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 .
Claims 1-15 are the currently pending claims hereby under examination
Claim Objections
Claims 10 and 11 are objected to because of the following informalities:
In claim 10 and 11, line 1: "method according to claims 1" is a typographical error and should be "method according to claim 1".
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 14 recites “a first data acquisition part,” “a reference database generation part,” “a second data acquisition part,” “an alert information generation part,” and “an output part” in lines 2 through 12. Each of these limitations recites a generic term (“part”) coupled with purely functional language (e.g., “acquisition,” “generation,” “output”) without reciting structure for performing the claimed functions. The term “part” is interpreted as a nonce term, and therefore each of these limitations is interpreted to invoke 35 U.S.C. § 112(f). The specification describes an information processing device (server) with a processor and memory, and identifies functional blocks labeled as “first data acquisition part,” “reference database generation part,” “second data acquisition part,” “alert information generation part,” and “alert information output part,” implemented by the processor executing an information processing program (Instant Application, ¶[0084]-[0086]; ¶[0090]-[0092]; ¶[0098]-[0103]; ¶[0129]-[0142]). Accordingly, for claim 14, the corresponding structure for each recited “part” is interpreted as the processor (for example, processor 321) and associated memory storing and executing instructions of the information processing program to perform the respective function, as illustrated by FIGS. 3 to 6 and described in the specification (Instant Application, ¶[0084]-[0086]; ¶[0090]-[0092]; ¶[0098]-[0103]; ¶[0129]-[0142]).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 1 recites "by a computer," in line 2, which is an unclear claim drafting format for a method step because the claim does not specify whether this phrase limits who performs all subsequent steps or only the immediately following step, and the phrase should be rewritten as an introductory limitation (e.g., "performed by one or more processors") or integrated into each step for clarity. The Examiner is interpreting that the computer performs all subsequent steps.
Claims 2-13 are rejected by virtue of their dependence from claim 1.
Claim 9 recites "a predetermined number of times" in line 8 and line 9, which introduces the phrase twice without clarifying whether the two instances refer to the same predetermined number or different predetermined numbers, and antecedent clarity should be provided. The Examiner is interpreting that they are referring to the same predetermined number.
Claim 10 recites “the second predetermined period for acquiring the evacuation related data is longer than the second predetermined period for acquiring the urination related data” in lines 4-5, but claim 1 introduces only one “second predetermined period", and therefore it is unclear how the claim requires two different second predetermined periods and how those periods are selected. The Examiner interprets claim 10 as intending to recite a longer second predetermined period for evacuation related data and a shorter second predetermined period for urination related data.
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]-[E1]
[A1] generating a reference database indicating an excretion tendency of the user based on the acquired first data
[B1] 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
[C1] generating alert information to the user based on the reference database and the second data
[D1] outputting the generated alert information
These elements [A1]-[D1] of claim 1 are drawn to an abstract idea since they at least (1) involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations (e.g., determining excretion tendency based on first data, and generating alert information based on comparing second data to a reference database); and/or (2) they involve a mental process that can be practically performed in the human mind including observation, evaluation, judgment, and opinion and using pen and paper (e.g., reviewing excretion related data over time, determining an excretion tendency, and deciding whether to generate an alert based on a change).
Step 2A – Prong Two: Claim 1 recites the following limitations that are beyond the judicial exception: [A2]-[C2]
[A2] by a computer
[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
[C2] outputting the generated alert information
These elements [A2]-[C2] of claim 1 do not integrate the exception into a practical application of the exception. In particular, the element [A2] is merely an instruction to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Also, the element [B2] is merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality - see MPEP 2106.04(d) and MPEP 2106.05(g). Furthermore, even if considered as an additional element beyond the abstract idea identified in Prong One, the 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, and therefore does not integrate the abstract idea into a practical application - see MPEP 2106.04(d) and MPEP 2106.05(f).
