DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2 April 2026 has been entered.
Status
This Office Action is in response to the communications filed on 2 April 2026 and 9 September 2026. Claims 1 and 13 have been cancelled, claims 2, 4-5, 8, 10-11, 16, 18-19, and 23-24 have been amended, and no new claims have been added. Therefore, claims 2-12 and 14-30 are pending and presented for examination.
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 Amendment
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s amendment overcomes the prospective claim objection(s); therefore, the Examiner withdraws the objection(s).
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101 based on abstract idea analysis (Point 6 at pp. 4-7 of the Non-Final Office Action); therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment overcomes the rejection(s) under 35 USC §§ 102 and/or 103; therefore, the Examiner places new grounds of rejection.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
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 2-12 and 14-30 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a method (claims 2-7 and 27-30), systems (claims 8-12, 14-15 and 23-26), and a non-transitory computer-readable storage medium (claims 16-22), which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a computer-implemented method of predicting therapeutic alliances between help seekers and care providers, the method comprising: receiving, via a network, care provider attribute data of one or more care providers, help seeker attribute data of one or more help seekers, and help seeker input data from one or more databases, wherein the care provider attribute data comprises personality variable data of the one or more care providers; storing the care provider attribute data, the help seeker attribute data, and the help seeker input data in one or more non-transitory computer-readable storage mediums; executing, by one or more processors in a cloud-based computing system, one or more machine learning models trained to: generate predictive positive working alliance feedback data based on the care provider attribute data; and generate a ranking of the one or more care providers based on the generated predictive positive working alliance feedback data; and transmitting, to one or more user interfaces, one or more indications of the generated ranking, which are predictive of the therapeutic alliance between the help seeker and the care provider.
Independent claims 8, 16, and 23 are analyzed in the same manner as claim 2 above since claim 8 is directed to a cloud-based computing system … comprising: one or more processors and one or more non-transitory computer-readable storage mediums storing instructions comprising one or more algorithms that when executed by the one or more processors cause the one or more processors to perform operations comprising the same or similar activity as indicated at claim 2 (where claim 2 also indicates use of the same system components). Claim 16 is directed to a non-transitory computer-readable storage medium storing instructions which, when executed by the one or more processors in a cloud-based computing system, cause the one or more processors to perform operations comprising the same or similar activity as indicated at claim 2. And Claim 23 is directed to a cloud-based computing system … comprising: one or more modules comprising one or more processors and one or more non-transitory computer-readable storage mediums storing instructions comprising one or more algorithms that when executed by the one or more processors cause the one or more processors to perform operations comprising the same or similar activity as indicated at claim 2 and claim 8, except designating the same system components as at claim 8 to be “one or more modules”, incorporating the attribute data type limitations of dependent claims 5, 11, and 19, and moving the determining and transmitting (i.e., providing) of pairings to dependent claim 24.
The dependent claims (claims 3-7, 9-15, 17-22, and 24-30) appear to be encompassed by the abstract idea of the independent claims since they merely indicate the particular type of machine learning model, i.e., a regression model (claims 3, 9, and 17), receiving additional data (of the same types or categories) and updating the model result (claims 4, 10, and 18), what the care provider and help seeker attribute data comprises (claims 5, 11, and 19), what the personality variable data comprises (claims 6, 12-14, and 20-21), the model has been trained to assess using Session Alliance Inventory (SAI) (claims 7, 15, and 22), determining and transmitting a pairing of help seeker and care provider – as at independent claims 1, 8, and 16 (claim 24), if/when the care provider ranking is greater than a threshold, transmitting the ranking or that the ranking meets the threshold (claim 25), the transmission including a hiring recommendation when the ranking is greater than the threshold (claim 26), the model has been trained using validated psychological scales or validated working alliance questionnaires, including a Counselor Activity Self-Efficacy Scale or a 6-item working alliance questionnaire (claims 27-29).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of pairing care providers to a help seeker based on attributes of each; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within the following grouping(s) of subject matter:
Certain methods of organizing human activity (e.g. …; commercial or legal interactions such as agreements, contracts, legal obligations, advertising, marketing or sales activities/behaviors, or business relations; and/or managing personal behavior or relationships between people such as social activities, teaching, and following rules or instructions); and
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion).
