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 .
DETAILED ACTION
Response to Amendment
The present Office Action is in response to the Request for Continued Examination dated 16 February 2026.
In the Amendment dated 16 February 2026, the following occurred: Claims 1, 7, 9, and 10; Claims 3-6 are cancelled.
Claims 1, 2, and 7-10 are pending.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 16 February 2026 has been entered.
Priority
This application claims priority to Japanese Application No. JP2023-071603 dated 25 April 2023.
Claim Objections
Claim 1 recites “an inference process of inferring…from an odor sensor associated with the service user.” The claim is objected to because “an odor sensor associated with the service user” was previously recited in the claim. From the context of the claim the Examiner believes the limitation should read “from [[an]] the odor sensor associated with the service user.” Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, and 7-10 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.
Claims 1 and 7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
The claim recites a device and computer-readable non-transitory storage medium (“CRM”) for inferring a type of excretion of a service user, which are within a statutory category.
Step 2A1
The limitations of (Claim 1 being representative) determining, in view of [sub-sensor information] from a sub-sensor provided close to an odor sensor associated with a service user, whether or not the odor sensor is worn by the service user; acquiring [odor sensor information]; acquiring type information indicative of a type of excretion of the service user, the type having being determined by a service provider; and generating training data which includes sensor information indicative of the output signal having been acquired in the first acquisition process from the odor sensor that has been, in the wear determination process, determined to be worn by the service user and which includes the type information having been acquired in the second acquisition process, wherein the training data is associated with the service user; generating, using training data generated in the training data generation process, a model into which sensor information indicative of an output signal from an odor sensor is to be inputted and from which type information indicative of a type of excretion is to be outputted; and inferring, with use of the model generated in the [generating step], a type of excretion of a service user in view of an output signal from [the] odor sensor associated with the service user, wherein the sensor information is image data including numerical data or a graph indicative of the output signal from the odor sensor, and the type information is a string or symbol, as drafted, is a process that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components, except as indicated below. The claims encompass a series of rules or instructions for a person or persons to follow, with or without the aid of a computer, to infer a type of excretion of a service user (see Spec. Para. 0001, 0003 describing the inferring as a human activity) in the manner described in the identified abstract idea, supra. The rules or instructions are the claimed steps of “determining… acquiring… acquiring… generating… and inferring” as indicated supra.
Other than reciting generic computer components (discussed infra), i.e., a system implemented by a data processor (computer), the claimed invention amounts to managing personal behavior or interaction between people. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The Examiner notes that the limitations of “generating training data” and “generating, through machine learning using training data generated…a model,” when given their broadest reasonable interpretation in light of the disclosure, represents the creation of mathematical interrelationships between data and the application of this data to a “model,” respectively. The particular way in which the training data is generated is not described in the as-filed disclosure. The particular way the training data is used to generate a model is also not described. As such, the Examiner is required to analyze the “generating training data” and “generating, through machine learning using training data generated…a model” steps given their broadest reasonable interpretation.
The generation of the training data represents a mathematical concept that is interpreted to be part of the identified abstract idea, supra. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. The training of the model is considered to be part of the abstract idea because it falls under data manipulations that humans perform and thus are part of the rules or instructions; humans routinely fit data to models.
Finally, the model itself is described in the Specification at Para. 0033 as “any model” which is further described as encompassing decision trees. As such, the “model” encompasses simplistic mathematical models that are interpreted to be part of the rules or instructions that a person or persons would follow; a person having skill in the art would be able to perform the noted types of data manipulation. The model is thus considered to be part of the abstract idea because it falls under data manipulations that humans perform and thus are part of the rules or instructions.
Step 2A2
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of at least one processor and at least one memory storing instructions (Claim 1) or a CRM/computer storing a program (Claim 7) that implements the identified abstract idea. The at least one processor/memory and/or CRM/computer are not described by the applicant and are recited at a high-level of generality (i.e., one or more generic computers or components thereof) such that it amounts no more than mere instructions to apply the exception using one or more generic computers or components thereof. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim further recites the additional elements of a sub-sensor and an odor sensor. The sensor and sub-sensor merely generally link the abstract idea to a particular technological environment or field of use. MPEP 2106.04(d)(I) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide a practical application. Accordingly, even in combination, this additional element does not integrate the abstract idea into a practical application.
