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 .
Drawings
The replacement drawings were received on 04/01/2026. These drawings are accepted and the objection to the drawings is withdrawn herein.
Response to Arguments
In response to the amendments, file 04/01/2026, the claim objections have been withdrawn. However, the newly amended portions of the claims have new claim objections.
Applicant's arguments filed 04/01/2026, regarding the rejection of the claims under 35 U.S.C. 101 have been fully considered but they are not persuasive. Specifically, Applicant argues, on page 12 of the remarks, that “
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Examiner disagrees. Applicant cites the Electric Power Group v. Alstom S.A. decision but the decision recites that claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and states that a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind (see MPEP 2106.04(a)(2)). Current independent claims 1, 8, and 15 recite collecting information such as physiological data of individuals, features of the individuals, and demographic information of individuals from video of the individuals; creating a graph with nodes as individuals and using the data collected to create edges between the nodes to reflect social interactions such as having a conversation is akin to Electric Power Group v. Alstom S.A. insomuch that these steps can all be practically done in the human mind and are simple evaluations and judgements; there is no step in the claims that discuss specific details on how to collect the data, analyze the data, and create the graph with enough technical specificity beyond what a human can accomplish with their own human vision and simple pen paper; for example, a human making these observations asks each person being videotaped demographic information and asks each person to take their heartbeat (biometric physiological information) to draw a relationship graph with edges indicating a stressful conversation between two individuals having high heartbeats that are of similar age.
Applicant argues that an independent edge with physiological information is new data; however, this is not new data, but rather displaying information in a graph format which falls under mental process as indicated in Electric Power Group v. Alstom S.A. (see MPEP 2106.05(a) reciting that the courts have indicated it may be sufficient to show an improvement in existing technology includes: components or methods, such as measurement devices or techniques, that generate new data, Electric Power Group, LLC v. Alstom, S.A). Examiner disagrees with the assertion that simply taking easily observable data and putting it into a graph format constitutes “new data” in a meaningful way analogous to Electric Power Group v. Alstom S.A. decision. The data found in Electric Power Group v. Alstom S.A. is highly technical beyond capacity of a human without technical assistance to do such as synchronized phasor measurements, and the analysis go beyond human capability such as frequency instability, voltages, power flows, phase angles, damping, and oscillation modes, derived from the phasor measurements and the other power system data sources.
Therefore, the rejection of the claims under 35 U.S.C. 101 is maintained.
Applicant’s arguments, see remarks, filed 04/01/2026, with respect to the rejection of independent claims 1 and 15 under 35 U.S.C. 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made under 35 U.S.C. 103, in view of International Patent Application Publication No.: WO 2024177033 A1 (Shintaro et al.).
Applicant's arguments filed 04/01/2026, regarding independent claim 8, have been fully considered but they are not persuasive. Specifically, Applicant argues on page 15 of the remarks, that “
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Examiner disagrees. Griffin teaches the newly amended limitations of claim 8 reciting “obtaining demographic information about the individual (Griffin, para. [0031]: “The identity information can be determined by, for example, analyzing data from video processing module 120 with one or more programs of feature extraction module 130 to identify features representative of identity. For example, graphing module 140 can also include one or more programs for identifying individuals 112A-N in video data 110A-N based on features extracted by feature extraction module 130. Graphing module 140 can further include one or more programs for associating features extracted by feature extraction module 130 with each of individuals 112A-N. An individual 112A-N can be identified by, for example, cross-referencing features with a table or array that relates features to identity. Additionally, and/or alternatively, a machine learning model trained to identify an individual 112A-N based on a training set of features from image, audio, and/or semantic text data can be used to identify individuals 112A-N for creating nodes of the relationship graph.”) and
