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 .
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.
Response to Arguments
This a response to Applicant’s amendment filed on 05 January 2026, wherein:
Claim 1 is amended.
Claims 2-4, 6-8, 14, 15, and 17-23 are original.
Claims 5, 10, 12, 13, and 16 are previously presented.
Claims 9, 11, and 24-26 are canceled.
Claims 1-8, 10, and 12-23 are pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 120 as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994)
The disclosure of the prior-filed applications, Application No. 16/291,225, 16/501,103, and 62/561,938, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
In particular, regarding limitation “monitoring interpersonal relations …. from the smart devices or from a wearable sensor in communication with the smart devices”, the following are paragraphs of the originally filed prior-filed applications related to such limitation:
Para. 6 discloses “In the context of the present invention, a proof-of-concept study was recently published in IEEE Computer. In this study, multimodal data generated from smartphone and wearable devices are used to detect when couples were having conflict with each other ….”
Para. 11 discloses “The method includes a step of monitoring interpersonal relations for a couple or a group of interpersonally connected users with a plurality of devices by collecting data from the smart devices.”
Such paragraphs clearly disclose the limitation “monitoring interpersonal relations …” is performed by collecting data from a plurality of smart devices, such as wearable devices as opposed to the “or” statement as recited in the independent claims of this application. For these reasons, the recited claimed language is not disclosed in the specification of US 16/501,103.
Regarding the limitation “classifying and/or quantifying the interpersonal relations into classification or quantification”, the following are paragraphs of the prior-filed applications related to such limitation:
Para. 12 discloses “In a variation, the representations of interpersonal relationships can be signal-derived and/or machine learning representations. In a refinement, the data streams include one or more components selected from the group consisting of physiological signals; audio measures; speech content …”.
Para. 14 discloses “In a variation, the method further includes a step of computing signal-derived representations of the data streams. The signal-derived representation can be computed by knowledge-based design and/or data-driven analyses, which can include clustering. In a refinement, the signal-derived representations is used as a foundation for machine learning, data mining, and statistical algorithms that can be used to determine what factors, or combinations of factors, predict a variety or relationship dimensions, such as conflict, relationship quality or positive interactions. …”
Such paragraphs disclose claim 2 and 3, but fail to disclose the limitation of the independent claims as indicated above. For these reasons, the recited claimed language is not disclosed in the specification of US 16/501,103.
Regarding priority of Provisional Application 62/561,938, such provisional was filed on 22 September 2017 and this application was filed 27 December 2021. The filing date of this application is filed more than 1 year after the Provisional Application 62/561,938, hence does not benefit from the date of Provisional Application 62/561,938. Furthermore, Provisional Application 62/561,938 provides the same disclosure as Non-Provisional Application 16/501,103 which is insufficient as identified above. (MPEP 211.01(a) and 35 USC 119(e)(1)).
Hence, per MPEP 2133.01, pending claims 1-8, 10, and 12-26 are not entitled to the benefit of US 16/501,103 and US 62/561,938.
Furthermore, the disclosure of the prior-filed applications, 16/291,225, 16/501,103, and 62/561,938, fail to provide sufficient written description for “classifying and/or quantifying the interpersonal relations using the signal-derived features by applying a neural network trained on previously obtained data with known classifications to generate interpersonal classifications; providing feedback and/or goals to one or more users to increase awareness about relationship functioning, wherein the goals include amplifying attachment bonds, increasing positivity, and decreasing the amount of aggression and conflict; and generating and displaying personalized feedback and goals on users' smart devices in real-time, with context-specific suggestions based on a relational classification, designed to enhance relationship awareness, and communication quality” in claim 1 to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). In particular, the specification of the prior-filed applications, at best, merely recites similar language as the claims without providing any substantive description for the claimed limitations identified above for the same reasons that the instant specification also fails as identified in the rejections of the claims under 35 USC 112(a) below for the same claim limitations.
Hence, per MPEP 2133.01, pending claims 1-8, 10, and 12-23 are not entitled to the benefit of US 16/291,225, 16/501,103 and US 62/561,938. Therefore, pending claims 1-8, 10, and 12-23 have an effective filing date of 27 December 2021.
Information Disclosure Statement
The information disclosure statements filed 29 February 2024 fail to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because the list of non-patent references contains either one or more non-compliances with format requirements. According to § 1.98(b)(5):
“Each publication listed in an information disclosure statement must be identified by publisher, author (if any), title, relevant pages of the publication, date, and place of publication.”
In particular, one or more references listed under “Non-Patent Literature Documents” lack one or more of these elements. Importantly, identification of relevant pages of each publication ensures that the Office was informed of the specific portion to be considered, especially for voluminous works, and that it has received all identified pages. Moreover, in the case of voluminous works such as books and websites, failure to cite relevant pages or webpages presents a boundless search. Specific references to particular contents within these works by page number or similar indices are suggested.
The IDS submission amounts to being clearly voluminous. The lack of explicit page numbers in many documents including foreign patent documents, especially those exceeding 100 pages, that sets forth subject matter relevant to the claimed invention provides a boundless search.
Accordingly, due to the voluminous length of the total number of pages accompanying the documents listed in the submission, only a cursory review of the references could be performed by the Examiner.
The information disclosure statement filed 29 February 2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. In particular, non-patent literature cite no. 9 (Heater) is incomplete (missing text); foreign patent document cite no. 4 (JP 5025800) is so blurry as to be illegible; non-patent literature cite no. 4 is missing; non-patent literature cite no. 11 and 19 are the same; and there are multiple, different copies of non-patent literature cite no. 6 (Furness).
The information disclosure statement filed 10 April 2024 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. In particular, non-patent literature cite no. 2 (Nahum-Shani) is so blurry as to be illegible.