Step 2B
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using at least one processor and at least on memory having instructions and/or a CRM/computer storing a program to perform the noted steps amounts to no more than mere instructions to apply the exception using one or more generic computers or components thereof. Mere instructions to apply an exception using one or more generic computers or components thereof cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a sub-sensor and an odor sensor were determined to generally link the abstract idea to a particular technological environment or field of use. This has been re-evaluated under the “significantly more” analysis and has also been found insufficient to provide significantly more. MPEP 2106.05(A) indicates that generally linking an abstract idea to a particular technological environment or field of use cannot provide significantly more. Accordingly, even in combination, this additional element does not provide significantly more. As such the claim is not patent eligible.
Claims 2, and 8-10 are similarly rejected because they either further define/narrow the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible even when considered individually or as an ordered combination.
Claim(s) 2 merely describe(s) receiving input and outputting information, which further defines the abstract idea.
Claim 2 also includes the additional element of “a notification terminal” which generally links the claimed invention to a particular technical environment or field of user and is insufficient to provide practical application or significantly more (see MPEP citations, supra).
Claim(s) 8 merely describe(s) performing (or reperforming, see 112(b) rejection) the functions of Claims 1, which further defines the abstract idea.
Claim 8 further defines which if the at least one processor and at least one memory of Claim 1 performs the abstract idea. These processors and memories are analyzed in the same manner as the processors and memories of Claim 1.
Claim(s) 9, 10 merely describe(s) the model, which further defines the abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 2, and 7-10 are rejected for lack of adequate written description.
Claim 1 recite functional step for which the Applicant has not adequately described the steps in sufficient detail for one of ordinary skill in the art to conclude that the Applicant had possession of the invention at the time of filing. This is a new matter rejection.
Specifically, the claim recites “wherein the sensor information is image data including numerical data or a graph indicative of the output signal from the odor sensor.” This limitation does not find support in the Specification. The Specification at Para. 0031 states: “Here, the sensor information may be, for example, numerical data indicative of an output signal from the odor sensor. Alternatively, the sensor information may be image data including a graph indicative of an output signal from the odor sensor (emphasis added).” The sensor data may be numerical data or the sensor data may be an image in the form of a graph. There is no disclosure of the image data including numerical data. See also Fig. 3.
By virtue of their dependence from Claim 1, this basis of rejection also applies to dependent Claims 2 and 7-10.
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.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 8 recites “the at least one other processor being configured to carry out, in accordance with instructions contained in a program stored in the at least one other memory, a model generation process of generating, through machine learning using the training data received from the at least one processor of the training data generation device, a model into which sensor information indicative of an output signal from an odor sensor is to be inputted and from which type information indicative of a type of excretion is to be outputted […] inferring, with use of a model received from the at least one other processor of the model generation device, a type of excretion of a service user in view of an output signal from an odor sensor associated with the service user. The claim is indefinite because it is unclear whether these features are required to be performed multiple times. Claim 1, from which Claim 8 depends, previously recited generating a model in the same manner as in Claim 8. Claim 1 also previously recited inferring the type of excretion in the same manner as Claim 8. It is unclear whether the claim performs these steps again (in which case there are multiple antecedent issues) or is attempting to claim that these steps of Claim 1 are performed on a different processor. The Examiner assumes the latter and has evaluated the claim as such. Given the number of issues, the Examiner suggests cancelling Claim 8.
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 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, and 4-10 is/are rejected under 35 U.S.C. § 103 as being unpatentable over Mizutani et al. (Japanese document JP2022-086951) in view of Khouri (U.S. Pre-Grant Patent Publication No. 2003/0120136) in view of Abraham et al. (U.S. Pre-Grant Patent Publication No. 2013/0110063).
Note: this Office Action will reference the translation of the Mizutani document provided by the Applicant in the IDS dated 07 October 2024.
REGARDING CLAIM 1
Mizutani teaches the claimed training data generation device comprising at least one processor and at least one memory, [Para. 0017 teaches a computer having a processor and memory.] the at least one processor being configured to carry out, in accordance with instructions contained in a program stored in the at least one memory: [Para. 0018 teaches a program stored in the memory.]
