffic, air traffic, or the like) for particular geographic areas and/or routes Col 14 ln 46-52 …scheduling platform 225 may identify at least one potential courier based on obtaining courier characteristics. For example, courier characteristics may include data indicating a cost associated with a courier, data indicating a range associated with a courier, data indicating availability for a courier, data indicating traffic conditions associated with a courier. Col 13 ln 6467 providing information obtained from user device 210, product location device(s) 220, courier device(s) 240, and/or third party device(s) 250 to an artificial intelligence model (e.g., a machine learning model)Col 14 ln 1-9 Scheduling platform 225 may use one or more of the product location characteristics to determine the fulfillment time in a variety of ways. Using the cashier's check example, scheduling platform 225 may obtain, from product location device 220 associated with a bank branch, the schedule or availability of one or more employees capable of authorizing a cashier's check, data indicating the availability of blank cashier's checks, and data indicating the availability of a printing device for printing the cashier's check; location device (surveillance equipment) provides resource utilization data. Col 17 ln 23-27 various pieces of information may be provided to a machine learning model training device (e.g., in a manner designed to update an existing model and/or generate a new model to facilitate scheduling product preparation and delivery)).
As per claim 12, Phillips teaches: wherein the traffic training data comprises dynamically updated street traffic information of transportation routes associated with the plurality of source locations (Col 14 ln 26-27 … model may be trained, using previous product orders, fulfillment times, and product location characteristics Col 17 ln 23-27 various pieces of information may be provided to a machine learning model training device (e.g., in a manner designed to update an existing model and/or generate a new model to facilitate scheduling product preparation and delivery). Col 8 ln 27-46 Third party device(s) 250 include one or more one or more devices capable of receiving, generating, storing, processing, and/or providing information associated with scheduling preparation and delivery of products… third party device 250 may be associated with a traffic server, which may provide traffic-related information (e.g., automobile traffic, air traffic, or the like) for particular geographic areas and/or routes Col 14 ln 46-52 …scheduling platform 225 may identify at least one potential courier based on obtaining courier characteristics. For example, courier characteristics may include data indicating a cost associated with a courier, data indicating a range associated with a courier, data indicating availability for a courier, data indicating traffic conditions associated with a courier).
As per claim 13, this claim recites limitations substantially similar to those addressed by the rejection of claim 3, above; therefore, the same rejection applies.
As per claim 14, this claim recites limitations substantially similar to those addressed by the rejection of claim 4, above; therefore, the same rejection applies.
As per claim 15, this claim recites limitations substantially similar to those addressed by the rejection of claim 5, above; therefore, the same rejection applies.
As per claim 16, this claim recites limitations substantially similar to those addressed by the rejection of claim 6, above; therefore, the same rejection applies.
As per claim 18, this claim recites limitations substantially similar to those addressed by the rejection of claim 1, above; therefore, the same rejection applies. Additionally, Phillips also teaches: surveillance equipment for detecting location information associated with a plurality of source locations, and a computing device communicatively coupled to the surveillance equipment, the computing device comprising a processor and memory comprising instructions (Col 1 ln 17-19 device may comprise: one or more memories; and one or more processors, communicatively coupled to the one or more memories Col 2 ln 58-65 gathers data from product location devices (e.g., devices located at area locations that house a product), from courier devices (e.g., devices associated with independent contractors, drones, carriers, or the like), and/or from third party devices (e.g., devices that provide traffic data, weather data, or the like). Using the data gathered, the scheduling platform may schedule delivery of a product to a user specified location)002E
As per claim 20, this claim recites limitations substantially similar to those addressed by the rejection of claim 2, above; therefore, the same rejection applies.
Claim(s) 8, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 10163070 (Phillips); in view of US 2019/0130354 (Han); US 2020/0344238 (Ainsworth).
As per claim 8, although not explicitly taught by Phillips, Ainsworth teaches: wherein the resource is determined via a surveillance camera configured to detect an individual utilizing a computer, wherein the individual is identified via coordinating with a human resources database comprising employee identification information ([0016] In embodiments, the tracking systems include a surveillance system including surveillance cameras and a facial recognition module for performing facial recognition of individuals in the image data from the surveillance cameras. [0047] The facial recognition module also maps each instance of facial recognition information (e.g. the facial signature or facial patch) for each employee to a user credential or other identifier (OD). In this way, the OD associated with each instance of stored facial recognition information can be used to identify the individual for which the facial signature was obtained. [0050] The VMS 110 then uses its facial recognition module 107 to determine whether the images of the individuals 60 in the image data 99 from the surveillance cameras match the stored facial recognition information for registered employees. If the individuals 60 are determined to be employees, the VMS 110 operates in conjunction with the SIS 80, the IT system 20, the ERM 138, and possibly the ERP system 44 to determine whether the individuals are: in the correct rooms within the building 50; attempting to access computer systems to which they are authorized [0056] In step 212, the facial recognition module 107 of the VMS 110 locates the individuals 60 in the image data 99. In step 214, the facial recognition module 107 then performs facial recognition of the individuals located in the image data 99 to identify the individuals 60. For this purpose, the facial recognition module 107 preferably uses the same facial recognition algorithms used when the security operators first registered the individuals as employees. In this way, the facial recognition module 107 can identify employees in the image data by reference to the badge photos 36 stored in the employee database 139 of the ERM system 138).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Phillips with the aforementioned teachings of Ainsworth with the motivation of performing facial recognition of individuals (Ainsworth [0016])). Further, one of ordinary skill in the art would have recognized that applying the teachings of Ainsworth to the system of Phillips would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the identification of employees using facial recognition.
As per claim 17, this claim recites limitations substantially similar to those addressed by the rejection of claim 8, above; therefore, the same rejection applies.
As per claim 19, although not explicitly taught by Phillips, Ainsworth teaches: wherein the surveillance equipment comprises at least one of a satellite system, a resource meter, or a surveillance camera ([0016] In embodiments, the tracking systems include a surveillance system including surveillance cameras and a facial recognition module for performing facial recognition of individuals in the image data from the surveillance cameras. [0047] The facial recognition module also maps each instance of facial recognition information (e.g. the facial signature or facial patch) for each employee to a user credential or other identifier (OD). In this way, the OD associated with each instance of stored facial recognition information can be used to identify the individual for which the facial signature was obtained).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Phillips with the aforementioned teachings of Ainsworth with the motivation of performing facial recognition of individuals (Ainsworth [0016])). Further, one of ordinary skill in the art would have recognized that applying the teachings of Ainsworth to the system of Phillips would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the identification of employees using facial recognition.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
10311459 (Babao) – discloses a system that uses cameras to identify individuals.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625