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 .
Election/Restrictions
Applicant's election without traverse of Group I corresponding to claims 1-6 and 9 in the reply filed on 10/08/2025 is acknowledged.
Claims 7, 8, and 10 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected groups II and III, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 10/08/2025.
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 limitations are: learning information storage unit in claims 1 and 9; sound information acquiring unit in claims 1, 5, and 9; predicting unit in claims 1, 5, and 9; output unit in claims 1 and 9.
Claims 1 and 9 recites “information storage unit.” The limitation has been interpreted under 112f as a means plus function limitation because of the combination of a non-structural generic placeholder term “unit” and functional language “learning information storage” without reciting sufficient structure to achieve the function.
Claims 1, 5, and 9 recites “sound information acquiring unit.” The limitation has been interpreted under 112f as a means plus function limitation because of the combination of a non-structural generic placeholder term “unit” and functional language “sound information” without reciting sufficient structure to achieve the function.
Claims 1, 5, and 9 recites “predicting unit.” The limitation has been interpreted under 112f as a means plus function limitation because of the combination of a non-structural generic placeholder term “unit” and functional language “predicting” without reciting sufficient structure to achieve the function.
Claims 1 and 9 recites “output unit.” The limitation has been interpreted under 112f as a means plus function limitation because of the combination of a non-structural generic placeholder term “unit” and functional language “output” without reciting sufficient structure to achieve the function.
Because this/these claim limitations 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.
Claims 1, 5, and 9 regarding “learning information storage unit, sound information acquiring unit, and predicting unit,” are being interpreted as a processor as set in Paragraph 0091.
Claims 1 and 9 regarding “output unit” is being interpreted as a screen or display as set forth in Paragraph 0093.
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 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.
Regarding claim 5 and 9, the claim recites the functions of “machine learning algorithm” respectively, but the specification never discloses the necessary steps and/or flowcharts of how this occurs. The term “machine learning algorithm” is treated as a black box and the specification does not describe the specifics of how to achieve the above-recited function(s) with this algorithm. For example, how many and what types of layers are there? How is the data propagated? What logics are programmed to help the machine learning algorithm make a decision? Is the training supervised or unsupervised? What are the weightings? Are other training concepts used such as regression? It is not enough that a skilled artisan could devise a way to accomplish the function because this is not relevant to the issue of whether the inventor has shown possession of the claimed invention. See MPEP 2161.01(I). Therefore, adequate disclosure is needed.
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 1-6 and 9 are 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In Claims 1 and 9, the claim limitation “learning information configured using….menstruation-related information…is stored,” and “applies the learning information…acquiring menstruation-related information,” renders the claim indefinite because the limitation is unclear. It is unclear whether the menstruation-relation information is the same or different. Additionally, the claim limitations “learning information configured using two or more pieces of training data having sound information” and “the learning information to the sound information” renders the claim indefinite because the limitation is unclear. It is unclear whether the sound information is the same or different. For purposes of examination, the claim limitation is interpreted as different menstruation-related information and sound information, where the process involves training a model with a pre-gathered dataset that correlates specific abdominal sounds, sound information, with menstruation-related information; the trained model can determine menstruation-related information from newly acquired sound information.
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.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed to a recording medium. Under the broadest reasonable interpretation, a recording medium covers an ineligible signal per se. A claim that covers both statutory and non-statutory embodiments (under the broadest reasonable interpretation of the claim when read in light of the specification and in view of one skilled in the art) embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. A recording medium can encompass non-statutory forms of signal transmission, such as, a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the broadest reasonable interpretation of recording medium encompasses transitory forms of signal transmission, the claim is directed to non-statutory subject matter. It is suggested that Applicant amend the claims to recite a “non-transitory computer-readable storage medium” in order to recite statutory subject matter. For the purposes of compact prosecution, the following 101 is made assuming the amendment is made.
Claims 1-6 and 9 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. Each of Claims 1-6 and 9 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 2A, Prong 1
Each of Claims 1 and 9 recites at least one step or instruction for estimating an individual’s blood glucose, which is grouped as a mental process under the 2019 PEG or a certain method of organizing human activity under the 2019 PEG. Accordingly, each of Claims 1 and 9 recites an abstract idea.
