Systems and methods for detecting pet eating patterns

Abstract

This application discloses a computer-implemented method for determining changes in pet eating behavior using historical pet eating data. The method includes receiving multiple historical pet eating data records from a database, determining a subset of the multiple historical pet eating data records, determining a expected distribution based on the subset of the multiple historical pet eating data records, wherein the expected distribution includes a baseline, an upper threshold, and a lower threshold, receiving current pet data from pet sensors, wherein the current pet data includes a total eating event value, analyzing whether the total eating event value exceeds the upper threshold or the lower threshold, and outputting a notification indicating the results of the analysis.

Description

Technical Field

[0001] Various embodiments of this disclosure generally relate to detecting a pet's eating patterns. In some embodiments, this disclosure relates to systems and methods for using historical pet eating data to determine changes in a pet's eating behavior. Background Technology

[0002] A pet's appetite and corresponding eating habits are important indicators of its well-being. For example, a pet eating for longer or shorter periods than average may indicate a health problem, such as dental issues. Eating monitoring and analysis are very useful tools that can detect potential pet problems before they become serious.

[0003] Traditional methods may rely on pet owners tracking and recording when their pets eat. However, pet owners cannot always see all instances of their pets eating or not eating, and subtle eating trends may be difficult to detect. For example, traditional methods may not cover the possibility that a pet steals food from other pets, or that another pet's food is stolen by another pet. Furthermore, traditional technologies may not account for unexpected eating events during the feeding detection process, such as eating when the pet owner is not present.

[0004] Furthermore, traditional methods may not be able to dynamically adapt to the individual feeding behavior of pets. However, feeding monitoring is not a one-size-fits-all approach. For example, different breeds of pets and different individual pets have different feeding habits. Small pet breeds often eat small bites throughout the day rather than consuming 2-3 large meals, which can make feeding monitoring more challenging.

[0005] This disclosure aims to address the challenges described above. The background description provided herein is intended to present the overall context of this disclosure. Unless otherwise stated herein, the material described in this section is not prior art to the claims of this application, nor is it acknowledged as prior art or a suggestion of prior art by virtue of its inclusion in this section. Summary of the Invention

[0006] According to certain aspects of this disclosure, methods and systems for using historical pet eating data to determine changes in pet eating behavior are disclosed.

[0007] In one aspect, an exemplary embodiment of a method for using historical pet feeding data to determine changes in pet feeding behavior includes: receiving, by one or more processors, multiple historical pet feeding data records from a database, each record including a historical feeding event value and a feeding event date. The method may further include determining, by one or more processors, a subset of the multiple historical pet feeding data records. The method may further include determining, by one or more processors, a desired distribution based on the subset of the multiple historical pet feeding data records, the desired distribution including a baseline, an upper threshold, and a lower threshold, wherein the upper threshold and the lower threshold correspond to the baseline. The method may further include receiving, by one or more processors, current pet data from pet sensors, the current pet data including a total feeding event value. The method may further include analyzing, by one or more processors, whether the total feeding event value exceeds the upper threshold or the lower threshold; and outputting, by one or more processors, a notification indicating the result of the analysis.

[0008] In another aspect, an exemplary embodiment of a computer system for determining changes in a companion pet's eating behavior using historical companion pet eating data is disclosed. The computer system includes at least one memory storing instructions and at least one processor configured to execute the instructions to perform operations. The operations may include receiving multiple historical pet eating data records from a database, wherein each record in the multiple historical pet eating data records includes a historical eating event value and a eating event date. The operations may also include determining a subset of the multiple historical pet eating data records. The operations may further include determining an expected distribution based on the subset of the multiple historical pet eating data records, the expected distribution including a baseline, an upper threshold, and a lower threshold, wherein the upper threshold and the lower threshold correspond to the baseline. The operations may further include receiving current pet data from pet sensors, the current pet data including a total eating event value; analyzing whether the total eating event value exceeds the upper threshold or the lower threshold; and outputting a notification indicating the results of the analysis.

[0009] In another aspect, a non-transitory computer-readable medium containing instructions, when executed by a processor, causes the processor to perform operations using historical companion pet feeding data to determine changes in companion pet feeding behavior. These operations may include receiving multiple historical pet feeding data records from a database, each record including a historical feeding event value and a feeding event date. The operations may also include determining a subset of the multiple historical pet feeding data records. The operations may further include determining a desired distribution based on the subset of the multiple historical pet feeding data records, the desired distribution including a baseline, an upper threshold, and a lower threshold, wherein the upper and lower thresholds correspond to the baseline. The operations may also include receiving current pet data from pet sensors, the current pet data including a total feeding event value; analyzing whether the total feeding event value exceeds the upper or lower threshold; and outputting a notification indicating the results of the analysis.

[0010] It should be understood that the foregoing general description and the following detailed description are merely exemplary and illustrative, and do not limit the claimed and disclosed embodiments. Attached Figure Description

[0011] The accompanying drawings, which are included in and form part of this specification, illustrate various exemplary embodiments and, together with the specification, serve to explain the principles of the disclosed embodiments.

[0012] Figure 1A-1J An exemplary environment for a platform for analyzing and displaying pet health data, according to one or more embodiments, is described.

[0013] Figure 2A-2C An exemplary environment for a platform for analyzing and displaying pet feeding data, according to one or more embodiments, is described.

[0014] Figure 3 A flowchart is depicted illustrating an exemplary method for determining changes in pet eating behavior using historical pet eating data according to one or more embodiments.

[0015] Figure 4 A flowchart illustrating an exemplary method for determining a subset of multiple historical pet feeding data records to determine an expected distribution, according to one or more embodiments, is depicted.

[0016] Figure 5 A flowchart illustrating an exemplary method for determining whether a baseline for a desired distribution is valid, according to one or more embodiments, is depicted.

[0017] Figure 6 Exemplary environments that can be used with the techniques described herein, according to one or more embodiments, are depicted.

[0018] Figure 7 Examples of computing devices capable of performing the techniques described herein, according to one or more embodiments, are depicted. Detailed Implementation

[0019] According to certain aspects of this disclosure, methods and systems for detecting and analyzing pet eating patterns are disclosed. Conventional techniques may not be suitable because they may rely on pet owners tracking and recording their pets' eating events. Furthermore, conventional techniques may not dynamically adapt to the individual eating habits of pets. Therefore, there is a need to improve the techniques related to detecting and analyzing pet eating patterns.

