DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In this reply filed on 16 September 2025, the following changes have been made: amendments to claims 1, 4, and 11-12.
Claims 1-4, 6-8, and 10-13 are currently pending and have been examined.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-4, 6-8, and 10-13 are rejected on the ground of provisional nonstatutory double patenting as being unpatentable over claims 1 and 11 of App# 18/176,040 in view of Shriberg et al. (US20210110895A1), Hayakawa (US20150227714A1), and further in view of Kodali (US20230224540A1). Although the claims at issue are not identical, they are not patentably distinct from each other because recite substantially similar limitations.
This is a provisional nonstatutory double patenting rejection because the indistinct claims have not been patented.
The table/chart below exhibits the similarity* between the independent claims where claims 1 and 11 of the current application are a broader variation of the claims of the reference application.
* Similarities highlighted in BOLD
App# 18/176,097
App# 18/176,040
Claim 1:
A clinical support system comprising: a clinical support apparatus; and a display device, the clinical support apparatus having processing circuitry, wherein the processing circuitry configured to: receive, from the display device, specifying of a patient from the display device; acquire patient information related to the specified patient
Claim 1:
A clinical support system comprising: a clinical support apparatus having processing circuitry; and a display device, and a patient terminal storing conversation information recording a conversation of a patient wherein the processing circuitry is configured to: acquire patient information related to a patient
interview information showing details of a response to an interview by the specified patient, and conversation information recording a conversation of the specified patient,
receive specifying of at least one analysis target item among analysis items belonging to any of a mental aspect and a social aspect
and interview information showing a response detail of the patient with respect to an interview, and electrically receive the conversation information from the patient terminal,
receive specifying of an analysis target item among analysis items for a mental aspect and a social aspect, the analysis target item being a target item to be analyzed for the patient
by causing a learned model to perform an analyzing process of extracting, for each category item, information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect from the patient information, the interview information, and the conversation information, generate patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item,
generate patient characteristic information showing an analysis result of the analysis target item among the mental aspect and the social aspect of the patient by an analyzing process using the patient information, the interview information, and the conversation information
determine a similar patient based on a degree of similarity between the patient
characteristic information generated and patient characteristic information of each of a
plurality of patients stored in a storage, and
generate information on the determined similar patient,
and the display device displays, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item, and
display information on the similar patient.
and determine at least one supporter based on the patient characteristic information, a medical-practice event of the patient, and a request condition for requesting support in the medical-practice event, the supporter being to be requested for the support, and the display device displays a screen including information related to the determined supporter.
Claim 11:
A clinical support apparatus comprising following processing circuitry configured to: receive specifying of a patient from the display device, acquire patient information related to the specified patient, interview information showing details of a response to an interview by the specified patient, and conversation information recording a conversation of the specified patient,
receive, from the display device, specifying of at least one analysis target item among analysis items belonging to any of a mental aspect and a social aspect
Claim 11:
A clinical support apparatus comprising processing circuitry, wherein the processing circuitry is configured to: acquire patient information related to a patient, and interview information showing a response detail of the patient with respect to an interview, electrically receive conversation information recording a conversation of the patient from a patient terminal storing the conversation information, receive specifying of an analysis target item among analysis items for a mental aspect and a social aspect, the analysis target item being a target item to be analyzed for the patient,
by causing a learned model to perform an analyzing process of extracting, for each category item, information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect from the patient information, the interview information, and the conversation information, generate patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item,
determine a similar patient based on a degree of similarity between the patient
characteristic information generated and patient characteristic information of each of a
plurality of patients stored in a storage, and
generate information on the determined similar patient,
and
generate, based on the patient characteristic information, a category screen in which the
information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item.
generate patient characteristic information showing ana analysis result of the analysis target item among the mental aspect and the social aspect of the patient by an analyzing process using the patient information, the interview information, and the conversation information
determine at least one supporter based on the patient characteristic information, a medical-practice event of the patient, and a request condition for requesting support in the medical-practice event, the supporter being to be requested for the support, and generate a screen including information related to the determined supporter.
The difference between the present application and 18/176,040 is that the present application discloses receiving specifying of a patient from a display device, using a learned model, categorizing into category items, determining a similar patient, generating information on the determined similar patient, and displaying a category screen in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized which are obvious over Shriberg et al. (US20210110895A1) ([0166], [0153], [0156], and [0206]) with the motivation to support clinical workflows (See Shriberg, Background) in view of Hayakawa et al. (US20150227714A1) ([0030] & [0033]) with the motivation to efficiently used accumulated data on patients, and further in view of Kodali (US20230224540A1) ([0041]) with the motivation to organize data to improve human efficiency.