Both of the above groupings also now, based on the amending including using mathematical models such as a regression model (especially indicated at dependent claims), also implicate mathematical concepts as an additional grouping.
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are the method being computer-implemented, receiving via a network, storing in one or more non-transitory computer-readable storage mediums; executing, by one or more processors in a cloud-based computing system (at claim 2), a cloud-based computing system … comprising: one or more processors and one or more non-transitory computer-readable storage mediums storing instructions comprising one or more algorithms that when executed by the one or more processors cause the one or more processors to perform operations (at claim 8), a non-transitory computer-readable storage medium storing instructions which, when executed by the one or more processors in a cloud-based computing system, cause the one or more processors to perform operations (at claim 16), and a cloud-based computing system … comprising: one or more modules comprising one or more processors and one or more non-transitory computer-readable storage mediums storing instructions comprising one or more algorithms that when executed by the one or more processors cause the one or more processors to perform operations (at claim 23). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding the words ‘apply it’ (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility.
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore, the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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.
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 2, 8, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (U.S. Patent Application Publication No. 2014/0324457, hereinafter Kim) in view of Abedini et al. (U.S. Patent Application Publication No. 2017/0177814, hereinafter Abedini).
Claim 2: Kim discloses a computer-implemented method of predicting therapeutic alliances between help seekers and care providers, the method comprising:
receiving, via a network, care provider attribute data of one or more care providers, help seeker attribute data of one or more help seekers, and help seeker input data from one or more databases, (see Kim at least at, e.g., ¶¶ 0025, “EHR cloud 105 provides a collection of electronic health information about users or patients on a HIPAA-compliant eBuyMed cloud Network Server also referred to as permission based server 107. A user or patient can input and keep a record of patients' medical information including demographics, medical history, medication, allergies, immunization status, laboratory test results, radiology images, vital signs, billing and insurance information on the EHR cloud 105. Also, EHR cloud 105 includes an EHR record of the services offered by the doctor including fever, cold, heart diseases and a list of the doctor's contacts”, 0026, “EHR cloud 105 will have the capacity to automatically sync with patient's PHR records stored in PHR cloud 113 subject to privacy settings and permission data settings on permission based server 107. The data and storage packages are stored on the servers” 0039, “The patient will input basic information to search for health care professionals (e.g., area, diseases).… In addition to the search criteria that the patient enters, SHCMS application program utilizes existing PHR records on PHR cloud 113 for ideally matched health care professionals for the patient”; citation hereafter by number only);
storing the care provider attribute data, the help seeker attribute data, and the help seeker input data in one or more non-transitory computer-readable storage mediums (0026, “EHR cloud 105 will have the capacity to automatically sync with patient's PHR records stored in PHR cloud 113 subject to privacy settings and permission data settings on permission based server 107. The data and storage packages are stored on the servers”);
executing, by one or more processors in a cloud-based computing system, one or more machine learning models (0039, “Smart Health Care Matching System (SHCMS) is a server-side software program that searches doctors that are predicted to give maximum satisfaction and best treatment outcome for each patient…. To achieve these approaches, this novel matching system, SHCMS application program, leverages a customized Machine Learning technology”, 0042, “SHCMS application program utilizes a customized Machine Learning Strategy in order to match the health care professional or insurance plans with the patient”).trained to:
generate predictive positive working alliance feedback data based on the care provider attribute data, the help seeker attribute data (0039, “Smart Health Care Matching System (SHCMS) is a server-side software program that searches doctors that are predicted to give maximum satisfaction and best treatment outcome for each patient”);
generate a ranking of the one or more care providers based on the generated predictive positive working alliance feedback data (0038, “the matching server 109 (FIG. 1) is able to operate in order to predict or match the patient with the best matched doctors or best health care professional. The matching server 109 includes a Smart Health Care Matching System (SHCMS) application program”, 0039, “Smart Health Care Matching System (SHCMS) is a server-side software program that searches doctors that are predicted to give maximum satisfaction and best treatment outcome for each patient”, 0052, “the final evaluation number which is calculated by sum of combination of normalized scores of patients' feedback, quantified outcome of treatment from PHR, and quantified outcome of treatment from EHR. This evaluation number is also exposed to the patients who are searching the health care professionals at the eBuyMed website”); and
transmitting, to one or more user interfaces, one or more indications of the generated ranking, which are predictive of the therapeutic alliance between the help seeker and the care provider (0052, “This evaluation number is also exposed to the patients who are searching the health care professionals at the eBuyMed website”, 0070, “the patient 115a is presented with a matching health care professional or service to verify if it is correct by the matching server 109 transmitting through PHR cloud 113 a display on the patient computer system 115 of the matching health care professional for the patient 115a to choose”).