[0020] A pet's eating times are very important. For example, if a pet takes longer or shorter than average to eat, the owner may need to check for underlying health problems. Longer eating times may indicate that the pet is eating more cautiously or is not feeling hungry. Underlying health problems corresponding to changes in eating patterns can include dental problems, gastrointestinal issues, or picky eating. Simple changes, such as changing the food bowl, type of food, and / or shape of food, can also affect a pet's eating habits.

[0021] There is a need for feeding detection techniques to detect feeding events in pets and analyze these events to determine patterns. The feeding detection techniques disclosed herein can objectively show pet owners whether their pet's feeding behavior is changing. Such changes in a pet's feeding behavior can indicate potential health problems and / or indicate whether the pet should be fed more or less food. Furthermore, pet owners can share information about their pet's feeding events with their veterinarian to help provide context for what is happening in their pet's daily life.

[0022] As will be discussed in more detail below, systems and methods for using historical pet eating data to determine changes in pet eating behavior are described in various embodiments. By collecting and analyzing historical pet eating data, the system and method are able to calculate an expected distribution of future pet eating events, where the expected distribution may include a baseline, an upper threshold, and a lower threshold. The system and method can then receive current pet eating data and compare such pet eating data with the expected distribution. The system and method can then output a notification indicating the comparison result to inform the pet owner of any changes in the pet eating data.

[0023] Exemplary health data platform

[0024] Figure 1A -J describes an exemplary environment for a platform for analyzing and displaying pet health data according to one or more embodiments.

[0025] Figure 1AA dashboard page of an exemplary environment for a platform for collecting, analyzing, and displaying pet data, according to one or more embodiments, is shown. The platform may display the pet's overall health score, as well as one or more of the following behavioral sections: "scratching," "licking," "sleeping," "eating," and / or "drinking." The platform may also display options for communicating with a veterinarian (e.g., "chatting with a veterinarian"), sending aggregated data to recipients (e.g., "sharing a health report"), and / or participating in research projects (e.g., "contributing to research").

[0026] In addition, during the initial phase of data collection from pets, the platform can display a "dashboard" page. For example, if data is still being collected from pets, there may not be enough data to analyze and display. Therefore, the platform can display "Data in progress" values ​​for one or all of the shown health scores, scratching data, licking data, sleep data, eating data, and / or drinking data.

[0027] Figure 1B A dashboard page of an exemplary environment for a platform for collecting, analyzing, and displaying pet health data, according to one or more embodiments, is further shown.

[0028] The platform can display the pet's overall health score, as well as one or more of the following behavioral sections: "scratching," "licking," "sleeping," "eating," and / or "drinking." If the platform is still collecting data to establish a baseline, it can display "Baseline Establishing" for any of the behavioral sections shown. In some embodiments, while the platform is in the process of establishing a baseline, it can display the duration of the behavior in the corresponding behavior section (e.g., "28 minutes" in the "licking" section, "235 seconds" in the "scratching" section, "8.4 duration (hours)" and "2 interruptions" in the "sleeping" section, "13 minutes" in the "eating" section, and / or "2 minutes" in the "drinking" section). For example, the duration can be expressed in seconds, minutes, and / or hours. The platform can also display options for communicating with a veterinarian (e.g., "Chat with veterinarian"), sending summary data to recipients (e.g., "Share health report"), and / or participating in research projects (e.g., "Contribute to research").

[0029] Figure 1C The "Health Insights" page is further illustrated as an exemplary environment of a platform for collecting, analyzing, and displaying pet health data according to one or more embodiments.

[0030] The platform can display a pet's overall health score, along with one or more of the following behavioral categories: "scratching," "licking," "sleeping," "eating," and / or "drinking." The pet's overall health score can be based on a comprehensive analysis of all (or some) behaviors (e.g., "88"). The platform can also display an image indicating the health score's position on a scale and a comparison of that health score with a previous health score (e.g., a decrease of "8" from the previous score, indicated by...). Figure 1C The value "8" and the downward arrow indicate this. The platform can also display a written description of the pet's current health status based on a health score (e.g., "Otto is in good health"). In some embodiments, such as... Figure 1D As shown, if the platform does not have enough data to display an overall health score, it can display an indicator indicating that the platform is "collecting data".

[0031] Once the platform has established baselines for specific behavioral categories, it can display images indicating a comparison between the pet's current behavior and these baselines. For example, in the "Scratching" category, compared to the pet's scratching baseline, the pet's current behavior could be "infrequent," "occasional," "intensified," and / or "severe." Similarly, in the "Licking" category, compared to the pet's licking baseline, the pet's current behavior could be "infrequent," "occasional," "intensified," and / or "severe." In the "Sleep" category, compared to the pet's sleep baseline, the pet's current behavior could be "steady," "slightly interrupted," and / or "intermittent." In the "Eating" category, compared to the eating baseline, the pet's current eating behavior could be "below average," "average," and / or "above average." In the "Drinking" category, compared to the drinking baseline, the pet's current drinking behavior could be "below average," "average," and / or "above average."

[0032] The platform can also display the duration of a behavior, the average duration of the behavior, the previous duration of the behavior, and / or a comparison of the behavior with a previous time period (e.g., the previous day). For example, in the "Scratching" section, the platform can display an average duration of "235 seconds / day" and a behavior comparison of "5 seconds less than the previous day." In the "Licking" section, the platform can display an average duration of "28 minutes / day" and a behavior comparison of "3 minutes less than the previous day." In the "Sleep" section, the platform can display "8.4 duration (hours)" and a behavior comparison of "10 seconds more than baseline." It can also display the number of interruptions (e.g., "2 interruptions") and / or a comparison of the interruption with the interruption baseline (e.g., "1 fewer interruption than baseline"). In the "Eating" section, the platform can display a previous behavior duration of "13 minutes" and a behavior comparison of "10 seconds more than baseline." In the "Drinking" section, the platform can display a previous behavior duration of "2 minutes" and a behavior comparison of "10 seconds more than baseline."