Desjardins: The specification was evaluated to determine if 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 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, and generating advice or notification information based on trends; however, the specification does not describe any improvement to the functioning of a computer itself, does not disclose a specific nonconventional data structure or processing technique, and does not describe any technical improvement to the operation of the sensors or output mechanisms. Instead, the specification frames the invention as applying conventional data acquisition, storage, aggregation, and comparison techniques to excretion-related information, such that claim 1 recites the abstract idea at a high level of generality as data acquisition, analysis, and alert output performed using generic computer technology.
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, and outputting alert information do not qualify as significantly more. The recitation of acquiring excretion related data using a sensor installed in a toilet constitutes insignificant extra-solution activity, namely mere 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. 4-5).
Sato (US 20230225714 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 15b 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, the element [A2] does not qualify as significantly more because this limitation is simply appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014)) and/or a claim to an abstract idea requiring no more than being stored on a computer readable medium which is a well-understood, routine and conventional activity previously known in the industry (see Electric Power Group, 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 110 USPQ2d 1976 (2014); SAP Am. v. InvestPic, 890 F.3d 1016 (Fed. Circ. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taking individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific 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, i.e., the computer is simply a tool to perform the process.
Claims 2-13 depend from claim 1, and recite the same abstract idea as claim 1. Furthermore, these claims only contain recitations that further limit the abstract idea (that is, the claims only recite limitations that further limit the data processing and comparison over time) with the following exceptions:Claim 11: “related data related to the generated alert information is further acquired” and “the related data is output together with the alert information”;Claim 12: “the related data includes a type of medicine taken by the user”;Claim 13: “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”.Each of these claim limitations does not integrate the exception into a practical application. In particular, the elements of claims 11-13 are merely adding insignificant extra-solution activity to the judicial exception, i.e., mere data gathering at a higher level of generality and appending additional information to be output with the alert - see MPEP 2106.04(d) and MPEP 2106.05(g). Also, each of these limitations does not recite additional elements that amount to significantly more than the judicial exception itself because they are merely insignificant extra-solution activity to the judicial exception, e.g., mere data gathering and/or simply displaying the results of the abstract idea using conventional, routine, and well known elements.
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of claims 2-13 as an ordered combination in conjunction with the claims from which they depend (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific 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, i.e., the computer is simply a tool to perform the process.
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] 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
[C1] generates alert information to the user based on the reference database and the second data
[D1] outputs the generated alert information
These elements [A1]-[D1] of claim 14 are drawn to an abstract idea since they at least (1) involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations (e.g., determining an excretion tendency based on first data, and generating alert information based on comparing second data to a reference database); and/or (2) involve mental processes that can be practically performed in the human mind including observation, evaluation, judgment, and opinion (e.g., reviewing excretion related data over time, determining an excretion tendency, and deciding whether to generate an alert based on a change).
Step 2A – Prong Two: Claim 14 recites the following limitations that are beyond the judicial exception:
[A2] an information processing device
[B2] the first data acquisition part 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, the reference database generation part, the second data acquisition part, the alert information generation part, and the output part
These elements [A2]-[B2] of claim 14 do not integrate the exception into a practical application of the exception. In particular, the recitation of an information processing device is merely an instruction to implement the abstract idea on a generic computer, or to use a computer as a tool to perform the abstract idea - see MPEP 2106.04(d) and MPEP 2106.05(f). Further, the recited "parts" are drafted at a high level of generality in purely functional terms and do not impose any technical constraints or improvements on the operation of the device, and therefore do not integrate the abstract idea into a practical application.
Desjardins: The specification was evaluated to determine if 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 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, and generating advice or notification information based on trends; however, the specification does not describe any improvement to the functioning of a computer itself, does not disclose a specific nonconventional data structure or processing technique, and does not describe any technical improvement to the operation of the sensors or output mechanisms. Instead, the specification frames the invention as applying conventional data acquisition, storage, aggregation, and comparison techniques to excretion-related information, such that claim 14 recites the abstract idea at a high level of generality as data acquisition, analysis, and alert output performed using generic computer technology.
