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 .
Applicant' s arguments, filed 4/20/2026, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Applicants have amended their claims, filed 4/20/2026, and therefore rejections newly made in the instant office action have been necessitated by amendment.
Claims 1-15 are the currently pending claims hereby under examination. Claims have 1-11 and 14-15 have been amended.
Claim Objections
Claim 8 is objected to because of the following informalities:
In claim 8, line 5, “]the” appears to be a typographical error and should read “the”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 1 is directed to a method of generating alert information based on excretion related data using data processing and comparison over predetermined time periods, which is an abstract idea. Claim 14 and claim 15 are directed to an information processing device and a non-transitory computer readable recording medium, respectively, that implement the same abstract idea using generic computer components and data acquisition/output. Claims 1-15 do not include additional elements that integrate the exception into a practical application or that are sufficient to amount to significantly more than the judicial exception for the reasons provided below which are in line with the 2014 Interim Guidance on Patent Subject Matter Eligibility (Federal Register, Vol. 79, No. 241, p 74618, December 16, 2014), the July 2015 Update on Subject Matter Eligibility (Federal Register, Vol. 80, No. 146, p. 45429, July 30, 2015), the May 2016 Subject Matter Eligibility Update (Federal Register, Vol. 81, No. 88, p. 27381, May 6, 2016), and the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 4, page 50, January 7, 2019).
The analysis of claim 1 is as follows:
Step 1: Claim 1 is drawn to a process.
Step 2A, Prong One: Claim 1 recites an abstract idea. In particular, claim 1 recites the following limitations:
[A1] generating a reference database indicating an excretion tendency of the user based on the acquired first data;
[B1] generating a plurality of types of alert information to the user based on the reference database and the second data; and
[C1] wherein the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output.
These elements [A1] through [C1] of claim 1 are drawn to an abstract idea because they at least involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations, such as determining an excretion tendency based on first data, determining or selecting a second predetermined period according to a type of alert information, and generating alert information based on comparing second data to a reference database. These elements also involve a mental process that can be practically performed in the human mind, including observation, evaluation, judgment, and opinion, with or without pen and paper, such as reviewing excretion related data over time, determining a user tendency, selecting an appropriate observation period based on the type of alert to be generated, and deciding whether to generate or output a particular alert based on a change or threshold condition.
In particular, the amended period-selection limitation falls at least within the mental-process category because it recites a judgment about which observation period should be used for a particular alert type. The limitation need not independently qualify as a mathematical concept in order to remain part of the identified abstract idea, because the claim as a whole recites data collection, data organization, comparison, period selection, and alert generation that can be performed as evaluation and judgment.
The newly added limitation that the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output falls within the same abstract idea identified above. This limitation merely specifies how the observation or data acquisition window is selected before the alert information is generated. Selecting a longer or shorter observation window according to the type of health-related alert is an evaluation or judgment about what information should be collected and analyzed for a given alert, and does not change the character of the claim from abstract data collection, data organization, comparison, and alert generation.
Step 2A, Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception:
[A2] by a computer;
[B2] acquiring first data in which excretion related data of a user acquired by a sensor installed in a toilet and a user ID for identifying the user are associated with each other in a first predetermined period;
[C2] acquiring second data in which the excretion related data of the user acquired by the sensor installed in the toilet and the user ID are associated with each other in a second predetermined period; and
[D2] outputting one of the plurality of types of the generated alert information.
These elements [A2] through [D2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, element [A2] is merely an instruction to implement the abstract idea on a computer, or merely uses a computer as a tool to perform the abstract idea. Elements [B2] and [C2] are merely adding insignificant extra solution activity to the judicial exception, namely data gathering at a high level of generality. Element [C2] is merely an instruction to present the result of the abstract idea using a generic computer output function, or merely uses a computer as a tool to perform the abstract idea.
The amended limitation regarding the second predetermined period also does not integrate the exception into a practical application. Applicant argues that determining the second predetermined period in advance according to the type of alert information improves health monitoring technology by increasing the accuracy of outputted alert information. However, under Step 2A, Prong Two, an asserted improvement to health monitoring technology must be reflected in the claim as a technical improvement to the monitoring system itself, rather than merely an improvement to the accuracy or usefulness of the information being analyzed. Claim 1 does not recite any improvement to the sensor hardware, the toilet structure, the user identification mechanism, the processing architecture, the data storage structure, or the output mechanism. The selection of a longer or shorter data acquisition window based on alert type is a decision about what information to collect and analyze, which does not alter how the computer, sensor, or any other technical component of the system functions. Although health monitoring may be considered a technical field, the claim does not reflect an improvement to health-monitoring technology as a technical field because the claim does not alter the technical operation of any component of the monitoring system. Rather, the claim improves, at most, the informational basis for generating the alert.
Furthermore, the amended claim language recites the period selection limitation at a high level of generality, namely determined in advance according to the one of the plurality of types of alert information to be output, without specifying any technical parameter, data structure, or processing step that would distinguish the claimed period selection from an abstract mental judgment or data analysis rule about appropriate observation windows for different health conditions. The claim does not recite any particular period length, any particular relationship between the period and a measured physiological frequency, any particular algorithm for selecting the period, or any particular improvement to the way sensor data is technically acquired, stored, processed, or output.
Desjardins: The specification was evaluated to determine whether the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field, and the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. Here, the specification describes collecting excretion-related data using sensors installed in a toilet, associating the data with user identification information, aggregating the data over time periods, generating a reference database, and generating advice, notification, or alert information based on trends. The specification paragraph relied upon by Applicant explains that evacuation may use a longer period because evacuation is performed less frequently and is affected by dietary intake or physical condition, while urination may use a shorter period because urination occurs more frequently and certain urinary conditions may change rapidly. This describes a medical or informational rationale for choosing different observation windows, but it does not describe a technical improvement to the computer, sensor, toilet device, memory, processor, database structure, or output mechanism. Instead, the alleged improvement is an improvement to the abstract analysis itself, namely selecting an observation period intended to improve the accuracy of health-related alert information.
Therefore, claim 1 does not integrate the judicial exception into a practical application. The claim still merely uses a computer and a toilet-installed sensor as tools to perform data acquisition, data organization, comparison, selection of a data analysis period, alert generation, and alert output.
Step 2B: Claim 1 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitations of a computer, a sensor installed in a toilet, associating data with a user ID, generating a reference database, using predetermined periods, determining the second predetermined period according to the type of alert information to be output, generating alert information, and outputting alert information do not qualify as significantly more. The recitation of acquiring excretion related data using a sensor installed in a toilet merely describes the source and type of data being collected and does not incorporate the sensor as part of any technical improvement or nonconventional arrangement. Such a limitation constitutes insignificant extra solution activity, namely data gathering, in conjunction with the abstract idea, using conventional, routine, and well known elements.
As evidenced by:
Park (Park et al., “A mountable toilet system for personalized health monitoring via the analysis of excreta,” Nature Biomedical Engineering, 2020) discloses building a smart toilet module using off the shelf components housed in a “commercially available electronic bidet,” and using conventional sensors and imaging components including commercially available “cameras (GoPro Hero 7, GoPro)” (Park, p. 4 to 5).
Sato (US 2023/0225714 A1) discloses using a generic camera as a sensor in a toilet environment both for acquiring excretion-related information and for identifying a user. In particular, Sato explains that imaging data is input from an image capture apparatus exemplified as a camera (Sato, ¶[0061]), demonstrating that the sensor for acquiring excretion-related data is a generic imaging device. Sato further discloses that the second camera may be an optical camera used to capture a face image of a user for identification purposes (Sato, ¶[0099]), and that user identification data may be obtained via a Bluetooth tag held by the user (Sato, ¶[0100]). These disclosures confirm that the claimed sensor and user identification components are implemented using ordinary, generic cameras and identification mechanisms, and are not incorporated into the claim as part of any technical improvement.
Further, element [A2] does not qualify as significantly more because this limitation simply appends well-understood, routine, and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception. The claim requires no more than a generic computer to perform generic computer functions that are well-understood, routine, and conventional activities previously known in the industry, such as receiving data, storing data, comparing data, generating information, and outputting information.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of selecting a data-acquisition period based on alert type and then comparing the acquired data against a reference database to generate alert information does not produce a technical improvement to the monitoring system. It produces, at most, more accurate information analysis, which remains within the abstract idea regardless of whether the elements are considered individually or in combination. There is no indication that the combination of elements improves the functioning of a computer, improves the operation of a toilet sensor, improves the operation of a user identification mechanism, improves an output device, or improves any other technology. There is no indication that the combination of elements permits automation of specific technical tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation of the abstract idea, with the computer and sensor used as tools to perform data collection, data analysis, and alert output.
Accordingly, claim 1 is directed to a judicial exception without significantly more and is not eligible under 35 U.S.C. § 101.
The analysis of claim 14 is as follows:
Step 1: Claim 14 is drawn to a machine.
Step 2A, Prong One: Claim 14 recites an abstract idea. In particular, claim 14 recites the following limitations:
[A1] generates a reference database indicating an excretion tendency of the user based on the acquired first data;
[B1] generates a plurality of types of alert information to the user based on the reference database and the second data; and
[C1] wherein the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output.
These elements [A1] through [C1] of claim 14 are drawn to an abstract idea because they at least involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations, such as determining an excretion tendency based on first data, determining or selecting a second predetermined period according to a type of alert information, and generating alert information based on comparing second data to a reference database. These elements also involve a mental process that can be practically performed in the human mind, including observation, evaluation, judgment, and opinion, with or without pen and paper, such as reviewing excretion related data over time, determining a user tendency, selecting an appropriate observation period based on the type of alert to be generated, and deciding whether to generate or output a particular alert based on a change or threshold condition.
In particular, the amended period-selection limitation falls at least within the mental-process category because it recites a judgment about which observation period should be used for a particular alert type. The limitation need not independently qualify as a mathematical concept in order to remain part of the identified abstract idea, because the claim as a whole recites data collection, data organization, comparison, period selection, and alert generation that can be performed as evaluation and judgment.
The newly added limitation that the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output falls within the same abstract idea identified above. This limitation merely specifies how the observation or data acquisition window is selected before the alert information is generated. Selecting a longer or shorter observation window according to the type of health-related alert is an evaluation or judgment about what information should be collected and analyzed for a given alert, and does not change the character of the claim from abstract data collection, data organization, comparison, and alert generation.
Step 2A, Prong Two: Claim 14 recites the following limitations that are beyond the judicial exception:
[A2] an information processing device comprising a processor;
[B2] acquires first data in which excretion related data of a user acquired by a sensor installed in a toilet and a user ID for identifying the user are associated with each other in a first predetermined period;
[C2] acquires second data in which the excretion related data of the user acquired by the sensor installed in the toilet and the user ID are associated with each other in a second predetermined period; and
[D2] outputs one of the plurality of types of the generated alert information.
These elements [A2] through [D2] of claim 14 do not integrate the exception into a practical application of the exception. In particular, element [A2] is merely an instruction to implement the abstract idea on a generic information processing device, or merely uses a processor as a tool to perform the abstract idea. Elements [B2] and [C2] are merely adding insignificant extra solution activity to the judicial exception, namely data gathering at a high level of generality. Element [C2] is merely an instruction to present the result of the abstract idea using a generic computer output function, or merely uses an information processing device as a tool to perform the abstract idea.
The amended limitation regarding the second predetermined period also does not integrate the exception into a practical application. Applicant argues that determining the second predetermined period in advance according to the type of alert information improves health monitoring technology by increasing the accuracy of outputted alert information. However, under Step 2A, Prong Two, an asserted improvement to health monitoring technology must be reflected in the claim as a technical improvement to the monitoring system itself, rather than merely an improvement to the accuracy or usefulness of the information being analyzed. Claim 14 does not recite any improvement to the sensor hardware, the toilet structure, the user identification mechanism, the processing architecture, the data storage structure, or the output mechanism. The selection of a longer or shorter data acquisition window based on alert type is a decision about what information to collect and analyze, which does not alter how the information processing device, processor, sensor, or any other technical component of the system functions. Although health monitoring may be considered a technical field, the claim does not reflect an improvement to health-monitoring technology as a technical field because the claim does not alter the technical operation of any component of the monitoring system. Rather, the claim improves, at most, the informational basis for generating the alert.
Furthermore, the amended claim language recites the period selection limitation at a high level of generality, namely determined in advance according to the one of the plurality of types of alert information to be output, without specifying any technical parameter, data structure, or processing step that would distinguish the claimed period selection from an abstract mental judgment or data analysis rule about appropriate observation windows for different health conditions. The claim does not recite any particular period length, any particular relationship between the period and a measured physiological frequency, any particular algorithm for selecting the period, or any particular improvement to the way sensor data is technically acquired, stored, processed, or output.
Desjardins: The specification was evaluated to determine whether the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field, and the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. Here, the specification describes collecting excretion-related data using sensors installed in a toilet, associating the data with user identification information, aggregating the data over time periods, generating a reference database, and generating advice, notification, or alert information based on trends. The specification paragraph relied upon by Applicant explains that evacuation may use a longer period because evacuation is performed less frequently and is affected by dietary intake or physical condition, while urination may use a shorter period because urination occurs more frequently and certain urinary conditions may change rapidly. This describes a medical or informational rationale for choosing different observation windows, but it does not describe a technical improvement to the computer, sensor, toilet device, memory, processor, database structure, or output mechanism. Instead, the alleged improvement is an improvement to the abstract analysis itself, namely selecting an observation period intended to improve the accuracy of health-related alert information.
Therefore, claim 14 does not integrate the judicial exception into a practical application. The claim still merely uses an information processing device, processor, and toilet-installed sensor as tools to perform data acquisition, data organization, comparison, selection of a data analysis period, alert generation, and alert output.
Step 2B: Claim 14 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitations of an information processing device, a processor, a sensor installed in a toilet, associating data with a user ID, generating a reference database, using predetermined periods, determining the second predetermined period according to the type of alert information to be output, generating alert information, and outputting alert information do not qualify as significantly more. The recitation of acquiring excretion related data using a sensor installed in a toilet merely describes the source and type of data being collected and does not incorporate the sensor as part of any technical improvement or nonconventional arrangement. Such a limitation constitutes insignificant extra solution activity, namely data gathering, in conjunction with the abstract idea, using conventional, routine, and well known elements.
