DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 12/30/2025.
Claims 11-23 are pending and have been examined.
All previous objections / rejections not mentioned in this Office Action have been withdrawn by the examiner.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments / Amendments
Regarding the Applicant’s arguments for the rejections under 35 U.S.C. § 101, applicant has amended independent claims 11, 14, 19, and 21 with the following limitation ”wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the [learning/estimation] process by selectively processing only the psychological-state/sensibility expressing words”. Applicant has also amended independent claims 12, 15, 20, and 22 with the following limitation “wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the [learning/estimation] process by selectively processing only the psychological-state/sensibility expressing words and their corresponding times”. Applicant asserts that independent claim limitations brings about a concrete improvement in computer technology (non-conventional efficiency) in the form of a "reduction in computational resource consumption" and solve two specific technical problems, "reduction in computational resource consumption" and "improvement in prediction accuracy," by means of specific, non-conventional data limitation. Examiner respectfully disagrees. During patent examination, pending claims must be “given their broadest reasonable interpretation consistent with the specification.” MPEP 2111. Also, claims should not be interpreted by reading limitations of the specification into the claim, to narrow the scope of the claim, by implicitly adding disclosed limitations that have no express basis in the claim language. In re Prater, 415 F.2d 1393. First, the improvement that applicant asserts and included in the claim as amended limitation is not disclosed in the as filed disclosure. The specification does not specifically disclose the problem that the invention is solving or improving. The disclosure lacks the information where processing psychological-state/sensibility expressing words would achieve a reduction in computational resource consumption. Second, MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Here, the claim recites a model learning from psychological-state/sensibility expressing words to estimate an incident occurrence quantitative value after a certain time. The claim does not describe any specific improvement to a problem or to the estimation model that would demonstrate an improvement in technology. Therefore, the claims as currently recited does not overcome the 35 U.S.C. § 101 abstract idea rejection.
Regarding the Applicant’s arguments for the rejections under 35 U.S.C. § 102 and § 103, applicant asserts that prior art references does not teach the learning data being “a combination including only a time series of two or more learning psychological-state/sensibility expressing words”. Examiner respectfully disagrees. Prior art reference Berlingerio teach that sentiment analysis can be based on the wording used by the user, any punctuation marks such as exclamation points that appear in the text. (Berlingerio P0019) Computing state of criticality can then be performed using sentiment analysis. (Berlingerio P0035) The “psychological-state/sensibility expressing words” is broadly defined that is consistent with P0005 of the specification. Words with exclamation points is interpreted as a psychological-state/sensibility expressing word.
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 11, 14, 19, and 21 are 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. Specifically, the as filed disclosure does not disclose ”wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the [learning/estimation] process by selectively processing only the psychological-state/sensibility expressing words”.
Claims 12, 15, 20, and 22 are 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. Specifically, the as filed disclosure does not disclose “wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the [learning/estimation] process by selectively processing only the psychological-state/sensibility expressing words and their corresponding times”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 11-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 11 and 19 the limitations of “a memory that stores at least a learning psychological-state/sensibility expressing word emitted in a predetermined region, and a learning incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event that occurred in the region when the learning psychological-state/sensibility expressing word was emitted”, “processing circuitry configured to learn an estimation model for estimating an incident occurrence quantitative value after a certain time in the region, with an input being only a time series of two or more psychological-state/sensibility expressing words emitted before the certain time in the region, using a plurality of sets of learning data, one set of learning data being a combination including only a time series of two or more learning psychological-state/sensibility expressing words emitted in the region before a time time(t) and a learning incident occurrence quantitative value in the region after the time time(t)”, and “wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the learning process by selectively processing only the psychological-state/sensibility expressing words”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human thinking of past words and incidents to determine a correlation where a human would think of incident potential from words heard in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Regarding claim 12 and 20 the limitations of “a memory that stores at least a learning psychological-state/sensibility expressing word emitted by a plurality of persons in a predetermined region, a time for learning at which the learning psychological-state/sensibility expressing word was emitted, and a learning incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event that occurred in the region when the learning psychological-state/sensibility expressing word was emitted”, “processing circuitry configured to learn an estimation model for estimating an incident occurrence quantitative value after a certain time in the region, with inputs being