Specifically, Claims 1 and 9 recite:
Claim 1 | “A menstruation-related information output apparatus comprising: a learning information storage unit in which learning information configured using two or more pieces of training data having sound information acquired from abdominal sounds of a user and menstruation- related information related to menstruation is stored (Judgement); a sound information acquiring unit that acquires sound information from abdominal sounds of the user (Observation); a predicting unit that applies the learning information to the sound information acquired by the sound information acquiring unit, thereby acquiring menstruation-related information (Judgement); and an output unit that outputs the menstruation-related information acquired (Evaluation/Opinion) by the predicting unit.”
Claim 9 | “A recording medium on which a program is recorded, the program causing a computer capable of accessing a learning information storage unit in which learning information configured using two or more pieces of training data having sound information acquired from abdominal sounds of a user and menstruation-related information related to menstruation is stored (Judgement), to function as: a sound information acquiring unit that acquires sound information from abdominal sounds of the user (Observation); a predicting unit that applies the learning information to the sound information acquired by the sound information acquiring unit, thereby acquiring menstruation-related information (Judgement); and an output unit that outputs the menstruation-related information acquired by the predicting unit (Evaluation/Opinion).”
Regarding the dependent claims, the following dependent claims are directed to steps that are also abstract or organizing human activity:
Claims 2, 3, 4, and 6 include steps that are also abstract as a mental process through additional data gathering or analysis
Claim 5 include steps that are also abstract because the learning unit and prediction unit are updated through a mental process.
Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrowing idea is still an abstract idea and an abstract idea with additional well-known equipment/functions are not significantly more than the abstract idea.
Accordingly, as indicated above, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 1 and 9 (and their respective dependent Claims 2-6) is not integrated into a practical application under 2019 PEG because the additional elements (identified above in independent Claims 1 and 9), either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use. More specifically, the additional elements of: learning information storage unit, sound information acquiring unit, predicting unit, output unit, machine learning algorithm, program, and computer are generically recited computer elements in independent Claims 1 and 9 (and their respective dependent claims) which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 1 and 9 (and their respective dependent claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method and system merely implements the above-identified abstract idea (e.g., mental process and certain method of organizing human activity) using rules (e.g., computer instructions) executed by a computer (e.g., learning information storage unit, sound information acquiring unit, predicting unit, output unit, machine learning algorithm, and program as claimed). In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 1 and 9 (and their respective dependent claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 1 and 9 (and their respective dependent claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 1-6 and 9 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of learning information storage unit, sound information acquiring unit, predicting unit, output unit, machine learning algorithm, program, and computer.
The above-identified additional elements are generically claimed computer components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Per Applicant’s specification, learning information storage unit stores information associated with the user (Paragraph 0066); sound information acquiring unit measures abdominal sounds and makes a menstruation-related information (Paragraph 0039); predicting unit utilizes the machine learning algorithm and program to determine menstrual-related information (Paragraph 0014); output unit and computer displays the menstruation-related information (Paragraph 0082, 0093).
Accordingly, in light of Applicant’s specification, the claimed term learning information storage unit, sound information acquiring unit, predicting unit, and output unit is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the learning information storage unit, sound information acquiring unit, predicting unit, and output unit. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 1-6 and 9 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the apparatuses of Claims 1-6 and 9 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 1-6 and 9 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 1 and 9 (and their dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply instruct the practitioner to implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 1-6 and 9 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 1-6 and 9 amounts to significantly more than the abstract idea itself. Accordingly, Claims 1-6 and 9 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, 5 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Lakdawala (US -20160139156 A1) in view of Momeni et al. (US 20190298295 A1).
Regarding Claim 1, Lakdawala discloses a menstruation-related information output apparatus (apparatus – element 20; Paragraphs 0050, 0079, 0099) comprising:
a storage unit (processor – element 34) acquiring the abdominal sound and menstruation-related information related to menstruation is stored (Paragraph 0050, 0099, 0105)
a sound information acquiring unit (processor – element 34) that acquires sound information from abdominal sounds of the user (Paragraph 0099; [Examiner’s note, the abdominal sounds of the user are collected by the sensing unit (50), then sent to the base unit (30) within the apparatus (20), which is then sent to the processor (34).]);
a predicting unit (processor – element 34) that applies the sound information acquired by the sound information acquiring unit, thereby acquiring menstruation-related information (Paragraph 0105); and
an output unit (portable device – element 12; Paragraph 0051) that outputs the menstruation-related information acquired by the predicting unit (Paragraphs 0049-0050, 0105).