Kim, however, does not appear to explicitly disclose wherein the care provider attribute data comprises personality variable data of the one or more care providers. Abedini, though, teaches that “Many existing systems try to match patients with physicians by match operations that depend on entities (search terms, such as “location, distance, expertise) and, as a result there is limited or no opportunity for the patients to be aware of how comfortable they would be in communicating with the doctor or with the treatment approach that was suggested by the doctor for the specific ailment and/or health concerns” (Abedini at 0005), “A computer-implemented methodology, which may implement a search engine including for example a machine learning component or device, is proposed that provides cognitive care to make the process of searching and communicating with specialized doctors or health care providers easy, for example, for an average patient, and for example, which addresses the above-described problems. In one embodiment, a machine may be trained, using a machine learning technique or algorithm, to provide a personalized and specialized health care apparatus with cognitive care for identifying best matches between doctors and patients. While this disclosure refers to doctors, the description in the present disclosure may apply to physicians and other health care providers” (Abedini at 0015), and “a cognitive care solution may look for a doctor suitable for a patient considering the following perspectives simultaneously: 1) The doctor who has the correct expertise pertaining to the patient's complaints, and 2) The doctor and the patient matching in personality, for example, as defined by Big Five factors of personality assessment used in academic psychology” (Abedini at 0017). Therefore, the Examiner understands and finds that to use care provider personality variable data is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations for care providers a patient would be comfortable communicating with and accepting care suggestions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the pairing patients and providers of Kim with the personality consideration of Abedini in order to use care provider personality variable data so as to provide recommendations for care providers a patient would be comfortable communicating with and accepting care suggestions.
The rationale for combining in this manner is that to use care provider personality variable data is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations for care providers a patient would be comfortable communicating with and accepting care suggestions as explained above.
Claims 8 and 16 are rejected on the same basis as claim 2 above since Kim discloses the processor, non-transitory computer-readable storage mediums, and cloud computing required by the system and medium of claims 8 and 16 (Kim as cited above at claim 2).
Claims 3-5, 9-11, 17-19 and 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Abedini in further view of Krishnan et al. (U.S. Patent Application Publication No. 2018/0322941, hereinafter Krishnan).
Claims 3, 9, and 17: Kim in view of Abedini discloses the computer-implemented method, system, and medium of claims 2, 8, and 16, but does not appear to explicitly disclose wherein the one or more machine learning models includes one or more machine learning regression models. Krishnan, however, teaches matching patients and providers where “the systems, devices, and methods described herein comprise an application comprising a software module providing at least one of a treatment recommendation and a healthcare provider recommendation generated by a machine learning algorithm based on a user profile of the individual, the analyte, the result, a location of the digital processing device, historical treatment outcome data for a cohort of patients matched to the individual, healthcare provider information, or a combination thereof” where “the one or more recommendations can suggest the nearest hospital with the requisite facilities or resources for treating the specific disorder the user is suffering from (according to the test results and/or user profile information)” (Krishnan at 0047), and where “[t]he machine learning algorithm may be selected from the group consisting of a supervised, semi-supervised and unsupervised learning, such as, for example, … regression algorithm [and/or others]…. Machine learning techniques include bagging procedures, boosting procedures, random forest algorithms, and combinations thereof. Illustrative algorithms for analyzing the data include but are not limited to methods that handle large numbers of variables directly such as statistical methods and methods based on machine learning techniques. Statistical methods include penalized logistic regression” (Krishnan at 0049, see also 0051). Therefore, the Examiner understands and finds that to use a regression machine learning model is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations for the nearest and/or most equipped facilities and resources.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the pairing patients and providers of Kim in view of Abedini with the regression modeling of Krishnan in order to use a regression machine learning model so as to provide recommendations for the nearest and/or most equipped facilities and resources.