The listing of references in the specification is not a proper information disclosure statement. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Objections
Claims 1-8, 10, and 12-23 are objected to because of the following informalities:
Claim 1 includes an errant comma following “enhance relationship awareness” at the end of the claim.
Dependent claims 2-8, 10, and 12-23 inherit the deficiencies of their respective parent claims, and are thus objected to under the same rationale.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code 112(b) not included in this action can be found in a prior Office action.
Claims 1-8, 10, and 12-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, it is unclear what constitutes a “relational classification”, and thus how “context-specific suggestions” can be based on it. The disclosure does not aid understanding as it is silent regarding this term. Thus, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought. Dependent claims 2-8, 10, and 12-23 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claim 4, it is unclear whether “a trained neural network” recited in claim 4 is the same neural network as that recited in independent claim 1 or a different neural network. Thus, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought. For the purposes of compact prosecution, they are construed as the same. Dependent claim 7 inherits the deficiencies of its respective parent claims, and is thus rejected under the same rationale.
The text of those sections of Title 35, U.S. Code 112(a) not included in this action can be found in a prior Office action.
Regarding claim 1, the disclosure fails to provide sufficient written description for “classifying and/or quantifying the interpersonal relations using the signal-derived features by applying a neural network trained on previously obtained data with known classifications to generate interpersonal classifications; providing feedback and/or goals to one or more users to increase awareness about relationship functioning, wherein the goals include amplifying attachment bonds, increasing positivity, and decreasing the amount of aggression and conflict; and generating and displaying personalized feedback and goals on users' smart devices in real-time, with context-specific suggestions based on a relational classification, designed to enhance relationship awareness, and communication quality” to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). The specification, at best, merely recites that this function is performed without providing sufficient, if any, description of the steps, calculations, or formulas necessary to perform the claimed functionality. See, for example, at least para. 35, 37, 39, 41-45, and 76 which merely recite a neural network may be used in the alternative without any meaningful description. Additionally, the only mention of amplifying attachment bonds, increasing positivity, and reducing conflict is found in para. 38 which only recites these as potential goals without any further disclosure. Similarly, the originally filed disclosure is silent regarding “relational classification”, “relationship awareness”, and “communication quality”. “Relationship awareness”, “communication quality”, “relational classification”, and “positive relational engagement” are not clearly related to anything in the disclosure and are thus considered new matter. Dependent claims 2-8, 10, and 12-23 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 13 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. In particular, independent claim 1 recites “classifying and/or quantifying the interpersonal relations using the signal-derived features by applying a neural network trained on previously obtained data with known classifications to generate interpersonal classifications”. The content of claim 13 is more broad than this limitation and thus does not further limit the subject matter upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code 101 not included in this action can be found in a prior Office action.
Claims 1-8, 10, and 12-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The claims are directed to a method and a product which fall under the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 1 recites:
A method for monitoring and understanding interpersonal relationships comprising:
monitoring interpersonal relations of a couple or group of interpersonally connected users with a plurality of smart devices by collecting data streams from the smart devices or from a wearable sensor in communication with the smart devices;
extracting signal-derived features from the data streams, wherein the signal-derived features include acoustic features, linguistic features, and physiological measures, wherein
the acoustic features include motor timing parameters of speech production, prosody and intonation, and frequency modulation;
the physiological measures include skin conductance level, mean skin conductance response frequency and amplitude, and rise and recovery time of skin conductance responses; and
the linguistic features include word count, frequency of parts of speech, and frequency of words related to affect, stress, or mood;
classifying and/or quantifying the interpersonal relations using the signal-derived features by applying a neural network trained on previously obtained data with known classifications to generate interpersonal classifications;
providing feedback and/or goals to one or more users to increase awareness about relationship functioning, wherein the goals include amplifying attachment bonds, increasing positivity, and decreasing the amount of aggression and conflict; and
generating and displaying personalized feedback and goals on users' smart devices in real-time, with context-specific suggestions based on a relational classification, designed to enhance relationship awareness, and communication quality.
All of the foregoing underlined elements identified above amount to the abstract idea grouping of a certain method of organizing human activity because they are managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) by collecting information, analyzing the information, and outputting the results of the collection and analysis. This collection, analysis, and outputting of results also amounts to the abstract idea grouping of mental processes as the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind (including observation, evaluation, judgment, opinion) but for the recitation of generic computer components. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process.
The dependent claims amount to merely further defining the judicial exception.