wherein the graph includes a plurality of nodes where at least one node is associated with one individual and includes demographic information associated with the one individual” (Griffin, para. [0018]; para. [0031]-[0032]; FIG. 1: “Memory 104 also stores relationship graph 200, which is an edge and node graph created by graphing module 140. Relationship graph 200 includes nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, and 222. Each of nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222 are connected to another of nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222 by at least one edge.”; see graph 200 of FIG. 1 above; “In examples where the individual 112A-N is identified, the nodes of the relationship graph can include, for example, descriptions of the name, title, or organizational position of the individual 112A-N, among other options. In other examples, descriptive identity information may not be available for one or more of individuals 112A-N. In these examples, the nodes of the relationship graph can include descriptions of the physical appearance, setting, built environment, or geographic location of the individual 112A-N, among other options.”); these citations of Griffin were cited in the previous non-final rejection dated 01/09/2026; Griffin finds using a feature extraction or machine learning on the images/video to find demographic information such as geographic location of the individuals which, then the individuals are turned into nodes in a relationship graphs that have the geographic location associated with the node for each individual; the broadest reasonable interpretation of the claim term “demographic information” includes geographic location; Supporting evidence of this interpretation states the definition of demographic data “are aspects of individuals or groups of participants in a study or registry, including basic descriptive information like age (date of birth), sex, race, and ethnicity. Other examples of demographic data that may be relevant to a patient registry, survey, or study include disease diagnosis, family history of disease, income level, employment, location, and level of education” (National Institute of Health; https://toolkit.ncats.nih.gov/glossary/demographic-data/). Therefore, the claim interpretation taken by examiner is appropriate, and therefore Griffith teaches the newly amended limitations. Therefore, the rejections of independent claim 8 and its respective dependent claims 9-14 are maintained under 35 U.S.C. 102(a)(1).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are: “graph module” in claims 1, 3, and 5-7, and “input” in claim 1.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Claim Objections
Claim 1 is objected to because of the following informalities: the claim term “indepdent edge” should recite “independent edge” for proper spelling. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1 and 15 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Specifically, the newly amended independent claims recite “wherein at least one of the plurality of edges comprises an independent edge with physiological information associated with the one individual”; it is unclear what defines an edge as “independent” since each edge connects two nodes which reflect the relationship between two individuals. Para. [0042] of the present specification recites that “other received information may be stored as edges 140 of the graph 130. For example, a video feature 142 may be stored as an edge 140 between a first person node 132a and a second person node 132b as illustrated. An associated audio feature 144 and physiological feature may be similarly stored as independent edges 140 … the physiological feature 146 may be the individuals’ respective biometric data from wearable devices temporally proximate to the detected conversation.” FIG. 1 shows the edges between the nodes:
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it appears that an “independent edge” may be a one-way directional edge from a first node 132a (first individual) to a second node 132b as well as vice-versa that encompass each individual’s respective biometric data, rather than single edge encompassing both individuals biometric data; however, this is not clear because Applicant’s specification and claims have not explicitly defined what an “independent edge” is; therefore, for examination, Examiner will be interpreting “independent edge” as meaning “edge”. Examiner recommends Applicant define this term on the record and/or amend the claims in such a way to better reflect the one-way directional edges encompassing a single person’s physiological data indicated in para. [0042] and FIG. 1. Proper Corrections are requested.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 8, and 15 and dependent claims 2-7, 9-14, and 16-20 are rejected are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without integration into a practical application or recitation of significantly more.
In the analysis below, the method of independent claim 1 is considered representative of independent claim 15 since all of the independent claims recite identical steps despite being directed to different statutory matter. Furthermore, independent claims 1, 8, and 15 are directed to one of the four statutory categories of eligible subject matter (a process for independent claim 8 and a non-transitory computer readable medium storing instructions for independent claim 15); thus, the claims pass Step 1 of the Subject Matter Eligibility Test (See flowchart in MPEP 2106).
Step 2A, prong 1 analysis:
Independent claims 1 and 15 are directed to receive video of a group of individuals from a video source, extract a feature from the video; identify an individual from the group associated with the feature; obtain physiological data for the individual; determine when a node in a graph is associated with the individual, and store the feature associatively with the node in the graph when the feature is associated only with the individual, wherein the feature includes data for determining group affect, and wherein the graph includes a plurality of nodes where at least one node is associated with one individual, and a plurality of edges between nodes where each edge is associated with a feature.