The rationale for combining in this manner is that to use a regression machine learning model is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide recommendations for the nearest and/or most equipped facilities and resources as explained above.
Claims 4, 10, and 18: Kim in view of Abedini discloses the computer-implemented method, system, and medium of claims 2, 8, and 16, but does not appear to explicitly disclose further comprising: receiving, via the network, additional care provider attribute data, additional help seeker attribute data, and/or additional help seeker input data from the one or more databases; storing the additional care provider attribute data, the additional help seeker attribute data, and/or the additional help seeker input data in the one or more non-transitory computer-readable storage mediums; executing, by the one or more processors in the cloud-based computing system, the one or more machine learning models trained to: generate updated predictive positive working alliance feedback data; generate an updated ranking of the one or more care providers based on the updated predictive positive working alliance feedback data; and transmitting, to the one or more user interfaces, one or more updated indications of the updated ranking. Krishnan further teaches “a user who obtains a positive test result for a highly infectious disease travels to the nearest hospital, for which no information is known according to the algorithm. However, during the course of the visit, the hospital turns out to have a quarantine space and established quarantine protocols that successfully resolve the potential outbreak. This information is uploaded to the platform's online databases along with other treatment information for the user. The algorithm then updates its decision making based on this information such that, for example, this hospital may be recommended for future users who require treatment for an infectious disease (pursuant to other relevant conditions such as proximity to the user or availability of comparable facilities)” and “the algorithm may provide a treatment recommendation based on the user's past responses to various treatments (e.g. a treatment option is removed from consideration because of past instances when the user experienced no effect or an adverse reaction to the treatment). In some embodiments, recommendations are only provided for predictions having an area under curve of at least about 0.6, about 0.7, about 0.8, about 0.9, about 0.95, or about 0.99 when assessed for predictive accuracy using data not used for training” (Krishnan at 0047) and Krishnan at 0215, “About six hours later, the individual begins experiencing abdominal pain while traveling…. The individual answers that he has had to visit the hospital for treatment due to abdominal pain. This information is then anonymized, and uploaded onto an encrypted database in a remote server”). Therefore, the Examiner understands and finds that to provide additional data to update the analysis is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide better or more current recommendations for the nearest and/or most equipped facilities and resources.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the pairing patients and providers of Kim in view of Abedini with the updating of Krishnan in order to provide additional data to update the analysis so as to provide better or more current recommendations for the nearest and/or most equipped facilities and resources.
The rationale for combining in this manner is that to provide additional data to update the analysis is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide better or more current recommendations for the nearest and/or most equipped facilities and resources as explained above.
Claims 5, 11, and 19: Kim in view of Abedini discloses the computer-implemented method, system, and medium of claims 2, 8, and 16, wherein demographics data of the one or more help seekers is provided (0025, “A user or patient can input and keep a record of patients' medical information including demographics”, 0040, “recommends best suitable or most popular insurance for her based on location, demographic, and other variables in patient PHR cloud 113”), but does not appear to explicitly disclose the care provider attribute data comprises: demographic data of the one or more care providers, and/or character trait variable data of the one or more care providers; and wherein the help seeker attribute data comprises: and/or personality variable data of the one or more help seekers. Krishnan, though, further teaches scoring and ranking of providers, including based on demographics such as a distance radius for providers close to the patient (by “current device location”) (Krishnan at 0052, i.e., demographics of the patient and the provider). Therefore, the Examiner understands and finds that to use at least help seeker (i.e., patient) and care provider demographics is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide better or more current recommendations for the nearest and/or most equipped facilities and resources.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the pairing patients and providers of Kim in view of Abedini with the demographics of Krishnan in order to use at least help seeker (i.e., patient) and care provider demographics so as to provide better or more current recommendations for the nearest and/or most equipped facilities and resources.