Therefore, the claims recite a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the claims do not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: a plurality of smart devices (claim 1); a wearable sensor in communication with the smart devices (claim 1); a neural network trained on previously obtained data with known classifications (claim 1); identifying representations as “machine-learned” (claim 3), a trained neural network (claim 4); video (claim 5); Global Positioning System (GPS) (claim 5); mobile, internet, network communications (claim 5); the internet (claim 7); network communications (claim 7); a peripheral device (claim 8); a mobile device (claim 10); Internet of Things (IoT) platform (claim 10); machine learning (claim 13); and a system (claim 15). Although the claims recite the components, identified above, these elements are recited at a high level of generality and are not tied to performing any of the steps of the claimed method. This is evidenced by at least Fig. 1A and 2A-2J which illustrate that the basis of the claimed invention is merely a smartphone app, with Fig. 1B illustrating the physical components as merely non-descript black boxes. Further evidence is provided by the specification. See, for example, at least para. 39-45 and 49-53. For instance, para. 49 explicitly identifies that “virtually any type of computer processor may be used”, the “computer memory 24 includes a computer-readable medium which can be any non-transitory (e.g., tangible) medium that participates in providing data that may be read by a computer”, and that any form of programming language may be used. Thus, the judicial exception is not implemented with, or used in, a particular machine or manufacture. Additionally, the claims do not recite any limitations that improve the functionality of the computer system because the claimed monitoring, forming representations, detecting, computing, and providing feedback and/or goals are merely performing the steps of processing data but are not tied to improving any functionality of the computer system. This is further evidenced by the absence of specificity of the components and their organization in the disclosure. Again, see, for example, at least Fig. 1A-2J and para. 39-45 and 49-53. None of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Furthermore, the inclusion of sensors and smart devices merely adds insignificant extrasolution activity to the judicial exception (e.g., mere extrasolution data gathering in conjunction with a law of nature or abstract idea). Similarly, the mere use of a trained neural network does not improve computer functionality as it merely invokes the use of a computer or other machinery in its ordinary capacity to process information. In other words, the neural network performs its normal operations as it would for any intended use (i.e., being trained on and applied to data relevant to the intended use). Additionally, the claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition nor do they apply or use a judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. For instance, while the claims indicate that the claimed invention is for monitoring and understanding interpersonal relationships and provides feedback and/or goals with the intent to increase awareness about relationship functioning, they are silent regarding any specific treatment or prophylaxis for any specific disease or medical condition. It should be noted that because the courts have made it clear that the mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the elements identified above does not affect this analysis. See MPEP 2106.05(I). Accordingly, based on all of the considered factors, these additional elements do not integrate the abstract idea into a practical application. Therefore, the claims are directed to the judicial exception. (STEP 2A, PRONG 2: NO).
Step 2B
The independent and dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As identified in Step 2A, Prong 2, above, the claimed system and the process it performs does not require the use of a particular machine, nor does it result in the transformation of an article. The claims do not involve an improvement in a computer or other technology. Although the claims recite components (identified in Step 2A, Prong 2) for performing at least some of the recited functions, these elements are recited at a high level of generality and are not tied to performing any of the steps of the claimed method. This is evidenced by at least Fig. 1A and 2A-2J which illustrate that the basis of the claimed invention is merely a smartphone app, with Fig. 1B illustrating the physical components as merely non-descript black boxes. Further evidence is provided by the specification. See, for example, at least para. 39-45 and 49-53. They are well-understood, routine, and conventional functions of a computer, as evidenced by the Applicant’s written description which describe the elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a). Thus, the judicial exception is not implemented with, or used in, a particular machine or manufacture. Furthermore, this also evidences that the components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. This further evidences that the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system as the claimed functions merely amount to collecting information, analyzing the collected information, and outputting the results of the collection and analysis. The inclusion of sensors and smart devices merely adds insignificant extrasolution activity to the judicial exception (e.g., mere extrasolution data gathering in conjunction with a law of nature or abstract idea). Similarly, the mere use of a trained neural network does not improve computer functionality as it merely invokes the use of a computer or other machinery in its ordinary capacity to process information. In other words, the neural network performs its normal operations as it would for any intended use (i.e., being trained on and applied to data relevant to the intended use). Viewed as a whole, these additional claim elements do not provide meaningful limitation to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea of itself (STEP 2B: NO). Therefore, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code 103 not included in this action can be found in a prior Office action.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8, 10, 12-17, 19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (US 2020/0065728, hereinafter referred to as Wilson) in view of Gil (US 2016/0328987) and Christie1 (Electrodermal activity in the 1980s: a review).
Regarding claim 1, Wilson teaches a method for monitoring and understanding interpersonal relationships (Wilson, Fig. 1 and 4-6) comprising:
monitoring interpersonal relations of a couple or group of interpersonally connected users with a plurality of smart devices (Wilson, Fig. 4, label 410A-410C as the users, labels 408,412,406 shows the smart devices of the users. Fig. 5, label 504 shows the monitoring of interpersonal relations by monitoring or collecting data from the one or more internet of things computing devices.) by collecting data streams from the smart devices or from a wearable sensor in communication with the smart devices (Wilson, Fig. 5, label 504, Fig. 4, label 418,420. Para. 68-69 discloses monitor, observe, and collect data using IoT devices collection component 418. Para. 12 discloses IoT devices.);
extracting signal-derived features from the data streams, the signal-derived features include acoustic features, linguistic features, and physiological measures (Wilson, para. 15, “the present invention may analyze audible communications, body language, biometric data, micro expressions/facial expression, or other detectible data relating to each party to determine risk factors at thresholds sent or provided by a user.”), wherein