Each of the above steps can be performed mentally. In particular, a human uses their own vision over time (equivalent to a video source) and identifies numerous human beings in a social interaction, such as a business/networking meeting by recognizing different features of each person as well as taking different physiological markers such as asking each individual for their heartbeat, for example; the human then creates a relationship graph with pen and paper (storing on paper) that labels each node of the graph as each individual (this could be the people’s specific names or simple unique descriptors that differentiate each person) as well as labeling each edge between two nodes of the graph as a specific interaction between the two people’s such as having a conversation and looking at one another, for example; further, the group affect (underlying experience of feeling, emotion, attachment, or mood of the group or between two people) is determined based on the different nodes and edges of the relationship graph; for example, if two people face one another and have angry facial expressions/features and the edge is labeled as them talking, the “group affect” of the 2 node and 1 edge interaction of those two people is labeled as angry, frustrated, or negative; further, the edge has the two different biometric physiological datapoints of heartbeat which for both which would be high if the two individuals are angry, frustrated or negative in the social interaction; this is done for everyone’s different interactions based of the human’s observations of the social situation; therefore, this process can all be done mentally.
Independent claim 8 is directed to substantially the same limitations as independent claim 1, but rather than taking physiological data and incorporating the data into edges of the relationship graph, demographic data is taken of individuals and incorporated into each node associated with each individual. The same process as described above with respect to claim 1 is done as well as taking demographic data of each individual observed such as age, gender, income, education, and race and noting those characteristics along with features for each node associated with the individual in the relationship graph via pen and paper; therefore, this process can all be done mentally.
As such, the description in independent claims 1, 8, and 15 is an abstract idea – namely, a mental process. Accordingly, the analysis under prong one of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Additional elements:
The additional element recited in independent claims 1, 8, and 15 are a system for generating a graph of group affect, comprising an input module having both an image processor and an input, a graph module, and a wearable device.
Step 2A, prong 2 analysis:
The above-identified additional elements do not integrate the judicial exception into a practical application. A human has abilities to draw a graph for group affect (graph module) based on their observations of features/physiological data/demographic data (image processor, input module, input, wearable device).
Each of the other additional elements (a system for generating a graph of group affect, comprising an input module having both an image processor and an input, a graph module, and a wearable device) amounts to merely using different devices as tools to perform the claimed mental process. Implementing an abstract idea on a computer or using known generic devices does not integrate a judicial exception into a practical application (See MPEP 2106.05(f)).
Moreover, the additional elements of the claims do not recite an improvement in the functioning of a computer or other technology or technical field, the claimed steps are not performed using a particular machine, the claimed steps do not effect a transformation, and the claims do not apply the judicial exception in any meaningful way beyond generically linking the use of the judicial exception to a particular technological environment (See MPEP 2106.04(d)). Therefore, the analysis under prong two of step 2A of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
Step 2B:
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Each of the other additional elements (a system for generating a graph of group affect, comprising an input module having both an image processor and an input, a graph module, and a wearable device) are generic computer features which perform generic computer functions that are well-understood, routine, and conventional and do not amount to more than implementing the abstract idea with a computerized system. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation, and mere implementation on a generic computer does not add significantly more to the claims. Accordingly, the analysis under step 2B of the Subject Matter Eligibility Test does not result in a conclusion of eligibility (See flowchart in MPEP 2106).
For all of the foregoing reasons, independent claims 1, 8, and 15 do not recite eligible subject matter under 35 USC 101.
Claims 2, 9, and 16 recite wherein the video source is selected from the group consisting of a video camera, a video streaming device, a pre-recorded video, and a segment of a video. The human uses their own vision over time to observe the social situation which is equivalent to using a video camera; therefore, this process can all be done mentally.
Claims 3, 10, and 17 recite to generate a new node in the graph when no node is associated with the individual, associate the new node in the graph with the individual, and store the feature associatively with the new node in the graph. The human uses their vision over time to observe the social situation and if a new person shows up the social situation, then the human adds another node and necessary edges to the relationship graph drawn on paper with pen; therefore, this process can all be done mentally.
Claims 4, 11, and 18 recite wherein at least one node is associated with a feature. The human drawing the relationship graph designates the node as each individual they observe in the social situation based off their features they observe (ex: blonde woman, short bald man, etc.) if they do not know the specific names of each person they are observing; therefore, this process can all be done mentally.
Claims 5, 12, and 19 recite to identify a second individual associated the feature, determine a second node associated with the second individual, generate an edge associated with the node and the second node, and store the feature associatively with the edge in the graph. The human draws the relationship graph on paper with different nodes designated as the people they are observing in the social situation; if two people are talking then the human draws an edge connecting the two nodes for those two people to indicate a conversation is happening; the human observes facial expressions of the two people and interprets whether the conversation between them is positive or negative and notes/draws this on the edge of the graph that connects the two nodes; therefore, this process can all be done mentally.