[…];
a first acquisition process of acquiring an output signal from the odor sensor; [Para. 0011, 0012 teaches an odor sensor device attached to a user the senses odor of an excretion. Para. 0012 teaches that the odor sensor outputs a signal.]
a second acquisition process of acquiring type information indicative of a type of excretion of the service user, the type having being determined by a service provider; and [Para. 0026 teaches that the service provider inputs excretion information (“type information” see Para. 0035) indicating the excretion mode of the service user (a type of excretion of the service user).]
a training data generation process of generating training data which includes sensor information indicative of the output signal […], [Para. 0029 teaches that the inputted excretion information and the associated odor signal from the odor sensor are used as teacher data for a learned model for estimating excretion.] and type information [Para. 0029 teaches that excretion information input by the service provider is also used as teacher data.] wherein the training process is associated with the service user. [Para. 0011, 0023, 0026 teaches that the data collection is associated with the service user.]
a model generation process of generating, using training data generated in the training data generation process, a model into which sensor information indicative of an output signal from an odor sensor is to be inputted and from which type information indicative of a type of excretion is to be outputted; and [Para. 0029 teaches that a trained model is built from the training data and that the trained model receives odor sensor data and outputs the excretion mode (information indicative of a type of excretion, see Para. 0005). The Examiner notes the “into which…” is all an intended use of the model; however, Para. 0029 teaches these features.]
an inference process of inferring, with use of the model generated in the model generation process, a type of excretion of a service user in view of an output signal from an odor sensor associated with the service user, [Para. 0029 teaches that a trained model is built from the training data and that the trained model receives odor sensor data and outputs the excretion mode (information indicative of a type of excretion, see Para. 0005).]
wherein the sensor information is […] the output signal from the odor sensor, and [Para. 0012, 0029 teaches a sensor output signal from the sensor is used to estimate (infer) the excretion mode.]
the type information is a string or symbol. [Para. 0035 teaches that the excretion information / type information are words (strings) such as “poop” or “pee.” See Spec. Para. 0031.]
Mizutani may not explicitly teach
a wear determination process of determining, in view of an output signal from a sub-sensor provided close to an odor sensor associated with a service user, whether or not the odor sensor is worn by the service user;
[…] having been acquired in the first acquisition process from the odor sensor that has been, in the wear determination process, determined to be worn by the service user and which includes the type information having been acquired in the second acquisition process […].
Khouri at Fig. 1, Para. 0009, 0010 teaches that it was known in the art of computerized healthcare, at the time of filing, to verify that a patient is wearing a sensor using data a different sensor
a wear determination process of determining, in view of an output signal from a sub-sensor provided close to an odor sensor associated with a service user, whether or not the odor sensor is worn by the service user; [Khouri at item 36, Fig. 1, Para. 0009 teaches a temperature sensor (sub-sensor; see Spec. Para. 0017) that is used to confirm that a patient is wearing a monitoring device. Khouri at Item 38, Fig. 1, Para. 0009 teaches that the device includes a 3rd sensor (the odor sensor of Mizutani). Khouri at item 40, Fig. 1, Para. 0010 teaches that data from both sensors are sent to a central computing device (the computer of Mizutani).]
[…] having been acquired in the first acquisition process from the odor sensor that has been, in the wear determination process, determined to be worn by the service user and which includes the type information having been acquired in the second acquisition process, […]. [Khouri at item 40, Fig. 1, Para. 0010 teaches that data from both sensors (sensor information indicative of the output signal having been acquired; the data of Mizutani) are sent to a central computing device (the computer of Mizutani) and analyzed.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the teaching of an odor excretion learned model using service provider labeling of Mizutani to verify that a patient is wearing a sensor using data a different sensor as taught by Khouri, with the motivation of improving accuracy of collected patient data.
Mizutani/Khouri may not explicitly teach
wherein the sensor information is image data including numerical data or a graph indicative of the output signal from the odor sensor, and
Abraham at Fig. 3, Para. teaches that it was known in the art of computerized healthcare, at the time of filing, to depict odor sensor information in the form of a graph
wherein the sensor information is image data including numerical data or a graph indicative of the output signal from the odor sensor, and [Abraham at Fig. 3, Para. 0031, 0032, 0065 teaches a graph indicating gas concentrations monitored by a sensor. The graph is interpreted to correspond to the information indicative of the sensor data of Mizutani.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art of computerized healthcare, at the time of filing, to modify the teaching of an odor excretion learned model using service provider labeling of Mizutani having the verification that a patient is wearing a sensor using data a different sensor of Khouri to depict odor sensor information in the form of a graph as taught by Abraham, with the motivation of improving usability of collected sensor data.
