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 .
Response to Amendment
The Amendment filed 07/03/2025 has been entered. Claims 1-4, 6-13, and 15-19 remain pending in the application. Claims 5, 14, and 20 are cancelled. Applicant’s amendments to the specification and claims have overcome each and every objection previously set forth in the Non-Final Office Action mailed 04/07/2025.
Applicant’s amendments to claims have overcome most of the 112(b) rejections except for the following:
In claims 1 (line 11) and 10 (line 7): lack of antecedent basis for “the one or more proximal servers”
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0030 line 33: “A Chute” chute should not be capitalized
Appropriate correction is required.
Claim Objections
Claim 1 is objected to because of the following informalities:
(Claim 1 page 2 lines 29-30) “at least of: wildlife, livestock”
Appropriate correction is required.
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.
Claims 1-4, 6-13, and 15-17 are 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.
With respect to claims 1 (line 11) and 10 (line 7): lack of antecedent basis for “the one or more proximal servers”. Their dependent claims (2/1, 4/1, 6/1, 9/1, 7/2/1, 8/2/1, 3/2/1, 11/10, 13/10, 15/10, 17/10, 12/11/10, 16/11/10) are also rejected.
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.
Claims 1-4, 6-8, 10-13 and 15-16 are rejected under over Neethirajan (Neethirajan, S. Affective State Recognition in Livestock—Artificial Intelligence Approaches. Animals 2022, 12, 759) in view of Kim et al (K. Kim and C. S. Hong, "Optimal Task-UAV-Edge Matching for Computation Offloading in UAV Assisted Mobile Edge Computing," 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS), Matsue, Japan, 2019, pp. 1-4), She (US 20100277334 A1), and Zweig (US 6658325 B2).
With respect to claim 1, Neethirajan teaches an Artificial intelligence (AI)-based computing system for monitoring health conditions (“Positive affective states in farm animals are essential
to monitor and foster, not only because they indicate good physical and mental health, but also because they can increase the productivity of livestock and the quality of products obtained from them [6,32]. Therefore, developing quantitative measures of farm animal affective state (Figure 1) to improve animal welfare could benefit both livestock and farmers by mitigating disease, reducing suffering, and increasing the quality and quantity of livestock output.” Page 2 lines 27-33), the AI-based computing system comprising: one or more hardware processors (“Common methods to identify affective and behavioural events in farm animals using sensors and AI enabled sensor data are: (a) An automatic affective state classification approach, capitalizing on preliminary work [102] conducted by FarmWorx of the Wageningen University. Pre-existing trained farm animals’ facial recognition platform such as WUR Wolf (Wageningen University and Research—Wolf Mascot) [102] can be used to classify changes in affective state over time in pigs and cows based on the video camera
data (Figure 4).” Page 8 paragraph 1 And Fig. 4, sensor and computer processors); and a memory coupled to the one or more hardware processors (“Common methods to identify affective and behavioural events in farm animals using sensors and AI enabled sensor data are: (a) An automatic affective state classification approach, capitalizing on preliminary work [102] conducted by FarmWorx of the Wageningen University. Pre-existing trained farm animals’ facial recognition platform such as WUR Wolf (Wageningen University and Research—Wolf Mascot) [102] can be used to classify changes in affective state over time in pigs and cows based on the video camera data (Figure 4).” Page 8 paragraph 1 And Fig. 4, sensor and computer processors and the memory used to store the pre-existing trained platform), wherein the memory comprises a plurality of modules in the form of programmable instructions executable by the one or more hardware processors (“Common methods to identify affective and behavioural events in farm animals using sensors and AI enabled sensor data are: (a) An automatic affective state classification approach, capitalizing on preliminary work [102] conducted by FarmWorx of the Wageningen University. Pre-existing trained farm animals’ facial recognition platform such as WUR Wolf (Wageningen University and Research—Wolf Mascot) [102] can be used to classify changes in affective state over time in pigs and cows based on the video camera data (Figure 4).” Page 8 paragraph 1 And Fig. 4, sensor and computer processors and the memory used to store the pre-existing trained platform), and wherein the plurality of modules comprises: a data capturing module configured to capture at real-time (“To respond rapidly to changes in the behaviour, affective state, and health of their animals, caretakers need to be continuously updated with the status of the animals under their charge. Digital twin models of individual animals promise a next generation approach to realizing this real-time flow of biometric information” page 14 Digital twin systems to report and predict affective states in real time lines 1-4) a multimedia