INFORMATION PROCESSING DEVICE, DATA GENERATION METHOD, GROUPING MODEL GENERATION METHOD, GROUPING MODEL LEARNING METHOD, EMOTION ESTIMATION MODEL GENERATION METHOD, AND GROUPING USER INFORMATION GENERATION METHOD

Application No. 18/254,953

• Filed: May 30, 2023
• Priority: Dec 07, 2020 — JP 2020-202690
  • Nov 15, 2021 — nonprovisional of PCTJP2021041866
• Examiner: THAI, HANH B
• Art Unit: 2163
• Tech Center: 2100 — Computer Architecture & Software
• Assignee: Sony Group Corporation
• OA Round: 4 (Final)

Office Action

DETAILED ACTION
This is Final Office Action in response to amendment filed on September 9, 2025. Claims 5, 7 and 17-18 have been canceled. Claims 1-4, 6, 8-16 and 19-25 are pending.
Response to Arguments
Applicant's arguments regarding the § 101 rejection have been fully considered but they are not persuasive.
Applicant's arguments have been fully considered but they are not persuasive.
Regarding Step 2A Prongs One and Two of the § 101 rejection, Applicant argues that the recitation of “detecting and collecting biological information data require biological acquisition device, an action that cannot be practically performed by the human mind” (response 9/9/2025, pages 2-4) is not directed to judicial exception, and is not an abstract idea. The examiner respectfully disagrees.
The element “detecting and collecting biological information data require biological acquisition device” applies the abstract idea of detecting and collecting biological information data” using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. Please see MPEP § 2106.07(a).
Applicant further argues the claim recites additional elements that amount to significantly more than the judicial exception (i.e., an inventive concept). This argument is not persuasive.
Upon closer examination, the claims are directed to an abstract idea and fail to integrate that idea into a practical application or to provide any additional elements that amount to significantly more than the judicial exception itself. Specifically, the claims merely recite the concept of grouping or classifying users based on biological information, followed by retraining the grouping model using that information. Grouping, classifying, or organizing data based on observed characteristics is a fundamental data analysis practice and a longstanding method of organizing human activity. Likewise, the use of biological information as an input does not alter the abstract nature of the concept, as the claims do not recite any specific technological improvement in how such the biological data is obtained, processed, generated, or transformed. Instead, the biological information is simply used as a parameter for classification’s grouping and generating grouping information based on the classification’s grouping. Furthermore, the step of retraining a model using user feedback based on newly available data is a routine and well-understood practice in the field of machine learning and data analytics. The claims do not specify any particular model architecture, training mechanism, or technical improvement that would distinguish the recited retraining from conventional and generic model updating techniques. Viewed as a whole, the claims amount to nothing more than applying an abstract idea of grouping users based on information and refining that grouping over time using generic computing components performing their ordinary functions. There is no indication that the claimed invention improves the functioning of a computer itself, improves another technology or technical field, or solves a technical problem in a novel way, Instead, the claims simply automate a mental or organizational process using a computer as a tool. Accordingly, the additional elements recited in the claims, whether considered individually or in combination, do not add an inventive concept that transforms the judicial exception into patent eligible subject matter, The claims therefor remain directed to an abstract idea without significantly more, and the applicants’ arguments to the contrary are unavailing. Consequently, the 101 rejection is maintained.

Applicant's arguments regarding the amended limitations “grouping users based on time-series biological information for the plurality of users during the same time period” (response 9/9/2025, pages 4-8) that are added to independent claims 1, 21, 22, 23, 24 and 25 have been fully considered and are NOT persuasive.
First, the specification does not clearly support the claimed limitation that “receiving or detecting biological information comprises time-series biological information for the plurality of users during the same time period.” The specification fails to describe collecting biological information from multiple users in a common “same” time frame.
Second, reference Kanao et al. (US 20230317065 A1) teaches obvious this limitation. In particular, Kanao discloses grouping users based on biological users based on biological information collected over time “time-series biological information” (¶[0048], [0050] and [0054]-[0059], Kanao) and the grouping is performed using data associated with multiple users withing time periods “time-series” (¶ [0050] and [0062]-[0063], Kanao).

Applicant argues “the Nemoto reference is silent regarding training a grouping model using machine learning and training data in which biological information and emotion information are correlated” of claim 25 (response 9/9/2025, page 8). The examiner respectfully disagrees.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
With respect to the applied reference, Nakashima discloses classifying biological information pattern variation amounts according to pieces of the emotion information associated with the biological information (see Fig.18 and ¶[0058]-[0059] and [0145]-[0146], Nakashima). Nakashima further discloses forming probability density distributions for each change in emotion represented by the emotion information, thereby establishing a correlation between biological information and emotion information. Additionally, Nakashima discloses generating groups of biological information pattern variation amounts associated with the same piece of the emotion information (see ¶[0083], [0143] and [0151]-[0153], Nakashima). Thompson discloses grouping users based on compatible characteristics and generating group matches based on parameters so that compatible individuals are matched (see Fig.8; ¶[0037],[0075], [0110],[0171] and [0178], Thompson). Thompson’s disclosure of grouping based on learned or evaluated parameters reasonably encompasses the use of machine learning techniques to perform such grouping. Nemoto, while not explicitly describing machine learning based training in isolation, contributes to the overall system context in which biological information is processed and utilized. When considered in combination, the references collectively teach or suggest training and using a grouping model based on correlated biological and emotion information. Accordingly, the combination of Nakashima/ Nemoto/ Thompson teaches or renders obvious the claimed limitations of training a grouping model using training data in which biological information and emotion information are correlated.

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 1-4, 6-16, 19-22 and 24-25 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. In particularly, independent claim 1, claim 20, claim 21 recite “receive or detect biological information comprises time-series biological information for the plurality of users during the same time period” However, the specification does not clearly provide objective boundaries for what qualifies as the “same time period”.
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 1-4, 6, 8-16 and 19-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental process without significantly more. The claims recite “…training a grouping model using machine learning and training data in which biological information and emotion information are correlated; receiving second biological information data for a plurality of users, wherein the second biological information data comprises time-series data of biological information acquired in real-time by a biosensor; extract feature values …, generate emotion estimation data.. and outputting grouping information …”. This judicial exception is not integrated into a practical application because the steps can be performed manually in human mind. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claim here merely uses the processor “processing hardware” as a tool to perform the otherwise mental processes. See October Update at Section I(C)(ii). Thus, the limitations recite concepts that fall into the “mental process” grouping of abstract ideas.