The remaining dependent claims in the present application also recite substantially similar limitations to 18/176,040 such as displaying with time and displaying associated data.
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, and 10-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
The claim(s) recite(s) subject matter within a statutory category as a machine (claims 1-4, 6-8, and 10-13).
INDEPENDENT CLAIMS
Step 2A Prong 1
Claim 1 recites steps of
a clinical support apparatus; and a display device, the clinical support
apparatus having processing circuitry, wherein
the processing circuitry is configured to: receive specifying of a patient from the display device,
acquire patient information related to the specified patient, interview information showing details of a response to an interview by the specified patient, and conversation information recording a conversation of the specified patient,
receive, from the display device, specifying of at least one analysis target item among analysis items belonging to any of a mental aspect and a social aspect, and
by causing a learned model to perform an analyzing process of extracting, for each category item, information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect from the patient information, the interview information, and the conversation information, generate patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item,
determine a similar patient based on a degree of similarity between the patient
characteristic information generated and patient characteristic information of each of a plurality of patients stored in a storage, and
generate information on the determined similar patient,
and
and the display device displays, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item, and display information on the similar patient.
Claim 11 recites similar limitations as claim 1.
These steps directed to supporting medical workers at a medical practice to help with patient treatment outcomes, as drafted, under the broadest reasonable interpretation, includes performance of the limitations in the mind but for recitation of generic computer components. That is, nothing in the claim element precludes the italicized portions from practically being performed in the mind through performing the same evaluation, judgement, and determination on data that medical workers already perform. This could be analogized to collecting information, analyzing it, and displaying certain results of the collection and analysis. The italicized portion containing the recitation of the learned model at a high level of generality has been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance. If a claim limitation, under its broadest reasonable interpretation, covers performance in the mind and mathematical calculations but for the recitation of generic computer components, then it falls within the “Mental Process” and “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2
This judicial exception is not integrated into a practical application. In particular, the additional elements non-italicized portions identified above for claims 1 and 11, do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as by
a clinical support apparatus; and a display device, the clinical support
apparatus having processing circuitry; the display device displays […] a category screen; and, display information on the similar patient amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of wherein the processing circuitry is configured to: receive specifying of a patient from the display device; acquire patient information related to the specified patient, interview information showing details of a response to an interview by the specified patient, and conversation information recording a conversation of the specified patient; and, receive, from the display device, specifying of at least one analysis target item among analysis items belonging to any of a mental aspect and a social aspect amounts to mere data gathering since it does not add meaningful limitations to the receiving and acquiring performed, see MPEP 2106.05(g))
Each of the above additional elements therefore only amounts to mere instructions to implement functions within the abstract idea using generic computer components or other machines within their ordinary capacity, and also add insignificant extra-solution activity to the abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. These elements are therefore not sufficient to integrate the abstract idea into a practical application. Therefore, the above claims, as a whole, are directed to an abstract idea.
Step 2B
The claim(s) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and also add insignificant extra-solution activity to the abstract idea. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to mere instructions to apply an exception in particular fields such as by a clinical support apparatus; and a display device, the clinical support apparatus having processing circuitry; the display device displays […] a category screen; and, display information on the similar patient, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f).