Lakdawala is silent in teaching a learning information storage unit in which learning information configured using two or more pieces of training data having sound information acquired from abdominal sounds of a user and menstruation- related information related to menstruation is stored;
Momeni teaches a learning information storage unit (Momeni | classifier process – element 140; Paragraphs 0034, 0039) in which learning information configured using two or more pieces of training data (Momeni | training data – element 156) having sound information acquired from abdominal sounds of a user (Momeni | sounds – element 114) and menstruation-related information related to menstruation is stored (Momeni | Paragraph 0033, 0039-0041, 0118, 0133; [Examiner’s note, the menstruation-related information can be recorded by the user through a mobile app, and that app may use a questionnaire where the user can input information regarding their digestive conditions, bowel movements, and menstrual cycle.]).
Lakdawala teaches the storage unit acquiring the abdominal sound and menstruation-related information related to menstruation is stored (Lakdawala | Paragraph 0050, 0099, 0105), and Momeni teaches of the learning information storage unit which learning information configured using two or more pieces of training data having sound information acquired from abdominal sounds of a user and menstruation- related information related to menstruation is stored (Momeni | Paragraphs 0033-0034, 0039-0041, 0118, 0133). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the storage unit within the system of Lakdawala to incorporate the teachings of the learning information storage unit from Momeni because doing so would allow the apparatus track user data accurately overtime, which would provide a proper diagnosis for the user (Momeni | Paragraph 0038).
Lakdawala is silent specifically on the learning information within the predicting unit;
Momeni teaches the predicting unit (Momeni | classifier output – element 142; Paragraphs 0035-0038) applies the learning information to the sound information acquired by the sound information acquiring unit, thereby acquiring menstruation-related information (Momeni | Paragraphs 0117-0118).
Lakdawala teaches predicting menstruation-related information and output the prediction of the menstruation-related information (Lakdawala | Paragraph 0105), and Momeni teaches a predicting unit and an output unit (Momeni | Paragraphs 0035-0038, 0117-0118). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the predicting menstruation-related information and outputting the prediction of the menstruation-related information within the system of Lakdawala in view of Momeni to incorporate the teachings of the predicting unit and output unit from Momeni because doing so would allow the apparatus to predict the user’s symptoms and provide a recommended diagnosis or treatment (Momeni | Paragraph 0008).
Regarding Claim 2, Lakdawala in view of Momeni teaches the menstruation-related information output apparatus according to claim 1, wherein the menstruation-related information is menstruation date- related information regarding a relationship with a date related to menstruation (Lakdawala | Paragraphs 0050, 0079).
Regarding Claim 4, Lakdawala in view of Momeni teaches the menstruation-related information output apparatus according to claim 1. Lakdawala is silent in teaching the two or more pieces of training data are constituted by training data having the sound information acquired from abdominal sounds acquired from the abdomen on different days in a menstrual cycle of the user and the menstruation-related information;
Momeni teaches the two or more pieces of training data (Momeni | training data – element 156) are constituted by training data having the sound information acquired from abdominal sounds acquired from the abdomen (Momeni | Paragraph 0039-0041; Paragraph 0118) on different days in a menstrual cycle of the user and the menstruation-related information (Momeni | Paragraph 0032, 0133, 0139; [Examiner’s note, the menstruation-related information can be recorded by the user through a mobile app, and that app may use a questionnaire where the user can input information regarding their digestive conditions, bowel movements, and menstrual cycle.]).
One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify measuring the menstruation-related information and abdominal sounds on different days within the system of Lakdawala in view of Momeni to incorporate the teachings of two or more training data based on the data collected from menstruation-related information and abdominal sounds on different days from Momeni because doing so would enable continuous data measurement, allowing the apparatus to adapt to changes, identify patterns, and provide accurate health predictions to the user (Momeni | Paragraphs 0025-0027).