ANALYSIS under Revised Guidance of 2019 PEG:
Statutory Category:
The claims 1-4, 6, 8-16 and 19-25 are directed to one of the four statutory category (claims 1-19 an information processing device, claims 20-25 a method or a process).
Step 2A – Prong 1: Judicial Exception Recited?
The independent claim 1 recites the limitations of “train a grouping model using machine learning and emotion information are correlated; receiving second biological information from the one or more biological acquisition devices for a plurality of users, wherein the second biological information comprises time-series biological information for the plurality of users during the same time period; extract feature values …, generate emotion estimation data.. , generate grouping information… and outputting grouping information” The limitations, as drafted, are steps or processes that, under their broadest reasonable interpretation, cover performance of the limitations in mind. That is, nothing in the claim 1 precludes the processes (the steps …) from practically being performed in the human mind. The claim 1 encompasses the limitations of the processes or steps of grouping a plurality of users based on their compatibility. The user manually use the data and does not take the claimed limitations out of the mental processes, which is one of the groupings of abstract ideas. Thus, the claim 1 recites an abstract idea under one of groupings of abstract idea, mental processes (concepts performed in the human mind including an evaluation, judgment, opinion, observation). (MPEP 2106.05(a)).
Step 2A – Prong 2: integrated into a practical application?
The claim 1 recites limitations or elements (one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the information processing device to execute operations) do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as identified in MPEP 2106.05(g).
Step 2B: The claim does not provide an incentive concept.
The claim 1 includes limitations or elements that are sufficient to amounts to no more than mere instructions to apply the judicial exception which cannot integrate a judicial exception into a practical application or provide an inventive concept. The same analysis applies here in 2B, that is, mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Thus, the claim 1 is ineligible as more detail explanation below.
At step 2A(i):
Independent claim 1 recites the following limitations directed to an abstract idea:
“train a grouping model using machine learning and emotion information are correlated” recites a mental process of grouping information by applying the abstract idea of grouping information using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.04(c)).
“receiving second biological information from the one or more biological acquisition devices for a plurality of users”. amount to insignificant extra-solution activity because they input and output (create, receive, transmit, and store data) in addition to the abstract idea. Please see 2106.05(d)(ii) and “v. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;” And the “biological information acquired in real-time by a biosensor” applies abstract idea using a computer at a high level of generality of generic computer components.
“extracting feature values correlated with emotions from the second biological information data” recites a mental process as retrieving “extracting” values. There is nothing specific recited as to how the values are extracted or retrieved. Instead this merely recites that the abstract idea of extracting feature values/ data is implemented on a computer in generic manner (e.g. at a high level of generality) as insignificant extra-solution activity. MPEP § 2106.05 (d)(II).
“generating emotion estimation data that correlates the second biological information data and emotions and “generating grouping information…” recites a mental process as creating. There is nothing specific recited as to how the emotion estimation data is created. Instead this merely recites that the abstract idea of creating the emotion estimation data is implemented on a generic computer. MPEP 2106.05(f).
“outputting the grouping information, wherein the grouping information groups users who have high compatibility with each other based on the emotion estimation data” recites a mental process as outputting the grouping’s results. MPEP 2106.05(f).
At step 2A(ii):
The claim recites the following additional elements:
That the processing device is “processors and memories” insignificant extra solution activities per MPEP 2106.05(g).
At step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
Dependent claim 2 recites “grouping the plurality of users based on the biological information of the plurality of users, and at least one of: image information of faces of the plurality of users as subjects, sound information including speech voices of the plurality of users, and location information of the plurality of users” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 3 recites “…grouping the plurality of users based on the biological information of the plurality of users and the attribute information of the plurality of users” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 4 recites “…stores grouping history information that is a result of grouping the plurality of users in the past based on the attribute information of the plurality of users” mental process of abstract idea under step 2A(ii), and “grouping of the plurality of users based on the biological information of the plurality of users includes grouping the plurality of users based on the biological information of the plurality of users, the attribute information of the plurality of users, and the grouping history information for each piece of the attribute information” mental process of abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 6 recites “grouping the plurality of users based on emotion estimation data generated using the biological information of the plurality of users, and at least one of: facial expression data generated using image information of faces of the plurality of users as subjects, speech data generated using sound information including speech voices of the plurality of users, and user-to-user distance data generated using location information of the plurality of users…” abstract idea under step2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 8 recites “creating user pairs each composed of two users included in the plurality of users based on the biological information of the plurality of users” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 9 recites “calculating a plurality of matchmaking indices for a user pair composed of two users included in the plurality of users, wherein the calculating of the plurality of matchmaking indices includes calculating the matchmaking indices based on the biological information of users of the user pair; calculating a distance between the users of the user pair based on a matchmaking index vector of each user in a matchmaking space composed of the plurality of matchmaking indices; calculating a degree of matchmaking between the users of the user pair by using the distance between the users in the matchmaking space; and grouping the plurality of users by using the degree of matchmaking,” abstract idea under mathematical concepts “calculating” step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 10 recites “…performing in-phase/anti-phase analysis using time-series data of the biological information of the two users of the user pair to calculate a first matchmaking index” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 11 recites “…calculating of the first matchmaking index includes calculating the first matchmaking index by Equation” abstract idea under mathematical concepts “mathematical formulas or equations” step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claims 12-13 recite “…calculating the degree of matchmaking by Equation” abstract idea under mathematical concepts “mathematical formulas or equations” step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 14 recites “…notify the plurality of users of group information on a result of grouping the plurality of users and prompt the plurality of users to act in a way that a physical distance between users in a same group becomes shorter” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 15 recites “…group the plurality of users based on the second biological information data…and notify the plurality of users of group information on a result of grouping the plurality of user and prompt the plurality of user…” abstract idea under step 2A(ii). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 16 recites “receiving feedback data from one or more users” abstract idea under step 2A(i). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
Dependent claim 19 recites “the plurality of users are a plurality of users who each carry a biological information acquisition device, and the biological information of the plurality of users is biological information of the plurality of users acquired by the biological information acquisition devices” abstract idea under step 2A(ii) as insignificant extra solution activities per MPEP 2106.05(g). Therefore, the claimed elements fail to integrate the judicial exception into a practical application.
The independent claim 20 recites the limitations of “detecting time-series biological information…, calculating a plurality of matchmaking indices…, calculating a distance between the two users of the user pair…, calculating a degree of matchmaking between the two users of the user pair…, and grouping the plurality of users …” The limitations, as drafted, are steps or processes that, under their broadest reasonable interpretation, cover performance of the limitations in mind. That is, nothing in the claim 20 precludes the processes (the steps …) from practically being performed in the human mind- mental processes and mathematical calculations of abstract ideas. The claim 20 encompasses the limitations of the processes or steps of detecting biological information and some calculating in performing the matchmaking. The mental processes and mathematical methods are both categories of abstract ideas. (MPEP 2106.04(a)(2) and MPEP 2106.04(a)(2)III).
Step 2A – Prong 2: integrated into a practical application?
The claim 20 does not recite any limitations or elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as identified in MPEP 2106.05(g).
Step 2B: The claim does not provide an incentive concept.
The claim 20 includes limitations or elements that are sufficient to amounts to no more than mere instructions to apply the judicial exception which cannot integrate a judicial exception into a practical application or provide an inventive concept. The same analysis applies here in 2B, that is, mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Thus, the claim 20 is ineligible as more detail explanation below.
At step 2A(i):
Independent claim 20 recites the following limitations directed to an abstract idea:
“calculating a plurality of matchmaking indices for a user pair composed of two users included in a plurality of users, wherein the calculating of the plurality of matchmaking indices includes calculating the matchmaking indices based on biological information of the two users of the user pair; calculating a distance between the two users of the user pair based on a matchmaking index vector of each user in a matchmaking space composed of the plurality of matchmaking indices; calculating a degree of matchmaking between the two users of the user pair by using the distance between the users in the matchmaking space” recites a mathematical calculation. (MPEP 2106.04(a)(2)).
“grouping the plurality of users by using the degree of matchmaking” recites a mental process as determination on grouping, observation or evaluation. This is mentally determining whether to group users based on the degree of matchmaking. (MPEP 2106.05(a)).
At step 2A(ii):
The claim recites the following additional elements:
That the method is “processors” insignificant extra solution activities per MPEP 2106.05(g).
At step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.