As evidenced by:
Park (Park et al., “A mountable toilet system for personalized health monitoring via the analysis of excreta,” Nature Biomedical Engineering, 2020) discloses building a smart toilet module using off the shelf components housed in a “commercially available electronic bidet,” and using conventional sensors and imaging components including commercially available “cameras (GoPro Hero 7, GoPro)” (Park, p. 4 to 5).
Sato (US 2023/0225714 A1) discloses using a generic camera as a sensor in a toilet environment both for acquiring excretion-related information and for identifying a user. In particular, Sato explains that imaging data is input from an image capture apparatus exemplified as a camera (Sato, ¶[0061]), demonstrating that the sensor for acquiring excretion-related data is a generic imaging device. Sato further discloses that the second camera may be an optical camera used to capture a face image of a user for identification purposes (Sato, ¶[0099]), and that user identification data may be obtained via a Bluetooth tag held by the user (Sato, ¶[0100]). These disclosures confirm that the claimed sensor and user identification components are implemented using ordinary, generic cameras and identification mechanisms, and are not incorporated into the claim as part of any technical improvement.
Further, element [A2] does not qualify as significantly more because this limitation simply appends well-understood, routine, and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception. The claim requires no more than a generic information processing device and processor to perform generic computer functions that are well-understood, routine, and conventional activities previously known in the industry, such as receiving data, storing data, comparing data, generating information, and outputting information.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of selecting a data-acquisition period based on alert type and then comparing the acquired data against a reference database to generate alert information does not produce a technical improvement to the monitoring system. It produces, at most, more accurate information analysis, which remains within the abstract idea regardless of whether the elements are considered individually or in combination. There is no indication that the combination of elements improves the functioning of a computer, improves the operation of a toilet sensor, improves the operation of a user identification mechanism, improves an output device, or improves any other technology. There is no indication that the combination of elements permits automation of specific technical tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation of the abstract idea, with the information processing device, processor, and sensor used as tools to perform data collection, data analysis, and alert output.
Accordingly, claim 14 is directed to a judicial exception without significantly more and is not eligible under 35 U.S.C. § 101.
The analysis of claim 15 is as follows:
Step 1: Claim 15 is drawn to a manufacture.
Step 2A, Prong One: Claim 15 recites an abstract idea. In particular, claim 15 recites the following limitations:
[A1] generate a reference database indicating an excretion tendency of the user based on the acquired first data;
[B1] generate a plurality of types of alert information to the user based on the reference database and the second data; and
[C1] wherein the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output.
These elements [A1] through [C1] of claim 15 are drawn to an abstract idea because they at least involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations, such as determining an excretion tendency based on first data, determining or selecting a second predetermined period according to a type of alert information, and generating alert information based on comparing second data to a reference database. These elements also involve a mental process that can be practically performed in the human mind, including observation, evaluation, judgment, and opinion, with or without pen and paper, such as reviewing excretion related data over time, determining a user tendency, selecting an appropriate observation period based on the type of alert to be generated, and deciding whether to generate or output a particular alert based on a change or threshold condition.
In particular, the amended period-selection limitation falls at least within the mental-process category because it recites a judgment about which observation period should be used for a particular alert type. The limitation need not independently qualify as a mathematical concept in order to remain part of the identified abstract idea, because the claim as a whole recites data collection, data organization, comparison, period selection, and alert generation that can be performed as evaluation and judgment.
The newly added limitation that the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output falls within the same abstract idea identified above. This limitation merely specifies how the observation or data acquisition window is selected before the alert information is generated. Selecting a longer or shorter observation window according to the type of health-related alert is an evaluation or judgment about what information should be collected and analyzed for a given alert, and does not change the character of the claim from abstract data collection, data organization, comparison, and alert generation.
Step 2A, Prong Two: Claim 15 recites the following limitations that are beyond the judicial exception:
[A2] a non-transitory computer readable recording medium storing an information processing program that causes a computer to function;
[B2] acquiring excretion related data of a user by a sensor installed in a toilet and associating the excretion related data with a user ID; and
[C2] outputting one of the plurality of types of the generated alert information.
These elements [A2] through [C2] of claim 15 do not integrate the exception into a practical application of the exception. In particular, element [A2] is merely an instruction to implement the abstract idea using a generic computer program stored on a generic non-transitory computer readable recording medium, or merely uses a computer as a tool to perform the abstract idea. Element [B2] is merely adding insignificant extra solution activity to the judicial exception, namely data gathering at a high level of generality. Element [C2] is merely an instruction to present the result of the abstract idea using a generic computer output function, or merely uses a computer program as a tool to perform the abstract idea.
The amended limitation regarding the second predetermined period also does not integrate the exception into a practical application. Applicant argues that determining the second predetermined period in advance according to the type of alert information improves health monitoring technology by increasing the accuracy of outputted alert information. However, under Step 2A, Prong Two, an asserted improvement to health monitoring technology must be reflected in the claim as a technical improvement to the monitoring system itself, rather than merely an improvement to the accuracy or usefulness of the information being analyzed. Claim 15 does not recite any improvement to the sensor hardware, the toilet structure, the user identification mechanism, the processing architecture, the data storage structure, the recording medium, or the output mechanism. The selection of a longer or shorter data acquisition window based on alert type is a decision about what information to collect and analyze, which does not alter how the computer, sensor, recording medium, or any other technical component of the system functions. Although health monitoring may be considered a technical field, the claim does not reflect an improvement to health-monitoring technology as a technical field because the claim does not alter the technical operation of any component of the monitoring system. Rather, the claim improves, at most, the informational basis for generating the alert.
Furthermore, the amended claim language recites the period selection limitation at a high level of generality, namely determined in advance according to the one of the plurality of types of alert information to be output, without specifying any technical parameter, data structure, or processing step that would distinguish the claimed period selection from an abstract mental judgment or data analysis rule about appropriate observation windows for different health conditions. The claim does not recite any particular period length, any particular relationship between the period and a measured physiological frequency, any particular algorithm for selecting the period, or any particular improvement to the way sensor data is technically acquired, stored, processed, or output.
Desjardins: The specification was evaluated to determine whether the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement in the functioning of a computer, or an improvement to other technology or a technical field, and the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. Here, the specification describes collecting excretion-related data using sensors installed in a toilet, associating the data with user identification information, aggregating the data over time periods, generating a reference database, and generating advice, notification, or alert information based on trends. The specification paragraph relied upon by Applicant explains that evacuation may use a longer period because evacuation is performed less frequently and is affected by dietary intake or physical condition, while urination may use a shorter period because urination occurs more frequently and certain urinary conditions may change rapidly. This describes a medical or informational rationale for choosing different observation windows, but it does not describe a technical improvement to the computer, sensor, toilet device, memory, processor, database structure, recording medium, or output mechanism. Instead, the alleged improvement is an improvement to the abstract analysis itself, namely selecting an observation period intended to improve the accuracy of health-related alert information.
Therefore, claim 15 does not integrate the judicial exception into a practical application. The claim still merely uses a computer program stored on a non-transitory computer readable recording medium, a computer, and a toilet-installed sensor as tools to perform data acquisition, data organization, comparison, selection of a data analysis period, alert generation, and alert output.
Step 2B: Claim 15 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitations of a non-transitory computer readable recording medium, an information processing program, a computer, a sensor installed in a toilet, associating data with a user ID, generating a reference database, using predetermined periods, determining the second predetermined period according to the type of alert information to be output, generating alert information, and outputting alert information do not qualify as significantly more. The recitation of acquiring excretion related data using a sensor installed in a toilet merely describes the source and type of data being collected and does not incorporate the sensor as part of any technical improvement or nonconventional arrangement. Such a limitation constitutes insignificant extra solution activity, namely data gathering, in conjunction with the abstract idea, using conventional, routine, and well known elements.
As evidenced by:
Park (Park et al., “A mountable toilet system for personalized health monitoring via the analysis of excreta,” Nature Biomedical Engineering, 2020) discloses building a smart toilet module using off the shelf components housed in a “commercially available electronic bidet,” and using conventional sensors and imaging components including commercially available “cameras (GoPro Hero 7, GoPro)” (Park, p. 4 to 5).
Sato (US 2023/0225714 A1) discloses using a generic camera as a sensor in a toilet environment both for acquiring excretion-related information and for identifying a user. In particular, Sato explains that imaging data is input from an image capture apparatus exemplified as a camera (Sato, ¶[0061]), demonstrating that the sensor for acquiring excretion-related data is a generic imaging device. Sato further discloses that the second camera may be an optical camera used to capture a face image of a user for identification purposes (Sato, ¶[0099]), and that user identification data may be obtained via a Bluetooth tag held by the user (Sato, ¶[0100]). These disclosures confirm that the claimed sensor and user identification components are implemented using ordinary, generic cameras and identification mechanisms, and are not incorporated into the claim as part of any technical improvement.
Further, element [A2] does not qualify as significantly more because this limitation simply appends well-understood, routine, and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception. The claim requires no more than a generic non-transitory computer readable recording medium storing a generic information processing program that causes a computer to perform generic computer functions that are well-understood, routine, and conventional activities previously known in the industry, such as receiving data, storing data, comparing data, generating information, and outputting information.
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of selecting a data-acquisition period based on alert type and then comparing the acquired data against a reference database to generate alert information does not produce a technical improvement to the monitoring system. It produces, at most, more accurate information analysis, which remains within the abstract idea regardless of whether the elements are considered individually or in combination. There is no indication that the combination of elements improves the functioning of a computer, improves the operation of a toilet sensor, improves the operation of a user identification mechanism, improves a recording medium, improves an output device, or improves any other technology. There is no indication that the combination of elements permits automation of specific technical tasks that previously could not be automated. There is no indication that the combination of elements includes a particular solution to a computer-based problem or a particular way to achieve a desired computer-based outcome. Rather, the collective functions of the claimed invention merely provide conventional computer implementation of the abstract idea, with the computer program, recording medium, computer, and sensor used as tools to perform data collection, data analysis, and alert output.
Accordingly, claim 15 is directed to a judicial exception without significantly more and is not eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 8, 10-12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (US 20230225714 A1), hereto referred as Sato, and further in view of Amin (US 20190062813 A1), hereto referred as Amin, and further in view of Ramesh et al. (US 20190046039 A1), hereto referred as Ramesh.
Regarding claim 1, Sato teaches an information processing method performed by a computer, comprising:(Sato, ¶[0072]: “The control unit can be implemented by, for example, a central processing unit (CPU), a working memory, a nonvolatile storage apparatus that stores a program, and the like”; ¶[0072]: “The program may be a program for causing the CPU to perform the processing of each of the units 1 a to 1 d”; disclosing computer-implemented processing using a CPU, memory, and stored program); acquiring first data in which excretion related data of a user acquired by a sensor installed in a toilet and a user ID for identifying the user are associated with each other in a first predetermined period (Sato, ¶[0061]: “The input unit 1 a inputs imaging data (image data) captured by an image capture apparatus (hereinafter exemplified as a camera) installed in such a way as to include, in a capturing range, an excretion range of excrement in a toilet bowl of a toilet”; ¶[0064]: “The excretion information is information indicating a content of excretion, and in a simpler example, can be information indicating whether excrement is feces (stool) or pee (urine)”; ¶[0100]: “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user from a Bluetooth tag held by the user”; ¶[0101]: “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information”; ¶[0170]: “The excrement analysis apparatus 10 collects excretion information (detailed information described above), and transmits the information (transmission information) together with user information and installation place information to the server apparatus 70 side”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; disclosing excretion related data acquired by a camera installed to capture excrement in a toilet bowl, user identification data associated with the excretion related information, and aggregation of the excretion related information by month, where a first month or other first aggregate period corresponds to the first predetermined period); generating a reference database indicating an excretion tendency of the user based on the acquired first data (Sato, ¶[0169]: “The excretion information DB 71 may include information (for example, an intensive information table 71 a in FIG. 16) acquired by summarizing, in the information processing unit 70 c, transmission information (for example, transmission information in FIG. 15) transmitted from the plurality of excrement analysis apparatuses 10”; ¶[0174]: “aggregate information tables 71 b to 71 d illustrated in FIGS. 17 to 19 may be generated in advance in the excretion information DB 71”; ¶[0175]: “The aggregate information table 71 b is a table indicating an aggregate result of a defecation shape”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; ¶[0178]: “the information processing unit 70 c preferably analyzes a tendency of a time change in a shape of defecation”; disclosing an excretion information database including aggregate information tables that indicate and are used to analyze an excretion tendency based on acquired excretion information); acquiring second data in which the excretion related data of the user acquired by the sensor installed in the toilet and the user ID are associated with each other in a second predetermined period; (Sato, ¶[0100]: “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user from a Bluetooth tag held by the user”; ¶[0101]: “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; disclosing the same type of excretion related data and user identification association discussed above, but during a subsequent month or other subsequent aggregate period corresponding to the second predetermined period); and generating a plurality of types of alert information to the user and outputting one of the plurality of types of the generated alert information (Sato, ¶[0146]: “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation and encourage the user and the carer to handle the situation”; ¶[0146]: “a tendency of a urination disorder can be recognized from a urination interval exceeding a fixed threshold, and early consultation at a hospital can be achieved by notifying a user and a carer of an alarm from the present system”; ¶[0146]: “the present system may be configured in such a way as to also similarly notify an alarm about a defecation interval”; ¶[0146]: “by checking information about a date and time of an excretion behavior and an excretion content and determining whether a set threshold is exceeded, the present system can recognize a urination disorder and a constipation tendency and notify a carer and a user of an alert”; disclosing multiple types of alert information, including at least a constipation alert, a urination disorder alarm, and a defecation interval alarm, and outputting such alert information to the user).