[0033] The platform can also display options to view previous and / or current data. If a user selects this option, the platform can display more details of previously collected data. For example, in the "Scratching" section, the platform can display an option to view the "average of the last 7 days." In the "Licking" section, the platform can display an option to view the "average of the last 7 days." In the "Sleep" section, the platform can display an option to view details of "Last Night." In the "Eating" section, the platform can display an option to view details of "Yesterday." In the "Drinking" section, the platform can display an option to view details of "Yesterday." The platform can also display options to communicate with a veterinarian (e.g., "Chat with a Veterinarian"), send summary data to recipients (e.g., "Share a Health Report"), and / or participate in research projects (e.g., "Contribute to Research").

[0034] Figure 1E A "Health Score" page is further shown as an exemplary environment of a platform for collecting, analyzing, and displaying pet health data according to one or more embodiments.

[0035] The platform can display a health score (e.g., "88") and / or a semicircle or arc indicating the position of the health score within a range. The health score can correspond to the pet's health condition. For example, the health score can be based on an analysis of one or all of the pet's behaviors. The platform can also display indicators corresponding to the health score's labels (e.g., "Low," "Good," and / or "Excellent"). A "Low" label can correspond to 0-59 points, a "Good" label to 60-79 points, and an "Excellent" label to 80-100 points. In some embodiments, the labels can have corresponding colors. For example, red can correspond to a "Low" label, yellow to a "Good" label, and green to an "Excellent" label. In some embodiments, the platform can display a bar chart indicating the health score for the previous day or several days and / or the previous month or several months. The chart can also be color-coded, with the color of a specific date corresponding to the label color of the health score.

[0036] In some embodiments, such as Figure 1F As shown, if there is insufficient data to generate a health score, the platform can display a "Data Insufficient" label. Furthermore, the "Data Insufficient" label can have a corresponding color, such as gray. For example, if a chart displays one or more days with the "Data Insufficient" label, the chart can display those days in gray.

[0037] The platform can also display options to learn more about feeding metrics (such as “Understanding Feeding Levels”) and / or get veterinary advice (such as “Chat with Veterinarian”).

[0038] Figure 1G The “Feeding” page is further illustrated as an exemplary environment of a platform for collecting, analyzing, and displaying pet health data according to one or more embodiments.

[0039] The platform can display whether a pet's current eating behavior is "below average," "average," or "above average" compared to its eating baseline. It can also display the pet's average eating time (e.g., "average 13 minutes / day").

[0040] The platform can also display a graph of the pet's feeding times, tracking all feeding events. In some embodiments, feeding time data can be collected by sensors and / or electronic devices worn by the pet. Feeding times can be displayed graphically, where the graph can include dates on the y-axis and time periods on the x-axis. The graph can include squares indicating the start of periods when the pet begins eating and the end of periods when the pet stops eating. The platform can also display the time of the most recent feeding event (e.g., "The most recent feeding event occurred at 4:42 PM (3 hours ago)").

[0041] The platform can also display visual representations of a pet's weekly activities (e.g., ...). Figure 1G (as shown) or monthly (e.g.) Figure 1H The platform uses a bar chart to represent the number of minutes a pet eats each day (as shown). The platform can display the bars in a color that corresponds to whether the pet's eating on a particular day is "below average," "average," or "above average" compared to its eating baseline. In some embodiments, if the platform is in the process of collecting pet eating data, the chart may not display any data (e.g., ...). Figure 1I (As shown).

[0042] The platform can also display options to learn more about feeding metrics (e.g., “Understand your feeding level”) and / or talk to a veterinarian (e.g., “Chat with your veterinarian”).

[0043] Figure 1J The “Drinking Water” page is further shown as an exemplary environment of a platform for collecting, analyzing, and displaying pet health data according to one or more embodiments.

[0044] The platform can display whether a pet's current drinking behavior is "below average," "average," or "above average" compared to its baseline drinking level. It can also display the pet's average drinking time (e.g., "average 3 minutes / day").

[0045] The platform can also display a bar chart visually representing the number of minutes a pet drinks water each day, weekly or monthly. The platform can use a color to display the bars, corresponding to whether the pet's water consumption on a particular day is "below average," "average," or "above average" compared to a baseline. In some embodiments, the chart may not display any data if the platform is in the process of collecting pet water consumption data.

[0046] Exemplary food data platform

[0047] Figure 2A -C describes an exemplary environment for a platform for analyzing and displaying pet feeding data according to one or more embodiments.

[0048] Figure 2A An exemplary environment for a platform for displaying pet feeding data, according to one or more embodiments, is shown as a "feeding time" interface. The platform can display the number of feeding events occurring within a specific time period (e.g., "2 events occurred yesterday"). Feeding events can correspond to the number of times the pet eats food, treats, etc. In some embodiments, sensors and / or electronic devices worn by the pet can collect feeding events. The platform can also display the duration of the pet's feeding (e.g., "120 seconds"). The duration of the pet's feeding can correspond to the total length of feeding events. Furthermore, for example, the duration of the pet's feeding can be described in seconds, minutes, and / or hours.

[0049] Figure 2B An exemplary "feeding" interface of a platform for displaying pet feeding data, according to one or more embodiments, is shown. The platform can display each feeding event occurring within a specific time period (e.g., a day, a month, or a year). The platform can provide a list of feeding events occurring within a specific time period (e.g., "2 / 9"). In some embodiments, each displayed feeding event may include a length (e.g., "Length: 120 seconds") and / or a confidence level (e.g., "Confidence Level: 58%"). The length may correspond to the duration of a particular feeding event. The confidence level may correspond to the level of confidence the platform assigns to the feeding event. Furthermore, in some embodiments, each feeding event may be a specific hue, where the hue corresponds to the confidence level. For example, when the confidence level is high, the hue of the feeding event may be darker (e.g., dark green when the confidence level is 99%), while when the confidence level is low, the hue of the feeding event may be lighter (e.g., light green when the confidence level is 25%).

[0050] Figure 2C An exemplary "feeding" interface of a platform for displaying pet feeding data, according to one or more embodiments, is shown. The platform can display a timeline corresponding to a specific time period (e.g., a day, a month, and / or a year). In some embodiments, the platform can indicate a line placed when a feeding event occurs. Furthermore, for example, the line can have a specific hue corresponding to a confidence level of the feeding event, where the confidence level may correspond to the platform's confidence level in determining the feeding event.