The independent claim 21 recites the limitations of “training a grouping model using machine learning and training data in which biological information and emotion information are correlated; detecting second biological information comprises time-series data of biological information acquired in real-time by the plurality of biological information acquisition devices; extract feature values …, generate emotion estimation data.. , generating grouping information…and outputting grouping information” The limitations, as drafted, are steps or processes that, under their broadest reasonable interpretation, cover performance of the limitations in mind. That is, nothing in the claim 21 precludes the processes (the steps …) from practically being performed in the human mind. The claim 21 encompasses the limitations of the processes or steps of generating group of information. The user manually generating the data and does not take the claimed limitations out of the mental processes, which is one of the groupings of abstract ideas. Thus, the claim 21 recites an abstract idea under one of groupings of abstract idea, mental processes (concepts performed in the human mind including an evaluation, judgment, opinion, observation). (MPEP 2106.05(a)).
Step 2A – Prong 2: integrated into a practical application?
The independent claim 21 does not recite any limitations or elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as identified in MPEP 2106.05(g).
Step 2B: The claim does not provide an incentive concept.
The claim 21 includes limitations or elements that are sufficient to amounts to no more than mere instructions to apply the judicial exception which cannot integrate a judicial exception into a practical application or provide an inventive concept. The same analysis applies here in 2B, that is, mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Thus, the claim 21 is ineligible as more detail explanation below.
At step 2A(i):
Independent claim 21 recites the following limitations directed to an abstract idea:
“train a grouping model using machine learning and emotion information are correlated” recites a mental process of grouping information by applying the abstract idea of grouping information using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.04(c)).
“detecting second biological information” recites a mental process as detecting/determination on information grouping, observation or evaluation. This is mentally determining whether to group users who have high compatibility with each other. (MPEP 2106.05(a)).
“receiving second biological information, wherein the second biological information comprises time-series data of biological information acquired in real-time by the plurality of biological information acquisition devices”. amount to insignificant extra-solution activity because they input and output (create, receive, transmit, and store data) in addition to the abstract idea. Please see 2106.05(d)(ii) and “v. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;” And the “biological information acquired in real-time by a biosensor” applies abstract idea using a computer at a high level of generality of generic computer components.
“extracting feature values correlated with emotions from the second biological information data” recites a mental process as retrieving “extracting” values. There is nothing specific recited as to how the values are extracted or retrieved. Instead this merely recites that the abstract idea of extracting feature values/ data is implemented on a computer in generic manner (e.g. at a high level of generality) as insignificant extra-solution activity. MPEP § 2106.05 (d)(II).
“generating emotion estimation data that correlates the second biological information data and emotions, and generating grouping information…” recites a mental process as creating. There is nothing specific recited as to how the emotion estimation data is created. Instead this merely recites that the abstract idea of creating the emotion estimation data is implemented on a generic computer. MPEP 2106.05(f).
“outputting the grouping information, wherein the grouping information groups users who have high compatibility with each other based on the emotion estimation data” recites a mental process as outputting the grouping’s results. MPEP 2106.05(f).
At step 2A(ii):
The claim recites the following additional elements:
That the method is “processors” insignificant extra solution activities per MPEP 2106.05(g).
At step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.

The independent claim 22 recites the limitations of “training a grouping model using machine learning and training data in which biological information and emotion information are correlated; extract feature values correlated with emotions from the time-series biological information, generate emotion estimation data.. , generating grouping information…and outputting grouping information” The limitations, as drafted, are steps or processes that, under their broadest reasonable interpretation, cover performance of the limitations in mind. That is, nothing in the claim 22 precludes the processes (the steps …) from practically being performed in the human mind. The claim 22 encompasses the limitations of the processes or steps of generating group of information. The user manually creating the data and does not take the claimed limitations out of the mental processes, which is one of the groupings of abstract ideas. Thus, the claim 22 recites an abstract idea under one of groupings of abstract idea, mental processes (concepts performed in the human mind including an evaluation, judgment, opinion, observation). (MPEP 2106.05(a)).
Step 2A – Prong 2: integrated into a practical application?
The independent claim 22 does not recite any limitations or elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as identified in MPEP 2106.05(g).
Step 2B: The claim does not provide an incentive concept.
The claim 22 includes limitations or elements that are sufficient to amounts to no more than mere instructions to apply the judicial exception which cannot integrate a judicial exception into a practical application or provide an inventive concept. The same analysis applies here in 2B, that is, mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Thus, the claim 22 is ineligible as more detail explanation below.
At step 2A(i):
Independent claim 22 recites the following limitations directed to an abstract idea:
“train a grouping model using machine learning and emotion information are correlated” recites a mental process of grouping information by applying the abstract idea of grouping information using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.04(c)).
“detecting second biological information” recites a mental process as detecting/determination on information grouping, observation or evaluation. This is mentally determining whether to group users who have high compatibility with each other. (MPEP 2106.05(a)).
“extracting feature values correlated with emotions from the second biological information data” recites a mental process as retrieving “extracting” values. There is nothing specific recited as to how the values are extracted or retrieved. Instead this merely recites that the abstract idea of extracting feature values/ data is implemented on a computer in generic manner (e.g. at a high level of generality) as insignificant extra-solution activity. MPEP § 2106.05 (d)(II).
“generating emotion estimation data that correlates the second biological information data and emotions, and generating grouping information…” recites a mental process as creating. There is nothing specific recited as to how the emotion estimation data is created. Instead this merely recites that the abstract idea of creating the emotion estimation data is implemented on a generic computer. MPEP 2106.05(f).
“outputting the grouping information, wherein the grouping information groups users who have high compatibility with each other based on the emotion estimation data” recites a mental process as outputting the grouping’s results. MPEP 2106.05(f).
At step 2A(ii):
The claim recites the following additional elements:
That the method is “processors” insignificant extra solution activities per MPEP 2106.05(g).
At step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.