Regarding Claim 5, Lakdawala in view of Momeni teaches the menstruation-related information output apparatus according to claim 1. Lakdawala is silent in teaching a learning unit that performs learning processing through a machine learning algorithm on the two or more pieces of training data, thereby acquiring learning information that serves as a learning model, wherein the predicting unit performs prediction processing through a machine learning algorithm using the sound information acquired by the sound information acquiring unit and the learning information, thereby acquiring menstruation-related information;
Momeni teaches a learning unit (Momeni | classifier process – element 140) that performs learning processing through a machine learning algorithm (Momeni | classifier process – element 140; [Examiner’s note, one skilled in the art can determine the classifier process is the machine learning algorithm which utilizes the training data.]) on the two or more pieces of training data (Momeni | training data – element 156), thereby acquiring learning information that serves as a learning model (Momeni | Momeni | Paragraph 0039), wherein the predicting unit (Momeni | classifier output – element 142; Paragraphs 0035-0038; [Examiner’s note, the classifier output can be a mobile device, where the menstruation-related information can be recorded by the user through a mobile app, and that app may use a questionnaire where the user can input information regarding their digestive conditions, bowel movements, and menstrual cycle.]) performs prediction processing through a machine learning algorithm (Momeni | classifier process – element 140) using the sound information acquired by the sound information acquiring unit (Momeni | sounds – element 114) and the learning information (Momeni | training data – element 156), thereby acquiring menstruation-related information (Momeni | Paragraph 0118).
One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the system of Lakdawala in view of Momeni to incorporate the teachings of the learning unit, machine learning algorithm, and predicting unit from Momeni because doing so would enable continuous data measurement, allowing the apparatus to adapt to changes, identify patterns, and provide accurate health predictions to the user (Momeni | Paragraphs 0025-0027).
Regarding Claim 9, Lakdawala teaches a recording medium (apparatus – element 20; Paragraph 0094; [Examiner’s note, when a device continuously monitor patient data, it contains the function of recording and capturing said data.]) on which a program is recorded (Paragraph 0052), the program causing a computer capable of accessing a storage unit in which having sound information acquired from abdominal sounds of a user and menstruation-related information related to menstruation is stored (Paragraphs 0050-0052, 0099), to function as: a sound information acquiring unit (processor – element 34) that acquires sound information from abdominal sounds of the user (Paragraph 0099; [Examiner’s note, the abdominal sounds of the user are collected by the sensing unit (50), then sent to the base unit (30) within the apparatus (20), which is then sent to the processor (34).]); a predicting unit (processor – element 34) that applies the sound information acquired by the sound information acquiring unit, thereby acquiring menstruation-related information (Paragraph 0105); and an output unit (portable device – element 12; Paragraph 0051) that outputs the menstruation-related information acquired by the predicting unit (Paragraphs 0049-0050, 0105).
Lakdawala is silent in teaching a learning information storage unit in which learning information configured using two or more pieces of training data having sound information acquired from abdominal sounds of a user and menstruation-related information related to menstruation is stored;
Momeni teaches a learning information storage unit (Momeni | classifier process – element 140; Paragraphs 0034, 0039) in which learning information configured using two or more pieces of training data (Momeni | training data – element 156) having sound information acquired from abdominal sounds of a user (Momeni | sounds – element 114) and menstruation-related information related to menstruation is stored (Momeni | Paragraph 0033, 0039-0041, 0118, 0133; [Examiner’s note, the menstruation-related information can be recorded by the user through a mobile app, and that app may use a questionnaire where the user can input information regarding their digestive conditions, bowel movements, and menstrual cycle.]).
Lakdawala teaches the storage unit acquiring the abdominal sound and menstruation-related information related to menstruation is stored (Lakdawala | Paragraph 0050, 0099, 0105), and Momeni teaches of the learning information storage unit which learning information configured using two or more pieces of training data having sound information acquired from abdominal sounds of a user and menstruation- related information related to menstruation is stored (Momeni | Paragraphs 0033-0034, 0039-0041, 0118, 0133). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the storage unit within the system of Lakdawala to incorporate the teachings of the learning information storage unit from Momeni because doing so would allow the apparatus track user data accurately overtime, which would provide a proper diagnosis for the user (Momeni | Paragraph 0038).