However, Sato does not expressly teach generating a plurality of types of alert information to the user based on the reference database and the second data. Rather, Sato teaches generating and outputting plural alert types based on excretion date and time information and excretion content, including that “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation” and that “a tendency of a urination disorder can be recognized from a urination interval exceeding a fixed threshold” (Sato, ¶[0146]). Sato also teaches an excretion information database and aggregate information tables, including that “aggregate information tables 71 b to 71 d illustrated in FIGS. 17 to 19 may be generated in advance in the excretion information DB 71” and that “[a] tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b” (Sato, ¶[0174]-¶[0175]). However, Sato does not expressly teach generating the plurality of alert types based on both the reference database derived from the first data and the second data acquired in the later second predetermined period. Sato’s aggregate tables are used for viewing or analyzing tendencies, whereas Sato does not expressly teach using those aggregate tables as a reference database for generating the alert information by comparison against subsequently acquired second data.
Amin teaches that prior waste analyses may be used as a baseline or reference for a current diagnosis or recommendation because “Prior waste analyses performed by the smart toilet system on the particular user can, in one or more embodiments, be leveraged to improve the efficacy of current diagnoses and/or recommendations” (Amin, ¶[0048]). Amin further teaches comparing current waste analysis results with past waste analysis results by “comparing the user’s current microflora profile with the user’s past microflora profiles to determine how the user’s microbiome has responded to past recommendations” (Amin, ¶[0043]). Amin also teaches comparison to reference values and a database because “the diagnostic component 416 can, in some cases, compare this medically significant information with known reference values” and “the diagnostic component 416 can access a local and/or remote database” (Amin, ¶[0115]). Amin further teaches outputting the resulting diagnoses or recommendations as alerts because “the smart toilet system can notify the user of the diagnoses and any recommended courses of action... by means of a visual message and/or alert... and/or... an audible message and/or alert... and/or... vibratory messages and/or alerts” (Amin, ¶[0051]).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Sato in view of Amin to generate the plurality of types of alert information to the user based on a reference database derived from previously acquired excretion related data and second data acquired in a later predetermined period, and to output one of the plurality of types of generated alert information. Sato already stores user-associated excretion information in an excretion information database, analyzes excretion tendencies over time, and outputs excretion-related alerts, including constipation alerts, urination disorder alarms, and defecation interval alarms. Amin teaches the known health-monitoring technique of leveraging a user’s prior waste analyses, past waste profiles, reference values, and databases to improve current diagnoses and recommendations that are communicated to the user by alerts. A person of ordinary skill in the art would have had reason to incorporate Amin’s prior-analysis and reference-value comparison technique into Sato’s excretion monitoring system because Sato is concerned with accurately recognizing health damage from excretion records, while Amin expressly teaches that prior waste analyses can improve the efficacy of current diagnoses and recommendations. The modification would have been a predictable use of Amin’s historical-comparison technique in Sato’s database-driven excretion alert system, and would have been feasible because Sato already collects, stores, aggregates, and analyzes user-associated excretion information over time. The benefit would have been more reliable user-specific alerts by accounting for the user’s prior excretion tendency and later-acquired excretion data, rather than relying only on isolated threshold determinations. Because Sato already discloses multiple distinct alert categories, including constipation alerts, urination disorder alarms, and defecation interval alarms (Sato, ¶[0146]), Amin’s prior-analysis and reference-value comparison technique would have been applicable to each of those Sato alert categories. The resulting modified Sato system would generate the constipation, urination disorder, and defecation interval alerts by accounting for later-acquired excretion information in view of the user’s prior excretion tendency stored in the excretion information database, thereby generating the plurality of alert types based on the reference database and the second data.
Also regarding claim 1, the modified Sato does not expressly teach wherein the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output. Rather, the modified Sato teaches multiple excretion-related alert types, including a constipation alert, a urination disorder alarm, and a defecation interval alarm (Sato, ¶[0146]), and the modified Sato further teaches generating those alert types using a reference database and later-acquired excretion related data in view of Amin as set forth above. However, the modified Sato does not expressly teach determining the second predetermined period in advance according to the particular one of the plurality of alert types to be output.
Under the broadest reasonable interpretation, the limitation determined in advance requires that the second predetermined period be preconfigured based on the alert type before, or at the initiation of, acquisition of the second data for that alert type, rather than being selected only after the data collection is complete. Ramesh teaches this limitation by teaching a health-monitoring framework in which different monitored conditions and sensor types use different predetermined sensing frequencies, time intervals, and monitoring windows. Specifically, Ramesh teaches that “each sensor has different sensing frequencies,” including “measuring BP and blood glucose only twice or thrice in a day,” while “the patient is on continuous ECG monitoring and the sensor transmits the data continuously” (Ramesh, ¶[0082]). Thus, Ramesh teaches that different health conditions and corresponding sensor data types may be monitored using different acquisition frequencies or acquisition periods selected in advance according to what condition is being evaluated. Ramesh further teaches condition-specific monitoring intervals and alert criteria, including that “the BP, heart rate (HR) or SpO2 severity levels are set for identification of long-term trends in obstructive sleep apnea” (Ramesh, ¶[0091]), and that, for blood glucose, “continuous blood glucose monitoring in high-risk patients is adopted” and “the severity levels is set based on blood glucose measurements over a long time interval depending upon the risk of the patients to prevent fatal events” (Ramesh, ¶[0093]). These passages provide the primary support for the predetermined nature of the monitoring period. Ramesh additionally teaches that monitoring intervals are alert-dependent variables by stating that “AMIs also serves as a feedback mechanism to modulate sensing frequency and alert computation instants,” and that “[a] low AMI is used to effect three adjustments: (1) Reduce the frequency F of future sensor measurements to a medically allowed minimum bound, (2) Increase the gap Γ between successive monitoring intervals, and (3) Increase the subsequent inter-alert window φ” (Ramesh, ¶[0106]). Accordingly, ¶[0106] is relied upon as corroborating evidence that Ramesh treats monitoring intervals as alert-related variables, while the preconfigured condition-specific acquisition periods are principally taught by ¶[0082], ¶[0091], and ¶[0093].
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Sato system in view of Ramesh such that the second predetermined period for acquiring the second data is determined in advance according to the one of the plurality of types of alert information to be output. The modified Sato system already generates different excretion-related alert types, including constipation alerts, urination disorder alarms, and defecation interval alarms, based on user-associated excretion information over time. A person of ordinary skill in the art implementing those sensor-based health alerts would have had reason to look to known health-monitoring techniques for selecting a monitoring period according to the condition being evaluated, such as Ramesh’s teaching that BP and blood glucose may be measured only twice or thrice per day while ECG may be continuously monitored, and that long-term trends are used for obstructive sleep apnea while other conditions use continuous or higher-frequency monitoring. Ramesh is reasonably pertinent to the problem addressed by the amended limitation because it addresses selection of data collection and assessment windows for health condition monitoring systems that generate condition-specific alerts. Because the modified Sato system’s alert types correspond to different excretion-related conditions, applying Ramesh’s condition-specific monitoring-period technique to the modified Sato system would have predictably resulted in selecting the second predetermined period according to the particular excretion-related alert type being evaluated. This application of Ramesh is not based on bodily sensor structure, but on the shared health-monitoring design principle that the data acquisition period should be selected according to the temporal characteristics of the monitored condition. Consistent with Ramesh’s teaching that lower-frequency or long-term biological conditions may use longer monitoring windows while continuously or more frequently monitored conditions may use shorter or more frequent windows, the modified Sato system would have used an observation period suited to the temporal characteristics of the particular excretion-related alert being generated. The modification would have been feasible because the modified Sato system already acquires user-associated excretion data over time, stores and aggregates that data, distinguishes among multiple excretion-related alert categories, and outputs alerts to a user-accessible terminal apparatus. The benefit would have been more reliable alert generation by avoiding an assessment window that is too short to meaningfully evaluate a less frequent excretion condition or unnecessarily long for a more rapidly indicated condition.
Regarding claim 4, the modified Sato teaches that the excretion related data includes evacuation amount data indicating an evacuation amount (Sato, ¶[0123]: “calculating a feces amount and a urine amount from acquired imaging data”, Sato teaches determining a “feces amount” from imaging data, which is evacuation amount data indicating an evacuation amount); the reference database includes a first evacuation amount indicating an average evacuation amount per time in the first predetermined period (Sato, ¶[0122]: “...a defecation count or the amount of defecation per unit time...”; the modified Sato teaches an “amount of defecation per unit time”, which corresponds to an evacuation-amount-per-time metric usable as an “average evacuation amount per time” for a period when computed/aggregated over that period; ¶[0175]: “the classified information may be aggregated in the aggregate information table 71 b”, “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”, the modified Sato teaches aggregating excretion-related information by predetermined periods so that tendencies across time periods can be evaluated, which supports storing a period-based evacuation amount metric in a reference database); in generation of the plurality of types of alert information, a second evacuation amount indicating an average evacuation amount per time in the second predetermined period is calculated (Sato, ¶[0122]: “...information indicating... a decrease situation of... the amount of defecation per unit time... is preferably output...”; outputting a “decrease situation” for “amount of defecation per unit time” requires evaluating later values of that metric against an earlier baseline/expected level, which corresponds to calculating/deriving the metric for a later period and assessing change; ¶[0146]: “...a tendency (such as an average interval)... can be analyzed" the modified Sato expressly contemplates analyzing a “tendency” using an “average” derived from excretion-behavior data over time, supporting period-based aggregation/comparison concepts; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”, the modified Sato teaches evaluating tendencies across different time periods, which requires deriving period-based evacuation metrics for later periods for comparison); in a case where the second evacuation amount is smaller than the first evacuation amount, third alert information ... is generated, and outputting the generated alert information (Sato, ¶[0123]: “...information indicating whether a feces amount and a urine amount subjected to threshold processing exceed a predetermined threshold...”; “It is desirable that the detailed information... is transmitted (notified)... With such a notification (may include an alert)...”; the modified Sato teaches threshold/comparison-based evaluation of feces amount and notifying/transmitting the result, which may include an alert, i.e., generating and outputting alert/notification information based on evaluated evacuation amount information).
Also regarding claim 4, the modified Sato does not fully teach that the third alert information of the plurality of types of alert information indicating that a meal intake amount of the user is likely insufficient is generated. Because Claim 4 depends from Claim 1, the first predetermined period stored in the reference database already serves as the comparison baseline for evaluating the second predetermined period. The modified Sato teaches determining evacuation amount data by calculating a feces amount from acquired imaging data (Sato, ¶[0123]) and aggregating classified excretion-related information by predetermined periods so that tendencies can be viewed across months (Sato, ¶[0175]). The modified Sato further evidences that statistical processing over a predetermined period and alert generation based on processed results are within the contemplation of the invention, teaching that information indicating whether feces-related data subjected to threshold processing exceeds a predetermined threshold is produced and transmitted, where the notification may include an alert (Sato, ¶[0123]). The modified Sato also expressly contemplates ratio-based statistical processing of biological information over a day or multiple days, describing performing “statistical processing on an Na/K ratio” and storing correlations based on a “statistical concentration ratio” (Sato, ¶[0010]).However, the modified Sato does not expressly teach generating third alert information indicating that a meal intake amount of the user is likely insufficient when a second average evacuation amount per time in a second predetermined period is smaller than a first average evacuation amount per time in a first predetermined period.
Amin teaches that waste analysis includes evaluation of waste “quantity” and that such quantity “indicates how well a user digests their food” (Amin, ¶[0097]), and further teaches generating user-facing dietary recommendations including determining whether a user should be “eating more… and/or less” and “how much… to eat” based on waste-derived results (Amin, ¶[0042]). While Amin does not expressly attribute a decrease in evacuation amount to insufficient meal intake, it evidences that waste quantity metrics are suitable inputs for generating eating-amount guidance. (see also: Amin, ¶[0043]-¶[0044]).
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Sato in view of Amin to generate alert information indicating that a meal intake amount of the user is likely insufficient when a second average evacuation amount per time in a second predetermined period is smaller than a first average evacuation amount per time in a first predetermined period. The modification would have been feasible because the modified Sato already teaches calculating feces amount, deriving an amount-per-unit-time metric, performing predetermined-period averaging, evaluating decreases relative to a prior reference or threshold, and generating notifications that may include alerts based on such comparison processing (Sato, ¶[0122]-¶[0123]; ¶[0009]). Amin further teaches that stool quantity is a food/digestion-relevant result and that waste-derived quantity metrics may be used to generate user-facing guidance about eating more or less and how much to eat, including through comparison of current results with past results and statistical processing across analyses performed at different times (Amin, ¶[0097]; ¶[0042]-¶[0044]). The benefit of the combination would be providing a more interpretable and actionable alerting output by converting evacuation amount trend information into a dietary-intake guidance message, thereby enabling earlier and more targeted user or caregiver intervention based on statistically aggregated waste analysis results.
Regarding claim 8, the modified Sato does not teach that the excretion related data includes urine specific gravity data indicating whether or not specific gravity of urine of the user is higher than a predetermined range; the reference database includes a first specific gravity ratio indicating a ratio in which specific gravity of the urine is higher than the predetermined range among all urinations in the first predetermined period; in generation of the plurality of types of alert information, a second specific gravity ratio indicating a ratio in which specific gravity of the urine is higher than the predetermined range among all urinations in the second predetermined period is calculated; and in a case where the second specific gravity ratio is higher than the first specific gravity ratio, seventh alert information of the plurality of types of alert information indicating that the user is likely to be dehydrated is generated. The modified Sato teaches an information processing framework in which excretion related data acquired by a sensor installed in a toilet is accumulated for a first predetermined period to generate a reference database, excretion related data is acquired for a second predetermined period, and alert information is generated based on a comparison between the first-period data and the second-period data (as shown above in claim 1). However, the modified Sato does not expressly teach urine specific gravity data, determining whether urine specific gravity exceeds a predetermined range, calculating ratios of high specific gravity urinations across predetermined periods, or generating alert information indicating dehydration based on such ratios.