REGARDING CLAIM 2
Mizutani/Khouri/Abraham teaches the claimed training data generation device comprising at least one processor of Claim 1. Mizutani/Khouri/Abraham further teaches
wherein:
the at least one processor further carries out a notification process of notifying a notification terminal used by the service provider that the excretion of the service user has been detected; and [Mizutani at Para. 0031, 0043, 0046 teaches that a notification screen is displayed on the notification device using the processor of the notification device.]
the type information is inputted by the service provider with use of the notification terminal. [Mizutani at Para. 0026 teaches that the service provider inputs excretion information indicating the excretion mode of the service user into the input screen of a notification device (notification terminal).]
REGARDING CLAIM(S) 7
Claim(s) 7 is/are analogous to Claim(s) 1, thus Claim(s) 7 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Mizutani/Khouri further teaches that the notification device includes a memory (computer-readable non-transitory storage medium) at Mizutani Para. 0017.
REGARDING CLAIM 8
Mizutani/Khouri/Abraham teaches the claimed system comprising the training data generation device according to claim 1 [see rejection of Claim 1], a model generation device [Mizutani at Para. 0029 teaches the functionality of the model generation device and thus teaches the model generation device.], and an inference device, [Mizutani at Para. 0029 teaches the functionality of the inference device and thus teaches the inference device.]
the model generation device [Mizutani at Para. 0029 teaches the functionality of the model generation device and thus teaches the model generation device.] comprising at least one other processor and at least […] memory, [Mizutani at Para. 0007, Claim 1 teaches at least one processor, meaning additional processors that perform the disclosed functionality are contemplated (one of which is interpreted as “at least one other processor.”)]
the at least one other processor being configured to carry out, in accordance with instructions contained in a program stored in the at least […] memory, a model generation process of generating, through machine learning using the training data received from the at least one processor of the training data generation device, a model into which sensor information indicative of an output signal from an odor sensor is to be inputted and from which type information indicative of a type of excretion is to be outputted, [Mizutani at Para. 0029 teaches creation of a machine learning trained model that estimates the excretion mode from the output signal of the odor sensor using such a training data set.]
the inference device comprising at least one further other processor and at least […] memory, [Mizutani at Para. 0007, Claim 1 teaches at least one processor, meaning additional processors that perform the disclosed functionality are contemplated (one of which is interpreted as “at least one further other processor.”)
the at least one further other processor being configured to carry out, in accordance with instructions contained in a program stored in the at least […] memory, an inference process of inferring, with use of a model received from the at least one other processor of the model generation device, a type of excretion of a service user in view of an output signal from an odor sensor associated with the service user. [Mizutani at Para. 0007, 0029, 0043, 0046 teaches that the trained system detects excretion based on sensor data, which i occurs via implementation of the trained ML model.]
Mizutani in view of Khouri may not explicitly teach that at least one other memory performs the model generation and at least one further other memory performs the inferring; however, the noted features would have been prima facie obvious to one of ordinary skill in the art at the time of the invention in view of the teaching of Mizutani/Khouri based on the duplication of parts rationale (see In re Harza, MPEP 2144.04(VI)(B)). Mizutani teaches model generation [Mizutani at Para. 0029] and inferring [Mizutani at Para. 0007, 0029, 0043, 0046]. The implementation of the recited features by additional memories produces no new and unexpected result which would result in patentable significance over the teaching of Mizutani/Khouri; the application of additional memories does not change how the claim effects model generation and inferring. As such these features are obvious in view of Mizutani/Khouri.
REGARDING CLAIM 9
Mizutani/Khouri/Abraham teaches the claimed training data generation device comprising at least one processor of Claim 1. Mizutani/Khouri/Abraham may not explicitly teach
the model is a special model which specifies a service user to which the model is applied.
However, the limitation claims information/labels that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system. The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because the Mizutani teaches a applying a trained model to a patient’s data, substituting the information/labels associated with the trained model of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the trained model of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow). The Examiner notes that the recitation that the model specifies a user in Claim 9 and specifically does not specify a user in Claim 10 indicates that this information is superfluous to the functionality of the claims. Additionally, nothing is ever does as a result of these labels further indicating that the information is non-functional.
REGARDING CLAIM 10
Mizutani/Khouri/Abraham teaches the claimed training data generation device comprising at least one processor of Claims 1 and 5. Mizutani/Khouri/Abraham may not explicitly teach
the model is a general model which does not specify a service user to which the model is applied.