data of a Region of Interest (ROI) via one or more image capturing devices located at specified locations of the ROI (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored.” Page 4 visual sensors-facial features and expression), wherein the multimedia data is indicative of health of one or more animals (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored. The movement and attitude of specific facial features have been noted to reflect affective state in farm animals, for instance, backward-pointing ears indicates fear in pigs [16]. However, at present no systematic analyses of facial expressions in farm animals have been conducted [9,15,17]; therefore, an AI/ML big data approach to affective state recognition and analysis can radically deepen our understanding of correlations between facial features and affective states in farm animals.” Pages 4-5 visual sensors-facial features and expression and “Common methods to identify affective and behavioural events in farm animals using sensors and AI enabled sensor data are: (a) An automatic affective state classification approach, capitalizing on preliminary work [102] conducted by FarmWorx of the Wageningen University. Pre-existing trained farm animals’ facial recognition platform such as WUR Wolf (Wageningen University and Research—Wolf Mascot) [102] can be used to classify changes in affective state over time in pigs and cows based on the video camera data (Figure 4).” Page 8 paragraph 1 And Fig. 4), wherein the ROI comprises one or more locations at which the one or more animals are placed (“In this article, I propose ways to identify robust predictors of farm animal affective state and behaviour by collecting multimodal biometric sensor data from real farm environments; methods to develop objective, scientifically validated scales and indices of animal welfare to predict affective state and behaviour at the individual and herd levels” page 3 lines 20-24). Neethirajan also teaches wherein the one or more image capturing devices are located at: an animal (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored.” Page 4 visual sensors-facial features and expression). Neethirajan, additionally teaches a health management module configured to: receive, from a server, multimedia data associated with a set of animals (“It has to focus on establishing the hardware infrastructure to reliably gather large quantities of multimodal sensor data, along with the high-performance cloud server architecture to store and process these data.” Page 8 Sensor Network Fusion Protocols and Instrumentation Framework paragraph 2 lines 4-6), wherein the multimedia data comprises a plurality of images (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored.” Page 4 visual sensors-facial features and expression) and a plurality of videos of the set of animals (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored.” Page 4 visual sensors-facial features and expression), wherein the set of animals comprises at least of: livestock (“In this article, I propose ways to identify robust predictors of farm animal affective state and behaviour by collecting multimodal biometric sensor data from real farm environments; methods to develop objective, scientifically validated scales and indices of animal welfare to predict affective state and behaviour at the individual and herd levels” page 3 lines 20-24); identify one or more characteristics of the set of animals in the received at least one of the plurality of images and the plurality of videos by using the data management-based AI model (see fig. 4 and Build Predicative Models of Affective State and Behavior section), wherein the data management-based AI model is at least one of a Machine Learning (ML) model and an AI model (see fig. 4 and Build Predicative Models of Affective State and Behavior section);extract one or more features from the identified one or more characteristics of the set of animals by using the data management-based AI model to determine one or more changes associated with the set of animals (see fig. 4 and Build Predicative Models of Affective State and Behavior section);detect, based on determined changes and predefined health information, at least one of: a presence and absence of one or more diseases (“The digital twin system can deepen our understanding of the factors contributing to physical and emotional resilience in the animals during their maturation. This in-depth understanding is important for making evidence-based changes to animal husbandry practices that can enhance animal welfare and facilitate the detection and prevention of disease” page 16 lines 24-28 and “Common methods to identify affective and behavioural events in farm animals using sensors and AI enabled sensor data are: (a) An automatic affective state classification approach, capitalizing on preliminary work [102] conducted by FarmWorx of the Wageningen University. Pre-existing trained farm animals’ facial recognition platform such as WUR Wolf (Wageningen University and Research—Wolf Mascot) [102] can be used to classify changes in affective state over time in pigs and cows based on the video camera data (Figure 4).” Page 8 paragraph 1 And Fig. 4), in the set of animals.