Amin teaches that urinalysis includes measuring urine specific gravity and that high urine specific gravity is indicative of dehydration: “specific gravity (e.g., where low specific gravity can indicate diabetes insipidus, excessive hydration, chronic renal failure, and so on, and high specific gravity can indicate diabetes mellitus, excessive dehydration, kidney inflammation, and so on)” (Amin, ¶[0100]). Amin therefore teaches that urine specific gravity is a recognized physiological indicator and that elevated urine specific gravity corresponds to dehydration, which directly fills the gap left by the modified Sato regarding the interpretation of urine specific gravity data.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the modified Sato in view of Amin to include acquiring urine specific gravity data, determining whether urine specific gravity exceeds a predetermined range, calculating ratios of high specific gravity urinations across predetermined periods, and generating alert information indicating that the user is highly likely to be dehydrated when the second specific gravity ratio is higher than the first specific gravity ratio. The modification would have been feasible because the modified Sato already provides a system architecture for acquiring excretion related data over time, calculating reference values, and generating alerts based on deviations, and Amin teaches well known urinalysis techniques and clinical interpretations of urine specific gravity as an indicator of dehydration. The benefit of the combination would be improved health monitoring by enabling early detection of dehydration using objective urine analysis metrics integrated into an automated toilet based monitoring system.
Regarding claim 10, the modified Sato, in view of claim 1, teaches wherein the excretion related data includes evacuation related data related to evacuation of the user acquired during a predetermined evacuation period and urination related data related to urination of the user acquired during a predetermined urination period (Sato, ¶[0170]: "the excretion information may include an excretion date and time (occurrence date and time), a kind of excrement (information indicating any of urination, defecation, and a foreign body), the amount of urination (for example, information indicating any of great, normal, and small), and a shape of defecation (for example, information indicating any of hard, normal, and diarrhea)"; disclosing excretion related data including both urination related data and defecation related data, including occurrence date and time and kind of excrement; ¶[0145]: “The output may be a tendency (such as an average interval) of urination and defecation”; disclosing that urination and defecation information is evaluated over time to determine respective tendencies; ¶[0146]: “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation”; ¶[0146]: “a tendency of a urination disorder can be recognized from a urination interval exceeding a fixed threshold”; disclosing different temporal frameworks for evacuation-related and urination-related information, including a predetermined number of days for defecation-related constipation evaluation and a urination interval threshold for urination-disorder evaluation).
Also regarding claim 10, the modified Sato does not fully teach wherein the predetermined evacuation period is longer than the predetermined urination period. Rather, as established in the claim 1 rejection above, the modified Sato generates excretion-related alerts based on both urination and defecation data. However, the modified Sato does not expressly teach configuring the predetermined evacuation period to be longer than the predetermined urination period.
Ramesh teaches a health-monitoring framework in which different monitored conditions and sensor types use different predetermined sensing frequencies, time intervals, and monitoring windows. Specifically, Ramesh teaches that “each sensor has different sensing frequencies,” including “measuring BP and blood glucose only twice or thrice in a day,” while “the patient is on continuous ECG monitoring and the sensor transmits the data continuously” (Ramesh, ¶[0082]). Ramesh further teaches that some condition-specific analyses use long-term monitoring, including that “the BP, heart rate (HR) or SpO2 severity levels are set for identification of long-term trends in obstructive sleep apnea” (Ramesh, ¶[0091]), and that, for blood glucose, “continuous blood glucose monitoring in high-risk patients is adopted” and “the severity levels is set based on blood glucose measurements over a long time interval depending upon the risk of the patients to prevent fatal events” (Ramesh, ¶[0093]). Ramesh therefore teaches selecting different predetermined acquisition or assessment periods based on the monitored condition and the temporal characteristics of the underlying physiological data.
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Sato system in view of Ramesh such that evacuation related data is acquired during a predetermined evacuation period, urination related data is acquired during a predetermined urination period, and the predetermined evacuation period is longer than the predetermined urination period. The modified Sato system already acquires and analyzes both defecation related data and urination related data, including counts and average intervals of urination and defecation, and uses those data to generate excretion related alerts. Ramesh teaches the known health-monitoring principle that different physiological conditions and sensor data types may use different predetermined monitoring frequencies or windows based on the temporal characteristics of the condition being evaluated. In view of Ramesh’s express contrast between lower-frequency BP/glucose measurements taken only twice or thrice per day and continuous ECG monitoring, one of ordinary skill in the art would have recognized that different excretion data streams likewise have different temporal characteristics, such that the lower-frequency evacuation data stream would use a longer acquisition period than the higher-frequency urination data stream. The modification would have been readily feasible within the modified Sato framework because Sato already separately identifies urination and defecation, records occurrence date and time information, stores and aggregates excretion information, and evaluates urination and defecation tendencies over time. Consistent with Ramesh’s teaching that conditions evaluated over different temporal scales benefit from independently configured monitoring windows, the benefit would have been improved accuracy of evacuation-related trend analysis by using a longer window suited to the lower frequency of defecation events, while preserving the responsiveness of urination monitoring through a shorter independently configured window.
Regarding claim 11, the modified Sato teaches that in generation of the plurality of types of alert information, related data related to the generated plurality of types of alert information is further acquired (Sato, ¶[0101], “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information”, Sato teaches further acquiring related data, namely face image data and identification data, that is associated with the notification information and detailed information); and in output of the one of the plurality of types of alert information, the related data is output together with the one of plurality of types of alert information (Sato, ¶[0101], “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information, and may be transmitted to the terminal apparatus 50 and the server 40, respectively”, Sato teaches outputting the notification information together with the related data by adding or embedding the face image data and identification data in the notification information that is transmitted).
Regarding claim 12, the modified Sato does not teach that the related data includes a type of medicine taken by the user. The modified Sato teaches generating output information based on threshold processing and transmitting a notification that may include an alert, and further teaches transmitting “detailed information” output as a result of the threshold processing to a terminal apparatus (Sato, ¶[0123]). However, the modified Sato does not expressly teach that the related data includes a type of medicine taken by the user.
Amin teaches that a smart toilet system can take into account “inputted and/or learned idiosyncrasies of the user … including … current medications” (Amin, ¶[0049]), and further teaches that such idiosyncratic information can include “currently prescribed and/or over-the-counter medications taken by the user” (Amin, ¶[0045]). Amin therefore provides express support that medication type data is acquired by the system and maintained as user related information for use in health analysis.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Sato in view of Amin to further acquire related data that includes a type of medicine taken by the user, and to output the related data together with the alert information. The modification would have been feasible because the modified Sato already teaches transmitting “detailed information” together with an alerting notification (Sato, ¶[0123]), and Amin teaches that medications taken by the user are a form of user related information that may be obtained and used by a smart toilet system (Amin, ¶[0045]), such that medication type can be stored and output as part of the transmitted related information alongside the alert information. The benefit of the combination would be improving the relevance and interpretability of alert information by providing medication context that may affect excretion related conditions and user follow up actions.
Regarding claim 14, Sato teaches an information processing device comprising: a processor that acquires first data in which excretion related data of a user acquired by a sensor installed in a toilet and a user ID for identifying the user are associated with each other in a first predetermined period (Sato, ¶[0072]: “The control unit can be implemented by, for example, a central processing unit (CPU), a working memory, a nonvolatile storage apparatus that stores a program, and the like”; ¶[0072]: “The program may be a program for causing the CPU to perform the processing of each of the units 1 a to 1 d”; disclosing an information processing device including a processor that performs the processing using a CPU, memory, and stored program; Sato, ¶[0061]: “The input unit 1 a inputs imaging data (image data) captured by an image capture apparatus (hereinafter exemplified as a camera) installed in such a way as to include, in a capturing range, an excretion range of excrement in a toilet bowl of a toilet”; ¶[0064]: “The excretion information is information indicating a content of excretion, and in a simpler example, can be information indicating whether excrement is feces (stool) or pee (urine)”; ¶[0100]: “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user from a Bluetooth tag held by the user”; ¶[0101]: “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information”; ¶[0170]: “The excrement analysis apparatus 10 collects excretion information (detailed information described above), and transmits the information (transmission information) together with user information and installation place information to the server apparatus 70 side”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; disclosing excretion related data acquired by a camera installed to capture excrement in a toilet bowl, user identification data associated with the excretion related information, and aggregation of the excretion related information by month, where a first month or other first aggregate period corresponds to the first predetermined period); wherein the processor further generates a reference database indicating an excretion tendency of the user based on the acquired first data (Sato, ¶[0169]: “The excretion information DB 71 may include information (for example, an intensive information table 71 a in FIG. 16) acquired by summarizing, in the information processing unit 70 c, transmission information (for example, transmission information in FIG. 15) transmitted from the plurality of excrement analysis apparatuses 10”; ¶[0174]: “aggregate information tables 71 b to 71 d illustrated in FIGS. 17 to 19 may be generated in advance in the excretion information DB 71”; ¶[0175]: “The aggregate information table 71 b is a table indicating an aggregate result of a defecation shape”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; ¶[0178]: “the information processing unit 70 c preferably analyzes a tendency of a time change in a shape of defecation”; disclosing an excretion information database including aggregate information tables that indicate and are used to analyze an excretion tendency based on acquired excretion information); acquires second data in which the excretion related data of the user acquired by the sensor installed in the toilet and the user ID are associated with each other in a second predetermined period (Sato, ¶[0100]: “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user from a Bluetooth tag held by the user”; ¶[0101]: “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; disclosing the same type of excretion related data and user identification association discussed above, but during a subsequent month or other subsequent aggregate period corresponding to the second predetermined period); and generates a plurality of types of alert information to the user and outputs one of the plurality of types of the generated alert information (Sato, ¶[0146]: “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation and encourage the user and the carer to handle the situation”; ¶[0146]: “a tendency of a urination disorder can be recognized from a urination interval exceeding a fixed threshold, and early consultation at a hospital can be achieved by notifying a user and a carer of an alarm from the present system”; ¶[0146]: “the present system may be configured in such a way as to also similarly notify an alarm about a defecation interval”; ¶[0146]: “by checking information about a date and time of an excretion behavior and an excretion content and determining whether a set threshold is exceeded, the present system can recognize a urination disorder and a constipation tendency and notify a carer and a user of an alert”; disclosing multiple types of alert information, including at least a constipation alert, a urination disorder alarm, and a defecation interval alarm, and outputting such alert information to the user).
However, Sato does not expressly teach generating a plurality of types of alert information to the user based on the reference database and the second data. Rather, Sato teaches generating and outputting plural alert types based on excretion date and time information and excretion content, including that “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation” and that “a tendency of a urination disorder can be recognized from a urination interval exceeding a fixed threshold” (Sato, ¶[0146]). Sato also teaches an excretion information database and aggregate information tables, including that “aggregate information tables 71 b to 71 d illustrated in FIGS. 17 to 19 may be generated in advance in the excretion information DB 71” and that “[a] tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b” (Sato, ¶[0174]-¶[0175]). However, Sato does not expressly teach generating the plurality of alert types based on both the reference database derived from the first data and the second data acquired in the later second predetermined period. Sato’s aggregate tables are used for viewing or analyzing tendencies, whereas Sato does not expressly teach using those aggregate tables as a reference database for generating the alert information by comparison against subsequently acquired second data.
Amin teaches that prior waste analyses may be used as a baseline or reference for a current diagnosis or recommendation because “Prior waste analyses performed by the smart toilet system on the particular user can, in one or more embodiments, be leveraged to improve the efficacy of current diagnoses and/or recommendations” (Amin, ¶[0048]). Amin further teaches comparing current waste analysis results with past waste analysis results by “comparing the user’s current microflora profile with the user’s past microflora profiles to determine how the user’s microbiome has responded to past recommendations” (Amin, ¶[0043]). Amin also teaches comparison to reference values and a database because “the diagnostic component 416 can, in some cases, compare this medically significant information with known reference values” and “the diagnostic component 416 can access a local and/or remote database” (Amin, ¶[0115]). Amin further teaches outputting the resulting diagnoses or recommendations as alerts because “the smart toilet system can notify the user of the diagnoses and any recommended courses of action... by means of a visual message and/or alert... and/or... an audible message and/or alert... and/or... vibratory messages and/or alerts” (Amin, ¶[0051]).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Sato in view of Amin to generate the plurality of types of alert information to the user based on a reference database derived from previously acquired excretion related data and second data acquired in a later predetermined period, and to output one of the plurality of types of generated alert information. Sato already stores user-associated excretion information in an excretion information database, analyzes excretion tendencies over time, and outputs excretion-related alerts, including constipation alerts, urination disorder alarms, and defecation interval alarms. Amin teaches the known health-monitoring technique of leveraging a user’s prior waste analyses, past waste profiles, reference values, and databases to improve current diagnoses and recommendations that are communicated to the user by alerts. A person of ordinary skill in the art would have had reason to incorporate Amin’s prior-analysis and reference-value comparison technique into Sato’s excretion monitoring system because Sato is concerned with accurately recognizing health damage from excretion records, while Amin expressly teaches that prior waste analyses can improve the efficacy of current diagnoses and recommendations. The modification would have been a predictable use of Amin’s historical-comparison technique in Sato’s database-driven excretion alert system, and would have been feasible because Sato already collects, stores, aggregates, and analyzes user-associated excretion information over time. The benefit would have been more reliable user-specific alerts by accounting for the user’s prior excretion tendency and later-acquired excretion data, rather than relying only on isolated threshold determinations. Because Sato already discloses multiple distinct alert categories, including constipation alerts, urination disorder alarms, and defecation interval alarms (Sato, ¶[0146]), Amin’s prior-analysis and reference-value comparison technique would have been applicable to each of those Sato alert categories. The resulting modified Sato system would generate the constipation, urination disorder, and defecation interval alerts by accounting for later-acquired excretion information in view of the user’s prior excretion tendency stored in the excretion information database, thereby generating the plurality of alert types based on the reference database and the second data.
Also regarding claim 14, the modified Sato does not expressly teach wherein the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output. Rather, the modified Sato teaches multiple excretion-related alert types, including a constipation alert, a urination disorder alarm, and a defecation interval alarm (Sato, ¶[0146]), and the modified Sato further teaches generating those alert types using a reference database and later-acquired excretion related data in view of Amin as set forth above. However, the modified Sato does not expressly teach determining the second predetermined period in advance according to the particular one of the plurality of alert types to be output.