only a plurality of psychological-state/sensibility expressing words emitted by a plurality of persons in the region before the certain time, and times corresponding to the respective psychological-state/sensibility expressing words or elapsed times since a predetermined time, using a plurality of sets of learning data, one set of learning data being a combination including only a plurality of learning psychological-state/sensibility expressing words emitted by a plurality of persons in the region before a time time(t), times for learning corresponding to the respective learning psychological-state/sensibility expressing words or elapsed times since a predetermined time, and a learning incident occurrence quantitative value after the time time(t) in the region” and “wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the learning process by selectively processing only the psychological-state/sensibility expressing words and their corresponding times”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human thinking of past words and incidents to determine a correlation where a human would think of incident potential from words heard in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Regarding claim 14 and 21 the limitations of “processing circuitry configured to estimate a future incident occurrence quantitative value in a predetermined region only on a basis of two or more inputted psychological-state/sensibility expressing words emitted in the region and an input order of the two or more psychological-state/sensibility expressing words, using an estimation model for estimating an incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event in the region after a certain time, with an input being only a time series of two or more psychological-state/sensibility expressing words emitted in the region before the certain time”, and “wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the estimation process by selectively processing only the psychological-state/sensibility expressing words”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human thinking of words and incident potential from the words heard in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Regarding claim 15 and 22 the limitations of “processing circuitry configured to estimate a future incident occurrence quantitative value in a predetermined region only on a basis of a plurality of psychological-state/sensibility expressing words emitted by a plurality of persons in the region, and times corresponding to the respective psychological-state/sensibility expressing words or elapsed times since a predetermined time, using an estimation model for estimating an incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event in the region after a certain time, with inputs being only a plurality of psychological- state/sensibility expressing words emitted by a plurality of persons before the certain time in the region, and times corresponding to the respective psychological-state/sensibility expressing words or elapsed times since a predetermined time”, and “wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the estimation process by selectively processing only the psychological-state/sensibility expressing words emitted by a plurality of persons and their corresponding times”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human thinking of words and incident potential from the words heard in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of an apparatus in claim 11, 12, 14, 15, and 19-22, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using P0024 in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to think of past words and incidents to determine a correlation where a human would think of incident potential from words heard in the mind amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 13 and 16, the claim recites “wherein the psychological-state/sensibility expressing word is onomatopoeia”, which reads on a human determining incident potential from onomatopoeia words in the mind. No additional limitations are present.
With respect to claim 17, the claim recites “the estimation model is a model for estimating an incident occurrence quantitative value after the certain time in the region, with an input being at least one piece of”, “experience information relating to an experience: or”, “communication information relating to communication, and”, “the processing circuitry configured to use the estimation model, and estimate the future incident occurrence quantitative value in the region, on a basis of at least one piece of:”, “experience information relating to an experience of a person who has made an input at a time of the input of a psychological-state/sensibility expressing word; or”, and “communication information relating to communication of a person who has made an input at a time of the input of a psychological-state/sensibility expressing word”, which reads on a human determining incident potential from words heard in the mind that include communication words. No additional limitations are present.
With respect to claim 18, the claim recites “the estimation model is a model for estimating an incident occurrence quantitative value after the certain time in the region, also using the biological information as an input” and “the processing circuitry configured to use the estimation model, and estimate the future incident occurrence quantitative value in the region also on a basis of the biological information about the person who has made the input at the time of the input of a psychological-state/sensibility expressing word”, which reads on a human determining incident potential from words heard in the mind that include biological information. No additional limitations are present.
With respect to claim 23, the claim recites “[a] non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to function as the learning apparatus”, which reads on a generic computer component that does not integrate the abstract idea into a practical application. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 11, 12, 14, 15, 17-23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Berlingerio et al. (U.S. PG Pub No. 20150186378), hereinafter Berlingerio.
Regarding claim 11 and 19 Berlingerio teaches:
(Claim 11) A learning apparatus comprising: (P0062, Apparatus, or device.)