Neethirajan does not explicitly detail mobile servers and their operation (see claim 1 lines 11-16 and 22-47) and preforming animal care operations (see claim 1 lines 48-50)
Kim et al teaches one or more proximal mobile servers configured to retrieve data from one or more image capturing devices upon navigating to specified locations of the ROI (“launched and various kind of mobile devices and Internet-of Things sensors emerged which are produce a wide variety of data. Therefore, new applications have recently emerged, combining with the technologies such as big data processing and machine learning, VR(Virtual Reality), AR(Augmented Reality). However, processing these services on mobile device is not appropriate because mobile devices have lower computing power and battery. To solve that problem, mobile edge computing has introduced which is network architecture concept that enables cloud computing capabilities and IT service environment at edge of the network such as base stations and Wi-Fi Access point” page 1 introduction paragraphs 1 and 2 and fig. 1 and “Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3), wherein the one or more proximal mobile servers comprises at least one of: one or more drones (fig. 1 UAVs). Kim et al also teaches a location identification module configured to identify location of the one or more image capturing devices based on the captured real-time multimedia data (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and “It is important to efficiently use the energy of UAV with a limited battery. When a task occurs, the UAV move to the task to obtain data for processing, and then move to connect to a matched mobile edge server.” Page 2 Energy Consumption paragraph 1 and “Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3); a server identification module configured to identify the one or more proximal mobile servers in proximity to the ROI based on the identified location of the one or more image capturing devices (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and “It is important to efficiently use the energy of UAV with a limited battery. When a task occurs, the UAV move to the task to obtain data for processing, and then move to connect to a matched mobile edge server.” Page 2 Energy Consumption paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations);a parameter retrieval module configured to retrieve one or more ROI parameters from a storage unit upon identifying the one or more proximal mobile servers (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), wherein the one or more ROI parameters comprises: a location of the ROI (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), one or more images of the one or more image capturing devices (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), type of the identified one or more proximal mobile servers (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), and layout of the ROI (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations); a parameter determination module configured to determine one or more travel parameters based on predefined location information, a current location of the one or more proximal mobile servers (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), identified location of the one or more image capturing devices (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations, task location interpreted as camera location), and the retrieved one or more ROI parameters wherein the one or more travel parameters comprises: a distance between the identified one or more proximal mobile servers and the ROI (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), optimal path and a set of most optimal mobile servers from the identified one or more proximal mobile servers to reach the ROI (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations and Proposed Algorithm sections A and B); a session establishing module configured to establish a communication session between the one or more image capturing devices and the set of most optimal mobile servers upon determining the one or more travel parameters (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations and Proposed Algorithm sections A and B). Kim et al additionally teaches wherein generated commands are transferred to the set of most optimal mobile servers for performing one or more operations (“The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function).
Kim et al. is analogous art reasonably pertinent to the problem faced by the inventor. Kim et al. is directed towards optimal mobile server selection and pathing (“Recently, the fifth-generation communication service has launched and various kind of mobile devices and Internet-of Things sensors emerged which are produce a wide variety of data. Therefore, new applications have recently emerged, combining with the technologies such as big data processing and machine learning, VR(Virtual Reality), AR(Augmented Reality). However, processing these services on mobile device is not appropriate because mobile devices have lower computing power and battery. To solve that problem, mobile edge computing has introduced which is network architecture concept that enables cloud computing capabilities and IT service environment at edge of the network such as base stations and Wi-Fi Access point” page 1 introduction paragraphs 1 and 2). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Neethirajan and Kim et al. by utilizing Kim et al.’s teachings of a location and task based optimize mobile server system in combination with the AI, picture data and camera system of Neethirajan would lead to optimizations in mobile server pathing, taking into account task processing needs and travel time (see table 1 and “However, when mobile device decide to offload their tasks to mobile edge server, the nearest mobile edge server is not always the optimal solution. In spite of the nearest distance, If there is congestion in communication environment and overhead in mobile edge server, It will not be able to meet the requirements of the task. Therefore, offloading proper mobile edge server consider communication and processing time and process requirements is very important.” Page 1 introduction paragraph 2 and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Page 3 last paragraph of Cost function and other considerations section). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Neethirajan and Kim et al. by utilizing Kim et al.’s teachings of a location and task based optimize mobile server system in combination with the AI, picture data and camera system of Neethirajan, with the expectation that doing so would lead to optimizations in mobile server pathing, taking into account task processing needs and travel time (see table 1 and “However, when mobile device decide to offload their tasks to mobile edge server, the nearest mobile edge server is not always the optimal solution. In spite of the nearest distance, If there is congestion in communication environment and overhead in mobile edge server, It will not be able to meet the requirements of the task. Therefore, offloading proper mobile edge server consider communication and processing time and process requirements is very important.” Page 1 introduction paragraph 2 and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Page 3 last paragraph of Cost function and other considerations section).