The independent claim 23 recites the limitations of “…grouping model generated by machine learning using teacher data in which biological information of a plurality of users and group information on a result of grouping the plurality of users based on the biological information of the plurality of users are associated with each other..” The limitations, as drafted, are steps or processes that, under their broadest reasonable interpretation, cover performance of the limitations in mind. That is, nothing in the claim 23 precludes the processes (the steps …) from practically being performed in the human mind. The claim 23 encompasses the limitations of the processes or steps of generating group of information. The user manually creating the data and does not take the claimed limitations out of the mental processes, which is one of the groupings of abstract ideas. Thus, the claim 23 recites an abstract idea under one of groupings of abstract idea, mental processes (concepts performed in the human mind including an evaluation, judgment, opinion, observation). (MPEP 2106.05(a)).
Step 2A – Prong 2: integrated into a practical application?
The independent claim 23 does not recite any limitations or elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as identified in MPEP 2106.05(g).
Step 2B: The claim does not provide an incentive concept.
The claim 23 includes limitations or elements that are sufficient to amounts to no more than mere instructions to apply the judicial exception which cannot integrate a judicial exception into a practical application or provide an inventive concept. The same analysis applies here in 2B, that is, mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Thus, the claim 23 is ineligible as more detail explanation below.
At step 2A(i):
Independent claim 23 recites the following limitations directed to an abstract idea:
“train data in which biological information and emotion information are correlated” recites a mental process of grouping information by applying the abstract idea of grouping information using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.04(c)).
“calculated distance between each user and other users in the plurality of users” recites a mathematical calculation. (MPEP 2106.04(a)(2)).
“training the grouping model using feedback data from users who are associated with each other” applies the abstract idea of group information on a result of grouping the plurality of users based on the biological information of the plurality of users using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.07(a).).
At step 2A(ii):
The claim recites the following additional elements:
That the method is “processors” insignificant extra solution activities per MPEP 2106.05(g).
At step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.

The independent claim 24 recites the limitations of “…training an emotion model using machine learning and training data in which human biological information and emotion information are correlated, detecting time-series human biological information ..and grouping users who have high compatibility with each other based on the time-series human biological information” The limitations, as drafted, are steps or processes that, under their broadest reasonable interpretation, cover performance of the limitations in mind. That is, nothing in the claim 24 precludes the processes (the steps …) from practically being performed in the human mind. The claim 24 encompasses the limitations of the processes or steps of training group of information using machine learning. The user manually creating the data and does not take the claimed limitations out of the mental processes, which is one of the groupings of abstract ideas. Thus, the claim 24 recites an abstract idea under one of groupings of abstract idea, mental processes (concepts performed in the human mind including an evaluation, judgment, opinion, observation). (MPEP 2106.05(a)).
Step 2A – Prong 2: integrated into a practical application?
The independent claim 24 does not recite any limitations or elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as identified in MPEP 2106.05(g).
Step 2B: The claim does not provide an incentive concept.
The claim 24 includes limitations or elements that are sufficient to amounts to no more than mere instructions to apply the judicial exception which cannot integrate a judicial exception into a practical application or provide an inventive concept. The same analysis applies here in 2B, that is, mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Thus, the claim 24 is ineligible as more detail explanation below.
At step 2A(i):
Independent claim 24 recites the following limitations directed to an abstract idea:
“training an emotion estimation model using machine learning and training data in which human biological information and emotion information are correlated” recites a mental process of grouping information by applying the abstract idea of grouping information using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.04(c)).
“detecting time-series human biological information…” recites a mental process as detecting/determination on information grouping, observation or evaluation. This is mentally determining whether to group users who have high compatibility with each other. (MPEP 2106.05(a)).
“grouping, using emotion estimation model, users who have high compatibility with each other based on the time-series human biological information” applies the abstract idea of group information on a result of grouping the plurality of users based on the biological information of the plurality of users using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.07(a).).
At step 2A(ii):
The claim recites the following additional elements:
That the method is “processors” insignificant extra solution activities per MPEP 2106.05(g).
At step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.

The independent claim 25 recites the limitations of “training a grouping model using machine learning and training data in which biological information and emotion information are correlated; receiving second biological information data for a plurality of users, wherein the second biological information data comprises time-series data of biological information acquired in real-time by a biosensor; aligning time axes of time-series data of biological information of a plurality of users; and outputting grouping information used to group the plurality of users…” The limitations, as drafted, are steps or processes that, under their broadest reasonable interpretation, cover performance of the limitations in mind. That is, nothing in the claim 25 precludes the processes (the steps …) from practically being performed in the human mind. The claim 25 encompasses the limitations of the processes or steps of aligning data information. The user manually aligning the data and does not take the claimed limitations out of the mental processes, which is one of the groupings of abstract ideas. Thus, the claim 21 recites an abstract idea under one of groupings of abstract idea, mental processes (concepts performed in the human mind including an evaluation, judgment, opinion, observation). (MPEP 2106.05(a)).
Step 2A – Prong 2: integrated into a practical application?
The independent claim 25 does not recite any limitations or elements that integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea as identified in MPEP 2106.05(g).
Step 2B: The claim does not provide an incentive concept.
The claim 25 includes limitations or elements that are sufficient to amounts to no more than mere instructions to apply the judicial exception which cannot integrate a judicial exception into a practical application or provide an inventive concept. The same analysis applies here in 2B, that is, mere instructions to apply a judicial exception, it cannot integrate a judicial exception into a practical application at step 2A or provide an inventive concept in step 2B. Thus, the claim 25 is ineligible as more detail explanation below.
At step 2A(i):
Independent claim 25 recites the following limitations directed to an abstract idea:
“training a grouping model using machine learning and training data in which biological information and emotion information are correlated” recites a mental process of grouping information by applying the abstract idea of grouping information using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.04(c)).
“detecting second biological information…” recites a mental process as detecting/determination on information grouping, observation or evaluation. This is mentally determining whether to group users who have high compatibility with each other. (MPEP 2106.05(a)).
“aligning time axes of time-series biological information of the plurality of users…” recites a mental process as determination on aligning, observation or evaluation. This is mentally determining whether to group users who have high compatibility with each other. (MPEP 2106.05(a)).
“grouping users who have high compatibility with each other based on the time-series biological information” applies the abstract idea of group information on a result of grouping the plurality of users based on the biological information of the plurality of users using generic computer technology recited at a high level of generality, such that they are considered generic computer components under Step 2A Prong Two. (MPEP 2106.07(a).).
“outputting the grouping information grouping information the plurality of users based on compatibility with other users” recites a mental process as outputting the grouping’s results. MPEP 2106.05(f).