Step 2B: Claim 14 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation of an information processing device and functional "parts" for acquiring data, generating a reference database, generating alert information, and outputting alert information do not qualify as significantly more. These limitations merely describe generic computer components performing routine data acquisition, storage, analysis, and output functions, specified at a high level of generality. The recitation of a first data acquisition part 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 constitute insignificant extra-solution activity, namely mere 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. 4-5).
Sato (US 20230225714 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 15b 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.
Accordingly, claim 14 amounts to no more than appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, requiring no more than a generic computer to perform generic computer functions that are well understood, routine and conventional in the industry (see Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016 (Fed. Cir. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of claim 14 as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific 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, i.e., the computer is simply a tool to perform the process.
The analysis of claim 15 is as follows:
Step 1: Claim 15 is drawn to an article of manufacture.
Step 2A – Prong One: Claim 15 recites an abstract idea. In particular, claim 15 recites the following limitations:
[A1] 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
[B1] generate a reference database indicating an excretion tendency of the user based on the acquired first data
[C1] 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
[D1] generate alert information to the user based on the reference database and the second data
[E1] output the generated alert information
These elements [A1]-[E1] of claim 15 are drawn to an abstract idea since they at least (1) involve mathematical concepts in the form of mathematical relationships, mathematical formulas or equations, and/or mathematical calculations (e.g., determining an excretion tendency based on first data, and generating alert information based on comparing second data to a reference database); and/or (2) involve mental processes that can be practically performed in the human mind including observation, evaluation, judgment, and opinion (e.g., reviewing excretion related data over time, determining an excretion tendency, and deciding whether to generate an alert based on a change).
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
[B2] the program causes a computer to function to perform the limitations
These elements [A2]-[B2] of claim 15 do not integrate the exception into a practical application of the exception. In particular, the recitation of a non-transitory computer readable recording medium storing a program is merely an instruction to implement the abstract idea using generic computer technology and/or to generally link the abstract idea to a field of use (a computer environment) - see MPEP 2106.04(d), MPEP 2106.05(f), and MPEP 2106.05(h). Further, the recitation that the program causes a computer to perform the abstract idea does not impose any technical constraints or improvements on computer operation, and therefore does not integrate the abstract idea into a practical application.
Desjardins: The specification was evaluated to determine if 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 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, and generating advice or notification information based on trends; however, the specification does not describe any improvement to the functioning of a computer itself, does not disclose a specific nonconventional data structure or processing technique, and does not describe any technical improvement to the operation of the sensors or output mechanisms. Instead, the specification frames the invention as applying conventional data acquisition, storage, aggregation, and comparison techniques to excretion-related information, such that claim 15 recites the abstract idea at a high level of generality as instructions for data acquisition, analysis, and alert output performed using generic computer technology.
Step 2B: Claim 15 does not recite additional elements that amount to significantly more than the judicial exception itself. In particular, the recitation of a non-transitory computer readable recording medium storing a program that causes a computer to perform the abstract idea does not qualify as significantly more. The non-transitory computer readable recording medium is a conventional storage medium and the computer is a generic computer used as a tool to execute instructions. 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 limitations constitute insignificant extra-solution activity, namely mere 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. 4-5).
Sato (US 20230225714 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 15b 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.
Accordingly, claim 15 amounts to no more than appending well-understood, routine and conventional activities previously known in the industry, specified at a high level of generality, to the judicial exception, requiring no more than a generic computer to perform generic computer functions that are well understood, routine and conventional in the industry and/or requiring no more than being stored on a computer readable medium which is a well-understood, routine, and conventional activity previously known in the industry (see Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016 (Fed. Cir. 2018)).
In view of the above, the additional elements individually do not integrate the exception into a practical application and do not amount to significantly more than the above-judicial exception (the abstract idea). Looking at the limitations of claim 15 as an ordered combination (that is, as a whole) adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer, for example, or improves any other technology. There is no indication that the combination of elements permits automation of specific 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, i.e., the computer is simply a tool to perform the process.
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.