Under the broadest reasonable interpretation, the limitation determined in advance requires that the second predetermined period be preconfigured based on the alert type before, or at the initiation of, acquisition of the second data for that alert type, rather than being selected only after the data collection is complete. Ramesh teaches this limitation by teaching a health-monitoring framework in which different monitored conditions and sensor types use different predetermined sensing frequencies, time intervals, and monitoring windows. Specifically, Ramesh teaches that “each sensor has different sensing frequencies,” including “measuring BP and blood glucose only twice or thrice in a day,” while “the patient is on continuous ECG monitoring and the sensor transmits the data continuously” (Ramesh, ¶[0082]). Thus, Ramesh teaches that different health conditions and corresponding sensor data types may be monitored using different acquisition frequencies or acquisition periods selected in advance according to what condition is being evaluated. Ramesh further teaches condition-specific monitoring intervals and alert criteria, including that “the BP, heart rate (HR) or SpO2 severity levels are set for identification of long-term trends in obstructive sleep apnea” (Ramesh, ¶[0091]), and that, for blood glucose, “continuous blood glucose monitoring in high-risk patients is adopted” and “the severity levels is set based on blood glucose measurements over a long time interval depending upon the risk of the patients to prevent fatal events” (Ramesh, ¶[0093]). These passages provide the primary support for the predetermined nature of the monitoring period. Ramesh additionally teaches that monitoring intervals are alert-dependent variables by stating that “AMIs also serves as a feedback mechanism to modulate sensing frequency and alert computation instants,” and that “[a] low AMI is used to effect three adjustments: (1) Reduce the frequency F of future sensor measurements to a medically allowed minimum bound, (2) Increase the gap Γ between successive monitoring intervals, and (3) Increase the subsequent inter-alert window φ” (Ramesh, ¶[0106]). Accordingly, ¶[0106] is relied upon as corroborating evidence that Ramesh treats monitoring intervals as alert-related variables, while the preconfigured condition-specific acquisition periods are principally taught by ¶[0082], ¶[0091], and ¶[0093].
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Sato system in view of Ramesh such that the second predetermined period for acquiring the second data is determined in advance according to the one of the plurality of types of alert information to be output. The modified Sato system already generates different excretion-related alert types, including constipation alerts, urination disorder alarms, and defecation interval alarms, based on user-associated excretion information over time. A person of ordinary skill in the art implementing those sensor-based health alerts would have had reason to look to known health-monitoring techniques for selecting a monitoring period according to the condition being evaluated, such as Ramesh’s teaching that BP and blood glucose may be measured only twice or thrice per day while ECG may be continuously monitored, and that long-term trends are used for obstructive sleep apnea while other conditions use continuous or higher-frequency monitoring. Ramesh is reasonably pertinent to the problem addressed by the amended limitation because it addresses selection of data collection and assessment windows for health condition monitoring systems that generate condition-specific alerts. Because the modified Sato system’s alert types correspond to different excretion-related conditions, applying Ramesh’s condition-specific monitoring-period technique to the modified Sato system would have predictably resulted in selecting the second predetermined period according to the particular excretion-related alert type being evaluated. This application of Ramesh is not based on bodily sensor structure, but on the shared health-monitoring design principle that the data acquisition period should be selected according to the temporal characteristics of the monitored condition. Consistent with Ramesh’s teaching that lower-frequency or long-term biological conditions may use longer monitoring windows while continuously or more frequently monitored conditions may use shorter or more frequent windows, the modified Sato system would have used an observation period suited to the temporal characteristics of the particular excretion-related alert being generated. The modification would have been feasible because the modified Sato system already acquires user-associated excretion data over time, stores and aggregates that data, distinguishes among multiple excretion-related alert categories, and outputs alerts to a user-accessible terminal apparatus. The benefit would have been more reliable alert generation by avoiding an assessment window that is too short to meaningfully evaluate a less frequent excretion condition or unnecessarily long for a more rapidly indicated condition.
Regarding claim 15, Sato teaches a non-transitory computer readable recording medium storing an information processing program that causes a computer to function to acquire first data in which excretion related data of a user acquired by a sensor installed in a toilet and a user ID for identifying the user are associated with each other in a first predetermined period (Sato, ¶[0215]: “the program may be stored by using various types of non-transitory computer-readable mediums, and may be supplied to a computer”; ¶[0215]: “The non-transitory computer-readable medium includes various types of tangible storage mediums”; ¶[0214]: “The functions of each apparatus described in the first to fifth example embodiments are implemented by the processor 101 that reads and executes the program stored in the memory 102”; disclosing a non-transitory computer readable recording medium storing a program that causes a computer to perform the recited processing; Sato, ¶[0061]: “The input unit 1 a inputs imaging data (image data) captured by an image capture apparatus (hereinafter exemplified as a camera) installed in such a way as to include, in a capturing range, an excretion range of excrement in a toilet bowl of a toilet”; ¶[0064]: “The excretion information is information indicating a content of excretion, and in a simpler example, can be information indicating whether excrement is feces (stool) or pee (urine)”; ¶[0100]: “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user from a Bluetooth tag held by the user”; ¶[0101]: “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information”; ¶[0170]: “The excrement analysis apparatus 10 collects excretion information (detailed information described above), and transmits the information (transmission information) together with user information and installation place information to the server apparatus 70 side”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; disclosing that the program causes the computer to acquire excretion related data by a camera installed to capture excrement in a toilet bowl, associate user identification data with the excretion related information, and aggregate the excretion related information by month, where a first month or other first aggregate period corresponds to the first predetermined period); generate a reference database indicating an excretion tendency of the user based on the acquired first data (Sato, ¶[0169]: “The excretion information DB 71 may include information (for example, an intensive information table 71 a in FIG. 16) acquired by summarizing, in the information processing unit 70 c, transmission information (for example, transmission information in FIG. 15) transmitted from the plurality of excrement analysis apparatuses 10”; ¶[0174]: “aggregate information tables 71 b to 71 d illustrated in FIGS. 17 to 19 may be generated in advance in the excretion information DB 71”; ¶[0175]: “The aggregate information table 71 b is a table indicating an aggregate result of a defecation shape”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; ¶[0178]: “the information processing unit 70 c preferably analyzes a tendency of a time change in a shape of defecation”; disclosing that the program causes the computer to generate an excretion information database including aggregate information tables that indicate and are used to analyze an excretion tendency based on acquired excretion information); acquire second data in which the excretion related data of the user acquired by the sensor installed in the toilet and the user ID are associated with each other in a second predetermined period (Sato, ¶[0100]: “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user from a Bluetooth tag held by the user”; ¶[0101]: “The face image data acquired by the second camera 15 b and the identification data acquired by the Bluetooth module 14 b may be added to or embedded in the notification information and the detailed information”; ¶[0175]: “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b”; disclosing the same type of excretion related data and user identification association discussed above, but during a subsequent month or other subsequent aggregate period corresponding to the second predetermined period); and generate a plurality of types of alert information to the user and output one of the plurality of types of generated alert information (Sato, ¶[0146]: “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation and encourage the user and the carer to handle the situation”; ¶[0146]: “a tendency of a urination disorder can be recognized from a urination interval exceeding a fixed threshold, and early consultation at a hospital can be achieved by notifying a user and a carer of an alarm from the present system”; ¶[0146]: “the present system may be configured in such a way as to also similarly notify an alarm about a defecation interval”; ¶[0146]: “by checking information about a date and time of an excretion behavior and an excretion content and determining whether a set threshold is exceeded, the present system can recognize a urination disorder and a constipation tendency and notify a carer and a user of an alert”; disclosing that the program causes the computer to generate multiple types of alert information, including at least a constipation alert, a urination disorder alarm, and a defecation interval alarm, and output such alert information to the user).
However, Sato does not expressly teach generating a plurality of types of alert information to the user based on the reference database and the second data. Rather, Sato teaches generating and outputting plural alert types based on excretion date and time information and excretion content, including that “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation” and that “a tendency of a urination disorder can be recognized from a urination interval exceeding a fixed threshold” (Sato, ¶[0146]). Sato also teaches an excretion information database and aggregate information tables, including that “aggregate information tables 71 b to 71 d illustrated in FIGS. 17 to 19 may be generated in advance in the excretion information DB 71” and that “[a] tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b” (Sato, ¶[0174]-¶[0175]). However, Sato does not expressly teach generating the plurality of alert types based on both the reference database derived from the first data and the second data acquired in the later second predetermined period. Sato’s aggregate tables are used for viewing or analyzing tendencies, whereas Sato does not expressly teach using those aggregate tables as a reference database for generating the alert information by comparison against subsequently acquired second data.
Amin teaches that prior waste analyses may be used as a baseline or reference for a current diagnosis or recommendation because “Prior waste analyses performed by the smart toilet system on the particular user can, in one or more embodiments, be leveraged to improve the efficacy of current diagnoses and/or recommendations” (Amin, ¶[0048]). Amin further teaches comparing current waste analysis results with past waste analysis results by “comparing the user’s current microflora profile with the user’s past microflora profiles to determine how the user’s microbiome has responded to past recommendations” (Amin, ¶[0043]). Amin also teaches comparison to reference values and a database because “the diagnostic component 416 can, in some cases, compare this medically significant information with known reference values” and “the diagnostic component 416 can access a local and/or remote database” (Amin, ¶[0115]). Amin further teaches outputting the resulting diagnoses or recommendations as alerts because “the smart toilet system can notify the user of the diagnoses and any recommended courses of action... by means of a visual message and/or alert... and/or... an audible message and/or alert... and/or... vibratory messages and/or alerts” (Amin, ¶[0051]).
It would have been prima facie obvious before the effective filing date of the claimed invention to have modified Sato in view of Amin to cause the computer, via the stored program, to generate the plurality of types of alert information to the user based on a reference database derived from previously acquired excretion related data and second data acquired in a later predetermined period, and to output one of the plurality of types of generated alert information. Sato already stores user-associated excretion information in an excretion information database, analyzes excretion tendencies over time, and outputs excretion-related alerts, including constipation alerts, urination disorder alarms, and defecation interval alarms. Amin teaches the known health-monitoring technique of leveraging a user’s prior waste analyses, past waste profiles, reference values, and databases to improve current diagnoses and recommendations that are communicated to the user by alerts. A person of ordinary skill in the art would have had reason to incorporate Amin’s prior-analysis and reference-value comparison technique into Sato’s excretion monitoring program because Sato is concerned with accurately recognizing health damage from excretion records, while Amin expressly teaches that prior waste analyses can improve the efficacy of current diagnoses and recommendations. The modification would have been a predictable use of Amin’s historical-comparison technique in Sato’s database-driven excretion alert program, and would have been feasible because Sato already causes the computer to collect, store, aggregate, and analyze user-associated excretion information over time. The benefit would have been more reliable user-specific alerts by accounting for the user’s prior excretion tendency and later-acquired excretion data, rather than relying only on isolated threshold determinations. Because Sato already discloses multiple distinct alert categories, including constipation alerts, urination disorder alarms, and defecation interval alarms (Sato, ¶[0146]), Amin’s prior-analysis and reference-value comparison technique would have been applicable to each of those Sato alert categories. The resulting modified Sato program would cause the computer to generate the constipation, urination disorder, and defecation interval alerts by accounting for later-acquired excretion information in view of the user’s prior excretion tendency stored in the excretion information database, thereby generating the plurality of alert types based on the reference database and the second data.
Also regarding claim 15, the modified Sato does not expressly teach wherein the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output. Rather, the modified Sato teaches multiple excretion-related alert types, including a constipation alert, a urination disorder alarm, and a defecation interval alarm (Sato, ¶[0146]), and the modified Sato further teaches generating those alert types using a reference database and later-acquired excretion related data in view of Amin as set forth above. However, the modified Sato does not expressly teach determining the second predetermined period in advance according to the particular one of the plurality of alert types to be output.
Under the broadest reasonable interpretation, the limitation determined in advance requires that the second predetermined period be preconfigured based on the alert type before, or at the initiation of, acquisition of the second data for that alert type, rather than being selected only after the data collection is complete. Ramesh teaches this limitation by teaching a health-monitoring framework in which different monitored conditions and sensor types use different predetermined sensing frequencies, time intervals, and monitoring windows. Specifically, Ramesh teaches that “each sensor has different sensing frequencies,” including “measuring BP and blood glucose only twice or thrice in a day,” while “the patient is on continuous ECG monitoring and the sensor transmits the data continuously” (Ramesh, ¶[0082]). Thus, Ramesh teaches that different health conditions and corresponding sensor data types may be monitored using different acquisition frequencies or acquisition periods selected in advance according to what condition is being evaluated. Ramesh further teaches condition-specific monitoring intervals and alert criteria, including that “the BP, heart rate (HR) or SpO2 severity levels are set for identification of long-term trends in obstructive sleep apnea” (Ramesh, ¶[0091]), and that, for blood glucose, “continuous blood glucose monitoring in high-risk patients is adopted” and “the severity levels is set based on blood glucose measurements over a long time interval depending upon the risk of the patients to prevent fatal events” (Ramesh, ¶[0093]). These passages provide the primary support for the predetermined nature of the monitoring period. Ramesh additionally teaches that monitoring intervals are alert-dependent variables by stating that “AMIs also serves as a feedback mechanism to modulate sensing frequency and alert computation instants,” and that “[a] low AMI is used to effect three adjustments: (1) Reduce the frequency F of future sensor measurements to a medically allowed minimum bound, (2) Increase the gap Γ between successive monitoring intervals, and (3) Increase the subsequent inter-alert window φ” (Ramesh, ¶[0106]). Accordingly, ¶[0106] is relied upon as corroborating evidence that Ramesh treats monitoring intervals as alert-related variables, while the preconfigured condition-specific acquisition periods are principally taught by ¶[0082], ¶[0091], and ¶[0093].