The rationale for combining in this manner is that to use at least help seeker (i.e., patient) and care provider demographics is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to provide better or more current recommendations for the nearest and/or most equipped facilities and resources as explained above.
Claims 23-24 are rejected on the same basis as claim 5 above since Kim in view of Abedini in further view of Krishnan disclose a cloud-based computing system for predicting therapeutic alliances between help seekers and care providers, screening the care providers, and/or optimizing pairing of the help seekers with the care providers, the system comprising: one or more modules comprising one or more processors and one or more non-transitory computer-readable storage mediums storing instructions comprising one or more algorithms that when executed by the one or more processors cause the one or more processors to perform operations comprising the same or similar activities as indicated at claims 2 and 5 above (see Kim at least at 0024, “Matching server 109, permission based server 107 are equivalent to a typical computer system that includes a processor, mass storage and memory. The matching server 109 also includes a smart health care matching system software program stored on the processor which will be described in FIG. 2”).
Claims 25 and 26: Kim in view of Abedini in view of Krishnan discloses the computer-implemented method of claim 23, further comprising: determining, by a care provider screening module of the one or more modules using the trained one or more machine learning models, if the generated ranking of a care provider of the one or more care providers is equal to or greater than a threshold value; and transmitting, by the care provider screening module, to one or more user interfaces, one or more indications of the generated ranking and/or the determination of whether the generated ranking meets the threshold value (at claim 25), and generating a hiring recommendation if the generated ranking of the one or more care providers is equal to or greater than the threshold value; and transmitting, by the care provider screening module, to one or more user interfaces, one or more indications of the hiring recommendation (at claim 26). See Krishnan at 0055 “In a case where the node corresponding to healthcare provider A has a value of 0.9 while the node corresponding to healthcare provider B has a value of 0.1, the output can be ranked with provider A as the number one option and provider B as the number two option. In some cases, treatment options are not ranked and/or presented when they fall below a minimum significance threshold” – as combined above and using the rationale as at the combination above.
Claims 6-7, 12, and 14-15, 20-22, and 27-30 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Abedini in further view of Krishnan and in further view of Applicant’s Admitted Prior Art (hereinafter “AAPA”) at Applicant ¶¶ 0016-0019 (as submitted and published).
Claims 6-7, 12, 14-15, and 20-22: Kim in view of Abedini in further view of Krishnan discloses the computer-implemented method, system, and medium of claims 5, 11, and 19, but does not appear to explicitly disclose wherein the personality variable data of the one or more care providers and/or the personality variable data of the one or more help seekers includes input data from one or more of a Ten-Item Personality Inventory (TIPI), Analogue to Multiple Broadband Inventories (AMBI) personality inventory, Counselor Activity Self-Efficacy Scales (CASES), or Adult Attachment Questionnaire (AAQ), or a combination of two or more thereof, and wherein the one or more machine learning models is trained to assess the personality variable data of the one or more care providers and/or the personality variable data of the one or more help seekers using the input data of the one or more of the Ten-Item Personality Inventory (TIPI), the Analogue to Multiple Broadband Inventories (AMBI) personality inventory, the Counselor Activity Self-Efficacy Scales (CASES), or the Adult Attachment Questionnaire (AAQ), or the combination of two or more thereof (at claims 6, 12-14, and 20-21) and wherein the one or more machine learning models is trained to assess the generated ranking using Session Alliance Inventory (SAI) (at claims 7, 15, and 22). However, AAPA at Applicant ¶¶ 0017, 0018, and 0019 recite the use of the claimed techniques or data (i.e., TIPI, AMBI, CASES, and AAQ) as known for at least several years in assessing personality traits and characteristics for forming a working alliance. Applicant does not indicate the AAPA as modified, altered or changed in any manner – the techniques and/or data described is/are used as intended. Gosling (entered of record per the IDS) indicates that TIPI is useful when “time is limited” and/or when “an extremely brief measure … or no measure at all” related to the Big-Five personality dimensions is desired. Therefore, the Examiner understands and finds that to use TIPI and/or AMBI, CASES, and AAQ as personality variable data and to train a model to rank pairings using SAI is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to match personality profiles accurately with little or no data input required.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the pairing patients and providers of Kim in view of Krishnan with the personality measures of AAPA in order to use TIPI and/or AMBI, CASES, and AAQ as personality variable data and to train a model to rank pairings using SAI so as to match personality profiles accurately with little or no data input required.