(Claim 19) A learning method, implemented by a learning apparatus that includes a memory and processing circuitry, comprising: (P0062, Apparatus, or device.; P0066, The hardware configuration preferably has at least one processor or central processing unit (CPU). The CPUs are interconnected via a system bus to a random access memory (RAM).)
a memory that stores at least a learning psychological-state/sensibility expressing word emitted in a predetermined region, and a learning incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event that occurred in the region when the learning psychological-state/sensibility expressing word was emitted; and (P0062, Computer readable storage medium.; P0016, Detection of an event or incident can be performed using, among other things, text analysis of the tweet content, and spatio-temporal network analysis of tweets, such as the number of tweets in the area that report the event. In addition, image analysis of possible photo attachments, and/or sentiment analysis of tweet text can be used.; P0019, Sentiment analysis on received tweets can be based, for example, on the wording used by the user.; P0021, In step S3, the system performs temporal profile matching by either matching the temporal profile of the incident (computed in step S2) to a diffusion model or to a historical related incident that is stored in the knowledge base.; P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. Accordingly, in step S4, the score of incident A after some amount of time (i.e., in the future) is estimated.)
processing circuitry configured to learn an estimation model for estimating an incident occurrence quantitative value after a certain time in the region, with an input being only a time series of two or more psychological-state/sensibility expressing words emitted before the certain time in the region, using a plurality of sets of learning data, one set of learning data being a combination including only a time series of two or more learning psychological-state/sensibility expressing words emitted in the region before a time time(t) and a learning incident occurrence quantitative value in the region after the time time(t), wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the learning process by selectively processing only the psychological-state/sensibility expressing words. (P0019, In step S2, the system performs scoring of each incident. … The score of the incident can be computed taking into account data from the social media sensors and the data can include characteristics such as social impact assessment, dynamic location of users. … As another contributing factor to the score of an incident, the humans' perception about the incident can be calculated, for example, by performing sentiment analysis on the received tweets by calculating the proximity of the user to the reported incident, etc. Sentiment analysis on received tweets can be based, for example, on the wording used by the user.; P0021, In step S3, the system performs temporal profile matching by either matching the temporal profile of the incident (computed in step S2) to a diffusion model or to a historical related incident that is stored in the knowledge base, or by positioning in time the current-score of the incident to a diffusion model (or historical incident). For example, an incident's temporal profile can be used to determine whether a matching event exists with some past profiles and/or historical models of events or incidents in the knowledge base. In one aspect, given the current-score of an incident A that is monitored in real time and a historical incident B, e.g., a historical related incident, (whose evolution is known), the current state of A can be mapped in B. For example, as shown in FIG. 3 (at 315), Incident 5 is the historical incident B (or it can be a diffusion model of a similar incident—it's the same). Then, in the timeline of Incident 5, the current incident A can be positioned (to capture where is “now”, shown as the dotted line). When this matching is performed accurately, then one can estimate how incident A is going to progress and/or evolve, using the timeline of incident B. Accordingly, the current-score of incident A can be positioned in time to the timeline of the historical incident (or diffusion model) B.; P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. … After time incident A is positioned to the historical incident B.)
Regarding claim 12 and 20 Berlingerio teaches:
(Claim 12) A learning apparatus comprising: (P0062, Apparatus, or device.)
(Claim 20) A learning method, implemented by a learning apparatus that includes a memory and processing circuitry, comprising: (P0062, Apparatus, or device.; P0066, The hardware configuration preferably has at least one processor or central processing unit (CPU). The CPUs are interconnected via a system bus to a random access memory (RAM).)
a memory that stores at least a learning psychological-state/sensibility expressing word emitted by a plurality of persons in a predetermined region, a time for learning at which the learning psychological-state/sensibility expressing word was emitted, and a learning incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event that occurred in the region when the learning psychological-state/sensibility expressing word was emitted; and (P0062, Computer readable storage medium.; P0016, Detection of an event or incident can be performed using, among other things, text analysis of the tweet content, and spatio-temporal network analysis of tweets, such as the number of tweets in the area that report the event. In addition, image analysis of possible photo attachments, and/or sentiment analysis of tweet text can be used.; P0019, Sentiment analysis on received tweets can be based, for example, on the wording used by the user.; P0021, In step S3, the system performs temporal profile matching by either matching the temporal profile of the incident (computed in step S2) to a diffusion model or to a historical related incident that is stored in the knowledge base.; P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. Accordingly, in step S4, the score of incident A after some amount of time (i.e., in the future) is estimated.)