the acoustic features include motor timing parameters of speech production, prosody and intonation, and frequency modulation (Wilson, para. 15, “the present invention may analyze audible communications, body language, biometric data, micro expressions/facial expression, or other detectible data relating to each party to determine risk factors at thresholds sent or provided by a user.” Para. 22, “perform the following functions: 1) Navigate the complexities of human language and understanding; 2) Ingest and process vast amounts of structured and unstructured data; 3) Generate and evaluate hypotheses; 4) Weigh and evaluate responses that are based only on relevant evidence; 5) Provide situation-specific advice, insights, estimations, determinations, evaluations, calculations, and guidance; 6) Improve knowledge and learn with each iteration and interaction through machine learning processes; 7) Enable decision making at the point of impact (contextual guidance); 8) Scale in proportion to a task, process, or operation; 9) Extend and magnify human expertise and cognition; 10) Identify resonating, human-like attributes and traits from natural language; 11) Deduce various language specific or agnostic attributes from natural language; 12) Memorize and recall relevant data points (images, text, voice) (e.g., a high degree of relevant recollection from data points (images, text, voice) (memorization and recall)); and/or 13) Predict and sense with situational awareness operations that mimic human cognition based on experiences.” Para. 68, “The IoT device collection component 418 may be used to monitor, observe, harvest, collect, and store personal data, behavior, biometric data (e.g., facial expressions, heart rate, etc.) in the database 455. The IoT device collection component 418 may identify, collect, and process data relating to age of the user, medical history, financial conditions, status of employment, a social media user profile, social media communication patterns, favorable and unfavorable interests, food preferences, profile types and characteristics of persons associated with the user, an emotional state of the user, biometric data, behavior patterns, or a combination thereof.” Para. 77, 1) One or more camera sensors may be integrated with a voice activated hub that may monitor an arbitration hearing occurring between two or more persons. 2) Communication, behavior, and/or activities or events of the user may be monitored to identify an escalation in hostility, a negative behavioral response, and/or an increase in a negative emotional state according to a threshold scale of seriousness (e.g., identified communication, behavior, or actions less than a threshold may be identified as inappropriate). For example, on or more cognitive identification operations may be employed such as, for example, a voice stress analysis, NLP keyword classification (e.g., negative communication vs. increased use of incendiary/offensive communication), a measurement of tone or speech inflection of the communication in order to detect early signals of confrontation, hostility, incendiary/offensive, or hazardous communication, behavior, or actions, increased amounts of gesticulations increasing over time, facial micro-expressions indicating various levels of escalated emotion and the type of emotions, increased heat rate/pulse, pacing of a user, bodily tremors/shakes, rapid movements of one or more extremities of a user (e.g., shaking a hand repeatedly towards another user), and/or other type of identification data.”);
the linguistic features include word count, frequency of parts of speech, and frequency of words related to affect, stress, or mood (Wilson, para. 15, “the present invention may analyze audible communications, body language, biometric data, micro expressions/facial expression, or other detectible data relating to each party to determine risk factors at thresholds sent or provided by a user.” Para. 22, “perform the following functions: 1) Navigate the complexities of human language and understanding; 2) Ingest and process vast amounts of structured and unstructured data; 3) Generate and evaluate hypotheses; 4) Weigh and evaluate responses that are based only on relevant evidence; 5) Provide situation-specific advice, insights, estimations, determinations, evaluations, calculations, and guidance; 6) Improve knowledge and learn with each iteration and interaction through machine learning processes; 7) Enable decision making at the point of impact (contextual guidance); 8) Scale in proportion to a task, process, or operation; 9) Extend and magnify human expertise and cognition; 10) Identify resonating, human-like attributes and traits from natural language; 11) Deduce various language specific or agnostic attributes from natural language; 12) Memorize and recall relevant data points (images, text, voice) (e.g., a high degree of relevant recollection from data points (images, text, voice) (memorization and recall)); and/or 13) Predict and sense with situational awareness operations that mimic human cognition based on experiences.” Para. 68, “The IoT device collection component 418 may be used to monitor, observe, harvest, collect, and store personal data, behavior, biometric data (e.g., facial expressions, heart rate, etc.) in the database 455. The IoT device collection component 418 may identify, collect, and process data relating to age of the user, medical history, financial conditions, status of employment, a social media user profile, social media communication patterns, favorable and unfavorable interests, food preferences, profile types and characteristics of persons associated with the user, an emotional state of the user, biometric data, behavior patterns, or a combination thereof.” Para. 77, 1) One or more camera sensors may be integrated with a voice activated hub that may monitor an arbitration hearing occurring between two or more persons. 2) Communication, behavior, and/or activities or events of the user may be monitored to identify an escalation in hostility, a negative behavioral response, and/or an increase in a negative emotional state according to a threshold scale of seriousness (e.g., identified communication, behavior, or actions less than a threshold may be identified as inappropriate). For example, on or more cognitive identification operations may be employed such as, for example, a voice stress analysis, NLP keyword classification (e.g., negative communication vs. increased use of incendiary/offensive communication), a measurement of tone or speech inflection of the communication in order to detect early signals of confrontation, hostility, incendiary/offensive, or hazardous communication, behavior, or actions, increased amounts of gesticulations increasing over time, facial micro-expressions indicating various levels of escalated emotion and the type of emotions, increased heat rate/pulse, pacing of a user, bodily tremors/shakes, rapid movements of one or more extremities of a user (e.g., shaking a hand repeatedly towards another user), and/or other type of identification data.”);
classifying and/or quantifying the interpersonal relations into classification or quantifications (Wilson, para. 70 discloses the conflict resolution component 450 interpret appropriateness and/or inappropriateness of communications, behavior, actions or events associated with one or more users according to a plurality of identified contextual factors during a conflict resolution. Para. 75 discloses a classifier that recognizes different behaviors, communications or events.) by applying a neural network trained on previously obtained data with known classifications to generate interpersonal classifications (Wilson, para. 59, historical data (e.g., cognitively learned appropriate and/or inappropriate communication and/or behavior)… The mechanisms are, among other aspects, rules driven, and the development of these rules may be based on interpretation of data of particular communications, behaviors, actions, and/or events.” Para. 75 discloses training the machine learning classifier with identified communications, behaviors, actions or events. Para. 77 discloses identified communications, behaviors, actions or events is determined from speech signals. Para. 73 discloses selection of action due to identified communications, behaviors, actions or events. Similarly, para. 81, “A multi-level neural networks machine learning classification model may also be used to categorize historical data and determine a next best or "optimal" action in order to take ameliorative actions.”); and
providing feedback and/or goals to one or more users to increase awareness about relationship functioning, wherein the goals include amplifying attachment bonds, increasing positivity, and decreasing the amount of aggression and conflict (Wilson, para. 58, “the mechanisms of the illustrated embodiments provide novel approaches for the facilitating cognitive conflict resolution to safeguard a user against communications, behavior, actions or events having a possible negative impact upon one or more entities.” Para. 72-73 discloses label 450 suggest and/or apply one or more corrective actions to mitigate a possible negative impact of communications, behavior, actions or events upon the one or more users if the interpreted appropriateness is less than a threshold. Para. 74 discloses one of the resolutions can be an encouragement or reminder (e.g., an email, or alert notification to the UE of each party 410a and 410b.); and
generating and displaying personalized feedback and goals on users' smart devices in real-time, with context-specific suggestions based on a relational classification, designed to enhance relationship awareness, and communication quality (It is noted that “designed to enhance relationship awareness and communication quality” merely indicates intended uses. Wilson, para. 72-73 discloses label 450 suggest and/or apply one or more corrective actions to mitigate a possible negative impact of communications, behavior, actions or events upon the one or more users if the interpreted appropriateness is less than a threshold. Para. 74 discloses one of the resolutions can be an encouragement or reminder (e.g., an email, or alert notification to the UE of each party 410a and 410b.).