The rationale for combining in this manner is that to use TIPI and/or AMBI, CASES, and AAQ as personality variable data and to train a model to rank pairings using SAI is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to match personality profiles accurately with little or no data input required as explained above.
Claims 27-30 are rejected on the same basis as claims 6-7, 12, 14-15, and 20-22 above since AAPA indicates the validated psychological scales (as at claim 27), Counselor Activity Self-Efficacy Scale (as at claim 28), validated working alliance questionnaires (as at claim 29), and use of a 6-item working alliance questionnaire (as at claim 30) – as cited above and using the rationale as at the combination above.
Response to Arguments
Applicant's arguments filed 2 April 2026 have been fully considered but they are not persuasive.
Applicant first argues the 101 rejection by alleging “the claims recite limitations that cannot practically be performed in the human mind. For example, claim 2 as amended recites ‘executing, by one or more processors in a cloud-based computing system, one or more machine learning models trained to ... generate predictive positive working alliance feedback data based on the care provider attribute data.’ Applicant submits that the human mind is not equipped to execute one or more machine learning models, and that it is plainly not practical to perform in the human mind machine learning inference as recited in claims 2, 8, 16, and 23” (Remarks at 18). However, per the claims, the model is pre-existing – it is merely executed. The model may be “machine learn[ed]” but it is still just an algorithm (i.e., at its core, mathematics). A human cannot, admittedly, execute code via a processor so as to use the model, but a human can execute (i.e., use) a model. That the executing is by one or more processors in a cloud-based computing system is indicated at the rejection as an additional element – i.e., the use of a computer to perform the analysis the model algorithm would otherwise provide. The algorithm itself need not be complex or difficult per the claim(s) – it merely needs to be a model that had been, at some time, developed via machine learning (i.e., on, or by, a computer). Applicant’s argument is literally that any claim reciting use of a computer and/or a machine learning model – since that cannot actually or literally be performed by a person in its entirety, is automatically eligible. Both precedent and the current examination guidelines do not support such a conclusion.
Applicant then argues that “because the claims as amended do not include commercial or legal interactions and do not recite managing personal behavior or relationships between people. For example, claim 8 recites determining a pairing of help seeker with care provider and transmitting an indication of this pairing, which Applicant submits does not recite the creation of any commercial or legal obligation, and further does not recite any management of personal behavior or relationships between people” and therefore, the claims should not be considered one of certain methods of organizing human activity (Remarks at 18). However, “determining a pairing of help seeker with care provider” very literally is generating or creating a relationship (or at least a potential relationship – assuming, or given that, there is any utility to the pairing, i.e., the pairing is not just thrown away or used in any manner) between the help seeker and the care provider. Further, the “transmitting an indication of this pairing” – other than possibly or potentially being performed by a computer (although the claims do not recite this as being performed by a computer – is merely one person providing the pairing via an interface (e.g., orally, or in writing). Similar to, or analogous to, the age-old practice of match-making (whether for marriage, or for other purposes such as an employment head hunter) between persons, the claims are apparently match-making (or head hunting) of care providers for care/help seekers.