She teaches a command generation module configured to generate a command by analyzing the retrieved one or more ROI parameters, the identified location of the one or more image capturing devices and the determined one or more travel parameters upon establishing the communication session (“The camera unit 106 captures an image of the earthquake location when the earthquake magnitude reaches the default threshold magnitude. After capturing the image, the camera unit 106 integrates the coordinate of the earthquake location into an Exchange image file format (EXIF) of the captured image and then sends the captured image to the message unit 108. The message unit 108 is configured for generating the emergency message embodying the captured image from the camera unit 106. The wireless transceiver 110 then transmits the emergency message to an emergency center 3“ paragraphs 0013 and 0014)
She is analogous art reasonably pertinent to the problem faced by the inventor. She is directed towards a location based communication system that is able to generate instructions for operations based on environmental conditions and transmit such instructions (“The camera unit 106 captures an image of the earthquake location when the earthquake magnitude reaches the default threshold magnitude. After capturing the image, the camera unit 106 integrates the coordinate of the earthquake location into an Exchange image file format (EXIF) of the captured image and then sends the captured image to the message unit 108. The message unit 108 is configured for generating the emergency message embodying the captured image from the camera unit 106. The wireless transceiver 110 then transmits the emergency message to an emergency center 3“ paragraphs 0013 and 0014). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Neethirjan, Kim et al. and She by utilizing She’s teachings of environmentally responsive communications in combination with Neethirajan’s environmental (animal) imaging system and Kim et al’s mobile networks would lead to a quick and effective communication between systems based on environmental images (“The camera unit 106 captures an image of the earthquake location when the earthquake magnitude reaches the default threshold magnitude. After capturing the image, the camera unit 106 integrates the coordinate of the earthquake location into an Exchange image file format (EXIF) of the captured image and then sends the captured image to the message unit 108. The message unit 108 is configured for generating the emergency message embodying the captured image from the camera unit 106. The wireless transceiver 110 then transmits the emergency message to an emergency center 3“ paragraphs 0013 and 0014). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Neethirajan, Kim et al. and She by utilizing She’s teachings of environmentally responsive communications in combination with Neethirajan’s environmental (animal) imaging system and Kim et al’s mobile networks, with the expectation that doing so would lead to a quick and effective communication between systems based on environmental images (“The camera unit 106 captures an image of the earthquake location when the earthquake magnitude reaches the default threshold magnitude. After capturing the image, the camera unit 106 integrates the coordinate of the earthquake location into an Exchange image file format (EXIF) of the captured image and then sends the captured image to the message unit 108. The message unit 108 is configured for generating the emergency message embodying the captured image from the camera unit 106. The wireless transceiver 110 then transmits the emergency message to an emergency center 3“ paragraphs 0013 and 0014).