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields such as recitation of wherein the processing circuitry is configured to: receive specifying of a patient from the display device; acquire patient information related to the specified patient, interview information showing details of a response to an interview by the specified patient, and conversation information recording a conversation of the specified patient; and, receive, from the display device, specifying of at least one analysis target item among analysis items belonging to any of a mental aspect and a social aspect, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
DEPENDENT CLAIMS
Step 2A Prong 1
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-4, 6-8, 10, and 12-13 reciting particular aspects of supporting medical workers at a medical practice to help with patient treatment outcomes such as
[Claim 2] wherein the processing circuitry is configured to generate the patient characteristic information showing at least any one of a tendency for making a decision, a preference, and a value of the patient as the mental aspect;
[Claim 3] wherein the processing circuitry is configured to generate the patient characteristic information showing at least any one of social background or a social environment of the patient as the social aspect;
[Claim 4] wherein the processing circuitry is configured to by causing the learned model to perform the analyzing process of extracting, for each category item, extract information corresponding to the at least one analysis target item belonging to any of a biological aspect, the mental aspect, and the social aspect, and generate the patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect is categorized into the category item, and the display device displays, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect is categorized into the category item;
[Claim 6] wherein the display device displays the category screen showing that the biological aspect, the mental aspect, and the social aspect of the patient has a time-course change based on the patient characteristic information;
[Claim 7] wherein the display device comparably displays information corresponding to the at least one analysis target item that has undergone the time-course change;
[Claim 8] wherein the display device displays an emotion in speech of the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect of the patient;
[Claim 10] wherein the display device displays any of the at least one analysis target item for which, the patient characteristic information has not been generated;
[Claim 12] wherein the processing circuitry is configured to generate, by causing the learned model to perform the analyzing process, patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized gradually by the category item, and the display device displays, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized gradually by the category item;
[Claim 13] wherein when a button corresponding to one of the category items included in the category screen is selected, the display device displays information corresponding to any of the at least one analysis target item that belongs to the same group as the one category item corresponding to the selected button;
these italicized portions covers performance of the limitations in the mind but for recitation of generic computer components since they merely describe types of data and determinations that can be performed by humans. The italicized portion containing the recitation of the learned model at a high level of generality has been treated as part of the abstract idea, specifically as mathematical calculations which falls within the abstract idea of mathematical concepts, in light of the 2024 USPTO AI Guidance).
Step 2A Prong 2
Dependent claims 4, 6-8, 10, and 12-13 recites additional subject matter which amount to limitations consistent with the additional elements in the independent claims (the additional limitations in claim 4 (the display device displays […] a category screen), claim 6 (wherein the display device displays the category screen showing that the biological aspect, the mental aspect, and the social aspect of the patient has a time-course change based on the patient characteristic information), claim 7 (wherein the display device comparably displays information corresponding to the at least one analysis target item that has undergone the time-course change), claim 8 (wherein the display device displays an emotion in speech of the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect of the patient), claim 10 (wherein the display device displays any of the at least one analysis target item for which, the patient characteristic information has not been generated), and claim 12 (the display device displays […] a category screen) amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f)); and, claim 13 (wherein when a button corresponding to one of the category items included in the category screen is selected, the display device displays information corresponding to any of the at least one analysis target item that belongs to the same group as the one category item corresponding to the selected button) amounts to generally linking the use of a judicial exception to a particular technological environment or field of use, see MPEP 2106.05(h)). 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.
Step 2B
Dependent claims 4, 6-8, and 10-12 recites additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, e.g., a commonplace business method or mathematical algorithm being applied on a general-purpose computer, Alice Corp. v. CLS Bank, MPEP 2106.05(f) ; and, dependent claim 13 which generally links the use of a judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010), MPEP 2106.05(h). Also, see [0022] which provides examples of terminal devices, [0033] which provides examples of memory devices, and [0037] disclosing examples of processors. There is no indication that these additional elements improve the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35
U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148
USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-8, and 11-13 are under 35 U.S.C. 103 as being unpatentable over Shriberg et al. (US20210110895A1) in view of Hayakawa (US20150227714A1) and further in view of Kodali at al. (US20230224540A1).
Regarding claim 1, Shriberg discloses a clinical support apparatus ([0553] “Various general-purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the methods of some embodiments.”)
and a display device ([0067] “The plurality of visual elements can be configured to be displayed on a graphical user interface of an electronic device.”)
the clinical support apparatus having processing circuitry ([0541] “(ii) logic implemented in electronic circuitry.”)
wherein the processing circuitry configured to: receive specifying of a patient from the display device ([0166] “the client devices 260 a-n of FIG. 2 […] During registration for screening or monitoring, patients may also enter information that specifies or constraints their interests.”)
acquire patient information related to the specified patient ([0197] “In step 808, generalized dialogue flow logic 602 receives an audiovisual signal of the patient's response to the question.”)
interview information showing details of a response to an interview by the specified patient ([0166] “In addition, medical care professionals may interview patients during or after a screening or monitoring event to obtain the demographic information.”)
and conversation information recording a conversation of the specified patient ([0501] “The system may record, with patient permission, conversation patients have with health care providers during appointments.”)
receive, from the display device, specifying of at least one analysis target item among analysis items belonging to any of a mental aspect and a social aspect ([0501] “The system may collect recordings of clinical encounters for physical complaints. The complaints may be regarding injuries, sicknesses, or chronic conditions.” [0517] “The patient may also express sadness, shame, or regret regarding not having followed the treatment plan.” [0365] “Topics identified within social media feeds are incorporated into the interaction to pique interest of the user.” [0384] “This data includes video/visual information as well as speech/audio information captured by the client device's camera(s) and microphone(s), respectively.”)