Wilson does not explicitly teach the physiological measures include skin conductance level, mean skin conductance response frequency and amplitude, and rise and recovery time of skin conductance responses.
However, in an analogous art, Gil teaches the physiological measures including skin conductance (Gil, para. 33, physiological measures of the members (e.g.,… skin conductance…)”) and Christie teaches that skin conductance level, mean skin conductance response frequency and amplitude, and rise and recovery time of skin conductance responses are the old and well-known elements of measuring skin conductance (Christie, Fig. 2 illustrate these elements; pg. 618, “Skin conductance levels (SCL) and responses (SCR) provide a range of information: data from the phasic changes of the skin conductance responses are seen in Figure 2, which shows a schematic skin conductance responses to a stimulus. Here is seen the measurement of amplitude (as the maximum displacement from the tonic level) together with three temporal measures of latency (between stimulus onset and response onset), rise time (between response onset and response peak) and recovery time (between response peak and return to pre-stimulus level, but usually reported as half-time (t/2) or sometimes in terms of the time constant). If a mean value for a number of response heights is reported, this may be calculated for all occasions where a response is given, and expressed as amplitude; or calculated for all occasions where a response might be given (i.e. all stimulus occasions) and expressed as magnitude.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the skin conductance measures of Gil in the physiological measures of Wilson because skin conductance is a common physiological measure for indicating changes in psychological state. It is further obvious that the skin conductance measures of Gil include skin conductance level, mean skin conductance response frequency and amplitude, and rise and recovery time of skin conductance responses because these are the old and well-known elements of skin conductance as taught by Christie above. Thus, it is merely combining prior art elements according known methods to yield predictable results.
Regarding claim 2, Wilson in view of Gil and Christie teaches the method of claim 1 wherein representations of interpersonal relationships are formed for increasing knowledge about relationship functioning and detecting interpersonally-relevant mood states and events (Wilson, para. 75 discloses training the classifier by the machine learning component to recognize each of the identified communications, behaviors, actions or events. Each of the identified communications, behaviors, actions or events is considered a representation of interpersonal relationships between users such as the users shown in Fig. 4.).
Regarding claim 3, Wilson in view of Gil and Christie teaches the method of claim 2 wherein the representations of interpersonal relationships are signal-derived and machine-learned representations (Wilson, para. 75 discloses the identified communications, behaviors, actions or events are determined from the machine learning component 460 and used to train 460. Para. 77 discloses the identified behaviors, events, actions or communications includes voice stress analysis, tone or speech inflection of communication.).
Regarding claim 4, Wilson in view of Gil and Christie teaches the method of claim 1 wherein signal-derived features are extracted from the data streams (Wilson, para. 77 discloses features, such as tone, speech inflection, voice analysis is extracted from data streams such as voice or speech.), and the signal derived features providing inputs to a trained neural network are interpersonal classifications that allow selection of a predetermined feedback to be sent (Wilson, para. 75 discloses training machine learning classifier with identified communications, behaviors, actions or events. Para. 77 discloses identified communications, behaviors, actions or events is determined from speech signals. Para. 73 discloses selection of action due to identified communications, behaviors, actions or events.).
Regarding claim 5, Wilson in view of Gil and Christie teaches the method of claim 1 wherein the data streams include one or more components selected from the group consisting of physiological signals, audio measures, speech content, video; Global Positioning System (GPS), light exposure, content consumed and exchanged through mobile, internet, network communications, sleep characteristics, interaction measures between individuals and across channels, and self-reported data about relationship quality, negative and positive interactions, and mood (Wilson, para. 77 discloses voice stress analysis, measurement of tone or speech inflection in communication.).
Regarding claim 6, Wilson in view of Gil and Christie teaches the method of claim 5 wherein pronoun use, negative emotion words, swearing, certainty words in speech content are evaluated (Wilson, para. 70 and 75 discloses inappropriateness and/or appropriateness is identified communications, which includes words such as pronoun use, negative emotion words, swearing, certainty words in speech. Para. 77 discloses identifying communications for inappropriateness and/or appropriateness includes speech analysis.).
Regarding claim 8, Wilson in view of Gil and Christie teaches the method of claim 1 wherein data or the data streams are stored separately in a peripheral device or integrated into a single platform (Wilson, Fig. 4, label 28 shows the memory storing data. Para. 69 discloses data collected by the IoT device collection component 418 from the IoT devices such as, for example, the audio/visual device 404, the wearable device 406, etc. may be wirelessly transmitted via a wireless transceiver 430 via a cloud computing infrastructure 432 to the cognitive conflict resolution service 402 via network 422. This indicates the data streams are stored separately in a peripheral device or cloud.).
Regarding claim 10, Wilson in view of Gil and Christie teaches the method of claim 8 wherein the single platform is a mobile device or Internet of Things (IoT) platform (Wilson, Fig. 4 shows the components of a mobile device or IoT platform; para. 42 discloses the computing device 12 (Fig. 1, label 12, Fig. 4, label 12) can be a handheld or laptop devices.).