Zweig teaches an operation performing module configured to perform the one or more operations for monitoring health conditions of the one or more animals based on generated command (“Other home functions where such a robot could interact with specially modified, robotic cooperative, appliances include sprinklers to water plants, equipment to care for pets, cleaning equipment, heating and air conditioning equipment, security equipment, and the like” page 6 lines 34-39 and “In a third example, the mobile robot of this invention might be used in a home situation to travel throughout the home and observe pets” page 6 line 27-30 and “To achieve these objectives, it is desirable to give a web browser/server controlled mobile robot an additional capability to manipulate its local environment by sending and receiving bi-directional short-range digital radio signals that can be used to control local devices that are external to the robot. In this context, “local” and “short-range” are defined to be distances between about 0 and 300 feet.” Page 7 lines 1-7)
Zweig is analogous art in the same field of endeavor as the claimed invention. Zweig is directed towards a mobile robot that can interface with animal care apparatuses and preform animal care operations (“Other home functions where such a robot could interact with specially modified, robotic cooperative, appliances include sprinklers to water plants, equipment to care for pets, cleaning equipment, heating and air conditioning equipment, security equipment, and the like” page 6 lines 34-39). A person of ordinary skill in the art before the effective filing date of the claimed invention could have reasoned that combining Neethirajan, Kim et al., She, and Zweig by using Zweig’s teaching of an animal care robot in concert with Neethirajan’s animal welfare teachings and the combined mobile server and communication system of Kim et al. and She would lead to greater flexibility in the system’s ability to perform animal specific care operations (“To enhance flexibility and generality, it is advantageous if the mobile robot is designed with general means to interact with various external sensors, external mechanical manipulators and external appliances. The robot may then travel into range of the external devices, and call upon them to assist the robot at the task at hand” page 8 lines 61-64 and “To achieve these objectives, it is desirable to give a web browser/server controlled mobile robot an additional capability to manipulate its local environment by sending and receiving bi-directional short-range digital radio signals that can be used to control local devices that are external to the robot. In this context, “local” and “short-range” are defined to be distances between about 0 and 300 feet.” Page 7 lines 1-7). Therefore, it would have been obvious for a person of ordinary skill before the effective filing date of the claimed invention to combine Neethirajan, Kim et al., She, and Zweig by using Zweig’s teaching of an animal care robot in concert with Neethirajan’s animal welfare teachings and the combined mobile server and communication system of Kim et al. and She, with the expectation that doing so would lead to greater flexibility in the system’s ability to perform animal specific care operations (“To enhance flexibility and generality, it is advantageous if the mobile robot is designed with general means to interact with various external sensors, external mechanical manipulators and external appliances. The robot may then travel into range of the external devices, and call upon them to assist the robot at the task at hand” page 8 lines 61-64 and “To achieve these objectives, it is desirable to give a web browser/server controlled mobile robot an additional capability to manipulate its local environment by sending and receiving bi-directional short-range digital radio signals that can be used to control local devices that are external to the robot. In this context, “local” and “short-range” are defined to be distances between about 0 and 300 feet.” Page 7 lines 1-7).
With respect to claim 2, Neethirajan, Kim et al., She, and Zweig teach the ai based computing system of claim 1. Neethirajan teaches wherein the multimedia data comprises a plurality of images and a plurality of videos corresponding to the ROI (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored.” Page 4 visual sensors-facial features and expression). Neethirajan also teaches where image capturing devices interact with the server using a wired means (see figure 2)
Kim et al teaches navigating the set of most optimal mobile servers from the current location of the set of most optimal mobile servers to the location of the one or more image capturing devices based on the generated command (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations); transfer the multimedia data (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3) from the one or more image capturing devices to at least one of a central server and one or more on-premises devices based on the generated command (“The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and equation 3), and upload the retrieved multimedia data (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3) to at least one of: the central server and the one or more on-premises devices via the set of most optimal mobile servers (“The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function); retrieve the multimedia data (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3) from the one or more image capturing devices via the set of most optimal mobile servers by using at least one of: one or more wireless means (“The UAV can be connected to one base station at a time through wireless channel” page 2 Network and Task Model lines 1-2) upon navigating the set of most optimal mobile servers to the one or more image capturing devices (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and “The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function).