[0051] Exemplary methods for identifying changes in a pet's eating behavior

[0052] Figure 3 An exemplary process 300 for determining changes in pet eating behavior using historical pet eating data according to one or more embodiments is shown.

[0053] The method may include receiving multiple historical pet feeding data records from a database by one or more processors, each record including a historical feeding event value and a feeding event date (step 302). The database may store each historical pet feeding data record in real time. Alternatively, the database may store each historical pet feeding data record after a predetermined time (e.g., at the end of the day). The database may receive historical pet feeding data from pet sensors and / or electronic devices worn by the pet, where the pet sensors and / or electronic devices collect feeding events in real time. The pet sensors and / or electronic devices may continuously transmit historical pet feeding data to the database in real time or after a predetermined time period. For example, the pet sensors and / or electronic devices may send the latest historical pet feeding data to the database every minute, hour, day, week, etc. Upon receiving historical pet feeding data, the database may store such historical pet feeding data in a historical pet feeding data record. Furthermore, if the database has already received and stored historical pet feeding data for a specific feeding event date in the historical pet feeding data record, the database can overwrite / update the stored historical pet feeding data record with the latest received historical pet feeding data for that feeding event date.

[0054] Each record in a series of historical pet feeding data entries can include a historical feeding event value and a feeding event date. The historical feeding event value can include the sum of the durations of all feeding events that occurred on the feeding event date. For example, a historical feeding event value could be "120 seconds," which could be the total duration of the pet's feeding, and the feeding event date could be "February 1, 2021." Historical feeding event values ​​can be expressed in seconds, minutes, and / or hours.

[0055] In addition, each historical pet feeding data record may include a historical pet identifier and / or a historical sensor wear rate. The historical pet identifier may include a unique identifier corresponding to the sensors and / or electronic devices worn by the pet. The historical sensor wear rate may correspond to the percentage of time the pet wore its sensor on the date of the feeding event. For example, a historical sensor wear rate of 0.5 might mean that the pet wore the sensor 50% of the time on the date of the feeding event.

[0056] The method may further include having one or more processors determine a subset of multiple historical pet feeding data records (step 304). This subset of historical pet feeding data records may include at least one pet feeding data record. In some embodiments, the subset of historical pet feeding data records may include all of the multiple historical pet feeding data records. In some embodiments, the subset may be stored in a database. Figure 4Steps 402-408 further discuss the process of determining a subset of multiple historical pet feeding data records.

[0057] The method may further include one or more processors determining a desired distribution based on a subset of multiple historical pet feeding data records, the desired distribution including a baseline, an upper threshold, and a lower threshold, wherein the upper and lower thresholds correspond to the baseline (step 306). The baseline may be the desired total length of feeding events, which may be based on the average of historical feeding event values ​​corresponding to the subset of multiple historical pet feeding data records. The baseline may be updated at each time period (e.g., daily) based on the latest average of the historical feeding event values. In some embodiments, the baseline may be the average of a subset of multiple historical pet feeding data records that excludes one or more existing outliers (further described in steps 402-408). Figure 5 Steps 502-508 further describe the validity of determining the baseline.

[0058] Upper and lower thresholds can correspond to a baseline, where the upper and / or lower thresholds can provide a range for the total length of mealtime events. For example, the upper and lower thresholds can be set to baseline ± 0.84 standard deviations. This would result in approximately 40% of a pet's daily activities being considered above or below average (20% for the upper threshold and 20% for the lower threshold), while 60% of a pet's daily activities would be considered average. The upper and / or lower thresholds can provide flexibility. For example, if a pet eats an extra meal, this behavior can be considered at the upper threshold ("above average"). Conversely, if a pet misses a meal, this behavior can be considered at the lower threshold ("below average").

[0059] The method may further include receiving current pet data from a pet sensor, including a total meal event value, by one or more processors (step 308). The pet sensor may be a pet sensor and / or electronic device worn by the pet. The current pet data may also include a pet identifier, a corresponding date, and / or a sensor wear rate. The pet identifier may include a unique identifier corresponding to the pet sensor. The corresponding date may include the date corresponding to the time the total meal event value occurred. The pet sensor wear rate may correspond to the percentage of time the pet wore the pet sensor on the corresponding date. The total meal event value may include the sum of the durations of all meal events that occurred on the corresponding date. For example, the total meal event value may be “50 seconds,” which could be the total duration of the pet's meal, and the corresponding date may be “December 21, 2021.” Furthermore, the total meal event value may be measured in seconds, minutes, and / or hours, for example.

[0060] The method may further include one or more processors analyzing whether the total meal event value exceeds an upper threshold or a lower threshold (step 310). The total meal event value may be compared with the upper threshold and the lower threshold. For the purposes of this disclosure, if the total meal event value is greater than the upper threshold, the total meal event value may exceed the upper threshold, and if the total meal event value is less than the lower threshold, the total meal event value may exceed the lower threshold.

[0061] The method may also include outputting a notification (step 312) by one or more processors in response to the results of the analysis. This output may result in the notification being displayed on the user interface of an electronic device (e.g., a mobile phone). The notification may also be in the form of an alert and / or a graphical image (e.g., a chart). Figure 1F -H can be an example user interface.

[0062] The notification may include at least one of the following: above-average eating, eating at average level, or below-average eating. An above-average eating notification may indicate that the pet is eating more than usual. For example, an above-average eating notification may correspond to a total meal event value reaching or exceeding an upper threshold. An above-average eating notification may be related to the pet eating extra food, eating more at mealtimes, or mealtimes being longer than usual. A eating at average level notification may indicate that the pet is eating normally. For example, a eating at average level notification may correspond to a total meal event value equal to or below an upper threshold and equal to or above a lower threshold. An below-average eating notification may indicate that the pet is eating less than usual. For example, an below-average eating notification may correspond to a total meal event value reaching or exceeding a lower threshold. An below-average eating notification may be related to the pet missing one (or two) meals, eating less at mealtimes, or eating faster than usual.

[0063] although Figure 3 An example block of exemplary method 300 is shown, but in some implementations, exemplary method 300 may include... Figure 3 The blocks depicted may be fewer, different, or arranged differently compared to additional blocks. Alternatively, two or more blocks in exemplary method 300 may be executed in parallel.