Lakdawala is silent specifically on the learning information within the predicting unit;
Momeni teaches the predicting unit (Momeni | classifier output – element 142; Paragraphs 0035-0038) applies the learning information to the sound information acquired by the sound information acquiring unit, thereby acquiring menstruation-related information (Momeni | Paragraphs 0117-0118).
Lakdawala teaches predicting menstruation-related information and output the prediction of the menstruation-related information (Lakdawala | Paragraph 0105), and Momeni teaches a predicting unit and an output unit (Momeni | Paragraphs 0035-0038, 0117-0118). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the predicting menstruation-related information and outputting the prediction of the menstruation-related information within the system of Lakdawala in view of Momeni to incorporate the teachings of the predicting unit and output unit from Momeni because doing so would allow the apparatus to predict the user’s symptoms and provide a recommended diagnosis or treatment (Momeni | Paragraph 0008).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Lakdawala (US 20160139156 A1) in view of Momeni et al. (US 20190298295 A1), and Li et al. (“Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data”, 26 May 2020, reference U on PTO-892).
Regarding Claim 3, Lakdawala in view of Momeni teaches the menstruation-related information output apparatus according to claim 1. Lakdawala in view of Momeni is silent in teaching the menstruation-related information is pain information regarding menstrual pain;
Li teaches the menstruation-related information is pain information regarding menstrual pain (Li | Figure 1; Tables 1-3; Page 1, Right Column, Paragraphs 2-3). One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the apparatus of Lakdawala in view of Momeni to incorporate the teachings of a self-tracked mobile-health data app which measures menstrual pain experienced by the user from Li. Doing so would allow the user to receive accurate and personalized predictions based on user-tracked data. Thus helping individuals track and manage their menstrual and reproductive health (Li | Right Column, Paragraphs 2-3).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Lakdawala (US 20160139156 A1) in view of Momeni et al. (US 20190298295 A1), and Wang et al. (“A flexible skin-mounted wireless acoustic device for bowel sounds monitoring and evaluation”, 2019, reference V on PTO-892).
Regarding Claim 6, Lakdawala in view of Momeni teaches the menstruation-related information output apparatus according to claim 1, wherein the sound information is the abdominal sounds of the user (Lakdawala | sensing unit – element 50; Paragraph 0099).
Lakdawala in view of Momeni does not explicitly teach the sound information is two or more feature values of abdominal sounds of the user;
--Wang teaches the sound information is two or more feature values of abdominal sounds of the user (Wang | Figure 4; Page 5, The special feature of the flexible, skin-mounted acoustic device, that is, its compatibility with direct placement on curvilinear regions of the skin, enable the application in long-term continuous monitoring for bowel sounds. This can reveal the pattern of intestinal activity, type of intaken food, and even the blood glucose levels [;Page 6, After food intake the activity of bowel sounds is at a relatively high level for more than an hour. This activity is then stabilized before the next meal time whereby another considerable increase is manifested (Figure 4(b)), just like a growling stomach. It is also found that drinking water may have minor influence on the production of bowel sounds. The spectrogram obtained with the use of short-time Fourier transform displays the frequency distribution as a sequence of vertical lines, and the higher power levels of the sound frequencies have darker color tones (Figure 4(c)). It appears that lower frequency components (below 600 Hz) exhibit larger changes. In conclusion, by using the flexible device for long time monitoring, all these characteristics of bowel sounds are shown to experience changes after food intake).
One having an ordinary skill in the art the time the invention was filed would have found it obvious to modify the apparatus of Lakdawala in view of Momeni to incorporate the teachings of capturing two or more abdominal sounds of the user from Wang. Doing so would allow the device to observe long-term recording of sounds for the tracking of changes over time and can reveal specific signature patterns or variations of the patient associated with particular diseases or pain that might not be detectable with a brief listening session (Wang | Page 2, Paragraphs 1-3).
Conclusion
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/SRISTI DIVINA GOMES/ Examiner, Art Unit 3791
/PATRICK FERNANDES/ Primary Examiner, Art Unit 3791
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