However, the limitation claims information/labels that constitute nonfunctional descriptive information that is/are not functionally involved in the recited system. The function described by the system would be performed the same regardless of whether the claimed information/labels was substituted with nothing. Because the Mizutani teaches a applying a trained model to a patient’s data, substituting the information/labels associated with the trained model of the claimed invention for the information/labels of the prior art would be an obvious substitution of one known element for another, producing predictable results. Therefore, would have been prima facie obvious to one of ordinary skill in the art at the time of filing to have substituted the information/labels applied to the trained model of the prior art with any other information/labels because the results would have been predictable. MPEP 2112.01, Section III (see also In re Ngai, Ex Parte Breslow). The Examiner notes that the recitation that the model specifies a user in Claim 9 and specifically does not specify a user in Claim 10 indicates that this information is superfluous to the functionality of the claims. Additionally, nothing is ever does as a result of these labels further indicating that the information is non-functional.
Response to Arguments
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims rejection of Claims 1-10, Applicant has cancelled Claims 3-6 rendering the rejection of those claims moot. Regarding the remaining claims, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
Amended claim 1 is patent-eligible because the claim is substantially analogous to claim 3 of Example 47, which is considered as patent-eligible in the 2019 Revised Patent Subject Matter Eligibility Guidelines (PEG 2019).
Regarding (a), the Examiner respectfully disagrees. Claim 3 of Example 47 was found to be eligible because it solved a technological problem. Applicant has not identified nor can the Examiner locate any technological problem caused by the technological environment to which the claim is confined, general-purpose computer. See also response to argument (b).
Claim 3 of Example 47 of PEG 2019 is considered as patent-eligible because the limitation in the claim, "a specific technical implementation that performs calculations in real time," ties the algorithm to a technological improvement in electronic payment processing.
Regarding (b), the Examiner respectfully submits that Example 47 is not directed to “electronic payment processing.” It is directed to network anomaly detection. It is also not part of the 2019 PEG. Further, the quoted "a specific technical implementation that performs calculations in real time" does not appear at all in Example 47. It also does not appear in the 2019 PEG or any of the other published examples. The Examiner is not sure what the Applicant is referencing.
More specifically, the PEG 2019 explains that the limitation:
(i) improves computer network operation by rejecting fraudulent transactions before authorization data propagate; and
(ii) prevents resource waste and system load caused by later chargebacks.
Likewise, the following limitations (i)-(ii) in amended claim 1 tie the model to a technological improvement in training data generation processing:
(i) a model generation process of generating, through machine learning using training data generated in the training data generation process, a model into which sensor information indicative of an output signal from an odor sensor is to be inputted and from which type information indicative of a type of excretion is to be outputted; and
(ii) an inference process of inferring, with use of the model generated in the model generation process, a type of excretion of a service user in view of an output signal from an odor sensor associated with the service user.
Regarding (c), the Examiner respectfully submits that Example 47 does not refer to “chargebacks” at all. Nor do any of the other published examples. In any event, training a machine learning model with specific training data is in no way an improvement to the model. See, e.g., Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 10 (Fed. Cir. April 18, 2025) (finding that claims that do no more than apply established methods of machine learning to a new data environment are ineligible). The “inferring” is part of the abstract idea; the abstract idea cannot provide the improvement.
According to MPEP 2106.04(d)(l), to test for this consideration, the specification should first be 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. […] According to the specification, in facilities with the purpose of providing care services and/ or medical services, a technique is needed to notify the service providers (e.g., staff) of types of excretion of the service users as well as of whether the service users (e.g., patients) have excreted. […] However, a technique has not been established of efficiently generating training data necessary for generating a model through machine learning, in particular, through supervised learning.
Regarding (c), the Examiner respectfully submits that the Specification was consulted and no “improvement” was found. Regarding the “providing care services” assertion, MPEP 2106.04(d)(1) states “the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” Here, the argued improvement is an improvement to the abstract idea; an improved abstract idea is still an abstract idea.