Claims 6-7, 13-14, and 20 recite to process the graph to determine at least one group affect associated with at least two individuals from the group, and store the determined group affect associatively with an edge of the graph. The human draws the relationship graph on paper with different nodes designated as the people they are observing in the social situation; if two people are talking then the human draws an edge connecting the two nodes for those two people to indicate a conversation is happening; the human observes facial expressions of the two people and interprets whether the conversation between them is positive or negative and notes/draws this on the edge of the graph that connects the two nodes as an indication of the “strength” of the group affect or attachment of the people observed to one another; therefore, this process can all be done mentally.
Therefore, dependent claims 2-7, 9-14, and 16-20 recite the same abstract idea of a mental process which can be performed in the mind with the aid of pen and paper, and are therefore also rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No.: 2023/0177834 (Griffin), in view of International Patent Application Publication No.: WO 2024177033 A1 (Shintaro et al.).
Regarding claim 1, Griffin teaches a system for generating a graph of group affect, comprising an input module that comprises: (Griffin, para. [0005]: “An embodiment of a system according to the present disclosure includes a camera device for acquiring digital video data, a processor, a user interface, and a memory. The memory is encoded with instructions that, when executed, cause the processor to acquire digital video data from the camera, identify a plurality of features in the digital video data, analyze the plurality of features to create a relationship graph, determine a relationship score based on the relationship graph, and output the relationship score by the user interface.")
an image processor configured to: receive video of a group of individuals from a video source, extract a feature from the video, and identify an individual from the group associated with the feature (Griffin, para. [0018]; para. [0029]; para. [0031]; FIG. 1: “FIG. 1 is a schematic diagram of relationship evaluator 100, which is a system for evaluating relationships of two or more individuals. Relationship evaluator 100 includes processor 102, memory 104, and user interface 106, and is connected to camera devices 108A-N. Camera devices 108A-N capture video data 110A-N of individuals 112A-N. Memory 104 includes video processing module 120, feature extraction module 130, graphing module 140, and relationship scoring module 146.”; “Feature extraction module 130 includes one or more programs for classifying the image data, audio data, and semantic text data extracted by video processing module 120. Feature extraction module 130 can include one or more programs for extracting classifiable features from the image data, audio data, and/or semantic text data.”; “An individual 112A-N can be identified by, for example, cross-referencing features with a table or array that relates features to identity. Additionally, and/or alternatively, a machine learning model trained to identify an individual 112A-N based on a training set of features from image, audio, and/or semantic text data can be used to identify individuals 112A-N for creating nodes of the relationship graph.”;
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a graph module configured to: determine when a node in a graph is associated with the individual, and store the feature associatively with the node in the graph when the feature is associated only with the individual, and wherein the graph includes a plurality of nodes where at least one node is associated with one individual, and a plurality of edges between nodes where each edge is associated with a feature (Griffin, para. [0018]; para. [0031]-[0032]; FIG. 1: “Memory 104 also stores relationship graph 200, which is an edge and node graph created by graphing module 140. Relationship graph 200 includes nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, and 222. Each of nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222 are connected to another of nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222 by at least one edge.”; see graph 200 of FIG. 1 above; “In examples where the individual 112A-N is identified, the nodes of the relationship graph can include, for example, descriptions of the name, title, or organizational position of the individual 112A-N, among other options. In other examples, descriptive identity information may not be available for one or more of individuals 112A-N. In these examples, the nodes of the relationship graph can include descriptions of the physical appearance, setting, built environment, or geographic location of the individual 112A-N, among other options. Feature extraction module 130 and/or graphing module 140 can include one or more programs for determining physical and/or environmental descriptions for each individual 112A-N represented as a node of the relationship graph.”; “Graphing module 140 can include one or more programs for creating an edge from each feature extracted by feature extraction module 130. Each feature extracted by feature extraction module 130 can be associated with at least one individual of individuals 112A-N captured in video data 110A-N by cameras 108A-N, such that each edge of the relationship graph can be associated with at least one node of the relationship graph. Graphing module 140 can also include one or more programs that, for each edge, are able to associate the edge with the nodes representative of the individuals associated with the feature represented by the edge. For example, for a feature that describes a statement (e.g., a feature that describes words spoken in the statement), can be associated with the speaker and/or the recipients of the statement. Processor 102 can use one or more programs of graphing module 140 to create an edge for each recipient of the statement and to associate those edges with the speaker, such that for each recipient of the statement, processor 102 creates an edge extending from the speaker to the recipient. In some examples, the features extracted by feature extraction module 130 are associated with all individuals involved in the interaction.”),