[0064] Figure 4 An exemplary method 400 for determining a subset of multiple historical pet feeding data records, according to one or more embodiments, is shown. It is worth noting that... Figure 4 Method 400 corresponds to Figure 3 Step 304.

[0065] The method may include identifying one or more historical pet feeding data records from a set of historical feeding data records, each of which indicates an outlier (step 402). For example, determining whether a historical pet feeding data record indicates an outlier may include determining whether the historical pet feeding data record reaches or exceeds an outlier threshold. This determination may include subtracting the baseline value of the historical pet feeding data record from the total number of feeding events and then dividing the result by the square root of the variance of the expected distribution. For example, if the result reaches or exceeds a predetermined outlier threshold, the method may skip / exclude that historical pet feeding data record (step 404) and continue analyzing the next historical pet feeding data record from the set of historical pet feeding data records.

[0066] The method may further include excluding one or more historical pet feeding data records, each indicating an outlier (step 404). As described in step 402, if the analysis of the historical pet feeding data records reaches or exceeds an outlier threshold, the method may further include excluding the historical pet feeding data record. In some embodiments, excluding one or more historical pet feeding data records may include skipping the one or more historical pet feeding data records and not including such records in a subset of the multiple historical pet feeding data records. Furthermore, in some embodiments, excluding one or more historical pet feeding data records may include removing the one or more historical pet feeding data records from the database.

[0067] The method may further include identifying one or more historical pet feeding data records from a plurality of historical feeding data records, each of which includes a historical sensor wearing rate less than a sensor wearing threshold (step 406). In some embodiments, a sensor wearing threshold may exist to ensure that the pet wears the pet sensor for the shortest possible time, wherein the pet sensor wearing threshold may correspond to the ratio of time the pet wears the pet sensor. For example, the value of the historical sensor wearing rate may be 0.5, which may mean that the sensor is worn for at least 50% of the time each day for several consecutive days.

[0068] In some embodiments, the historical sensor wearing rate of one or more historical pet feeding data records not excluded in step 404 can be compared with a sensor wearing threshold. One or more historical pet feeding data records with a historical sensor wearing rate that reaches or exceeds the sensor wearing threshold can be added to a subset of the multiple historical pet feeding data records. Furthermore, one or more historical pet feeding data records with a historical sensor wearing rate less than the sensor wearing threshold can be excluded from the subset of the multiple historical pet feeding data records (further described in step 408).

[0069] The method may further include excluding one or more historical pet feeding data records, each containing a historical sensor wearing rate less than a sensor wearing threshold (step 408). As previously described in step 406, if the historical sensor wearing rate of each record in one or more historical pet feeding data records is less than the sensor wearing threshold, then the one or more historical pet feeding data records may be excluded. In some embodiments, excluding one or more historical pet feeding data records may include skipping one or more historical pet feeding data records and not including such records in a subset of the multiple historical pet feeding data records. Furthermore, in some embodiments, excluding one or more historical pet feeding data records may include removing the one or more historical pet feeding data records from the database.

[0070] Although Figure 4 An example block of example method 400 is shown. In some implementations, exemplary method 400 may include... Figure 4 The blocks depicted may be fewer, different, or arranged differently compared to additional blocks. Alternatively, two or more blocks in exemplary method 400 may be executed in parallel.

[0071] Figure 5 An exemplary method 500 for determining whether a baseline is valid, according to one or more embodiments, is shown. It is worth noting that... Figure 5 Method 500 can be used in one or more aspects with Figure 3 Step 306 corresponds to or is related to this.

[0072] The method may include determining whether a baseline is valid (step 502), which can be further described in detail in steps 504 and 506 below. As discussed in steps 402-408, the baseline may be determined based on a subset of multiple historical pet feeding data records that do not include one or more outliers.

[0073] The method may include determining whether a subset of multiple historical pet feeding data records includes a predetermined number of historical pet feeding data records, each associated with a feeding event date within a predetermined time range (step 504). In some embodiments, to establish a baseline, the subset may need to include a predetermined number of historical pet feeding data records, each associated with a feeding event date within a predetermined time range, to ensure that sufficient data has been acquired to establish a baseline. The predetermined time range can be several days, weeks, months, and / or years. For example, the predetermined number of pet feeding history data records could be 7, where the predetermined time range could be 30 days. This example would result in determining whether the subset includes at least seven historical pet feeding data records with feeding event dates within the past 30 days.

[0074] The method may include determining whether each record in a predetermined number of historical pet feeding data records is also associated with a historical sensor wear rate that reaches or exceeds a baseline sensor wear threshold (step 506). In some embodiments, the historical sensor wear rate of one or more historical pet feeding data records may be compared with the baseline sensor wear threshold. If the historical sensor wear rate of each of the one or more historical pet feeding data records reaches or exceeds the baseline sensor wear threshold, the baseline can be determined to be valid.

[0075] The method may further include: displaying the baseline via a user interface in response to determining that the baseline is valid (step 508). For example, the baseline may be displayed on the user interface of a mobile device. The baseline may be displayed as a chart, graph, etc. Furthermore, it can be based on... Figure 1A-1J The exemplary environment shown is used to illustrate the baseline.

[0076] Although Figure 5 An exemplary block of an exemplary method 500 is shown. In some embodiments, the exemplary method 500 may include... Figure 5 The blocks depicted may be fewer, different, or arranged differently compared to additional blocks. Alternatively, two or more blocks in exemplary method 500 may be executed in parallel.

[0077] Exemplary environment and exemplary device

[0078] Figure 6An exemplary environment 600 is depicted that can be used with the techniques described herein. One or more user devices 605, one or more external systems 610, and one or more server systems 615 can communicate via network 601. As will be discussed in further detail below, one or more server systems 615 can communicate with one or more other components of environment 600 via network 601. One or more user devices 605 can be associated with a user.

[0079] In some embodiments, components of environment 600 are associated with a common entity, such as a veterinarian, clinic, animal specialist, research center, etc. In some embodiments, one or more components of the environment are associated with another different entity. The systems and devices of environment 600 can communicate in any arrangement.