processing circuitry configured to learn an estimation model for estimating an incident occurrence quantitative value after a certain time in the region, with inputs being only a plurality of psychological-state/sensibility expressing words emitted by a plurality of persons in the region before the certain time, and times corresponding to the respective psychological-state/sensibility expressing words or elapsed times since a predetermined time, using a plurality of sets of learning data, one set of learning data being a combination including only a plurality of learning psychological-state/sensibility expressing words emitted by a plurality of persons in the region before a time time(t), times for learning corresponding to the respective learning psychological-state/sensibility expressing words or elapsed times since a predetermined time, and a learning incident occurrence quantitative value after the time time(t) in the region, wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the learning process by selectively processing only the psychological-state/sensibility expressing words and their corresponding times. (P0019, In step S2, the system performs scoring of each incident. … The score of the incident can be computed taking into account data from the social media sensors and the data can include characteristics such as social impact assessment, dynamic location of users. … As another contributing factor to the score of an incident, the humans' perception about the incident can be calculated, for example, by performing sentiment analysis on the received tweets by calculating the proximity of the user to the reported incident, etc. Sentiment analysis on received tweets can be based, for example, on the wording used by the user.; P0021, In step S3, the system performs temporal profile matching by either matching the temporal profile of the incident (computed in step S2) to a diffusion model or to a historical related incident that is stored in the knowledge base, or by positioning in time the current-score of the incident to a diffusion model (or historical incident). For example, an incident's temporal profile can be used to determine whether a matching event exists with some past profiles and/or historical models of events or incidents in the knowledge base. In one aspect, given the current-score of an incident A that is monitored in real time and a historical incident B, e.g., a historical related incident, (whose evolution is known), the current state of A can be mapped in B. For example, as shown in FIG. 3 (at 315), Incident 5 is the historical incident B (or it can be a diffusion model of a similar incident—it's the same). Then, in the timeline of Incident 5, the current incident A can be positioned (to capture where is “now”, shown as the dotted line). When this matching is performed accurately, then one can estimate how incident A is going to progress and/or evolve, using the timeline of incident B. Accordingly, the current-score of incident A can be positioned in time to the timeline of the historical incident (or diffusion model) B.; P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. … After time incident A is positioned to the historical incident B.)
Regarding claim 14 and 21 Berlingerio teaches:
(Claim 14) An estimation apparatus comprising (P0062, Apparatus, or device.)
(Claim 21) An estimation method, implemented by an estimation apparatus that includes processing circuitry, comprising (P0062, Apparatus, or device.; P0066, The hardware configuration preferably has at least one processor or central processing unit (CPU). The CPUs are interconnected via a system bus to a random access memory (RAM).)
processing circuitry configured to estimate a future incident occurrence quantitative value in a predetermined region only on a basis of two or more inputted psychological-state/sensibility expressing words emitted in the region and an input order of the two or more psychological-state/sensibility expressing words, using an estimation model for estimating an incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event in the region after a certain time, with an input being only a time series of two or more psychological-state/sensibility expressing words emitted in the region before the certain time, wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the estimation process by selectively processing only the psychological-state/sensibility expressing words. (P0016, Detection of an event or incident can be performed using, among other things, text analysis of the tweet content, and spatio-temporal network analysis of tweets, such as the number of tweets in the area that report the event. In addition, image analysis of possible photo attachments, and/or sentiment analysis of tweet text can be used.; P0019, Sentiment analysis on received tweets can be based, for example, on the wording used by the user.; P0021, In step S3, the system performs temporal profile matching by either matching the temporal profile of the incident (computed in step S2) to a diffusion model or to a historical related incident that is stored in the knowledge base.; P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. Accordingly, in step S4, the score of incident A after some amount of time (i.e., in the future) is estimated.)
Regarding claim 15 and 22 Berlingerio teaches:
(Claim 15) An estimation apparatus comprising (P0062, Apparatus, or device.)
(Claim 22) An estimation method, implemented by an estimation apparatus that includes processing circuitry, comprising (P0062, Apparatus, or device.; P0066, The hardware configuration preferably has at least one processor or central processing unit (CPU). The CPUs are interconnected via a system bus to a random access memory (RAM).)
processing circuitry configured to estimate a future incident occurrence quantitative value in a predetermined region only on a basis of a plurality of psychological-state/sensibility expressing words emitted by a plurality of persons in the region, and times corresponding to the respective psychological-state/sensibility expressing words or elapsed times since a predetermined time, using an estimation model for estimating an incident occurrence quantitative value that is a quantitative value of an occurrence of a predetermined event in the region after a certain time, with inputs being only a plurality of psychological- state/sensibility expressing words emitted by a plurality of persons before the certain time in the region, and times corresponding to the respective psychological-state/sensibility expressing words or elapsed times since a predetermined time, wherein the processing circuitry is configured to achieve a reduction in computational resource consumption during the estimation process by selectively processing only the psychological-state/sensibility expressing words emitted by a plurality of persons and their corresponding times. (P0016, Detection of an event or incident can be performed using, among other things, text analysis of the tweet content, and spatio-temporal network analysis of tweets, such as the number of tweets in the area that report the event. In addition, image analysis of possible photo attachments, and/or sentiment analysis of tweet text can be used.; P0019, Sentiment analysis on received tweets can be based, for example, on the wording used by the user.; P0021, In step S3, the system performs temporal profile matching by either matching the temporal profile of the incident (computed in step S2) to a diffusion model or to a historical related incident that is stored in the knowledge base.; P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. Accordingly, in step S4, the score of incident A after some amount of time (i.e., in the future) is estimated.)