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Sato program in view of Ramesh such that the second predetermined period for acquiring the second data is determined in advance according to the one of the plurality of types of alert information to be output. The modified Sato program already causes the computer to generate different excretion-related alert types, including constipation alerts, urination disorder alarms, and defecation interval alarms, based on user-associated excretion information over time. A person of ordinary skill in the art implementing those sensor-based health alerts would have had reason to look to known health-monitoring techniques for selecting a monitoring period according to the condition being evaluated, such as Ramesh’s teaching that BP and blood glucose may be measured only twice or thrice per day while ECG may be continuously monitored, and that long-term trends are used for obstructive sleep apnea while other conditions use continuous or higher-frequency monitoring. Ramesh is reasonably pertinent to the problem addressed by the amended limitation because it addresses selection of data collection and assessment windows for health condition monitoring systems that generate condition-specific alerts. Because the modified Sato program’s alert types correspond to different excretion-related conditions, applying Ramesh’s condition-specific monitoring-period technique to the modified Sato program would have predictably resulted in selecting the second predetermined period according to the particular excretion-related alert type being evaluated. This application of Ramesh is not based on bodily sensor structure, but on the shared health-monitoring design principle that the data acquisition period should be selected according to the temporal characteristics of the monitored condition. Consistent with Ramesh’s teaching that lower-frequency or long-term biological conditions may use longer monitoring windows while continuously or more frequently monitored conditions may use shorter or more frequent windows, the modified Sato program would have used an observation period suited to the temporal characteristics of the particular excretion-related alert being generated. The modification would have been feasible because the modified Sato program already causes the computer to acquire user-associated excretion data over time, store and aggregate that data, distinguish among multiple excretion-related alert categories, and output alerts to a user-accessible terminal apparatus. The benefit would have been more reliable alert generation by avoiding an assessment window that is too short to meaningfully evaluate a less frequent excretion condition or unnecessarily long for a more rapidly indicated condition.
Claims 2-3 are rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (US 20230225714 A1), hereto referred as Sato, and further in view of Amin (US 20190062813 A1), hereto referred as Amin, and further in view of Ramesh et al. (US 20190046039 A1), hereto referred as Ramesh, and further in view of Oguri et al. (US 20180368818 A1), hereto referred as Oguri.
The modified Sato teaches claim 1 as described above.
Regarding claim 2, the modified Sato teaches that the excretion related data includes feces shape data indicating whether a shape of excreted feces is hard feces, normal feces, or watery feces (Sato, ¶[0170]: “the excretion information may include an excretion date and time (occurrence date and time), a kind of excrement (information indicating any of urination, defecation, and a foreign body), the amount of urination (for example, information indicating any of great, normal, and small), and a shape of defecation (for example, information indicating any of hard, normal, and diarrhea)”, the modified Sato teaches feces shape data including “hard” and “normal” and “diarrhea”, where “diarrhea” corresponds to watery feces).
Also regarding claim 2, the modified Sato does not fully teach that the reference database includes a first hard feces ratio indicating a ratio of the hard feces excreted among all evacuations in the first predetermined period, and in generation of the alert information, a second hard feces ratio indicating a ratio of the hard feces excreted among all evacuations in the second predetermined period is calculated, and in a case where the second hard feces ratio is higher than the first hard feces ratio, first alert information indicating that the user is highly likely to have constipation is generated. The modified Sato teaches acquiring excretion information including defecation shape and aggregating extracted defecation shape information by occurrence month in an aggregate information table, such that “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b” (Sato, ¶[0170]; ¶[0175]) . The modified Sato further evidences that statistical processing over a predetermined period and alert generation based on processed results are within the contemplation of the invention, teaching that in non-real time analysis, information indicating whether feces-related data subjected to threshold processing exceeds a predetermined threshold is transmitted and may include an alert (Sato, ¶[0123]). The modified Sato also teaches aggregating classified excretion information by period so that tendencies can be viewed across months (Sato, ¶[0175]). Further, the modified Sato expressly contemplates ratio-based statistical processing of biological information over a day or multiple days, describing performing ‘statistical processing on an Na/K ratio’ and storing correlations based on a ‘statistical concentration ratio’ (Sato, ¶[0010]). The modified Sato also expressly teaches prediction and constipation alerting using an excretion diary, stating “The output may be a tendency (such as an average interval) of urination and defecation” and that “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation” (Sato, ¶[0145]; ¶[0146]). These teachings demonstrate that (i) period-based aggregation of defecation shape information, (ii) calculating statistics/tendencies over predetermined periods, and (iii) generating constipation-related alert information based on analyzed excretion data are within the contemplation of the modified Sato. However, the modified Sato does not expressly teach calculating a first hard feces ratio indicating a ratio of hard feces among all evacuations in a first predetermined period, calculating a second hard feces ratio indicating a ratio of hard feces among all evacuations in a second predetermined period, comparing whether the second hard feces ratio is higher than the first hard feces ratio, or generating first alert information indicating that the user is highly likely to have constipation when the second hard feces ratio is higher than the first hard feces ratio.
Oguri teaches that hard feces is indicative of a constipation condition, expressly stating that the feces property patterns include “( 2 ) hard and barrel-shape” and that “( 1 ) and ( 2 ) are defined as properties of feces discharged in constipation condition”, and further that “( 1 ) to ( 3 ) are also defined as hard feces” (Oguri, ¶[0043]). Oguri further teaches extracting feces-shape features and using tendencies of those features to estimate the feces property pattern, stating “features related to the shape are extracted from the photographed still images”, and “This makes it possible to grasp the tendency of the feature amounts in each property pattern. By comparing this tendency with the tendency indicated by the feature amounts extracted from the estimation target image of the property of the feces, which one of the property patterns of the above (1) to (6) the feces belong to is estimated” (Oguri, ¶[0044]-¶[0045]). Oguri also teaches estimating time-series change in feces properties across images in time series, stating “estimates the property of the feces in each still image photographed in time series, and then estimates the change in the property of the feces” (Oguri, ¶[0046]). Oguri further teaches outputting the estimation result and storing past estimation results, stating “the estimation result provided by the fecal properties estimation part 30 is data-transmitted to a display terminal” and that “past estimation result data of a change in property of feces may be stored so that the estimation result data can be confirmed as needed” (Oguri, ¶[0040]). Thus, Oguri supplies express support for interpreting hard feces classifications as an indicator of constipation and further supports generating and outputting user-facing health-condition related information based on feces property estimation.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Sato in view of Oguri to calculate a first hard feces ratio indicating a ratio of hard feces among all evacuations in a first predetermined period, calculate a second hard feces ratio indicating a ratio of hard feces among all evacuations in a second predetermined period, compare whether the second hard feces ratio is higher than the first hard feces ratio, and generate first alert information indicating that the user is highly likely to have constipation when the second hard feces ratio is higher than the first hard feces ratio. The modification would have been feasible because the modified Sato already aggregates defecation shape information by period in an aggregate information table so that (i) the number of hard-defecation events for a period and (ii) the total number of defecation events for the period are available as a basis for ratio calculation, and further teaches calculating statistical values/tendencies over predetermined periods and generating alert information based on analyzed excretion data (Sato, ¶[0175]; ¶[0123], ¶[0175], and ¶[0010]; ¶[0145]-¶[0146]) . Computing the claimed hard feces ratio is a straightforward statistical derivation from the aggregated counts already maintained by the modified Sato, and the period-to-period comparison is a routine extension of Sato’s teaching to view “tendency” across months, while Oguri provides express support that hard feces corresponds to a constipation condition (Sato, ¶[0175]; Oguri, ¶[0043]) . The benefit of the combination would be enabling constipation alert generation using a normalized measure (hard feces ratio) that is less sensitive to variations in the total number of evacuations between periods, while still leveraging the same period-based aggregation and alerting framework already taught by the modified Sato.
Regarding claim 3, the modified Sato teaches that the excretion related data includes feces shape data indicating whether a shape of excreted feces is hard feces, normal feces, or watery feces (Sato, ¶[0170]: “the excretion information may include an excretion date and time (occurrence date and time), a kind of excrement (information indicating any of urination, defecation, and a foreign body), the amount of urination (for example, information indicating any of great, normal, and small), and a shape of defecation (for example, information indicating any of hard, normal, and diarrhea”, the modified Sato teaches feces shape data including “hard” and “normal” and “diarrhea”, where “diarrhea” corresponds to watery feces).
Also regarding claim 3, the modified Sato does not fully teach that the reference database includes a first watery feces ratio indicating a ratio of the watery feces excreted among all evacuations in the first predetermined period, and in generation of the plurality of types of alert information, a second watery feces ratio indicating a ratio of the watery feces excreted among all evacuations in the second predetermined period is calculated, and in a case where the second watery feces ratio is higher than the first watery feces ratio, second alert information of the plurality of the types of alert information indicating that the user is likely to have diarrhea is generated. The modified Sato teaches acquiring excretion information including defecation shape and aggregating extracted defecation shape information by occurrence month in an aggregate information table, such that “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b” and a viewer can recognize the person is “more likely to get out of condition in a month at frequent occurrence of diarrhea, for example” (Sato, ¶[0170]: “a shape of defecation (for example, information indicating any of hard, normal, and diarrhea”; Sato, ¶[0175]: “the classified information may be aggregated in the aggregate information table 71 b”; “A tendency of the defecation shape can be viewed for each month from the aggregate information table 71 b, and a person who views the information can recognize that the person is more likely to get out of condition in a month at frequent occurrence of diarrhea, for example”). The modified Sato further evidences that statistical processing over a predetermined period and alert generation based on processed results are within the contemplation of the invention, teaching that in non-real time analysis, information indicating whether feces-related data subjected to threshold processing exceeds a predetermined threshold is transmitted and may include an alert (Sato, ¶[0123]). The modified Sato also teaches aggregating classified excretion information by period so that tendencies can be viewed across months (Sato, ¶[0175]). Further, the modified Sato expressly contemplates ratio-based statistical processing of biological information over a day or multiple days, describing performing ‘statistical processing on an Na/K ratio’ and storing correlations based on a ‘statistical concentration ratio’ (Sato, ¶[0010]). The modified Sato also expressly teaches prediction and tendency analysis using an excretion diary, stating “The output may be a tendency (such as an average interval) of urination and defecation” and that “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation” (Sato, ¶[0145]; ¶[0146]). These teachings demonstrate that calculating statistical tendencies from historical excretion data over defined periods and generating health-related alerts based on those tendencies is within the contemplation of the modified Sato. However, the modified Sato does not expressly teach calculating a first watery feces ratio indicating a ratio of watery feces among all evacuations in a first predetermined period, calculating a second watery feces ratio indicating a ratio of watery feces among all evacuations in a second predetermined period, comparing whether the second watery feces ratio is higher than the first watery feces ratio, or generating second alert information indicating that the user is highly likely to have diarrhea when the second watery feces ratio is higher than the first watery feces ratio.
Oguri teaches that watery feces is indicative of a diarrhea condition, expressly stating that the feces property patterns include “( 6 ) watery” and that “( 5 ) and ( 6 ) are defined as properties of feces discharged in diarrhea condition” (Oguri, ¶[0043]). Oguri further teaches estimating feces property patterns by comparing tendencies of extracted features, stating “This makes it possible to grasp the tendency of the feature amounts in each property pattern” and that, “By comparing this tendency with the tendency indicated by the feature amounts extracted from the estimation target image of the property of the feces, which one of the property patterns of the above (1) to (6) the feces belong to is estimated” (Oguri, ¶[0045]). Oguri also teaches evaluating predominance of watery feces over time, stating “a period when the feces have been discharged in the property pattern of (6) water-like makes up most of the total time” (Oguri, ¶[0046]). Oguri further teaches outputting a health-condition estimation result and advice to a user-accessible terminal, stating “the estimation result of the health condition is displayed on a display terminal” and that “The advice for improving fecal properties is displayed based on the estimation result of the change in the fecal properties” (Oguri, ¶[0048]-¶[0049]). Thus, Oguri supplies the missing teaching of interpreting watery feces as a diarrhea condition indicator and further supports generating and outputting user-facing health-condition related information based on changes in feces properties.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Sato in view of Oguri to calculate a first watery feces ratio indicating a ratio of watery feces among all evacuations in a first predetermined period, calculate a second watery feces ratio indicating a ratio of watery feces among all evacuations in a second predetermined period, compare whether the second watery feces ratio is higher than the first watery feces ratio, and generate second alert information indicating that the user is likely to have diarrhea when the second watery feces ratio is higher than the first watery feces ratio. The modification would have been feasible because the modified Sato already aggregates defecation shape information by period in an aggregate information table so that the number of diarrhea defecation events and the total number of defecation events for a period are available, and further teaches calculating statistical tendencies over predetermined periods and generating alerts when later-acquired data deviates from registered statistics or thresholds (Sato, ¶[0175]; ¶[0123], ¶[0175], and ¶[0010]; ¶[0145]-¶[0146]). Computing the claimed watery feces ratio is a straightforward statistical derivation from the aggregated counts already maintained by the modified Sato, and the comparison between periods follows directly from Sato’s teaching of analyzing tendencies over time and notifying alerts when conditions indicative of poor health are recognized, while Oguri provides express support that watery feces corresponds to diarrhea. The benefit of the combination would be enabling generation of more precise and condition-specific alert information indicating that the user is highly likely to have diarrhea based on comparative analysis of watery feces occurrence across predetermined periods using excretion data already collected and analyzed by the system.
Claims 5, 7, 9, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (US 20230225714 A1), hereto referred as Sato, and further in view of Amin (US 20190062813 A1), hereto referred as Amin, and further in view of Ramesh et al. (US 20190046039 A1), hereto referred as Ramesh, and further in view of Hall et al. (US 20210076950 A1), hereto referred as Hall.
The modified Sato teaches claim 1 as described above.