At step 2A(ii):
The claim recites the following additional elements:
That the method is “processors” insignificant extra solution activities per MPEP 2106.05(g).
At step 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.

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.

Claims 1-3, 6, 16, 19, 21-22 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Nakashima et al. (US 20170188927 A1) in view of Ogasawara et al. (US 20210338089 A1) and further in view of Thompson (US 20040210661 A1).
Regarding claim 1, Nakashima discloses an information processing device, comprising:
one or more biological acquisition devices configured to detect biological information (abstract; processor of emotion recognition device 18, Fig.18, Nakashima); and
one or more memories storing instructions (memory of emotion recognition device 18, Fig.18, Nakashima) that, when executed by the one or more processors (emotion recognition device 18, Fig.18, Nakashima), cause the information processing device to:
train a grouping model using machine learning (first and second distribution formation in learning unit 18, Fig.18, Nakashima) and training data in which biological information and emotion information are correlated (Fig.18 and ¶[0058]-[0059] and [0145]-[0146], Nakashima, the biological information pattern variation amounts are classified according to pieces of the emotion information associated with the biological information pattern variation amounts, the first distribution formation unit forms the probability density distribution for each change in emotion represented by the pieces of the emotion information), wherein the grouping model outputs grouping information in response to input of received biological information (Fig.18 and ¶[0147], Nakashima, outputting learning result);
extract feature values correlated with emotions from the second biological information (Fig.3 and Fig.18; ¶[0054]-[0055] and [0085], Nakashima, extracts a feature quantity representing biological information from the data of the biological information, wherein the extracted feature quantities are used in machine learning by the emotion recognition, the combinations of all the feature quantities used by the emotion recognition are referred to as “biological information pattern”);
generate emotion estimation data that correlates the second biological information and the emotions (¶[0082]-[0083], Nakashima, generation of a distribution and estimation of a probability density distribution based on the distribution while sequentially selecting one emotion, wherein the emotion of a test subject is estimated with high accuracy in the case of measuring a biological information data associated with an unknown emotion as “test data”); and
output the grouping information, wherein the grouping information groups users who have high compatibility with each other based on the emotion estimation data (¶[0083], [0143] and [0151]-[0153], Nakashima, generating groups of biological information pattern variation amounts associated with the same piece of the emotion information in the biological information pattern).
Nakashima, however, does not explicitly disclose receiving second biological information from one or more biological acquisition devices for a plurality of users, wherein the second biological information comprises time-series biological information for the plurality of user.
Ogasawara discloses disclose receiving biological information from biological acquisition devices for a plurality of users (terminals 200 and 300 of Fig.5; ¶[0040]-[0041], [0153] and [0162], Ogasawara), wherein the biological information data comprises time-series data of biological information (Fig.2, Fig.5; ¶[0076]-[0077] and [0083]-[0085], Ogasawara, biological information acquired by a biosensor 105 and measurement of biological information based on time-series data).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having both Nakashima and Ogasawara before them to incorporate time-series analysis of Ogasawara into Nakashima, as taught by Ogasawara. One of ordinary skill in the art would be motivated to integrate measurement biological information based on the time-series analysis into Nakashima, with a reasonable expectation of success, in order to enhance measured biological information.
Nakashima/Ogasawara combination does not explicitly disclose grouping information that groups users who have high compatibility with each other based on the emotion estimation data.
Thompson, however, disclose searching for compatible matches of grouping information that groups users who match compatible individuals (Fig.8; ¶[0037],[0075], [0110],[0171] and [0178], Thompson) and generating group matching based on adjustable parameters so that truly compatible persons are matched (Fig.8; ¶[0062]-[0063], [0110]-[0111] and [0178], Thompson).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Thompson before them to incorporate compatible matching of Thompson into the modified Nakashima, as taught by Thompson. One of ordinary skill in the art would be motivated to integrate compatible matches in biological information into the modified Nakashima, with a reasonable expectation of success, in order to enhance automated system performance as such matching driven by AI (¶[0002] and [0051], Thompson).
Regarding claim 2, Nakashima/Ogasawara combination discloses grouping the plurality of users based on at least one of: image information of faces of the plurality of users as subjects, sound information including speech voices of the plurality of users, and location information of the plurality of users (¶[0063], Nakashima).
Regarding claim 3, Nakashima/Ogasawara combination discloses store attribute information of the plurality of users (¶[0060]-[0063], Nakashima), and the grouping of the plurality of users based on the biological information of the plurality of users includes grouping the plurality of users based on the biological information of the plurality of users (¶[0083], [0143] and [0151]-[0153], Nakashima) and the attribute information of the plurality of users (¶[0060]-[0063] and [0151]-[0153], Nakashima, the states of the emotions are classified into a set of the states of the emotions depending on the characteristics “attributes” of the states).
Regarding claim 6, Nakashima/Ogasawara combination discloses the grouping of the plurality of users based on the biological information of the plurality of users (¶[0083], [0143] and [0151]-[0153], Nakashima), and at least one of: facial expression data generated using image information of faces of the plurality of users as subjects, speech data generated using sound information including speech voices of the plurality of users, and user-to-user distance data generated using location information of the plurality of users (¶[0063], Nakashima).
Regarding claim 16, Nakashima/Ogasawara combination discloses wherein the operations include receiving feedback data from one or more users (¶[0173]-[0175], Nakashima).
Regarding claim 19, Nakashima/Ogasawara combination discloses wherein the plurality of users are a plurality of users who each carry a biological information acquisition device (¶[0047]-[0048] and [0076]-[0077], Ogasawara).
Regarding claim 21, Nakashima discloses a data generation method executed by one or more processors, the data generation method comprising:
training a grouping model using machine learning (first and second distribution formation in learning unit 18, Fig.18, Nakashima) and training data in which biological information and emotion information are correlated (Fig.18 and ¶[0058]-[0059] and [0145]-[0146], Nakashima, the biological information pattern variation amounts are classified according to pieces of the emotion information associated with the biological information pattern variation amounts, the first distribution formation unit forms the probability density distribution for each change in emotion represented by the pieces of the emotion information), wherein the grouping model outputs grouping information in response to input of received biological information (Fig.18 and ¶[0147], Nakashima, outputting learning result);
extracting feature values correlated with emotions from the biological information data (Fig.3 and Fig.18; ¶[0054]-[0055] and [0085], Nakashima, extracts a feature quantity representing biological information from the data of the biological information, wherein the extracted feature quantities are used in machine learning by the emotion recognition, the combinations of all the feature quantities used by the emotion recognition are referred to as “biological information pattern”);
generating emotion estimation data that correlates the biological information data and the emotions (¶[0082]-[0083], Nakashima, generation of a distribution and estimation of a probability density distribution based on the distribution while sequentially selecting one emotion, wherein the emotion of a test subject is estimated with high accuracy in the case of measuring a biological information data associated with an unknown emotion as “test data”); and