by causing a learned model to perform an analyzing process of extracting, for each category item, information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect from the patient information, the interview information, and the conversation information ([0153] “In this illustrative embodiment, patient device 112 may also reachable by call center system 104 through a public-switched telephone network (PSTN) 120 or directly. Health screening or monitoring server 102 may be a server computer system that administers the health screening or monitoring test […] to produce results 1820 (FIG. 18), using clinical data retrieved from clinical data server 106, social data retrieved from social data server 108, and patient data collected from past screenings or monitoring to train the models of runtime model server 304 (FIG. 18).” [0154] “The system may be used to assess the mental state of the subject in a single session or over multiple sessions. Subsequent sessions may be informed by assessment results from prior assessments. This may be done by providing assessment data as inputs to machine learning algorithms.” [0156] “the screening or monitoring test may be similar to the subject matter of standardized depression screening or monitoring tests such as the PHQ-9” [0206] “Categories may correlate to (i) specific health diagnoses such as depression, anxiety, etc.; (ii) specific symptoms such as insomnia, lethargy, general disinterest, etc.; and/or (iii) aspects of a patient's treatment such as medication, exercise, etc.” [0519] “The system may be able to establish a baseline profile for each individual patient.”)
generate patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item ([0561] “The real time results may include a score corresponding to a mental state. In a fourth step, the case manager may update a care plan based on the real time results. For example, a particular score that exceeds a particular threshold may influence a future interaction between a care provider and a patient and may cause the provider to ask different questions of the patient. The score may even trigger the system to suggest particular questions associated with the score. The conversation may be repeated with the updated care plan.”)
Note: the real time results are a representation of the patient characteristic information of the analysis target item gleaned from each of the biological aspect, the mental aspect, and the social aspect of the patient through speech and stored data.
Shriberg does not explicitly disclose however Hayakawa teaches determine a similar patient based on a degree of similarity between the patient characteristic information generated and patient characteristic information of each of a plurality of patients stored in a storage ([0030] “The specifying unit 2 specifies a second patient whose medical record is similar to that of a first patient with reference to the medical records of the plurality of patients. For example, the specifying unit 2 calculates the degree of similarity between each of the plurality of record notes 1a-1, 1a-2, 1a-3, . . . included in the medical record 1a of the first patient and each of the plurality of record notes 1b-1, 1b-2, 1b-3, . . . included in the medical records 1b, 1c, . . . of the other patients.” [0123] “A term array table 161, 162, 163, . . . for each record note of the target patient is stored in the similar-patient-record-based term storage unit 160.”)
generate information on the determined similar patient ([0033] “Then, the extraction unit 4 extracts terms that are considered useful for the medical care of the first patient, from the medical record 1b of the second patient.”)
and display information on the similar patient ([0033] The extraction unit 4 then outputs the extracted terms, for example, as terms (reference terms 7) that are useful for the medical care of the first patient 1.”)
It would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Shriberg determining a similar patient, generating information on the determined similar patient, and displaying information on the similar patient as taught by Hayakawa since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Shriberg in view of Hayakawa does not explicitly disclose however Kodali teaches and the display device displays, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item ([0041] “which can be displayed at the GUI 502 […] Moreover, the multi-data source personality profile generator 110 can generate a summary 510 and/or cause the summary 510 to be presented as part of the timeline view 504. For instance, the summary 510 can include an aggregation of behavior metrics 124 generated from the behavioral insight categories and the categorized user data. By way of example, the summary depicted in FIG. 5 may indicate “high interest/enthusiasm for sports, movies, fitness, and travel;” “medium interest in holidays;” “high willingness to make purchase for home;” “medium or low willingness to click on ads;” “high willingness to subscribe to digital media;” “medium level of social media participation;” and the like.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Shriberg and Hayakawa the display device displaying, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect is categorized into the category item as taught by Kodali since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Shriberg discloses wherein the processing circuitry configured to generate the patient characteristic information showing at least any one of a tendency for making a decision, a preference, and a value of the patient as the mental aspect ([0190] “Interaction control logic generator 702 receives data from screening or monitoring data store 210 that represents subjective preferences of the patient”)
Regarding claim 3, Shriberg discloses wherein the processing circuitry configured to generate the patient characteristic information showing at least any one of social background or a social environment of the patient as the social aspect ([0158] “Social data may include information collected from a patient's social networks, including social media postings, from databases detailing patient's purchases, and from databases containing patient's economic, educational, residential, legal and other social determinants. This information may be compiled together with additional preference data, metadata, annotations, and voluntarily supplied information, to populate the user database 220.”)