Regarding claim 5, the modified Sato teaches that
the excretion related data includes evacuation color data indicating a color of excreted feces (Sato, ¶[0179]: “the information indicating a color of defecation is preferably included in the detailed information in the transmission information”, the modified Sato teaches including/using “information indicating a color of defecation”, which is evacuation color data indicating a color of excreted feces; Sato, ¶[0135]: “a color occupying a largest area in the extracted feces image can be set as a feces color”, the modified Sato teaches determining a feces color from imaging data)
Also regarding claim 5, the modified Sato does not fully teach that the reference database includes a first evacuation color ratio indicating a ratio of excretion of feces of a predetermined color among all evacuations in the first predetermined period (Sato, ¶[0179]: “the information processing unit 70c preferably analyzes a tendency of a time change in a shape and a color of defecation...”, the modified Sato teaches analyzing a time-change tendency of feces color but does not expressly teach calculating or storing a “ratio... among all evacuations” for a predetermined color in the reference database); in generation of the plurality of types of alert information, a second evacuation color ratio indicating a ratio of excretion of feces of the predetermined color among all evacuations in the second predetermined period is calculated (Sato, ¶[0179]: “the information processing unit 70c preferably analyzes a tendency of a time change in a shape and a color of defecation...”, the modified Sato teaches analyzing time-change tendency of feces color across multiple evacuations/periods but does not expressly teach calculating a second evacuation color ratio as claimed); in a case where the first evacuation color ratio and the second evacuation color ratio are different, fourth alert information of the plurality of types alert information indicating that a color of feces of the user changes is generated (Sato, ¶[0180]: “receives caution information that gives caution (including an alert) about spread of an infectious disease...”, the modified Sato teaches generating/receiving caution information including an alert based on analyzed defecation information but does not expressly teach generating alert information based on a determination that first and second evacuation color ratios are different).
Because Claim 5 depends from Claim 1, the first predetermined period stored in the reference database already serves as the comparison baseline for evaluating the second predetermined period. As shown above, the modified Sato teaches determining and recording feces color as excretion-related data, including setting “a feces color” from imaging data and including “information indicating a color of defecation” in detailed information. The modified Sato further teaches analyzing “a tendency of a time change in... a color of defecation” (Sato, ¶[0179]). The modified Sato also evidences that statistical processing over a predetermined period and subsequent alerting based on processed results are within the contemplation of the invention, teaching that classified excretion-related information is aggregated by period so that a tendency can be viewed across months (Sato, ¶[0175]), that non-real time analysis includes threshold processing of feces-related data and transmitting the resulting information, which may include an alert (Sato, ¶[0123]), and that ratio-based statistical processing over a day or multiple days is contemplated, describing performing ‘statistical processing on an Na/K ratio’ and storing correlations based on a ‘statistical concentration ratio’ (Sato, ¶[0010]). The modified Sato further references performing “statistical processing on an Na/K ratio” and storing data representing a correlation between a “statistical concentration ratio acquired by performing statistical processing” and an Na/K ratio over a day or multiple days (Sato, ¶[0010]), which further evidences that ratio-based statistical metrics over a period are contemplated as a way to recognize a “tendency” in excretion-related data. Additionally, in non-real time analysis, the modified Sato teaches that “information indicating whether a feces amount and a urine amount subjected to threshold processing exceed a predetermined threshold” may be output/added as detailed information, and that such threshold-processing results are desirably “transmitted (notified)” and “may include an alert” (Sato, ¶[0123]). However, the modified Sato does not expressly teach calculating or storing, in the reference database, a first evacuation color ratio indicating a ratio of excretion of feces of a predetermined color among all evacuations in the first predetermined period, calculating a second evacuation color ratio indicating a ratio of excretion of feces of the predetermined color among all evacuations in the second predetermined period, or generating fourth alert information based on a determination that the first evacuation color ratio and the second evacuation color ratio are different.
Hall teaches presenting health and wellness data to a user with accompanying instructions (Hall, FIG.12; ¶[0018]: “FIG. 12 shows a printout of health and wellness data for a user, with accompanying instructions”), generating reports about “trends” detected in such data (Hall, ¶[0061]: “The app can also create reports about the trends detected in the health and wellness reports”), and creating an “alert” when health and wellness data is outside a parameter (Hall, ¶[0062]: “The app may enable the app to create an alert when any of the health and wellness data is outside of a parameter set by the user”). Thus, Hall supports aggregating excretion-related values over multiple evacuations to detect trend-based changes and output user-facing alerts, which corresponds to using a period-based statistical metric such as a ratio of a predetermined feces color among all evacuations and generating alert information when that ratio changes between periods.
It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Sato in view of Hall to calculate/store, in the reference database, a first evacuation color ratio indicating a ratio of excretion of feces of a predetermined color among all evacuations in the first predetermined period, calculate a second evacuation color ratio indicating a ratio of excretion of feces of the predetermined color among all evacuations in the second predetermined period, and generate fourth alert information indicating that a color of feces of the user changes when the ratios differ. The modification would have been feasible because the modified Sato already teaches assigning a feces color per excretion event and storing feces color information for aggregation/analysis (Sato, ¶[0135]; ¶[0179]) and analyzing a time-change tendency of feces color and generating alert information (Sato, ¶[0179]-¶[0180]), such that counting evacuations of a predetermined feces color and dividing by the total evacuations in a period is a routine statistical processing of Sato’s per-event feces color data. Hall further reinforces trend-based alerting outputs by teaching reporting detected trends and creating alerts when data deviates from a parameter (Hall, ¶[0061]-¶[0062]). The benefit of the combination would be improving the robustness and interpretability of feces-color change detection by quantifying color occurrence over predetermined periods, thereby reducing sensitivity to one-off anomalous evacuations and enabling clearer trend-based alerts to the user.
Regarding claim 7, the modified Sato teaches that the excretion related data includes excrement type data indicating that excrement of the user is urine (Sato, ¶[0170]: “the excretion information may include an excretion date and time, an amount of excretion, a kind of excrement (information indicating any of urination, defecation, and a foreign body), a shape of defecation, a color of defecation, and may further include a count (a count of urination and defecation in one day)”, Sato teaches that excretion information may include “a kind of excrement (information indicating any of urination, defecation, and a foreign body)”, which corresponds to excrement type data including that the excrement is urine); the reference database includes a first number of times of urination indicating an average number of times of urination per day in the first predetermined period (Sato, ¶[0170]: “the excretion information may include an excretion date and time, an amount of excretion, a kind of excrement (information indicating any of urination, defecation, and a foreign body), a shape of defecation, a color of defecation, and may further include a count (a count of urination and defecation in one day)”, Sato teaches collecting a “count (a count of urination and defecation in one day)”, which corresponds to a number of times of urination per day that can be aggregated over the first predetermined period and used to determine an average number of times of urination per day for that first predetermined period); and in generation of the plurality of types of alert information, a second number of times of urination indicating an average number of times of urination per day in the second predetermined period is calculated (Sato, ¶[0145]: “The output may be a tendency (such as an average interval) of urination and defecation”, Sato teaches outputting a “tendency” of urination and defecation based on non-real time analysis, which corresponds to calculating a period-based statistical value for urination in a later period for comparison to a prior baseline; ¶[0170], “may further include a count (a count of urination and defecation in one day)”; ¶[0175], “the extracted information may be classified into information about an occurrence date and time (occurrence month in this example)”; “A tendency of the defecation shape can be viewed for each month”, Sato teaches collecting a per day “count of urination” and also teaches classifying aggregated excretion information by “occurrence month” so that a “tendency” can be viewed “for each month”, which is conceptually consistent with calculating, for a given second predetermined period defined by month, an average number of times of urination per day based on the per day urination counts in that month).
Also regarding claim 7, the modified Sato does not fully teach that in a case where the second number of times of urination is smaller than the first number of times of urination, sixth alert information of the plurality of types of types of information indicating a water intake amount of the user is likely insufficient is generated. Because Claim 7 depends from Claim 1, the first predetermined period stored in the reference database already serves as the comparison baseline for evaluating the second predetermined period. The modified Sato teaches collecting per day urination-count information as part of excretion information, stating that the excretion information “may further include a count (a count of urination and defecation in one day)” (Sato, ¶[0170]). The modified Sato also teaches non-real time analysis and outputting a urination-related tendency, stating, “The output may be a tendency (such as an average interval) of urination and defecation” (Sato, ¶[0145]). However, the modified Sato does not expressly teach generating sixth alert information indicating that a water intake amount of the user is likely insufficient based on a determination that a second average number of times of urination per day in a later period is smaller than a first average number of times of urination per day in an earlier period.
Hall teaches generating hydration-related reporting based on urine metrics, stating: “The report will indicate that a user may be under-hydrating, based on their urine flow, for their goals, even if the urine flow falls within otherwise normal ranges” (Hall, ¶[0065]). Hall further teaches alerting a user based on detected measurements, stating: “the user can adjust their app settings so that their smart device alerts them if any out-of-range or pre-specified measurement is detected” (Hall, ¶[0068]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the modified Sato in view of Hall to, in a case where the second number of times of urination is smaller than the first number of times of urination, generate sixth alert information indicating a water intake amount of the user is likely insufficient. The modification would have been feasible because the modified Sato already teaches collecting per day urination-count information for a user and deriving urination-related tendencies from non-real time analysis outputs (Sato, ¶[0170]; ¶[0145]), and Hall teaches interpreting urine-related measurements to indicate under-hydration and generating user alerts based on detected measurements (Hall, ¶[0065]; ¶[0068]), such that applying Hall’s under-hydration interpretation and alerting to urine-related trends derived from Sato’s collected excretion data would have been a straightforward integration of known reporting and notification techniques. The benefit of the combination would be providing a more actionable alert output by converting urine-related measurements into user-understandable water-intake guidance, thereby enabling earlier corrective hydration behavior.
Regarding claim 9, the modified Sato does not fully teach that the excretion related data includes bleeding data indicating that the user bleeds at a time of evacuation or urination, the reference database includes a first number of times of bleeding indicating the number of times of bleeding in the first predetermined period, and in generation of the alert information, a second number of times of bleeding indicating the number of times of bleeding in the second predetermined period is calculated, and in a case where the first number of times of bleeding is smaller than a predetermined number of times and the second number of times of bleeding is equal to or more than a predetermined number of times, eighth alert information indicating that there is a high possibility that the user is bleeding at the time of evacuation or urination is generated. Because Claim 9 depends from Claim 1, the first predetermined period stored in the reference database already serves as the comparison baseline for evaluating the second predetermined period. The modified Sato teaches collecting excretion information that includes a kind of excrement that can indicate urination, stating “the excretion information may include an excretion date and time (occurrence date and time), a kind of excrement (information indicating any of urination, defecation, and a foreign body)…” (Sato, ¶[0170]). The modified Sato teaches generating an alert based on evaluating an excretion record condition relative to a predetermined number over a period, stating “when there is no record of defecation for a predetermined number of days, the present system can notify a user and a carer of an alert of constipation” (Sato, ¶[0146]). However, the modified Sato does not expressly teach bleeding data indicating that the user bleeds at a time of evacuation or urination, counting a number of times of bleeding in a first predetermined period, calculating a number of times of bleeding in a second predetermined period, or generating alert information indicating that there is a high possibility that the user is bleeding at the time of evacuation or urination based on the first number of times of bleeding being smaller than a predetermined number of times and the second number of times of bleeding being equal to or more than the predetermined number of times.
Hall teaches detecting bleeding in excreta, including detecting “Blood in Feces” during evacuation and detecting “presence of blood in urine” during urination (Hall, Fig. 12; ¶[0044]). Hall further teaches generating “unique excreta event data” for each station visit and storing that data (Hall, ¶[0053]; ¶[0055]), as well as generating period summaries over defined time intervals such as weekly or monthly reports (Hall, ¶[0069]). Hall also teaches generating alerts when measured excreta data meets a pre-specified or out-of-range condition (Hall, ¶[0068]). Thus, Hall provides express support for detecting bleeding at the time of evacuation or urination, counting individual bleeding events over predetermined periods, and generating alerts when bleeding-related metrics satisfy a predetermined condition.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Sato in view of Hall to, in a case where a first number of times of bleeding in a first predetermined period is smaller than a predetermined number of times and a second number of times of bleeding in a second predetermined period is equal to or more than the predetermined number of times, generate eighth alert information indicating that there is a high possibility that the user is bleeding at the time of evacuation or urination. The modification would have been feasible because the modified Sato already applies predetermined-number threshold logic over a period to generate alerts (Sato, ¶[0146]), and Hall teaches detecting bleeding during excreta events, recording each event as unique excreta data, aggregating those events over defined periods, and generating alerts when measured excreta data meets a pre-specified condition (Hall, ¶[0044]; ¶[0053]; ¶[0055]; ¶[0068]; ¶[0069]). The benefit of the combination would be enabling earlier detection and user notification of potentially concerning bleeding trends based on automatically monitored excreta events, thereby improving health monitoring and prompting timely follow-up.
Regarding claim 13, the modified Sato does not fully teach that the related data includes at least one of meal content of the user, an environment in a house of the user, and an activity amount of the user. Rather, the modified Sato teaches receiving and using user information, address information, and environmental information, and analyzing detailed excretion information in view of that environmental information for each user (Sato, ¶[0266]: “the reception unit receives user information… and receives environmental information including weather information and infectious disease spread information…”, Sato, ¶[0267]: “the information processing unit analyzes, from the user information, the address information, the environmental information, and the detailed information, a tendency of a time change in the defecation according to the environmental information for each user”). However, it does not expressly teach acquiring and outputting, as related data associated with the alert information, meal content of the user, an environment in a house of the user, or an activity amount of the user.
Amin teaches that a smart toilet system can generate user-directed health information that incorporates meal content and activity-related information, including recommendations about “foods to avoid”, “foods to eat”, and “exercises/actions to perform” (Amin, ¶[0088]). Amin further teaches acquiring and tracking actual user meal behavior, for example by learning “via user input” that the user “properly increased consumption of yogurt” as previously suggested (Amin, ¶[0048]). Amin also teaches determining meal content based on sample analysis by identifying “foods consumed” and related nutritional components (Amin, ¶[0098]).