output the grouping information, wherein the grouping information groups users who have high compatibility with each other based on the emotion estimation data (¶[0083], [0143] and [0151]-[0153], Nakashima, generating groups of biological information pattern variation amounts associated with the same piece of the emotion information in the biological information pattern).
Nakashima, however, does not explicitly disclose detecting biological information data associated with a plurality of users.
Ogasawara discloses disclose receiving biological information from biological acquisition devices for a plurality of users (terminals 200 and 300 of Fig.5; ¶[0040]-[0041], [0153] and [0162], Ogasawara), wherein the biological information data comprises time-series data of biological information (Fig.2, Fig.5; ¶[0076]-[0077] and [0083]-[0085], Ogasawara, biological information acquired by a biosensor 105 and measurement of biological information based on time-series data).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having both Nakashima and Ogasawara before them to incorporate time-series analysis of Ogasawara into Nakashima, as taught by Ogasawara. One of ordinary skill in the art would be motivated to integrate measurement biological information based on the time-series analysis into Nakashima, with a reasonable expectation of success, in order to enhance measured biological information.
Nakashima/Ogasawara combination does not explicitly disclose generating grouping information that groups users who have high compatibility with each other based on the emotion estimation data.
Thompson, however, disclose searching for compatible matches of grouping information that groups users who match compatible individuals (Fig.8; ¶[0037],[0075], [0110],[0171] and [0178], Thompson) and generating group matching based on adjustable parameters so that truly compatible persons are matched (Fig.8; ¶[0062]-[0063], [0110]-[0111] and [0178], Thompson).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Thompson before them to incorporate compatible matching of Thompson into the modified Nakashima, as taught by Thompson. One of ordinary skill in the art would be motivated to integrate compatible matches in biological information into the modified Nakashima, with a reasonable expectation of success, in order to enhance automated system performance as such matching driven by AI (¶[0002] and [0051], Thompson).
Regarding claim 22, Nakashima discloses a grouping model generation method executed by one or more processors, the grouping model generation method comprising:
training a grouping model using machine learning (first and second distribution formation in learning unit 18, Fig.18, Nakashima) and training data in which biological information and emotion information are correlated (Fig.18 and ¶[0058]-[0059] and [0145]-[0146], Nakashima, the biological information pattern variation amounts are classified according to pieces of the emotion information associated with the biological information pattern variation amounts, the first distribution formation unit forms the probability density distribution for each change in emotion represented by the pieces of the emotion information);
extracting feature values correlated with emotions from the biological information data (Fig.3 and Fig.18; ¶[0054]-[0055] and [0085], Nakashima, extracts a feature quantity representing biological information from the data of the biological information, wherein the extracted feature quantities are used in machine learning by the emotion recognition, the combinations of all the feature quantities used by the emotion recognition are referred to as “biological information pattern”);
generating emotion estimation data that correlates the biological information data and the emotions (¶[0082]-[0083], Nakashima, generation of a distribution and estimation of a probability density distribution based on the distribution while sequentially selecting one emotion, wherein the emotion of a test subject is estimated with high accuracy in the case of measuring a biological information data associated with an unknown emotion as “test data”); and
output the grouping information, wherein the grouping information groups users who have high compatibility with each other based on the emotion estimation data (¶[0083], [0143] and [0151]-[0153], Nakashima, generating groups of biological information pattern variation amounts associated with the same piece of the emotion information in the biological information pattern).
Nakashima, however, does not explicitly disclose detecting time-series biological associated with a plurality of users.
Ogasawara discloses disclose receiving biological information from biological acquisition devices for a plurality of users (terminals 200 and 300 of Fig.5; ¶[0040]-[0041], [0153] and [0162], Ogasawara), wherein the biological information data comprises time-series data of biological information (Fig.2, Fig.5; ¶[0076]-[0077] and [0083]-[0085], Ogasawara, biological information acquired by a biosensor 105 and measurement of biological information based on time-series data).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having both Nakashima and Ogasawara before them to incorporate time-series analysis of Ogasawara into Nakashima, as taught by Ogasawara. One of ordinary skill in the art would be motivated to integrate measurement biological information based on the time-series analysis into Nakashima, with a reasonable expectation of success, in order to enhance measured biological information.
Nakashima/Ogasawara combination does not explicitly disclose grouping information that groups users who have high compatibility with each other based on the emotion estimation data.
Thompson, however, disclose searching for compatible matches of grouping information that groups users who match compatible individuals (Fig.8; ¶[0037],[0075], [0110],[0171] and [0178], Thompson) and generating group matching based on adjustable parameters so that truly compatible persons are matched (Fig.8; ¶[0062]-[0063], [0110]-[0111] and [0178], Thompson).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Thompson before them to incorporate compatible matching of Thompson into the modified Nakashima, as taught by Thompson. One of ordinary skill in the art would be motivated to integrate compatible matches in biological information into the modified Nakashima, with a reasonable expectation of success, in order to enhance automated system performance as such matching driven by AI (¶[0002] and [0051], Thompson).
Regarding claim 24, Nakashima discloses an emotion estimation model generation method comprising:
Training an emotion estimation model (first and second distribution formation in learning unit 18, Fig.18, Nakashima) using machine learning and training data in which human biological information and emotion information are correlated (Fig.18 and ¶[0058]-[0059] and [0145]-[0146], Nakashima, the biological information pattern variation amounts are classified according to pieces of the emotion information associated with the biological information pattern variation amounts, the first distribution formation unit forms the probability density distribution for each change in emotion represented by the pieces of the emotion information), wherein the emotional estimation model outputs grouping information in response to input of received biological information (Fig.18 and ¶[0147], Nakashima, outputting learning result).
Nakashima, however, does not explicitly disclose detecting time-series biological associated with a plurality of users.
Ogasawara discloses disclose receiving biological information from biological acquisition devices for a plurality of users (terminals 200 and 300 of Fig.5; ¶[0040]-[0041], [0153] and [0162], Ogasawara), wherein the biological information data comprises time-series data of biological information (Fig.2, Fig.5; ¶[0076]-[0077] and [0083]-[0085], Ogasawara, biological information acquired by a biosensor 105 and measurement of biological information based on time-series data). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having both Nakashima and Ogasawara before them to incorporate time-series analysis of Ogasawara into Nakashima, as taught by Ogasawara. One of ordinary skill in the art would be motivated to integrate measurement biological information based on the time-series analysis into Nakashima, with a reasonable expectation of success, in order to enhance measured biological information.
Nakashima/Ogasawara combination does not explicitly disclose grouping information that groups users who have high compatibility with each other based on the emotion estimation data.