Regarding claim 1, Sato teaches that an excrement analysis apparatus includes a computer (e.g., CPU and memory storing a program) that receives excretion related imaging data from a toilet bowl and performs analysis to output notification information and detailed information: an information processing method comprising: by a computer (Sato, ¶[0072], “The excrement analysis apparatus 1 may include a control unit (not illustrated) that controls the entire excrement analysis apparatus 1… 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”, Sato teaches performing the processing using a computer implemented by a CPU and memory storing a 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, ¶[0100], “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user… The Bluetooth tag held by the user may be set as a different ID for each user”, Sato teaches receiving a user ID for identifying 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”, Sato teaches associating identification data for identifying the user with excretion related data by adding or embedding the identification data into the excretion related output; ¶[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”, Sato teaches acquiring and aggregating excretion related data for each month, such that data acquired and aggregated for one month corresponds to first data associated with the user ID during a first predetermined period); generating a reference database indicating an excretion tendency of the user based on the acquired first data (Sato, ¶[0246], “a storage unit that stores the detailed information received by the reception unit”, Sato teaches storing excretion related detailed information as a database; ¶[0248], “the detailed information includes at least information indicating an excretion date and time, a kind of excrement, and a shape of defecation”, Sato teaches storing time and content information usable to characterize an excretion tendency; ¶[0256], “the information processing unit analyzes a tendency of a time change in the shape of the defecation”, Sato teaches generating an analysis of excretion tendency based on stored excretion related data, where the stored and analyzed data corresponds to the first data acquired and aggregated during the first predetermined period); 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… The Bluetooth tag held by the user may be set as a different ID for each user”, Sato teaches receiving a user ID for identifying 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”, Sato teaches associating identification data for identifying the user with excretion related data by adding or embedding the identification data into the excretion related output; ¶[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”, Sato teaches acquiring and aggregating excretion related data for each month, such that data acquired and aggregated for a subsequent month corresponds to second data associated with the user ID during a second predetermined period).
Also regarding claim 1, Sato does not fully teach generating alert information to the user based on the reference database and the second data; and outputting the generated alert information. Sato teaches acquiring excretion related data, storing excretion related information, analyzing temporal tendencies in that data, generating notification or health advice information for a user, and outputting that information to an external user-accessible apparatus (Sato, ¶[0246]: “a storage unit that stores the detailed information received by the reception unit”; ¶[0256]: “the information processing unit analyzes a tendency of a time change in the shape of the defecation”; ¶[0204]; ¶[0301]: “a first analysis step of analyzing first analysis target data being imaging data input in the input step, and outputting notification information to an observer who observes a user of the toilet”; ¶[0228]; ¶[0297]: “the provision step includes, by the server apparatus, providing a processing result in the information processing step to an external apparatus”). However, Sato does not expressly teach generating alert information based on a comparison between a reference database derived from first-period data and second data acquired in a subsequent predetermined period.
Amin teaches generating user-directed alerts or recommendations by explicitly comparing newly acquired waste data against previously analyzed historical data serving as a baseline. In particular, Amin teaches that “the smart toilet system can determine/estimate the level of efficaciousness of a recommendation by… comparing current waste analysis results with past results”, thereby expressly disclosing comparison of second data against prior analyses (Amin, ¶[0043]: "( e.g., 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, and so on)"). Amin further teaches accessing a database of reference values to evaluate a user’s health and diagnose corresponding conditions (Amin, ¶[0115]: “the diagnostic component 416 can, in some cases, compare this medically significant information with known reference values… the diagnostic component 416 can access a local and/or remote database”), which corresponds to using a reference database derived from prior data. Amin also expressly teaches notifying the user of diagnoses or recommendations by means of visual, audible, or other alerts (Amin, ¶[0051]: “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”). Accordingly, Amin supplies the specific missing concept of generating and outputting alert information by comparing current excretion data against a stored baseline of previously acquired excretion 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 Sato in view of Amin to generate 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 the generated alert information. The modification would have been feasible because Sato already stores, analyzes, and outputs time stamped excretion related information, and Amin teaches using prior analyses as a baseline for generating current user-directed diagnoses or recommendations. The benefit of the combination would be improved personalization and accuracy of alerts by evaluating changes in a user’s excretion condition relative to that user’s historical excretion tendency.