Regarding claim 4, Shriberg discloses wherein the processing circuitry configured to by causing the learned model to perform the analyzing process of extracting, for each category item, extract information corresponding to the at least one analysis target item belonging to any of a biological aspect, the mental aspect, and the social aspect, ([0154] “The system may be used to assess the mental state of the subject in a single session or over multiple sessions. Subsequent sessions may be informed by assessment results from prior assessments. This may be done by providing assessment data as inputs to machine learning algorithms.” [0158] “The health screening or monitoring system 200 includes a backend infrastructure designed to administer the screening or monitoring interaction and analyze the results. The web server 240 and model server(s) 230 leverage user data 220 which is additionally populated by clinical and social data 210.” [0206] “Topic 906 includes data specifying a hierarchical topic category to which the question belongs. Categories may correlate to (i) specific health diagnoses such as depression, anxiety, etc.; (ii) specific symptoms such as insomnia, lethargy, general disinterest, etc.; and/or (iii) aspects of a patient's treatment such as medication, exercise, etc.”)
Shriberg in view of Hayakawa does not explicitly disclose however Kodali teaches and generate the patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect is categorized into the category item ([0041] “The behavioral insight categories generator 120 can generate behavioral insight categories by detecting activity patterns 402 from the activity timeline 118 generated from the categorized user data.” By way of example, the summary depicted in FIG. 5 may indicate “high interest/enthusiasm for sports, movies, fitness, and travel;” “medium interest in holidays;” “high willingness to make purchase for home;” “medium or low willingness to click on ads;” “high willingness to subscribe to digital media;” “medium level of social media participation;” and the like.” [0031] “For instance, the fitness bin 154 can categorize the action items related to health, diet, sleep, fitness, and/or physical activity.”)
and the display device displays, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect is categorized into the
category item ([0041] “which can be displayed at the GUI 502 […] Moreover, the multi-data source personality profile generator 110 can generate a summary 510 and/or cause the summary 510 to be presented as part of the timeline view 504. For instance, the summary 510 can include an aggregation of behavior metrics 124 generated from the behavioral insight categories and the categorized user data. By way of example, the summary depicted in FIG. 5 may indicate “high interest/enthusiasm for sports, movies, fitness, and travel;” “medium interest in holidays;” “high willingness to make purchase for home;” “medium or low willingness to click on ads;” “high willingness to subscribe to digital media;” “medium level of social media participation;” and the like.” [0031] “For instance, the fitness bin 154 can categorize the action items related to health, diet, sleep, fitness, and/or physical activity.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Shriberg and Hayakawa generating the patient characteristic information in which the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect is categorized into the category item; and, the display device displaying, based on the patient characteristic information, a category screen in which the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect is categorized into the category item as taught by Kodali since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 6, Shriberg in view of Hayakawa does not explicitly disclose however Kodali teaches wherein the display device displays the category screen showing that the biological aspect, the mental aspect, and the social aspect of the patient has a time-course change based on the patient characteristic information ([0041] “which can be displayed at the GUI 502” [0046] “Using the techniques discussed herein, the system 700 can determine the behavioral insight categories by generating the activity timeline 118 and identifying the activity patterns (e.g., based on the associations to the content-based bin(s) 114).” [0052] “In some examples, the one or more interest category identifiers 702 corresponding to behavioral insight categories can include one or more of art, culture, entertainment, automobiles, vehicles, news, family, parenting, sports, recreation, hobbies, interests, geography, travel, home, garden, health, fitness, law, government, politics, food, drink, pets, style, fashion, cosmetics, personal care, history, events, human activities, philosophy, finance, education, careers, business, industrial, real estate, religion, spirituality, science, shopping, society, technology, computing, kids, combinations thereof, and the like.”)