Regarding claim 17 Berlingerio teaches claim 14 and 15 and further teaches:
the estimation model is a model for estimating an incident occurrence quantitative value after the certain time in the region, with an input being at least one piece of: (P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. … After time incident A is positioned to the historical incident B.)
experience information relating to an experience; or communication information relating to communication, and (P0019, The score of the incident can be computed taking into account data from the social media sensors and the data can include characteristics such as social impact assessment, dynamic location of users, human perception.)
the processing circuitry configured to use the estimation model, and estimate the future incident occurrence quantitative value in the region, on a basis of at least one piece of: (P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. … After time incident A is positioned to the historical incident B.)
experience information relating to an experience of a person who has made an input at a time of the input of a psychological-state/sensibility expressing word; or communication information relating to communication of a person who has made an input at a time of the input of a psychological-state/sensibility expressing word. (P0019, The score of the incident can be computed taking into account data from the social media sensors and the data can include characteristics such as social impact assessment, dynamic location of users, human perception. … the humans' perception about the incident can be calculated, for example, by performing sentiment analysis on the received tweets by calculating the proximity of the user to the reported incident)
Regarding claim 18 Berlingerio teaches claim 14 and 15 and further teaches:
the estimation model is a model for estimating an incident occurrence quantitative value after the certain time in the region, also using the biological information as an input, and (P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. … After time incident A is positioned to the historical incident B.; P0019, As another contributing factor to the score of an incident, the humans' perception about the incident can be calculated, for example, by performing sentiment analysis on the received tweets by calculating the proximity of the user to the reported incident, etc. Sentiment analysis on received tweets can be based, for example, on the wording used by the user, any punctuation marks such as exclamation points that appear in the text, etc.)
the processing circuitry configured to use the estimation model, and estimate the future incident occurrence quantitative value in the region also on a basis of the biological information about the person who has made the input at the time of the input of a psychological-state/sensibility expressing word. (P0022, In step S4, a score projection, e.g., “projected-score” of the incident can be computed. … After time incident A is positioned to the historical incident B.
P0019, As another contributing factor to the score of an incident, the humans' perception about the incident can be calculated, for example, by performing sentiment analysis on the received tweets by calculating the proximity of the user to the reported incident, etc. Sentiment analysis on received tweets can be based, for example, on the wording used by the user, any punctuation marks such as exclamation points that appear in the text, etc.)
Regarding claim 23 Berlingerio teaches claim 11, 12, 14, and 15 and further teaches:
A non-transitory computer-readable recording medium having recorded thereon a program for causing a computer to function as the learning apparatus according to claim 11 or 12, or the estimation apparatus according to claim 14 or 15. (P0062, A computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with a system, apparatus, or device running an instruction.)
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 13 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Berlingerio in view of Geller (U.S. PG Pub No. 20200084607).
Regarding claim 13 Berlingerio teaches claim 11, 12, 14, and 15.
Berlingerio does not specifically teach:
wherein the psychological-state/sensibility expressing word is onomatopoeia.
Geller, however, teaches:
wherein the psychological-state/sensibility expressing word is onomatopoeia. (P0080, Other preferred sounds may include onomatopoeia, such as animal sounds (e.g., “Meow”) or action sounds (e.g., “BAM!”).)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have onomatopoeia as a psychological-state/sensibility expressing word. It would have been obvious to combine the references because onomatopoeia is known distress signal that yields a predictable result of expressing or transmitting the distress event through the onomatopoeia to inform others of the distress incident. (Geller P0080).
Conclusion
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 DANIEL WONSUK CHUNG whose telephone number is (571)272-1345. The examiner can normally be reached Monday - Friday (7am-4pm)[PT].
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PIERRE-LOUIS DESIR can be reached at (571)272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL W CHUNG/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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