Zweig teaches wherein in performing the one or more operations for monitoring the health conditions of the one or more animals based on the generated command (“Other home functions where such a robot could interact with specially modified, robotic cooperative, appliances include sprinklers to water plants, equipment to care for pets, cleaning equipment, heating and air conditioning equipment, security equipment, and the like” page 6 lines 34-39 and “In a third example, the mobile robot of this invention might be used in a home situation to travel throughout the home and observe pets” page 6 line 27-30 and “To achieve these objectives, it is desirable to give a web browser/server controlled mobile robot an additional capability to manipulate its local environment by sending and receiving bi-directional short-range digital radio signals that can be used to control local devices that are external to the robot. In this context, “local” and “short-range” are defined to be distances between about 0 and 300 feet.” Page 7 lines 1-7), the operation performing module is configured to interface with external and internal servers (“Remote browser commands that are essentially requests for additional web pages (for example, a command to access a particular robotic function that has an interface on a different web page) are handled directly by the robot's onboard web server (3), which sends additional web pages from HTML file (4) back to remote browser (6). Remote browser CGI commands for general robotics intelligence or additional web page functionality (for example, to read from or update a robotic database) are passed through CGI interface (7) to other programs (e.g. database systems, such as SQL servers, and the like) (17) in the robot's onboard memory, also running under operating system (2). Other interpreters (JAVA, PERL, etc) may optionally run these other programs (17). (16) as needed.” Page 9 col. 10 lines 23-37).
With respect to claim 3, Neethirajan, Kim et al., She, and Zweig teach the ai based computing system of claim 2. Neethirajan teaches wherein the one or more image capturing devices are configured to: capture at real-time the multimedia data of the ROI (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored.” Page 4 visual sensors-facial features and expression);
Kim et al. teaches uploading the retrieved multimedia data to at least one of: the central server (“The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and equation 3) and the one or more on-premises devices (“The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and equation 3).
With respect to claim 4, Neethirajan, Kim et al., She, and Zweig teach the ai based computing system of claim 1. Neethirajan teaches using data management-based AI model (see fig. 4 and Build Predicative Models of Affective State and Behavior section)
Kim et al. teaches retrieving one or more location parameters from the storage unit (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), wherein the one or more location parameters comprises: one or more predefined locations (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations and figure 1) and current location of the set of most optimal mobile servers (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), and wherein the one or more predefined locations comprises: location of one of: base station (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations and figure 1); determine one or more distance parameters based on the retrieved one or more location parameters (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations), wherein the one or more distance parameters comprises: distance between the set of most optimal mobile servers and the one or more predefined locations (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations and figure 1) and optimal route between the set of most optimal mobile servers and the one or more predefined locations (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations and figure 1); navigate the set of most optimal mobile servers from the location of the ROI to the one or more predefined locations based on the retrieved one or more location parameters and the determined one or more distance parameters (table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function and other considerations and figure 1); and upload the multimedia data to at least one of the central server and the one or more on-premises devices from the one or more predefined locations by using the set of most optimal mobile servers (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and “The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function) upon navigating the set of most optimal mobile servers to the one or more predefined locations (“Task can be observation and data acquirement such as photo and video.” Page 2 lines 2-3 and “The UAV can be connected to one base station at a time through wireless channel. The uplink data rate takes into account to calculate the delay in the data transfer.” Page 2 Network and Task model paragraph 1 and table 1 considered variables and “Note that when match task-UAV-mobile edge server, we should consider not only current location of UAV but also return position for next task matching from energy efficiency aspect.” Last paragraph of Cost function).