Applicant then argues Step 2A, Prong 2, alleging that “Claims 2-30 [sic] recite improvements to pattern matching technologies to optimize pairing based upon collaborative relationship” (Remarks at 19).
However, first, this actually argues that the claims ARE establishing, or based on a relationship between people (i.e., a “collaborative relationship”), despite having earlier argued that the claims ARE NOT based on relationships between persons.
Second, it does not appear that the claims can reasonably be interpreted as actually “improving” the matching or any technology – the claims recite the machine-learning model as being already established or in existence. Merely using what already exists, including for its intended purpose as the claims indicate the machine learning model being used for, is not reasonably considered any form of improvement. The technology being used, i.e., the elements additional to the abstract idea, is not changed or altered such that it could reasonably be considered improved, either. Merely alleging, or even if it were documented, that “that many help seekers quit therapy after limited sessions, often for a perceived lack of fit with the mental health provider” does NOT mean that suggesting a different care provider is necessarily any improvement – the help seeker need not ever use the suggestion transmitted as a ranking, the suggested provider may be “worse” somehow than the previous one, etc. etc.
Applicant then argues the Step 2B analysis, alleging that “the claims recite improvements to a technology or technical field (e.g., improved efficient computer technology for pattern matching)” (Remarks at 21). However, as noted above, there is no apparent or recited improvement to technology. Also, there is no indication related to efficiency of any computer technology – technology is merely recited as being used (per the elements additional to the abstract idea) without any change or improvement.
As such, Applicant’s argument is not persuasive.
Applicant then argues the § 102 prior art rejections (Remarks at 21-24), repeating claim 2 (Id. at 22), then alleging that since the EHR records of Kim include “patient-doctor ratings”, “the patient can rate health care professionals”, and Kim can refer to medical and treatment history stored on electronic health records, “Kim fails to disclose ‘generate predictive positive working alliance feedback data based on the care provider attribute data’ as recited in amended claim 2” (Id. at 23, emphasis omitted). However, how a patient and/or doctor relate to, or rate, each other (as well as the medical and treatment history information) is very literally “feedback data” that is used to predict whether a working alliance would be, or may be, any of positive, negative, or neutral. Applicant’s specification does not describe exactly what an “attribute” would be, or encompass, but Applicant Fig. 5 is referenced for both care provider and help seeker attributes (see Applicant ¶¶ 0036, 0040, 0043, and 0044 – all indicating items 508 and 510), and Fig. 5, items 508 and 510 indicate this is “Help Seeker Input 508” and “Care Provider Input 510”. That appears to quite literally be just what Kim is using – input from the help seeker and care provider – to make the determination.
Applicant then argues the amendment reciting “personality variable data of the one or more care providers” (Remarks at 24). The amendment necessitates new grounds of rejection – please see the current rejection(s) above.
Therefore, Applicant’s argument is not persuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Raduchel et al. (U.S. Patent Application Publication No. 2017/0161439, hereinafter Raduchel) indicates that “user 120 may submit, from an application program at electronic device 130, a request to provide healthcare service for a particular condition (1210). The bids may be collected from network 110 by the application program on user electronic device 130 (1220). The bids may be presented to a requesting user through an informative interface on user electronic device 130 (1240). In some instances, the application program may rank the received bids. In one example, the ranking may be according to the degree of match between an interest of the healthcare provider and the requested healthcare service” (Raduchel at 0190), “A number of processing steps can be required to perform this comparison and pattern matching, including but not limited to data smoothing, time series analysis, cross-correlation analysis, convolution analysis, regression analysis, and Artificial Intelligence (AI) related technologies, such as neural networks, machine learning, deep learning, hierarchical temporal memory, and cogent confabulation” (Raduchel at 0248), and that Docker containers are known and used (Raduchel at 0360 and 0364).
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/SCOTT D GARTLAND/
Primary Examiner, Art Unit 3685
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