[0080] User equipment 605 may be configured to enable a user to access and / or interact with other systems in environment 600. For example, user equipment 605 may be a computer system, such as a desktop computer, mobile device, tablet computer, etc. In some embodiments, user equipment 605 may include one or more electronic applications, such as programs, plug-ins, browser extensions, etc., installed on the memory of user equipment 605.

[0081] User equipment 605 may include a display / user interface (UI) 605A, a processor 605B, a memory 605C, and / or a network interface 605D. User equipment 605 may execute an operating system (O / S) and at least one electronic application (each stored in memory 605C) via processor 605B. The electronic application may be a desktop program, a browser program, a web client, or a mobile application (which may also be a browser program in a mobile O / S), an applicant-specific program, system control software, system monitoring software, software development tools, etc. For example, environment 600 may extend information on a web client accessible via a web browser. In some embodiments, the electronic application may be associated with one or more other components in environment 600. The application may manage memory 605C (e.g., a database) to transmit streaming data to network 601. The display / UI 605A may be a touchscreen or a display with other input systems (e.g., a mouse, keyboard, etc.) so that the user can interact with the application and / or O / S. The network interface 605D may be a TCP / IP network interface for, for example, Ethernet or wireless communication with network 601. When executing an application, processor 605B may generate data and / or receive user input from display / UI 605A and / or receive / transmit messages to server system 615, and may further perform one or more operations before providing output to network 601.

[0082] External system 610 may be one or more third-party and / or auxiliary systems that integrate with and / or communicate with server system 615, for example, when performing various feeding detection tasks. External system 610 may communicate with other devices or systems in environment 600 via one or more networks 601. For example, external system 610 may communicate with server system 615 via API (Application Programming Interface) on one or more networks 601, or with user device 605 via a web browser on one or more networks 601.

[0083] In various embodiments, network 601 may be a wide area network (“WAN”), a local area network (“LAN”), a personal area network (“PAN”), etc. In some embodiments, network 601 includes the Internet, and information and data between the systems are provided online. “Online” can mean connecting to or accessing source data or information from a location remotely coupled to other devices or networks connected to the Internet. Alternatively, “online” can refer to connecting to or accessing the network via a mobile communication network or device (wired or wireless). The Internet is a global computer network system in which a party, a computer or other device connected to the network, can obtain information from any other computer and communicate with parties, other computers or devices. The most widely used part of the Internet is the World Wide Web (often abbreviated as “WWW” or simply “Web”). A “website page” typically encompasses location, data storage, etc., for example, hosted and / or operated by a computer system for online access, and may include data configured to cause programs (e.g., web browsers) to perform operations such as sending, receiving, or processing data, generating visual displays, and / or interactive interfaces.

[0084] Server system 615 may include an electronic data system, such as a computer-readable storage device, such as a hard disk drive, flash drive, disk, etc. In some embodiments, server system 615 includes and / or interacts with an application programming interface for exchanging data with other systems (e.g., one or more other components of the environment).

[0085] Server system 615 may include database 615A and at least one server 615B. Server system 615 may be a computer, computer system (e.g., rack server), and / or cloud service computer system. Server system may store or access database 615A (e.g., hosted on a third-party server or in storage 615E). Server may include display / UI 615C, processor 615D, storage 615E, and / or network interface 615F. Display / UI 615C may be a touchscreen or a display with other input systems (e.g., mouse, keyboard, etc.) so that an operator of server 615B can control the functions of server 615B. Server system 615 may execute an operating system (O / S) and at least one servlet program instance (each stored in storage 615E) via processor 615D.

[0086] Despite Figure 6 While depicted as separate components, it should be understood that in some embodiments, a component or a portion of a component in environment 600 may be integrated with or incorporated into one or more other components. For example, a portion of display 615C may be integrated into user equipment 605, etc. In some embodiments, the operation or aspects of one or more of the components discussed above may be distributed across one or more other components. Any suitable arrangement and / or integration of various systems and devices can be used with environment 600.

[0087] In the foregoing and following methods, various actions can be described as being performed by... Figure 6 The components within the environment 600 (e.g., server system 615, user equipment 605, or components thereof) execute or implement the actions. However, it should be understood that in various embodiments, the various components of the environment 600 discussed above can execute instructions or perform actions including the actions discussed above. Actions performed by a device can be considered as being performed by a processor, actuator, etc., associated with that device. Furthermore, it should be understood that in various embodiments, various steps can be added, omitted, and / or rearranged in any suitable manner.

[0088] Generally, any process or operation discussed in this disclosure that is understood to be computer-implementable (e.g., the process shown in Figures 1-5) can be performed by a computer system (e.g., Figure 6The process, as described above, is executed by one or more processors in any system or device within the environment 600. A process or process step executed by one or more processors may also be referred to as an operation. One or more processors may be configured to execute such a process by accessing instructions (e.g., software or computer-readable code) that, when executed by one or more processors, cause the one or more processors to perform the process. These instructions may be stored in the memory of the computer system. The processor may be a central processing unit (CPU), a graphics processing unit (GPU), or any suitable type of processing unit.

[0089] A computer system (e.g., a system or device that implements the processes or operations in the examples above) may include one or more computing devices, such as... Figure 6 A computer system may contain one or more systems or devices. One or more processors of a computer system may be located in a single computing device or distributed across multiple computing devices. The memory of a computer system may include the respective memory of each of the multiple computing devices.

[0090] Figure 7 This is a simplified functional block diagram of a computer 700, which can be configured to perform the environment and / or methods of Figures 1-5 according to exemplary embodiments of the present disclosure. For example, device 700 may include a central processing unit (CPU) 720. CPU 720 can be any type of processor device, including, for example, any type of dedicated or general-purpose microprocessor device. As those skilled in the art will understand, CPU 720 can also be a single processor in a multi-core / multi-processor system that operates independently, or in a cluster of computing devices operating in a cluster or server farm. CPU 720 can be connected to data communication infrastructure 710, such as a bus, message queue, network, or multi-core messaging scheme.