Regarding claim 12, Wilson in view of Gil and Christie teaches the method of claim 1, wherein the signal-derived features are computed by knowledge-based feature design and/or data-driven clustering (Wilson, para. 77 discloses signal derived features such as voice analysis, measurement of tone or speech inflections includes NLP keyword classification.).
Regarding claim 13, Wilson in view of Gil and Christie teaches the method of claim 1, wherein the signal-derived features are used as a foundation for machine learning, data mining, and statistical algorithms that are used to determine what factors, or combination of factors, predict a variety of relationship dimensions, such as conflict, relationship quality, or positive interactions (Wilson, para. 77 discloses the use of NLP or natural language processing classification for keywords and measurements of tone or speech inflections indicating conflict, relationship quality such as behavior. Para. 70 discloses appropriateness of communications, behavior, actions or events such as positive interactions.).
Regarding claim 14, Wilson in view of Gil and Christie teaches the method of claim 1 wherein individualized models increase classification accuracy, since patterns of interaction may be specific to individuals, couples, or groups of individuals (Wilson, para. 75 disclose machine learning component 460 from one or more users, collecting personal data of a user (para. 68) and notification is performed to one or more users (para. 74). Para. 76 discloses types of models or machine learning that can be performed. Since the machine learning component monitors interpersonal relations between one or more users, this indicates individualized models may be specific to individuals, couples or group of individuals.).
Regarding claim 15, Wilson in view of Gil and Christie teaches the method of claim 1 where active and semi-supervised learning are applied to increase predictive power as people continue to use a system implementing the method (Such limitation recites the result of using active and semi-supervised learning. The result holds no patentable weight due to intended use. Wilson, para. 76 discloses active and semi active such as supervised, unsupervised learning, etc. can be performed at label 460.).
Regarding claim 16, Wilson in view of Gil and Christie teaches the method of claim 1 wherein relationship functioning includes indices selected from the group consisting of a ratio of positive to negative interactions, number of conflict episodes, an amount of time two users spent together, an amount of quality time two users spent together, amount of physical contact, exercise, time spent outside, sleep quality and length, and coregulation or linkage across these measures (Wilson, para. 79 discloses 18 discloses inappropriateness during a conversation may be indicated via volume of one or more users. This indicates an amount of time two users spent together such as duration of the conversation.).
Regarding claim 17, Wilson in view of Gil and Christie teaches the method of claim 16 wherein further comprising suggesting goals for these indices and allows users to customize their goals (Wilson, para. 18 discloses appropriate communication is determined based on settings set by the user, such as health factors, user’s profile, etc. Suggestions for appropriate behavior or deescalating a conversation according to the settings such as volume of one or more user’s voice. (para. 18, 74, 78)).
Regarding claim 19, Wilson in view of Gil and Christie teaches the method of claim 1. Wilson also teaches series of corrective actions to deescalate the possible negative impact of communications, behavior, actions or events upon the one or more users based on the identified contextual factors includes notifications or reminders to one or more users. See Wilson at para. 74.
Although Wilson does not explicitly teach creating daily, weekly, monthly, and yearly reports of relationship functioning, Wilson teaches sending a series of corrective actions includes notifications (Wilson, para. 74).
Hence, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for Wilson’s notifications to be created weekly, daily, monthly and yearly reports since a series of corrective actions are performed by the device such as notifications or reminders to one or more users to deescalate any possible negative impact of communications, behavior, actions or events upon the one or more users. See Wilson at para. 74.
Regarding claim 21, Wilson in view of Gil and Christie teaches the method of claim 1 analyzing each data stream to provide a user with covariation of user’s mood, relationship functioning, and various relationship-relevant events (Wilson, para. 70 discloses identifying communications, behavior, events or actions that are inappropriate and/or appropriate. Such includes analyzing each data stream. Para. 77 discloses data streams.).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (US 2020/0065728, hereinafter referred to as Wilson) in view of Gil (US 2016/0328987) and Christie (Electrodermal activity in the 1980s: a review) as applied to claim 4, in view of Moturu et al. (US 2018/0158538, hereinafter referred to as Moturu).
Regarding claim 7, Wilson in view of Gil and Christie teaches the method of claim 4.
Wilson does not explicitly teach wherein content of text messages and emails, time spent on the internet, number or length of texts and phone calls in network communications are measured.
Moturu teaches wherein content of text messages and emails, time spent on the internet, number or length of texts and phone calls in network communications are measured (Moturu, para. 57 discloses features related to communication behavior, interaction diversity, mobility behavior in feature vectors includes text messages, emails, phone calls, a set of communication actions for a particular time period.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wilson’s interpersonal relationship interpretations by measuring text messages, emails, time of texts and phone calls as disclosed by Moturu to determine communication behavior, interaction diversity, mobility behavior in order to effectively determine interpersonal relations. (Moturu, para. 57)
Claims 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (US 2020/0065728, hereinafter referred to as Wilson) in view of Gil (US 2016/0328987) and Christie (Electrodermal activity in the 1980s: a review) as applied to claim 1 above, in view of Abbas (US 2017/0080346).
Regarding claim 18, Wilson in view of Gil and Christie teaches the method of claim 1.
Wilson does not explicitly teach feedback is provided as ongoing tallies and/or graphs viewable on the smart devices.
Abbas teaches feedback is provided as ongoing tallies and/or graphs viewable on the smart devices (Abbas, para. 191 discloses a human biometric representation of a user’s current state such as emotions, state of mind, etc. is shown. Fig. 3 shows graphs of the user’s current state.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wilson by incorporating human biometric representation as disclosed by Abbas so to provide the user with better ability to monitor their behavior and prevent conflict during interaction with others.
