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
In the Amendment dated 09 June 2026, the following occurred: Claim 1 was amended.
Claims 1, 2, and 7-10 are pending.
Priority
This application claims priority to Japanese Application No. JP2023-071603 dated 25 April 2023.
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 the 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 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.
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.
The claim further recites the additional element of using a trained neural network to evaluate sensor data and determine whether an excretion has occurred. This represents mere instructions to implement the abstract idea on a generic computer. Implementing an abstract idea using a generic computer or components thereof does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. 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). Alternatively, or in addition, the implementation of the trained neural network to evaluate sensor data and determine whether an excretion has occurred merely confines the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (neural networks) and thus fails to add an inventive concept to the claims.
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.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using the trained neural network to evaluate sensor data and determine whether an excretion has occurred was found to represent mere instructions to implement the abstract idea on a generic computer and/or confine the use of the abstract idea (i.e., the trained model) to a particular technological environment or field of use (neural networks). This has been re-evaluated under the “significantly more” analysis and determined to be insufficient to provide significantly more. MPEP 2106.05(I) indicates that mere instructions to implement the abstract idea on a generic computer and/or confining the use of the abstract idea to a particular technological environment or field of use cannot provide significantly more. See also Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 at 17 (Fed. Cir. April 18, 2025) (finding that applying machine learning to an abstract idea does not transform a claim into something significantly more).
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 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 7-10 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 Maggioni et al. (U.S. Pre-Grant Patent Publication No. 2024/00003681).
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 the 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 the 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
a neural network model
wherein the sensor information is image data including numerical data or a graph indicative of the output signal from the odor sensor, and
Maggioni at Fig. 24(a), Para.0441, 0459 teaches that it was known in the art of computerized healthcare, at the time of filing, to analyze odor data utilizing a trained neural network and to depict odor sensor information in the form of a graph
a neural network model [Maggiono at Para. 0441 teaches analyzing sensor information to identify a smell using a trained neural network (the machine learning model of Mizutani.]
wherein the sensor information is image data including a graph indicative of the output signal from the odor sensor, and [Maggioni at Fig. 24(a), Para. 0459 teaches sensor response data in the form of a graph that is indicative of the output of the sensor. Maggioni at Para. 0038, 0123, 0459 teaches this data (the graph) is used for smell training of the neural network via pattern matching. 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 analyze odor data utilizing a trained neural network and to depict odor sensor information in the form of a graph as taught by Maggioni, with the motivation of improving the accuracy of electronic smell processing (see Maggioni at Para. 0038).
REGARDING CLAIM 2
Mizutani/Khouri/Maggioni teaches the claimed training data generation device comprising at least one processor of Claim 1. Mizutani/Khouri/Maggioni 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/Maggioni further teaches that the notification device includes a memory (computer-readable non-transitory storage medium) at Mizutani Para. 0017.
REGARDING CLAIM 8
Mizutani/Khouri/Maggioni 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/Maggioni teaches the claimed training data generation device comprising at least one processor of Claim 1. Mizutani/Khouri/Maggioni 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/Maggioni teaches the claimed training data generation device comprising at least one processor of Claims 1 and 5. Mizutani/Khouri/Maggioni 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
Claim Objections
Regarding the objection to Claim 1, Applicant has amended the claim to render the objection moot.
Rejection under 35 U.S.C. § 101
Regarding the rejection of Claims rejection of Claims 1, 2, and 7-10, the Examiner has considered the Applicant’s arguments; however, the arguments are not persuasive. Applicant argues:
The present invention not only addresses a general problem in the caregiving field (detecting excretion) but also solves a specific technical problem in the field of computer technology: "how to efficiently generate high-quality training data and build a reliable model in sensor-based machine learning." The existence of this technical problem is explicitly supported in the specification, Paragraph [0005]: "a technique has not been established of efficiently generating training data."