[0091] Device 700 may also include main memory 740, such as random access memory (RAM), and may also include secondary memory 730. Secondary memory 730 (e.g., read-only memory (ROM)) may be, for example, a hard disk drive or a removable storage drive. Such a removable storage drive may include, for example, a floppy disk drive, a magnetic tape drive, an optical disk drive, flash memory, etc. The removable storage drive in this example reads from and / or writes to the removable storage unit in a well-known manner. The removable storage unit may include floppy disks, magnetic tapes, optical disks, etc., which are read from and written to by the removable storage drive. As those skilled in the art will understand, such a removable storage unit typically includes a computer-usable storage medium in which computer software and / or data are stored.

[0092] In an alternative implementation, secondary storage 730 may include other similar means to allow computer programs or other instructions to be loaded into device 700. Examples of such means may include program cartridge memory and cartridge interfaces (such as those in video game devices), removable memory chips (such as EPROM or PROM) and associated slots, as well as other removable storage units and interfaces that allow software and data to be transferred from removable storage units to device 700.

[0093] Device 700 may also include a communication interface (“COM”) 760. Communication interface 760 allows the transfer of software and data between device 700 and external devices. Communication interface 760 may include a modem, network interface (e.g., Ethernet card), communication port, PCMCIA slot, and card, etc. Software and data transferred via communication interface 760 may be in the form of signals, which may be electrical signals, electromagnetic signals, optical signals, or other signals that can be received by communication interface 760. These signals may be provided to communication interface 760 via a communication path of device 700, which may be implemented using, for example, wires or cables, optical fibers, telephone lines, cellular telephone links, RF links, or other communication channels.

[0094] The hardware components, operating system, and programming language of such devices are conventional in nature and are presumed to be sufficiently familiar to those skilled in the art. Device 700 may also include input / output ports 750 for connecting input / output devices such as keyboards, mice, touchscreens, monitors, displays, etc. Of course, various server functions can be implemented in a distributed manner on many similar platforms to distribute the processing load. Alternatively, a server can be implemented by appropriately programming a computer hardware platform.

[0095] The programmatic aspect of this technology can be considered a "product" or "manufactured item," typically in the form of executable code and / or related data, carried on or embodied in a machine-readable medium. "Storage" type media includes any or all tangible memory of computers, processors, etc., or related modules thereof, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage for software programming at any time. All or part of the software can sometimes communicate via the Internet or various other telecommunications networks. Such communication, for example, enables the loading of software from one computer or processor to another, such as from a management server or host computer of a mobile communication network to a server's computer platform and / or from a server to a mobile device. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, for example, on physical interfaces between local devices, through wired and optical ground networks, and on various air links. Physical elements carrying such waves (e.g., wired or wireless links, optical links, etc.) can also be considered as media carrying software. As used herein, unless limited to non-transitory tangible "storage" media, the term "readable medium" for a computer or machine refers to any medium that participates in providing instructions to a processor for execution.

[0096] References to any particular activity in this disclosure are for convenience only and are not intended to limit the scope of this disclosure. Those skilled in the art will recognize that the concepts upon which the disclosed devices and methods are based can be used for any suitable activity. This disclosure can be understood with reference to the following description and accompanying drawings, wherein like elements are indicated by like reference numerals.

[0097] Although the terminology used above is used in conjunction with the detailed description of certain specific examples of this disclosure, these terms may be interpreted in their broadest and most reasonable manner. In fact, some terms may even be emphasized above; however, any term intended to be interpreted in any limiting manner will be clearly and specifically defined in this Detailed Description section. The general description and detailed description are exemplary and illustrative only, and not intended to limit the features claimed.

[0098] In this disclosure, the term “based on” means “at least partially based on”. The singular forms “a”, “an”, and “the” include plural references unless the context otherwise specifies. The term “exemplary” means “example” rather than “ideal”. The terms “comprises,” “comprising,” “includes,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or product that comprises a list of elements does not necessarily include only those elements, but may also include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term “or” is used in a separate form, so “at least one of A or B” includes (A), (B), (A and A), (A and B), etc. Relative terms, such as “substantially” and “generally”, are used to indicate that the stated or understood value may vary by ±10%.

[0099] As used herein, terms such as “user” typically encompass one parent and / or both parents of a pet. Terms such as “pet” typically encompass a user’s pets, where the term may include multiple pets. Terms such as “provider” typically encompass pet care services.

[0100] It should be understood that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes combined in a single embodiment, drawing, or description thereof in order to simplify the disclosure and aid in understanding one or more of the various inventive aspects. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspect does not lie in all the features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim exists independently as a separate embodiment of the invention.

[0101] Furthermore, while some embodiments described herein include certain features but not others in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments, as will be understood by those skilled in the art. For example, any claimed embodiments may be used in any combination in the following claims.

[0102] Therefore, although certain embodiments have been described, those skilled in the art will recognize that other and further modifications can be made thereto without departing from the spirit of the invention, and it is intended that all such changes and modifications fall within the scope of the invention. For example, functions can be added or removed from the block diagrams, and the functions can be interchanged. Steps can be added or removed from the methods described within the scope of the invention.

[0103] The subject matter disclosed above should be considered illustrative rather than restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other implementations falling within the true spirit and scope of this disclosure. Therefore, to the fullest extent permitted by law, the scope of this disclosure should be determined by the broadest permissible interpretation of the following claims and their equivalents, and should not be limited or constrained by the foregoing detailed description. Although various embodiments of this disclosure have been described, it will be apparent to those skilled in the art that further embodiments are possible within the scope of this disclosure. Therefore, this disclosure is not limited except as provided in the appended claims and their equivalents.

Claims

1. A computer-based method for determining changes in a pet's eating behavior using historical pet eating data, the method comprising: One or more processors receive multiple historical pet feeding data records from a database. Each of the multiple historical pet feeding data records is generated based on historical pet data collected by a pet sensor worn by the pet, and includes a historical feeding event value and a corresponding historical feeding event date, as well as a historical sensor wearing rate, wherein the historical sensor wearing rate indicates the proportion of time the pet wore the pet sensor on the corresponding historical feeding event date. The one or more processors determine a subset of the plurality of historical pet feeding data records, each record in the subset including a historical sensor wearing rate that has reached or exceeded a sensor wearing threshold; The expected distribution is determined by one or more processors based on a subset of the multiple historical pet feeding data records. The expected distribution includes a baseline, an upper threshold, and a lower threshold, wherein the upper threshold and the lower threshold correspond to the baseline. The one or more processors receive current pet data from pet sensors worn by the pet, the current pet data including total feeding event values; The one or more processors analyze whether the total eating event value exceeds the upper threshold or the lower threshold; and The one or more processors output a notification indicating the result of the analysis.