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 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 indicates that there is a high possibility that a meal intake amount of the user is insufficient. 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 there is a high possibility that a meal intake amount of the user is 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 a high possibility that a meal intake amount of the user is 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 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 indicating that the user is highly 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 teaches that the excretion related data includes evacuation related data related to evacuation of the user and urination related data related to urination of the user (Sato, ¶[0170], “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 teaches that excretion related data includes both urination related data and defecation related data).
Also regarding claim 10, the modified Sato does not fully teach that the second predetermined period for acquiring the evacuation related data is longer than the second predetermined period for acquiring the urination related data. Rather, the modified Sato teaches acquiring and analyzing both urination related data and evacuation related data, including aggregating excretion information by time units such as an occurrence month and analyzing tendencies over time. However, the modified Sato does not expressly teach configuring different second predetermined acquisition periods such that the evacuation related data is acquired over a longer period than the urination related 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 configured the modified Sato such that the second predetermined period for acquiring evacuation related data is longer than the second predetermined period for acquiring urination related data. Evacuation related events generally occur less frequently than urination events, and thus meaningful trend analysis for evacuation requires a longer observation window, whereas urination related data can be evaluated over a shorter window due to higher daily frequency. Configuring different acquisition periods for different types of excretion data would have been a routine design choice in view of the differing statistical characteristics of the underlying data streams and would have been readily feasible within the modified Sato framework, which already supports time-based aggregation and tendency analysis. The benefit of the modification would be improved accuracy and stability of evacuation related trend analysis while maintaining timely detection of changes in urination related behavior.
Regarding claim 11, the modified Sato teaches that in generation of the alert information, related data related to the generated 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 alert information, the related data is output together with the 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 that an information processing device comprises: a first data acquisition part 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 excrement analysis apparatus 1 may include a control unit (not illustrated) that controls the entire excrement analysis apparatus 1… 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”, Sato teaches an information processing device implemented by a CPU and memory executing a program; ¶[0100], “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user… The Bluetooth tag held by the user may be set as a different ID for each user”, Sato teaches acquiring a user ID; ¶[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 associating excretion related data with the user ID; ¶[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”, Sato teaches acquiring and aggregating excretion related data for a predetermined period); a reference database generation part that generates a reference database indicating an excretion tendency of the user based on the acquired first data (Sato, ¶[0246], “a storage unit that stores the detailed information received by the reception unit”, Sato teaches a storage unit forming a database of excretion related information; ¶[0256], “the information processing unit analyzes a tendency of a time change in the shape of the defecation”, Sato teaches generating excretion tendency information based on stored data); and a second data acquisition part that 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…”, Sato teaches acquiring the user ID; ¶[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 associating the user ID with excretion related data; ¶[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”, Sato teaches acquiring and aggregating excretion related data for a second predetermined period).
Also regarding claim 14, Sato does not fully teach an alert information generation part that generates alert information to the user based on the reference database and the second data. Rather, Sato teaches an information processing device that acquires excretion related data, associates the excretion related data with a user ID, stores the data, analyzes temporal tendencies in the data, and outputs notification information to an external apparatus (Sato, ¶[0072]; ¶[0100]; ¶[0101]; ¶[0175]; ¶[0246]; ¶[0256]; ¶[0301]; ¶[0228]; ¶[0297]). However, Sato does not expressly teach an alert information generation part that generates alert information to the user based on the reference database and the second data.
Amin teaches not merely comparing two data sets, but generating user-directed diagnoses or recommendations based on an evaluation of current waste data in view of previously analyzed waste data serving as a reference (Amin, ¶[0048]: “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 further teaches that such diagnoses or recommendations are actively communicated to the user via alerts (Amin, ¶[0051]: “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…”), thereby filling the gap in Sato with respect to generating alert information to the user based on the reference database and the second data rather than merely analyzing or comparing data internally.