Therefore, it would have obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include in the system of Shriberg and Hayakawa the display device displaying the category screen showing that the biological aspect, the mental aspect, and the social aspect of the patient has a time-course change based on the patient characteristic information as taught by Kodali since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 7, Shriberg discloses wherein the display device comparably displays information corresponding to the at least one analysis target item that has undergone the time-course change ([0169] “Depressed patients may also use a higher frequency of first-person singular pronouns (e.g., “I”, “me”) and a lower frequency of second- or third-person pronouns when compared to the general population. The system may be able to train a machine learning algorithm to perform semantic analysis of word clouds of depressed and non-depressed people, in order to be able to classify people as depressed or not depressed based on their word clouds.” [0170] “The systems described herein can output an electronic report identifying whether a patient is at risk of a mental or physiological condition.” [0171] “The electronic report can include visual graphical elements. For example, if the patient has multiple scores from multiple screening or monitoring sessions that occurred at several different times, the visual graphical element may be a graph that shows the progression of the patient's scores over time.”)
Regarding claim 8, Shriberg discloses wherein the display device displays an emotion in speech of the information corresponding to the at least one analysis target item belonging to any of the biological aspect, the mental aspect, and the social aspect of the patient ([0071] “The method can comprise: obtaining speech data from the subject and storing the speech data in computer memory, processing the speech data using in part natural language processing to identify one or more features indicative of the mental or physiological condition, and outputting an electronic report identifying whether the subject is at a risk of the mental or physiological condition” [0271] “Assessment test administrator 2202 recognizes durations that are too long when (i) the patient explicitly indicates so (e.g., saying “Hello?” or “Are you still there?”) and/or (ii) the patient's response indicates increased frustration or agitation relative to the patient's speech earlier in the same conversation.” [0406] “Perhaps more so than others of language models 2214, speech fluency model 3906 may be specific to the individual patient. For example, rates of speech (word rates) vary widely across geographic regions. The normal rate of speech for a patient from New York City may be significantly greater than the normal rate of speech for a patient from Minnesota.”)
Regarding claim 11, Shriberg discloses a patient’s aspect display comprising following processing circuitry ([0067] “The plurality of visual elements can be configured to be displayed on a graphical user interface of an electronic device.” [0541] “(ii) logic implemented in electronic circuitry.”))
wherein the processing circuitry configured to: receive specifying of a patient from the display device ([0166] “the client devices 260 a-n of FIG. 2 […] During registration for screening or monitoring, patients may also enter information that specifies or constraints their interests.”)
acquire patient information related to the specified patient ([0197] “In step 808, generalized dialogue flow logic 602 receives an audiovisual signal of the patient's response to the question.”)
interview information showing details of a response to an interview by the specified patient ([0166] “In addition, medical care professionals may interview patients during or after a screening or monitoring event to obtain the demographic information.”)
and conversation information recording a conversation of the specified patient ([0501] “The system may record, with patient permission, conversation patients have with health care providers during appointments.”)
receive, from the display device, specifying of at least one analysis target item among analysis items belonging to any of a mental aspect and a social aspect ([0501] “The system may collect recordings of clinical encounters for physical complaints. The complaints may be regarding injuries, sicknesses, or chronic conditions.” [0517] “The patient may also express sadness, shame, or regret regarding not having followed the treatment plan.” [0365] “Topics identified within social media feeds are incorporated into the interaction to pique interest of the user.” [0384] “This data includes video/visual information as well as speech/audio information captured by the client device's camera(s) and microphone(s), respectively.”)
by causing a learned model to perform an analyzing process of extracting, for each category item, information corresponding to the at least one analysis target item belonging to any of the mental aspect and the social aspect from the patient information, the interview information, and the conversation information, ([0153] “In this illustrative embodiment, patient device 112 may also reachable by call center system 104 through a public-switched telephone network (PSTN) 120 or directly. Health screening or monitoring server 102 may be a server computer system that administers the health screening or monitoring test […] to produce results 1820 (FIG. 18), using clinical data retrieved from clinical data server 106, social data retrieved from social data server 108, and patient data collected from past screenings or monitoring to train the models of runtime model server 304 (FIG. 18).” [0154] “The system may be used to assess the mental state of the subject in a single session or over multiple sessions. Subsequent sessions may be informed by assessment results from prior assessments. This may be done by providing assessment data as inputs to machine learning algorithms.” [0156] “the screening or monitoring test may be similar to the subject matter of standardized depression screening or monitoring tests such as the PHQ-9” [0206] “Categories may correlate to (i) specific health diagnoses such as depression, anxiety, etc.; (ii) specific symptoms such as insomnia, lethargy, general disinterest, etc.; and/or (iii) aspects of a patient's treatment such as medication, exercise, etc.” [0519] “The system may be able to establish a baseline profile for each individual patient.”)
generate patient characteristic information in which the information corres