2. The computer-implemented method of claim 1, wherein, The baseline was determined based on a subset of historical pet feeding data records that do not include one or more outliers.

3. The computer-implemented method of claim 2, wherein, The upper and lower thresholds are determined based on at least one standard deviation from the baseline.

4. The computer implementation method according to claim 1, wherein determining a subset of multiple historical pet feeding data records includes: The one or more processors determine one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records indicates an outlier; and The processors exclude one or more historical pet feeding data records, each indicating an outlier.

5. The computer implementation method according to claim 1, wherein determining a subset of multiple historical pet feeding data records includes: The one or more processors determine one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records includes a historical sensor wearing rate less than a sensor wearing threshold; and The processors exclude one or more historical pet feeding data records, each containing a historical sensor wearing rate below the sensor wearing threshold.

6. The computer implementation method according to claim 1, wherein determining a subset of multiple historical pet feeding data records includes: The one or more processors determine one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records indicates an outlier; The one or more processors determine one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records includes a historical sensor wearing rate less than a sensor wearing threshold; and The processor excludes one or more historical pet feeding data records, each indicating an outlier, and one or more historical pet feeding data records, each including a historical sensor wearing rate less than the sensor wearing threshold.

7. The computer-implemented method of claim 1, wherein, The current pet data includes a pet identifier, the corresponding date, and a sensor wearing rate, whereby the sensor wearing rate indicates the percentage of time the companion pet wears the pet sensor on the corresponding date.

8. The computer implementation method according to claim 7, wherein, The total eating event value includes the sum of the durations of all eating events that occurred on the corresponding date.

9. The computer implementation method according to claim 8, wherein, The total eating event value is measured in seconds, minutes, or hours.

10. The computer implementation method according to claim 1, wherein, The notification includes at least one of the following: notification of eating above average, notification of eating at average level, or notification of eating below average.

11. The computer implementation method according to claim 1, wherein, The baseline is determined to be valid when a subset of the plurality of historical pet feeding data records includes a predetermined number of historical pet feeding data records, wherein each record in the predetermined number of historical pet feeding data records is associated with a historical feeding event date within a predetermined time range.

12. The computer implementation method according to claim 11, wherein, Each record in the predetermined number of historical pet feeding data records is also associated with a historical sensor wearing rate that has reached or exceeded the baseline sensor wearing threshold.

13. A computer system for determining changes in a companion pet's eating behavior using historical companion pet eating data, the computer system comprising: At least one memory for storing instructions; and At least one processor is configured to execute the instructions to perform operations including: Receive multiple historical pet feeding data records from the database. Each of the multiple historical pet feeding data records is generated based on historical pet data collected by a pet sensor worn by the pet, and includes a historical feeding event value and the corresponding historical feeding event date, as well as a historical sensor wearing rate. The historical sensor wearing rate indicates the proportion of time the pet wore the pet sensor on the corresponding historical feeding event date. A subset of the multiple historical pet feeding data records is determined, and each record in the subset includes a historical sensor wearing rate that has reached or exceeded the sensor wearing threshold; A predicted distribution is determined based on a subset of the multiple historical pet feeding data records. The predicted distribution includes a baseline, an upper threshold, and a lower threshold, wherein the upper threshold and the lower threshold correspond to the baseline. Receive current pet data from pet sensors worn by the pet, the current pet data including total feeding event values; Analyze whether the total number of eating events exceeds the upper threshold or the lower threshold; and The output indicates a notification in response to the results of the analysis.

14. The computer system according to claim 13, wherein, The baseline was determined based on a subset of historical pet feeding data records that do not include one or more outliers.

15. The computer system according to claim 14, wherein, The upper and lower thresholds are determined based on at least one standard deviation from the baseline.

16. The computer system of claim 13, wherein determining a subset of multiple historical pet feeding data records includes: Identify one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records indicates an outlier; and Exclude one or more historical pet feeding data records where each record indicates an outlier.

17. The computer system of claim 13, wherein determining a subset of multiple historical pet feeding data records comprises: Identify one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records includes a historical sensor wearing rate that is less than the sensor wearing threshold; and Exclude one or more historical pet feeding data records where each record includes a historical sensor wearing rate below the sensor wearing threshold.

18. The computer system according to claim 13, wherein determining a subset of multiple historical pet feeding data records includes: Identify one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records indicates an outlier; Identify one or more historical pet feeding data records from the plurality of historical pet feeding data records, wherein each of the one or more historical pet feeding data records includes a historical sensor wearing rate that is less than the sensor wearing threshold; and Exclude one or more historical pet feeding data records where each record indicates an outlier, and exclude one or more historical pet feeding data records where each record includes a historical sensor wearing rate below the sensor wearing threshold.

19. A non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations of using historical companion pet feeding data to determine changes in companion pet feeding behavior, the operations comprising: Receive multiple historical pet feeding data records from the database. Each of the multiple historical pet feeding data records is generated based on historical pet data collected by a pet sensor worn by the pet, and includes a historical feeding event value and the corresponding historical feeding event date, as well as a historical sensor wearing rate. The historical sensor wearing rate indicates the proportion of time the pet wore the pet sensor on the corresponding historical feeding event date. A subset of the multiple historical pet feeding data records is determined, and each record in the subset includes a historical sensor wearing rate that has reached or exceeded the sensor wearing threshold; A predicted distribution is determined based on a subset of the multiple historical pet feeding data records. The predicted distribution includes a baseline, an upper threshold, and a lower threshold, wherein the upper threshold and the lower threshold correspond to the baseline. Receive current pet data from pet sensors worn by the pet, the current pet data including total feeding event values; Analyze whether the total number of eating events exceeds the upper threshold or the lower threshold; and The output indicates a notification in response to the results of the analysis.

20. The non-transitory computer-readable medium according to claim 19, wherein, The current pet data includes a pet identifier, the corresponding date, and a sensor wearing rate, whereby the sensor wearing rate indicates the percentage of time the companion pet wears the pet sensor on the corresponding date.

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