Thompson, however, disclose searching for compatible matches of grouping information that groups users who match compatible individuals (Fig.8; ¶[0037],[0075], [0110],[0171] and [0178], Thompson) and generating group matching based on adjustable parameters so that truly compatible persons are matched (Fig.8; ¶[0062]-[0063], [0110]-[0111] and [0178], Thompson). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Thompson before them to incorporate compatible matching of Thompson into the modified Nakashima, as taught by Thompson. One of ordinary skill in the art would be motivated to integrate compatible matches in biological information into the modified Nakashima, with a reasonable expectation of success, in order to enhance automated system performance as such matching driven by AI (¶[0002] and [0051], Thompson).
Claims 4 is rejected under 35 U.S.C. 103 as being unpatentable over Nakashima et al. (US 20170188927 A1) in view of Ogasawara et al. (US 20210338089 A1), further in view of Thompson (US 20040210661 A1), and further in view of Suzuki et al. (US 20200143435 A1).
Regarding claim 4, Nakashima/Ogasawara combination discloses the grouping of the plurality of users based on the biological information of the plurality of users includes grouping the plurality of users based on the biological information of the plurality of users (¶[0083], [0143] and [0151]-[0153], Nakashima), the attribute information of the plurality of users, and the grouping information for each piece of the attribute information (¶[0060]-[0063] and [0151]-[0153], Nakashima). However, modified Nakashima does not disclose stores grouping history information that is a result of grouping the plurality of users in the past based on the attribute information of the plurality of users. Suzuki discloses information processing method including storing grouping history information (¶[044]-[0045], Suzuki). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having the modified Nakashima and Suzuki before them to manage storage’s grouping history information of Suzuki into the modified Nakashima, as taught by Suzuki. One of ordinary skill in the art would be motivated to integrate managing data grouping into the modified Nakashima, with a reasonable expectation of success, in order to enhance the evaluation information stored to be associated with the user’s group in storage file system (¶[0009], Suzuki).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Nakashima et al. (US 20170188927 A1) in view of Ogasawara et al. (US 20210338089 A1), further in view of Thompson (US 20040210661 A1), and further in view of Kogure (US 20200029832 A1).
Regarding claim 8, Nakashima/Ogasawara combination discloses all of the claimed limitations as discussed above, except creating user pairs each composed of two users. Kogure discloses creating user pairs each composed of two users included in the plurality of users based on the biological information of the plurality of users (¶[0130] and [0141], Kogure). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Kogure before them to manage biological activity’s information of Kogure into the modified Nakashima, as taught by Kogure. One of ordinary skill in the art would be motivated to integrate managing user’s biological activity into the modified Nakashima, with a reasonable expectation of success, in order to presume a status of a user with high accuracy on the basis of a biological information value of the user (¶[0010], Kogure).
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Nakashima et al. (US 20170188927 A1) in view of Ogasawara et al. (US 20210338089 A1), further in view of Thompson (US 20040210661 A1), and further in view of Rapaport et al. (US 10691726 B2).
Regarding claim 14, the modified Nakashima discloses all of the claimed limitations as discussed above, except processing for prompting the plurality of users to act in a way that a physical distance between users in a same group becomes shorter. Rapaport discloses processing for notifying the plurality of users of group information on a result of grouping the plurality of users (col.10, line37 to col.11, line 20, Rapaport) and processing for prompting the plurality of users to act in a way that a physical distance between users in a same group becomes shorter (step 421 of Fig.4C; col.10, line37 to col.11, line 20, Rapaport, i.e. user-to-user match-making or clustering and/or for purpose of matching with predefined chat rooms (user-to-room match-making) such as closeness of matching or closeness of clustering of users to one another and/or to available rooms is expressed as a machine-utilized co-compatibility distance between users, where more closely matched users are deemed to be less distant from one another in a co-compatibility space and thus are deemed to be closely clustered to one another for purposes of being automatically invited into a common chat room). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having the modified Nakashima and Rapaport before them to incorporate the physical distance between users based on the match-making of Rapaport into the modified Nakashima, as taught by Rapaport. One of ordinary skill in the art would be motivated to integrate managing user’s group co-compatibility into the modified Nakashima, with a reasonable expectation of success, in order to enhance the match-marking in user group (col.2, lines 1-28, Rapaport).
Regarding claim 15, the modified Nakashima discloses wherein the operations include repeating: grouping the plurality of users based on the biological information of the plurality of users (¶[0083]-[0085], Nakashima); and at least one of: processing for notifying the plurality of users of group information on a result of grouping the plurality of users and processing for prompting the plurality of users to act in a way that a physical distance between users in a same group becomes shorter (col.10, line37 to col.11, line 20, Rapaport). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having the modified Nakashima and Rapaport before them to incorporate the physical distance between users based on the match-making of Rapaport into the modified Nakashima, as taught by Rapaport. One of ordinary skill in the art would be motivated to integrate managing user’s group co-compatibility into the modified Nakashima, with a reasonable expectation of success, in order to enhance the match-marking in user group (col.2, lines 1-28, Rapaport).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Nakashima et al. (US 20170188927 A1) in view of Nishimura et al. (US 20200301398 A1) in view of Kanao et al. (US 20230317065 A1).
Regarding claim 23, Nakashima discloses a grouping model learning method executed by one or more processors, the grouping model learning method comprising:
causing a grouping model generated by machine learning using training data (first and second distribution formation in learning unit 18, Fig.18, Nakashima) and training data in which biological information and emotion information are correlated (Fig.18 and ¶[0058]-[0059] and [0145]-[0146], Nakashima, the biological information pattern variation amounts are classified according to pieces of the emotion information associated with the biological information pattern variation amounts, the first distribution formation unit forms the probability density distribution for each change in emotion represented by the pieces of the emotion information), wherein the grouping model outputs grouping information in response to input of received biological information (Fig.18 and ¶[0147], Nakashima, outputting learning result).
Nakashima, however, does not explicitly disclose causing a grouping model generated by machine learning using feedback data from users who are associated with each other. Nishimura discloses causing a grouping model generated by machine learning (¶[0064]-[0065] and [0090]-[0092], Nishimura) and feedback data from the users are associated with each other (¶[0072]-[0075], Nishimura). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Nishimura before them to manage biological information of Nishimura into the modified Nakashima, as taught by Nishimura. One of ordinary skill in the art would be motivated to integrate managing data grouping into the modified Nakashima, with a reasonable expectation of success, in order to collect data related to emotions of users more efficiently and realizing more highly precise emotion estimation (¶[0005], Nishimura).
Modified Nakashima, however, does not explicitly disclose wherein the emotion estimation data is generated from input of received time-series biological information and a calculated distance between each user and other users in the plurality of users.