Regarding the “training” assertion, the Examiner consulted the Specification and cannot locate any improvement to how the training data is generated. Initially, the Examiner notes that there is no description as to how the training data is generated other than stating that it is “trough supervised learning.” See Spec. Para. 0032, 0033. There is no description as to how the supervised learning is used and thus the Examiner must interpret this a normal supervised learning (otherwise there would be a written description issue). The Claims and Specification merely describe the data that is used to train the model without actually providing a description as to how this data is used such that more efficient training data is generated. Merely generating training data using specific data is not an improvement to how the training data is generated. See Recentive Analytics, Inc. v. Fox Corp. which held that non-specifically claimed training of an AI/ML algorithm is insufficient to provide a practical application or significantly more because it does not result in “improving the mathematical algorithm or making machine learning better” and “[i]terative training using selected training material…are incident to the very nature of machine learning.” The closest the Specification comes to describing the particulars of the generating process is Spec. Para. 0041 which describes setting parameters; however, this is how training data generation is normally performed. The Applicant is invited to point to the portion of the Specification that describes how the data is generated in a more efficient manner, as opposed to using normal training data generation methods.
Rejection under 35 U.S.C. § 112
Regarding the indefiniteness rejection of Claim7, the Applicant has amended the claims to overcome the basis of rejection.
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claims 1-10, Applicant has cancelled Claims 3-6 rendering the rejection of those claims moot. Regarding the remaining claims, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
Applicants note that according to MPEP 2153.01(a), a disclosure made within the grace period is not prior art under AIA 35 U.S.C. 102(a)(l) if it is apparent from the disclosure itself that it is an inventor-originated disclosure. Specifically, Office personnel may not apply a disclosure as prior art under AIA 35 U.S.C. 102(a)(1) if the disclosure: (1) was made one year or less before the effective filing date of the claimed invention; (2) names the inventor or a joint inventor as an author or an inventor; and (3) does not name additional persons as authors on a printed publication or joint inventors on a patent. […] In the present case, the application names additional persons as joint inventors relative to the persons named on the '951 publication. As such, the reference fails to qualify as prior art.
Regarding (a), the Examiner respectfully disagrees. The Applicant reiterates the test of 35 USC 102(a)(1) and MPEP 2153.01 and then fails to actually implement it correctly. As described by the Applicant, a prior art document is not available prior art if it meets all three of the conditions noted above. The Examiner does not dispute that items one and two are met; however, item three is not met in the present case. Item three states that a reference is not prior art if it “does not name additional persons as authors on a printed publication.” Put another way and taking out the double negative, a reference is prior art if it does name additional persons as authors on a printed publication. This is the case here. The inventors of the present application are:
Yoshihisa Suzuki
Masataka Shiraki
Saki Ogaeri
Yuya Suzuki.
The inventors of the ‘951 reference are:
Manase Mitutani
Masataki Shiraki
Yoshihisa Suzuki
Yasuto Yajima
As is plainly evident, Manase Mitutani and Yasuto Yajima are addition authors (inventors) on a printed publication (published Japanese patent application) published prior to the current application. This is the exact circumstance described in the previously cited section of MPEP 2153.01(a). The reference constitutes prior art. The Examiner suggests adding Manase Mitutani and Yasuto Yajima as co-inventors of the current application, which would then disqualify the reference.
This means that in circumstances where an application names additional persons as joint inventors relative to the persons named as authors in the publication (e.g., the application names as joint inventors A, B, and C, and the publication names as authors A and B), […] the publication is not prior art under AIA 35 U.S.C. 102(a)(l).
Regarding (b), the Examiner disagrees. This may or may not be a correct statement; however, this is not the issue and is an incorrect summarization of the above-cited section of MPEP 2153.01. The issue is that the prior art reference names different inventors (Manase Mitutani and Yasuto Yajima) that are not named inventors on the current application. The “additional persons” of MPEP 2153.01 is in reference to the prior art document; where the prior art document names additional authors/inventors as compared to the application, it is not to the same inventive entity and constitutes prior art assuming the other conditions are met. “Office personnel may not apply a disclosure as prior art under AIA 35 U.S.C. 102(a)(1) if the [prior art] disclosure: … (3) does not name additional persons as authors.” See discussion above.
Conclusion
Prior art made of record though not relied upon in the present basis of rejection are noted in the attached PTO 892 and include:
Suzuki et al. (U.S. Pre-Grant Patent Publication No. 2022/0093233) which discloses a system disposed in a toilet for analyzing a patient’s waste to determine an amount of nutrients present.
Yamada et al. (U.S. Pre-Grant Patent Publication No. 2022/0172843) which discloses analyzing the smell of a patient’s urine to determine whether the urine smell is indicative of cancer or not.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683