Regarding (a), the Examiner respectfully disagrees. The general problem in the caregiving field is not a technical problem, it is a medical problem as admitted by the Applicant. Even assuming for the sake of argument that "how to efficiently generate high-quality training data and build a reliable model in sensor-based machine learning" is a technical problem, this problem is not solved by the claimed invention. There is no claimed description as to how the training data is generated. Nor is there a claimed description as to how the sensor data is used to build a “reliable model.” Applicant is claiming generic training that uses data in an unspecified manner. This is performing training in its normal manner. Put another way, generically training a model using specific data does not improve the actual model. The model is still the same. Again, Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. April 18, 2025) is directly on point and fully supports the Examiner’s position.
The core of the present invention lies in the wear determination process. This is not merely a part of data collection, but a technical step to actively exclude low-quality (noisy) data from the machine learning process.
Regarding (b), the Examiner respectfully submits that there is no claimed “technical step to actively exclude low-quality (noisy) data from the machine learning process” and thus this argument is irrelevant. Even if it was claimed, this is part of the abstraction and cannot provide a practical application. It also would not improve the machine learning model; it would improve the data (i.e., the abstraction).
The sub-sensor in the present invention is not merely a data source. […] Such a configuration, which utilizes a specific sensor in a specific manner to improve the data processing process itself,….
Regarding (c), the Examiner respectfully submits that the second sensor is absolutely merely a data source. And, the data processing is the abstraction. Thus, as admitted by the Applicant, the abstraction is where the improvement lies. An improved abstract idea is still an abstract idea.
The main focus of the present invention is not the improvement of the machine learning algorithm itself. Rather, it is to construct an improved data processing pipeline for the computer to execute the algorithm effectively.
Regarding (d), the Examiner respectfully thanks the Applicant for admitting on the record that the machine learning algorithm is not improved. Again, the improvement described by the Applicant is an improvement to the analysis of the data. This is the abstraction. An improvement to the abstraction cannot provide a practical application or significantly more.
The invention at issue in the Recentive Analytics case, cited by the Office Action, simply applied machine learning to a new field. It did not, unlike the present invention, introduce a technical improvement to the data generation process itself. Therefore, it is irrelevant and not appropriate to compare the present invention with the Recentive Analytics case when examining its patent subject matter eligibility.
Regarding (e), the Examiner respectfully submits that the Recentive decision is directly relevant to the claimed invention because it evidences (as is now admitted by the Applicant, supra) that there is no improvement to the machine learning. There is also no improvement to the data generation by the sensors. The sensors are operating in their normal capacity. Any improvement is to how the data is analyzed—the abstraction. The Examiner cannot make this point any clearer.
The Examiner cannot suggest a path forward with respect to the lack of subject matter eligibility.
Rejection under 35 U.S.C. § 112
Regarding the written description rejection of Claim 1, the Applicant has amended the claims to overcome the basis of rejection.
Regarding the indefiniteness rejection of Claim 8, the Applicant argues that the present amendments have rendered the rejection of Claim 8 moot. The Examiner disagrees for the reasons presented in the basis of rejection. Given the number of issues, the Examiner suggests cancelling claim 8.
Rejection under 35 U.S.C. § 103
Regarding the rejection of Claims 1, 2, and 7-10, the Examiner has considered the Applicant’s arguments; however, the arguments are moot given the new grounds of rejection necessitated by amendment.
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:
Stockton et al. (U.S. Pre-Grant Patent Publication No. 2021/0365673) which discloses a system that receives body odor sensor data and uses the data in a machine learning model to identify an individual from their smell.
Brown (U.S. Pre-Grant Patent Publication No. 2023/0184652) which discloses an automated assistant that is capable of interpreting smells via a machine learning model.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JASON S TIEDEMAN whose telephone number is (571)272-4594. The examiner can normally be reached 7:00am-4:00pm, off alternate Fridays.
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/JASON S TIEDEMAN/Primary Examiner, Art Unit 3683
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