Kanao discloses grouping users based on biological users based on biological information collected over time “time-series biological information” (¶[0048], [0050] and [0054]-[0059], Kanao), the grouping is performed using data associated with multiple users withing time periods “time-series” (¶[0050] and [0062]-[0063], Kanao) and a calculated distance between each user and other users in the plurality of users (¶[0046]-[0050], Kanao).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Kanao before them to incorporate compatible matching of Kanao into the modified Nakashima, as taught by Kanao. One of ordinary skill in the art would be motivated to integrate compatible matches in biological information into the modified Nakashima, with a reasonable expectation of success, in order to enhance automated system performance as such matching driven by AI.
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Nakashima et al. (US 20170188927 A1) in view of Nemoto et al. (US 20150193588 A1) and further in view of Thompson (US 20040210661 A1).
Regarding claim 25, Nakashima discloses a grouping user information generation method executed by one or more processors, the grouping user information generation method comprising:
training a grouping model using machine learning (first and second distribution formation in learning unit 18, Fig.18, Nakashima) and training data in which biological information and emotion information are correlated (Fig.18 and ¶[0058]-[0059] and [0145]-[0146], Nakashima, i.e., the biological information pattern variation amounts are classified according to pieces of the emotion information associated with the biological information pattern variation amounts, the first distribution formation unit forms the probability density distribution for each change in emotion represented by the pieces of the emotion information), wherein the grouping model outputs grouping information in response to input of received biological information (Fig.18 and ¶[0147], Nakashima, outputting learning result); and output grouping information used to group the plurality of users, wherein users are grouped based on the biological information of the plurality of users (¶[0083], [0143] and [0151]-[0153], Nakashima, generating groups of biological information pattern variation amounts associated with the same piece of the emotion information in the biological information pattern).
Nakashima, however, does not explicitly disclose aligning time axes of time-series data of biological information. Nemoto discloses aligning time axes of time-series data of biological information of a plurality of users (¶[0091], Nemoto, i.e., generating health information graph and aligning in the horizontal axis); and outputting grouping user information used to group the plurality of users (¶[0065] and [0091], Nemoto, i.e., deriving an analysis result for the secondary use service. In addition, the secondary use service providing outputs the analysis result derived by the PHR big data analyzing unit 121, thereby providing the analysis result for various companies (a medical institution, a food/supplements seller, a pharmaceutical company, a medical device manufacturer, a distribution company, a security company)). It would have been obvious to a person having ordinary skill in the art before the effective filing date, having both Nakashima and Nemoto before them to manage biological activity’s information of Nakashima into Nemoto, as taught by Nakashima. One of ordinary skill in the art would be motivated to integrate managing user’s biological activity into Nemoto, with a reasonable expectation of success, in order to presume a status of a user with high accuracy on the basis of a biological information value of the user.
Modified Nakashima, however, does not explicitly disclose grouping information that groups users who have high compatibility with each other based on the emotion estimation data.
Thompson, however, disclose searching for compatible matches of grouping information that groups users who match compatible individuals (Fig.8; ¶[0037],[0075], [0110],[0171] and [0178], Thompson) and generating group matching based on adjustable parameters so that truly compatible persons are matched (Fig.8; ¶[0062]-[0063], [0110]-[0111] and [0178], Thompson).
It would have been obvious to a person having ordinary skill in the art before the effective filing date, having modified Nakashima and Thompson before them to incorporate compatible matching of Thompson into the modified Nakashima, as taught by Thompson. One of ordinary skill in the art would be motivated to integrate compatible matches in biological information into the modified Nakashima, with a reasonable expectation of success, in order to enhance automated system performance as such matching driven by AI (¶[0002] and [0051], Thompson).
Allowable Subject Matter
Claims 9-13 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. And claims 9-13 are allowable if rewritten in forms that overcome the 101 issues above.
Regarding claim 9, the modified Nakashima discloses all of the claimed limitations as discussed above, except calculating a plurality of matchmaking indices for a user pair composed of two users included in the plurality of users, wherein calculating the matchmaking indices based on the biological information of the two users of the user pair; calculating a distance between the users of the user pair based on a matchmaking index vector of each user in a matchmaking space composed of the plurality of matchmaking indices; calculating a degree of matchmaking between the users of the user pair by using the distance between the users in the matchmaking space; and grouping the plurality of users by using the degree of matchmaking.
Regarding claim 10, the modified Nakashima discloses all of the claimed limitations as discussed above, except wherein the calculating of the matchmaking indices based on the biological information of the two users of the user pair includes performing in-phase/anti-phase analysis using time-series data of the biological information of the users of the two user pair to calculate a first matchmaking index.
Regarding claim 11, the modified Nakashima discloses all of the claimed limitations as discussed above, except wherein the calculating of the first matchmaking index includes calculating the first matchmaking index by Equation (1):
r= f(t) * g(t-ᴫ)... (I)
where when the users of the two user pair are user A and user B, in Equation (1), r indicates the first matchmaking index, f(t) indicates time-series data of biological information of user A at a certain time, g(t) indicates time-series data of biological information of user B at a certain time, and t indicates a deviation of timing of a physiological index with respect to an external stimulus that takes positive and negative values.
Regarding claim 12, the modified Nakashima discloses all of the claimed limitations as discussed above, except wherein the calculating of the matchmaking indices based on the biological information of the two users of the user pair includes performing calculation of an index of absolute difference value using time-series data of the biological information of the two users of the user pair to calculate a second matchmaking index.
Regarding claim 13, the modified Nakashima discloses all of the claimed limitations as discussed above, except wherein the calculating of the degree of matchmaking between the two users of the user pair by using the distance between the users in the matchmaking space includes calculating the degree of matchmaking by Equation (IID): [Math. 1].
Regarding independent 20, the claim is allowable if it is rewritten in the form that overcome the 101 issues.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Saigal et al. (US 8548937 B2) disclose medical care treatment decision support system.
Nemoto et al. (US 20150193588 A1) disclose health information processing apparatus and method and health information display apparatus and method.
Lohi et al. (US 20170032080 A1) disclose method and arrangement for matching mammals by comparing genotypes.
Seabrooke (US 20150006086 A1) disclose personal compatibility using HLA.
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 HANH B THAI whose telephone number is [phone]. The examiner can normally be reached Mon-Friday 7-4:30.
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/HANH B THAI/Primary Examiner, Art Unit 2163
December 18, 2025

Prosecution Timeline

• May 30, 2023: Application Filed
• Apr 25, 2024: Non-Final Rejection mailed — §101, §103, §112
• Jul 02, 2024: Examiner Interview (Telephonic)
• Jul 03, 2024: Examiner Interview Summary
• Jul 25, 2024: Response Filed
• Nov 04, 2024: Final Rejection mailed — §101, §103, §112
• Dec 11, 2024: Examiner Interview (Telephonic)
• Dec 11, 2024: Examiner Interview Summary
• Jan 06, 2025: Response after Non-Final Action
• Mar 04, 2025: Request for Continued Examination
• Mar 10, 2025: Response after Non-Final Action
• Jun 09, 2025: Non-Final Rejection mailed — §101, §103, §112
• Sep 09, 2025: Response Filed
• Dec 23, 2025: Final Rejection mailed — §101, §103, §112 (current)