DETAILED ACTION
This is Non-Final Office Action in response to amendment filed on January 6, 2025 and RCE filed on March 4, 2025. Claims 5, 7 and 17-18 have been canceled. Claims 1-4, 6, 8-16 and 19-25 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on March 4, 2025 has been entered.
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 a “detecting and collecting biological information data require biological acquisition device” is not directed to judicial exception, and furthermore the claimed recitation an improvement technology “accuracy of matchmaking and generate grouping information based on users’ compatibility with each other” (response 1/6/2023, pages 11-13) 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). The steps of “creating and matchmaking in the memory storage of the server” 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;” Consequently, the 101 rejection is maintained.
Applicant further argues that the claims are directed to a practical application because they recited technical improvements to accuracy of matchmaking (response 1/6/2023, pages 12-13). The examiner respectfully disagrees because it merely recited grouping information based on users’ compatibility. The claims do not recite the technical improvements to accuracy of matchmaking index vector.
Applicant's arguments regarding the amended limitations “grouping information that groups users who have high compatibility with each other based on the emotion estimation data” that are added to independent claims 1, 21, 22, 23, 24 and 25 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Thompson (US 20040210661 A1).
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 “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” 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:
“grouping information in response to input of receive biological information” recites a mental process as determination on 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 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”. 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” 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 “…generating group information on a result of grouping a plurality of users based on biological information of 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. The claim 20 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 20 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 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; 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” 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:
“grouping information in response to input of receive biological information” recites a mental process as determination on 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 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”. 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” 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; 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” 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:
“grouping information in response to input of receive biological information” recites a mental process as determination on 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 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”. 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” 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:
“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” 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 “…performing machine learning using teacher data in which human biological information and human emotions are associated with each other, generating an emotion estimation model that outputs emotion estimation data of a plurality of users used to group the plurality of users in response to input of biological information of 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 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 generating 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:
“performing machine learning using teacher data in which human biological information and human emotions are associated with each other, generating an emotion estimation model that outputs emotion estimation data of a plurality of users used to group the plurality of users in response to input of biological information of the plurality of users” 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:
“aligning time axes of time-series data of biological information of a 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)).
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 and 21-22 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 processors (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 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”);
generate 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 biological acquisition devices configured to detect biological information in real-time.
Ogasawara discloses disclose biological acquisition devices configured to detect biological information (acquisition device 200 of Fig.5; ¶[0040]-[0041], [0047], wherein the biological information data comprises time-series data of biological information acquired in real-time by a biosensor (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 biological acquisition devices configured to detect biological information (acquisition device 200 of Fig.5; ¶[0040]-[0041], [0047], wherein the biological information data comprises time-series data of biological information acquired a biosensor (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), wherein