Hall teaches that wellness information tracked by user devices includes “wearable fitness trackers” and “digital food diaries” (Hall, ¶[0030]), which correspond to acquiring related data indicating an activity amount of the user and meal content of the user.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Sato in view of Amin and Hall to acquire related data including at least one of meal content of the user, an environment in a house of the user, and an activity amount of the user, and to output that related data together with the generated alert information. The modification would have been feasible because Sato already receives and uses contextual environmental and user information alongside excretion information for analysis, and Amin and Hall teach specific types of user context and tracked lifestyle information, including food intake and exercise or activity information, that can be acquired and associated with user-directed health outputs. The benefit of the combination would be improving interpretability and usefulness of alert information by providing lifestyle context and actionable guidance aligned with the user’s habits and environment.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Sato et al. (US 20230225714 A1), hereto referred as Sato, and further in view of Amin (US 20190062813 A1), hereto referred as Amin, and further in view of Ramesh et al. (US 20190046039 A1), hereto referred as Ramesh, and further in view of Takasu et al. (U 20060115540 A1), hereto referred as Takasu.
The modified Sato teaches claim 1 as described above.
Regarding claim 6, the modified Sato teaches that the excretion related data includes excrement type data indicating that excrement of the user is urine (Sato, ¶[0170], “the excretion information may include an excretion date and time (occurrence date and time), a kind of excrement (information indicating any of urination, defecation, and a foreign body), the amount of urination (for example, information indicating any of great, normal, and small), and a shape of defecation (for example, information indicating any of hard, normal, and diarrhea)”, Sato teaches that the excretion information includes a “kind of excrement” that can indicate “urination”, which is excrement type data indicating that excrement of the user is urine); the reference database includes a first number of times of urination indicating an average number of times of urination per day in the first predetermined period (Sato, ¶[0170], “the excretion information may include a color of defecation, and may further include a count (a count of urination and defecation in one day)”, Sato teaches collecting a “count of urination … in one day”, which is a number of times of urination per day that can be aggregated over the first predetermined period and used to determine an average per day for that first predetermined period); in generation of the plurality of types of alert information, a second number of times of urination indicating an average number of times of urination per day in the second predetermined period is calculated (Sato, ¶[0145]: “The output may be a tendency (such as an average interval) of urination and defecation”, Sato teaches outputting a “tendency” of urination and defecation based on non-real time analysis, which corresponds to calculating a period-based statistical value for urination in a later period for comparison to a prior baseline; ¶[0170], “may further include a count (a count of urination and defecation in one day)”; ¶[0175], “the extracted information may be classified into information about an occurrence date and time (occurrence month in this example)”; “A tendency of the defecation shape can be viewed for each month”, Sato teaches collecting a per day “count of urination” and also teaches classifying aggregated excretion information by “occurrence month” so that a “tendency” can be viewed “for each month”, which is conceptually consistent with calculating, for a given second predetermined period defined by month, an average number of times of urination per day based on the per day urination counts in that month).
Also regarding claim 6, the modified Sato does not fully teach that in a case where the second number of times of urination is larger than the first number of times of urination, fifth alert information of the plurality of types of alert information indicating that the user tends to have frequent urination is generated. The modified Sato teaches collecting excretion information including “a kind of excrement (information indicating any of urination, defecation, and a foreign body)” and “a count (a count of urination and defecation in one day)” (Sato, ¶[0170]). Thus, the modified Sato supports determining a first number of times of urination per day across a first predetermined period and calculating a first average number of times of urination per day for that first predetermined period, and also supports determining a second number of times of urination per day across a second predetermined period and calculating a second average number of times of urination per day for that second predetermined period (Sato, ¶[0170]). However, the modified Sato does not expressly teach, in a case where the second number of times of urination is larger than the first number of times of urination, generating fifth alert information indicating that the user tends to have frequent urination.
Takasu teaches that “Urinary frequency is a state where number of times of urination is more than the normal one and is said to be not less than about two times at night and not less than about 8 times during 24 hours” (Takasu, ¶[0019]). Takasu therefore provides express support that an increased number of times of urination corresponds to a recognized frequent urination condition, which fills the gap left by the modified Sato by providing technical context for generating alert information indicating that the user tends to have frequent urination when the user’s average number of times of urination per day increases relative to a baseline.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the modified Sato in view of Takasu to, in a case where the second number of times of urination is larger than the first number of times of urination, generate fifth alert information indicating that the user tends to have frequent urination. The modification would have been feasible because the modified Sato already teaches collecting per day urination counts and generating notifications that may include alerts (Sato, ¶[0123]; ¶[0170]), and Takasu teaches a clinical definition of urinary frequency based on an increased number of times of urination (Takasu, ¶[0019]), such that applying Takasu’s urinary frequency interpretation to the modified Sato’s determined urination count averages across predetermined periods is a straightforward application of known symptom interpretation to the monitored urination count data. The benefit of the combination would be enabling clearer, user understandable alerting tied to a recognized frequent urination condition, thereby improving monitoring and prompting earlier user attention or further evaluation.
Response to Arguments
Objections
Applicant's arguments filed 4/20/2026, page 10, regarding the previous Objections of claims 10 and 11 have been fully considered and are persuasive. The previous Objections have been withdrawn. However, there is a new objection as shown above.
35 U.S.C. §112(f)
Applicant's arguments filed 4/20/2026, page 11, regarding the previous 112(f) interpretations of claim 14 has been fully considered and are persuasive. The previous 112(f) interpretations have been withdrawn.
35 U.S.C. §112(b)
Applicant's arguments filed 4/20/2026, page 11, regarding the previous 112(b) Rejections of claims 1-13 have been fully considered and are persuasive. The previous 112(b) rejections have been withdrawn.
35 U.S.C. §101
Applicant's arguments filed 4/20/2026, pages 11-12, regarding the previous 101 Rejections of claims 1-15 have been fully considered but not are persuasive.
Applicant’s Argument: Applicant argues that independent claims 1, 14, and 15 have been amended to recite an improvement in health-monitoring technology by increasing the accuracy of outputted alert information for a patient. Applicant asserts that this improvement is accomplished by determining in advance, according to the type of alert information to be output, the second predetermined period for acquiring data associating excretion-related sensor data and a user ID. Applicant further argues that an evacuation-related alert uses a relatively long data-acquiring period, while a urination-related alert uses a relatively short data-acquiring period, and that this integrates the claims into a practical application.
Examiner’s Response: Applicant’s argument has been considered but is not persuasive. As set forth in the updated rejection of claims 1, 14, and 15 under 35 U.S.C. § 101 in this Office Action, the amendments do not alter the conclusion that claims 1, 14, and 15 are directed to collecting excretion-related data, associating that data with a user ID, generating a reference database indicating an excretion tendency, acquiring additional excretion-related data over a predetermined period, generating alert information based on the reference database and the additional data, and outputting the alert information.
The newly added limitation that the second predetermined period is determined in advance according to the one of the plurality of types of alert information to be output falls within the same abstract idea identified in the rejection. In particular, the amended period-selection limitation falls at least within the mental-process category because it recites a judgment about which observation period should be used for a particular alert type. Selecting a longer or shorter observation window according to the type of health-related alert is an evaluation or judgment about what information should be collected and analyzed for a given alert, and does not change the character of the claim from abstract data collection, data organization, comparison, and alert generation.
The amended limitation also does not integrate the judicial exception into a practical application under Step 2A, Prong Two. Applicant characterizes the amendment as an improvement to health-monitoring technology, but the claim does not recite any improvement to the sensor hardware, toilet structure, user identification mechanism, processing architecture, data storage structure, or output mechanism. Although health monitoring may be considered a technical field, the claim does not reflect an improvement to health-monitoring technology as a technical field because the claim does not alter the technical operation of any component of the monitoring system. Rather, the claim improves, at most, the informational basis for generating the alert.
The cited portion of the specification relied upon by Applicant confirms this point. Applicant identifies paragraph [0095] as explaining that evacuation may use a longer period because evacuation is performed less frequently and is affected by dietary intake or physical condition, while urination may use a shorter period because urination occurs more frequently and certain urinary conditions may change rapidly. This disclosure describes the medical or informational reason for choosing different observation windows, but it does not describe a technical improvement to the computer, sensor, toilet device, memory, processor, database structure, recording medium, or output mechanism. The improvement alleged by Applicant is therefore an improvement to the abstract analysis itself, namely selecting an observation period intended to improve the accuracy of health-related alert information.
Furthermore, the amended claim language recites the period-selection limitation at a high level of generality, namely determined in advance according to the one of the plurality of types of alert information to be output, without specifying any technical parameter, data structure, or processing step that would distinguish the claimed period selection from an abstract mental judgment or data-analysis rule about appropriate observation windows for different health conditions. The claims do not recite any particular period length, any particular relationship between the period and a measured physiological frequency, any particular algorithm for selecting the period, or any particular improvement to the way sensor data is technically acquired, stored, processed, or output.
Under Step 2B, the additional elements, whether considered individually or as an ordered combination, do not amount to significantly more than the judicial exception. The combination of selecting a data-acquisition period based on alert type and then comparing the acquired data against a reference database to generate alert information does not produce a technical improvement to the monitoring system. It produces, at most, more accurate information analysis, which remains within the abstract idea regardless of whether the elements are considered individually or in combination. The recited computer, processor, non-transitory computer readable recording medium, sensor installed in a toilet, user ID association, reference database, predetermined data-acquisition periods, alert generation, and alert output are each recited at a high level of generality and are used in their ordinary capacities to perform the abstract data-analysis process. The analysis of claim 1 applies equally to independent claims 14 and 15 because claims 14 and 15 implement the same abstract data-analysis process using the same conventional components, namely a processor in claim 14 and a non-transitory computer readable recording medium storing a program in claim 15, and neither claim recites a technical improvement to those components.
For at least these reasons, the rejection of claims 1-15 under 35 U.S.C. § 101 is maintained.
35 U.S.C. §103
Applicant's arguments filed 4/20/2026, pages 13-14, regarding the previous 103 Rejections of claims 1-15 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. That is, there are new grounds of rejection.
Applicant’s Argument: Applicant argues that independent claims 1, 14, and 15 have been amended to require that the second predetermined period, for acquiring data associating excretion related sensor data and the user ID, is determined in advance according to the one of a plurality of types of alert information to be output. Applicant asserts that Sato and Amin fail to disclose this feature. Applicant further argues that Sato ¶[0100], ¶[0101], and ¶[0175] merely disclose user identification and monthly aggregation of excretion-related data, and do not disclose that the second predetermined period is determined in advance according to a type of alert information to be output. Applicant also asserts that Amin fails to disclose this feature.
Examiner’s Response: Applicant’s argument has been considered but is not persuasive in view of the rejection as updated in this Office Action. The Examiner acknowledges that Sato ¶[0100], ¶[0101], and ¶[0175], standing alone, are not relied upon as expressly teaching that the second predetermined period is determined in advance according to the particular type of alert information to be output. Rather, Sato is relied upon for the toilet-based excretion monitoring framework, user-associated excretion data, the excretion information database and aggregate tables, and multiple excretion-related alert categories, including constipation alerts, urination disorder alarms, and defecation interval alarms. Amin is relied upon for using prior waste analyses, past profiles, reference values, and databases to improve current diagnoses or recommendations that are communicated to the user by alerts.
The rejection has been updated to further apply Ramesh for the amended limitation requiring the second predetermined period to be determined in advance according to the one of the plurality of types of alert information to be output. Under the broadest reasonable interpretation, determined in advance requires that the second predetermined period be preconfigured based on the alert type before, or at the initiation of, acquisition of the second data for that alert type, rather than being selected only after the data collection is complete. Ramesh teaches a health-monitoring framework in which different monitored conditions and sensor types use different predetermined sensing frequencies, time intervals, and monitoring windows. In particular, Ramesh teaches that “each sensor has different sensing frequencies,” including BP and blood glucose measured “only twice or thrice in a day,” while ECG may be continuously monitored and transmitted. Ramesh further teaches condition-specific monitoring intervals and alert criteria, including long-term trend analysis for obstructive sleep apnea and blood glucose severity levels based on measurements over a long time interval.
Thus, the rejection does not depend on Sato’s monthly aggregation alone to satisfy the amended alert-type-dependent period limitation. The modified Sato system already generates multiple excretion-related alert types, and Ramesh teaches that health-monitoring acquisition and assessment periods may be preconfigured according to the monitored condition and the temporal characteristics of the relevant physiological data. It would have been prima facie obvious before the effective filing date of the claimed invention to have further modified the modified Sato system in view of Ramesh so that the second predetermined period for acquiring the second data is determined in advance according to the particular excretion-related alert type to be output. Ramesh is reasonably pertinent to the problem addressed by the amended limitation because it addresses selection of data collection and assessment windows for health condition monitoring systems that generate condition-specific alerts.
Applicant’s argument that Sato and Amin do not expressly disclose the amended limitation does not overcome the rejection because the rejection is based on the combined teachings of Sato, Amin, and Ramesh. The applied combination uses Sato for the toilet-based excretion alert system, Amin for generating alerts based on prior/reference data and later-acquired data, and Ramesh for condition-specific predetermined monitoring periods. Accordingly, the rejection of independent claims 1, 14, and 15 is maintained.
Applicant’s Argument: Applicant argues that the dependent claims are allowable for the reasons given for the independent claims and because the dependent claims recite features that are patentable in their own right. Applicant requests individual consideration of the dependent claims.
Examiner’s Response: Applicant’s argument has been considered but is not persuasive. The dependent claims have been individually considered. Applicant has not presented separate substantive arguments identifying a particular limitation of any dependent claim that is not taught or suggested by the applied references. Therefore, the dependent claims are not allowable merely by virtue of their dependency from independent claims 1, 14, and 15.
To the extent the dependent claims were amended to refer to the plurality of types of alert information or one of the plurality of types of alert information, those amendments are addressed by the updated independent-claim rejections and do not separately patentably distinguish the dependent claims. Claim 10 has also been separately addressed in view of the modified Sato system and Ramesh, including Ramesh’s teaching that different monitored conditions and sensor data types may use different predetermined monitoring frequencies or windows. The remaining dependent limitations are addressed by the specific applied combinations set forth in the claim rejections.
Accordingly, the rejections of the dependent claims are maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791
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