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 Sato in view of Amin to configure the alert information generation part to generate alert information to the user by applying user-specific evaluation logic that uses a previously generated reference database when assessing subsequently acquired excretion related data, and to output the generated alert information. The modification would have been feasible because Sato already discloses a programmable control unit that stores excretion related information, analyzes temporal tendencies, and outputs notification information to an external apparatus, and Amin teaches applying previously analyzed waste data as a user-specific baseline for generating current diagnoses or recommendations that are communicated to the user via alerts. The benefit of the combination would be improved personalization and clinical relevance of alerts by explicitly linking current excretion conditions to a user’s historical excretion tendencies.
Regarding claim 15, the modified Sato teaches that 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”, Sato teaches a non-transitory computer-readable recording medium storing a program on a computer; ¶[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”, Sato teaches that the stored program causes a computer to implement the described information processing functions; ¶[0100], “The Bluetooth module 14 b is an example of a receiver that receives identification data for identifying a user… The Bluetooth tag held by the user may be set as a different ID for each user”, Sato teaches acquiring a user ID; ¶[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 associating the user ID with excretion related data; ¶[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”, Sato teaches acquiring and aggregating excretion related data for a predetermined period such that the acquired and aggregated data corresponds to first data in the first predetermined period); a reference database generation part that generates a reference database indicating an excretion tendency of the user based on the acquired first data (Sato, ¶[0246], “a storage unit that stores the detailed information received by the reception unit”, Sato teaches storing excretion related detailed information as a database; ¶[0256], “the information processing unit analyzes a tendency of a time change in the shape of the defecation”, Sato teaches generating excretion tendency information based on stored data); and a second data acquisition part that 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…”, Sato teaches acquiring the user ID; ¶[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 associating the user ID with excretion related data; ¶[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”, Sato teaches acquiring and aggregating excretion related data for a second predetermined period).
Also regarding claim 15, Sato does not fully teach an alert information generation part that generates alert information to the user based on the reference database and the second data. Rather, Sato teaches a program stored on a non-transitory computer-readable recording medium that, when executed by a computer, causes the computer to acquire excretion related data, associate the excretion related data with a user ID, store the data, analyze temporal tendencies in the data, and output notification information to an external apparatus (Sato, ¶[0215]; ¶[0214]; ¶[0100]; ¶[0101]; ¶[0175]; ¶[0246]; ¶[0256]; ¶[0301]; ¶[0228]; ¶[0297]). However, Sato does not expressly teach, via the stored program, generating alert information to the user based on the reference database and the second data.
Amin teaches not merely comparing two data sets, but generating user-directed diagnoses or recommendations based on an evaluation of current waste data in view of previously analyzed waste data serving as a reference (Amin, ¶[0048]: “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 further teaches that such diagnoses or recommendations are actively communicated to the user via alerts (Amin, ¶[0051]: “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…”), thereby filling the gap in Sato with respect to causing the computer, via the stored program, to generate alert information to the user based on the reference database and the second data, rather than merely analyzing or comparing data internally.
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 program stored on the non-transitory computer-readable recording medium of Sato in view of Amin to cause the computer to generate alert information to the user by applying user-specific evaluation logic that uses a previously generated reference database when assessing subsequently acquired excretion related data, and to output the generated alert information. The modification would have been feasible because Sato already discloses a program stored on a non-transitory computer-readable medium that causes a computer to perform data acquisition, storage, analysis, and output functions, and Amin teaches applying previously analyzed waste data as a user-specific baseline for generating current diagnoses or recommendations that are communicated to the user via alerts. The benefit of the combination would be improved personalization and clinical relevance of alerts by explicitly linking current excretion conditions to a user’s historical excretion tendencies.
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 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 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 indicating that the user is highly 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 highly 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 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 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 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 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 smaller than the first number of times of urination, sixth alert information indicating that there is a high possibility that a water intake amount of the user is 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 there is a high possibility that a water intake amount of the user is 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 that there is a high possibility that a water intake amount of the user is 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 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 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 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.
Conclusion
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/AARON MERRIAM/Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791