Zweig teaches wherein in performing the one or more operations for monitoring the health conditions of the one or more animals based on the generated command (“Other home functions where such a robot could interact with specially modified, robotic cooperative, appliances include sprinklers to water plants, equipment to care for pets, cleaning equipment, heating and air conditioning equipment, security equipment, and the like” page 6 lines 34-39 and “In a third example, the mobile robot of this invention might be used in a home situation to travel throughout the home and observe pets” page 6 line 27-30 and “To achieve these objectives, it is desirable to give a web browser/server controlled mobile robot an additional capability to manipulate its local environment by sending and receiving bi-directional short-range digital radio signals that can be used to control local devices that are external to the robot. In this context, “local” and “short-range” are defined to be distances between about 0 and 300 feet.” Page 7 lines 1-7), the operation performing module is configured to: retrieve location parameters wherein the one or more predefined locations comprises: location of one of: one or more nearest regions with internet connectivity (“Remote browser commands that are essentially requests for additional web pages (for example, a command to access a particular robotic function that has an interface on a different web page) are handled directly by the robot's onboard web server (3), which sends additional web pages from HTML file (4) back to remote browser (6). Remote browser CGI commands for general robotics intelligence or additional web page functionality (for example, to read from or update a robotic database) are passed through CGI interface (7) to other programs (e.g. database systems, such as SQL servers, and the like) (17) in the robot's onboard memory, also running under operating system (2). Other interpreters (JAVA, PERL, etc) may optionally run these other programs (17). (16) as needed.” Page 9 col. 10 lines 23-37) and on-premises location (“Other home functions where such a robot could interact with specially modified, robotic cooperative, appliances include sprinklers to water plants, equipment to care for pets, cleaning equipment, heating and air conditioning equipment, security equipment, and the like” page 6 lines 34-39 and “In a third example, the mobile robot of this invention might be used in a home situation to travel throughout the home and observe pets” page 6 line 27-30 and “To achieve these objectives, it is desirable to give a web browser/server controlled mobile robot an additional capability to manipulate its local environment by sending and receiving bi-directional short-range digital radio signals that can be used to control local devices that are external to the robot. In this context, “local” and “short-range” are defined to be distances between about 0 and 300 feet.” Page 7 lines 1-7)
With respect to claim 6, Neethirajan, Kim et al., She, and Zweig teach the ai based computing system of claim 1. Neethirajan teaches wherein the one or more image capturing devices comprises at least one of a stationary camera (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored.” Page 4 visual sensors-facial features and expression).
Zweig teaches wherein the one or more image capturing devices comprises at least one of a movable camera (“The robot's motorized wheels (2) are controlled through an interface to the Axis developer board's parallel port (3). The robot additionally has an onboard internet capable digital camera (Axis 2100 network camera (7)) that can take video pictures of the robots surroundings” page 10 col. 12 lines 1-6).
With respect to claim 7, Neethirajan, Kim et al., She, and Zweig teach the ai based computing system of claim 2. Kim et al. teaches that the one or more wireless means comprises at least one of a Wireless Fidelity (Wi-Fi) (“To solve that problem, mobile edge computing has introduced which is network architecture concept that enables cloud computing capabilities and IT service environment at
edge of the network such as base stations and Wi-Fi Access point” page 1 introduction paragraph 2).
With respect to claim 8, Neethirajan, Kim et al., She, and Zweig teach the ai based computing system of claim 2. Neethirajan teaches the one or more wired means comprises Universal Serial Bus (USB) (see figure 2).
With respect to claim 9, Neethirajan, Kim et al., She, and Zweig teach the ai based computing system of claim 1. Neethirajan teaches wherein the health management module is further configured to: identify one or more characteristics of the set of animals comprising at least one or more eyes, one or more retinas, one or more muzzles and one or more ears (“Using video cameras, the entire facial expression, or the appearance of particular facial features, such as the eyes, ears, nose (snout), cheeks, or jaw, can be monitored. The movement and attitude of specific facial features have been noted to reflect affective state in farm animals, for instance, backward-pointing ears indicates fear in pigs [16]. However, at present no systematic analyses of facial expressions in farm animals have been conducted [9,15,17]; therefore, an AI/ML big data approach to affective state recognition and analysis can radically deepen our understanding of correlations between facial features and affective states in farm animals.” Pages 4-5 visual sensors-facial features and expression and “Common methods to identify affective and behavioural events in farm animals using sensors and AI enabled sensor data are: (a) An automatic affective state classification approach, capitalizing on preliminary work [102] conducted by FarmWorx of the Wageningen University. Pre-existing trained farm animals’ facial recognition platform such as WUR Wolf (Wageningen University and Research—Wolf Mascot) [102] can be used to classify changes in affective state over time in pigs and cows based on the video camera data (Figure 4).” Page 8 paragraph 1 And Fig. 4); extract one or more features from the identified one or more characteristics of the set of animals comprising at least one or more eyes features, one or more retinas features, one or more muzz