Regarding claim 20, Wilson in view of Gil and Christie teaches the method of claim 1. Wilson also teaches a device monitoring interpersonal relations between one or more users (Fig. 1 and 4-6).
Wilson does not explicitly teach allowing users to view, track, and monitor each of these data streams and their progress on their goals via customizable dashboards.
Abbas teaches allowing users to view, track, and monitor each of these data streams and their progress on their goals via customizable dashboards (Abbas, para. 191 discloses an avatar human biometric representation that allows users to view the status of their current state, such as emotional, states of mind, etc. By providing the users the ability to view an avatar of their current state, the users can track and monitor their current state and their progress. Fig. 3a shows a customizable dashboard of the avatar).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wilson by incorporating human biometric representation as disclosed by Abbas so to provide the user with better ability to monitor their behavior and prevent conflict during interaction with others.
Claims 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson et al. (US 2020/0065728, hereinafter referred to as Wilson) in view of Gil (US 2016/0328987) and Christie (Electrodermal activity in the 1980s: a review) as applied to claim 1 above, in view of Barnett et al. (US 2018/0300917, hereinafter referred to as Barnett).
Regarding claim 22, Wilson in view of Gil and Christie teaches the method of claim 1. Wilson also teaches personalized networks (Wilson, para. 68 discloses collection of data such as user profile including people associated with the user.) and specify relationship types for each person in their network (Wilson, para. 84 discloses selecting appropriate person for intervening during conflict resolution, wherein such person can be identified based on knowledge in the area of discussion, availability of the person, or other means, etc. A person can be an associate/colleague, family member, etc. (para. 84).).
Wilson does not explicitly teach user can create user profiles or personalized networks.
Barnett teaches a user may specify privacy settings for a user profile page (Barnett, para. 194) and specifying relationship such as a social graph (Barnett, para. 194).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wilson’s user profile by allowing user to create the user profile and include settings such as relationships with other users as disclosed by Barnett so to gather data about the user and improve conflict resolution suggestions to the user.
Regarding claim 23, Wilson in view of Gil and Christie teaches the method of claim 1. Wilson also teaches user profile is collected (Wilson, para. 68) for one or more users (Wilson, para. 69) and identifying a person suitable for conflict resolution, such as a family member (Wilson, para. 84).
Wilson does not explicitly teach the user profile includes users set person-specific privacy settings and customize personal data that can be accessed by others in their networks.
Barnett teaches users set person-specific privacy settings (Barnett, para. 194) and customize personal data that can be accessed by others in their networks (Barnett, para. 194).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Wilson’s user profile by users setting profiles as disclosed by Barnett so to gather data about the user and improve conflict resolution suggestions to the user.
Response to Arguments
Applicant’s acknowledgement of the denial of benefit of priority to earlier filed applications is noted. The denial of benefit of priority is updated to address the amendments to the claims.
Applicant's arguments against the claim objections have been fully considered. The canceling of claims 24-26 render the associated objections moot. Thus, these objections are withdrawn. However, amendments to claim 1 necessitate a new objection.
Applicant’s arguments against the rejections of claim 1 under 35 USC 112(b) regarding “relational functioning”, antecedent basis, and “positive relational engagement” have been fully considered. The amendments to the claims obviate the rejections. Thus, these rejections are withdrawn.
Applicant’s arguments against the remaining rejections of the claims under 35 USC 112(b) have been fully considered but are not persuasive. Applicant asserts that the specification clearly defines what constitutes “relational classification” in para. 37 and asserts that it describes “general categories such as attachment, emotion regulation, and enmity” and provides specific examples including “frequency of positive interactions between people in relationships, feelings of closeness, and the amount of quality time spent together (e.g., minutes spent together and interacting), mood of each person, covaration or coregulation mood.”
Examiner is not persuaded. Para. 37 of the specification is silent regarding “a relational classification”. In particular, Applicant misrepresents the citations from this paragraph. For instance, the “general categories” are recited to be examples of representations and “frequency of positive interactions between people in relationships, feelings of closeness, and the amount of quality time spent together (e.g., minutes spent together and interacting), mood of each person, covaration or coregulation mood” are recited as aspects of the general categories.
Applicant also asserts that the rejection of claim 4 regarding “trained neural network” is moot in light of the amendments to claim 1.
Examiner is not persuaded. Applicant is directed to the rejection of claim 4 which has been updated to address the claim amendments.
Applicant’s arguments against the rejections of the claims under 35 USC 112(a) have been fully considered but are not persuasive. Applicant asserts that para. 37-45 and Fig 1A, 1B, and 2A-2J provide support for the amended claims.
Examiner is not persuaded. Applicant is directed to the rejection above which has been updated to address the claim amendments. It is noted that para. 37-45 merely recite that neural networks may be used in the alternative without any meaningful description. Additionally, Fig. 1A and 2A-2J merely illustrate screenshots of a display of smartphone while Fig. 1B illustrates a generic computer system using black boxes. In other words, these drawings are silent regarding the claim limitations at issue.
Applicant’s arguments against the rejections of the claims under 35 USC 101 have been fully considered but are not persuasive. In pg. 9-10, Applicant asserts that the amended claims are directed to a technological solution that employs specific hardware sensors and signal processing techniques in an unconventional combination. Here, Applicant asserts that the data extraction are specific, measurable technical parameters that require specialized digital signal processing algorithms and hardware sensors operating in coordinated fashion.
Examiner is not persuaded. This is merely a conclusory statement made without substantive support, and is not persuasive. The claimed data extraction is merely collecting information. Merely identifying types of data has routinely been identified by the courts as unpatentable. Applicant’s assertions only emphasizes that the focus of the claimed invention is on the analysis of the collected data, which is itself at best merely an improvement within the abstract idea. See pg. 2-3 in SAP America Inc. v. lnvestpic, LLC (890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018) which proffered “[w]e may assume that the techniques claimed are groundbreaking, innovative, or even brilliant, but that is not enough for eligibility. Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. The claims here are ineligible because their innovation is an innovation in ineligible subject matter. Their subject is nothing but a series of mathematical calculations based on selected information and the presentation of the results of those calculations.” Additionally, the Court in Electric Power Group, which SAP America also utilizes in its decision, held that “[i]nformation as such is an intangible. Accordingly, we have treated collecting information, including when limited to particular content (which does not change its character as information), as within the realm of abstract ideas. In a similar vein, we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category. And we have recognized that merely presenting the results of abstract processes of collecting and analyzing information, without more (such as identifying a particular tool for presentation), is abstract as an ancillary part of such collection and analysis. Here, the claims are clearly focused on the combination of those abstract-idea processes. The advance they purport to make is a process of gathering and analyzing information of a specified content, then displaying the results, and not any particular assertedly inventive technology for performing those functions. They are therefore directed to an abstract idea.” See Electric Power Group at pg. 7-8, citations removed for clarity. As identified in the rejection, the mere use of signal processing and hardware sensors in their conventional arrangement act to merely add extrasolution data gathering activity to the judicial exception.
Applicant also asserts that the neural network classification represents a technological advancement in sensor fusion and human-computer interaction systems.
Examiner is not persuaded. In particular, as identified in the rejection, the mere use of a trained neural network does not provide a technological advancement because it merely invokes the use of a computer or other machinery in its ordinary capacity to process information. The neural network, as claimed and organized, performs its normal operations as it would for any intended use (i.e., being trained on and applied to data relevant to the intended use) as the claims merely recite collecting information, analyzing the collected information, and outputting the results of the collection and analysis which the courts have repeatedly identified as wholly encompassed in the judicial exception.
In pg. 10-11, Applicant follows with asserting that the claimed method requires specific physical hardware including smart devices, wearable sensors, skin conductance monitors, microphones, and processing systems that work together to perform real-time multimodal sensing analysis.
Examiner is not persuaded. The computerized elements would operate as they normally would indicating that they merely provide a field of use. Furthermore, regarding “real-time”, Application is again directed to the decision in Electric Power Group wherein the Court identified that “[t]he claims in this case specify what information in the power-grid field it is desirable to gather, analyze, and display, including in “real time”; but they do not include any requirement for performing the claimed functions of gathering, analyzing, and displaying in real time by use of anything but entirely conventional, generic technology. The claims therefore do not state an arguably inventive concept in the realm of application of the information-based abstract ideas.” See Electric Power Group at pg. 11.
In pg. 11, Applicant asserts that the combination of acoustic feature extraction, physiological monitoring, and linguistic analysis is specific and requires unconventional and non-routine implementation using specialized algorithms and sensor hardware.
Examiner is not persuaded. This is merely a conclusory statement made without substantive support, and is not persuasive. Furthermore, such assertions are not represented in the language in the claims as they mere recite collecting information, analyzing the collected information, and outputting the results of the collection and analysis which has been repeatedly identified by the courts as directed to a judicial exception without significantly more.
Applicant's arguments against the rejections of the claims under 35 USC 103 have been fully considered but they are not persuasive. Here, Applicant asserts that Wilson does not teach monitoring of interpersonal relationships between couples or groups in their normal daily interactions for the purpose of understanding and enhancing relationship functioning through personalized feedback and goal setting.
Examiner is not persuaded. Applicant’s assertion amounts to an assertion of a difference in unclaimed intended use. Applicant is directed to the rejections above which have been updated to address the amendments to the claims and illustrate that the cited prior art teaches the claims.
Applicant also asserts that Wilson does not teach the acoustic features and the linguistic features.
Examiner is not persuaded. Applicant misrepresents the rejections which include significantly more than the paragraphs recited by Applicant. When viewed as whole, Wilson clearly teaches these limitations.
In pg. 13, Applicant asserts that Gil is directed to a different application context – enterprise group dynamics versus personal relationship enhancement - and is thus distinguished from the claimed invention. Similarly, Applicant asserts that Christie’s clinical measurement protocols cannot be directly applied to the claimed consumer device-based system.
Examiner is not persuaded. The rejections utilize Gil and Christie to teach the extraction of physiological measures including skin conductance level, mean skin conductance response frequency and mean skin conductance response amplitude, and rise and recovery time of skin conductance responses which are old and well-known physiological measures. It has been held that the test for obviousness is not whether the features of one reference may be bodily incorporated into the other to produce the claimed subject matter but simply what the combination of references makes obvious to one of ordinary skill in the art in the pertinent art. In re Bozek, 163 USPQ 545 (CCPA 1969).
Applicant then asserts that Wilson does not teach newly added language regarding neural network use.
Examiner is not persuaded. Applicant is directed to the rejections of the claims which have been updated to address the amendments to the claims.
Applicant follows with asserting that the rejection lacks proper motivation requires impermissible hindsight.
Examiner is not persuaded. This is merely a conclusory statement made without substantive support, and is not persuasive. In contrast, the motivation to combine the prior art references properly follows at least MPEP 2141. Regarding hindsight, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Applicant also asserts that the dependent claims are allowable due to their dependencies from the independent claims.
Examiner is not persuaded. Applicant is directed to the rejections above which have been updated to address the amendments to the claims.
The rejections stand.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DANIEL LANE/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
1 Christie, M. J. (1981). Electrodermal activity in the 1980s: a review. Journal of the Royal Society of Medicine, 74(8), 616–622. https://doi.org/10.1177/014107688107400812