wherein the feature includes data for determining group affect (Griffin, para. [0074]: “The relationship scores created using method 600 are based on the strength and types of connections between individuals of a group. The relationship strength of a group can influence the positivity of outcome associated with a particular interacting event. For example, a high relationship score can be associated with a positive event outcome and a negative relationship score can be associated with a negative event outcome. As a specific example, where the interacting event scored using method 600 is a work meeting, strong relationships may be correlated with a highly productive and/or effective work meeting. Accordingly, the relationship score generated by method 600 can also act as a score for the overall effectiveness of the work meeting. As a further example, where the interacting event is a sales event, strong relationships may be associated with strong client-vendor relations. Accordingly, a relationship score of a sales event can also act as a score for how the overall effectiveness of a sales event.”; the broadest reasonable interpretation of the claim term “group affect” is the is the underlying experience of feeling, emotion, attachment, or mood of a group of individuals; the relationship score is based on the extracted features used to create the relationship graph with individuals as nodes and edges as interacting events between the individual/person nodes to determine the strength of the relationships; this is an example of the strength of group affect regarding level/strength of attachment of the individuals in the social event captured by cameras; the relationship scores are thus group affect).
Griffin fails to teach
an input configured to obtain physiological data for the individual; and wherein at least one of the plurality of edges comprises an independent edge with physiological information associated with the one individual.
Shintaro teaches
an input configured to obtain physiological data for the individual (Shintaro, page 7, para. 8: “The information acquisition unit 51 acquires biometric information of the person to be predicted from an external server device, etc. As in the first embodiment, the biometric information includes gender, age, height, weight, blood pressure, creatinine clearance (CCr), Ivmass, cholesterol level, etc.”); and
wherein at least one of the plurality of edges comprises an independent edge with physiological information associated with the one individual (Shintaro, page 7, para. 10-11; page 4, para. 8; page 5, para. 1-2; FIG. 4: “The predictive model 20 is generated by the following steps (a) to (d). (a) generating a graph composed of nodes representing data points and edges representing relationships between the nodes from a group of data including a person's biometric information and information indicating the occurrence or non-occurrence of a disease in the person”; “As shown in FIG. 4, in a graph 31, edges 33 connect nodes 32 whose entities are similar. For example, in the example of FIG. 4, because patient A and patient B have similar attributes, the node representing patient A and the node representing patient B are connected by an edge 33. Each node 32 contains the biometric information of the corresponding patient. In addition, in the first embodiment, the graph generation unit 11 generates a graph by inputting the acquired data group into the graph generation model 30. The graph generation model 30 performs machine learning to learn the relationships between nodes that should be connected by edges.”;
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both the nodes and edges reflect the individual biometric/physiological data from the two individuals; the edges reflect the similarity of each node’s individual physiological data that is found via machine learning; the term “independent node” is indefinite and therefore is examined as “node”; see rejection of claim 1 under 35 U.S.C. 112(b) above).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify 1) the input module, as taught by Griffin, to include an input configured to obtain physiological data for the individual, as taught by Shintaro, and 2) the plurality of edges between nodes where each edge is associated with a feature, as taught by Griffin, so at least one of the plurality of edges includes an independent edge with physiological information associated with the one individual, as taught by Shintaro.
The suggestion/motivation for doing so would have been that “the learning model generation device 10 complements missing data that is to be the training data for the learning model by graph completion; the missing data is complemented with high accuracy by graph completion; therefore, the learning model generation device 10 improves the accuracy of disease prediction even when there is variability in the training data of the prediction model that predicts the disease” (Shintaro, page 3, para. 7).
Therefore, it would have been obvious to combine Griffin, with Shintaro, to obtain the invention as specified in claim 1.
Regarding claim 2, Griffin, in view of Shintaro, teaches the system of claim 1, wherein the video source is selected from the group consisting of a video camera, a video streaming device, a pre-recorded video, and a segment of a video (Griffin, para. [0025]: “Camera devices 108A-N are capable of capturing video data 110A-N of one or more individuals 112A-N. In the depicted example, camera devices 108A and 108N are depicted as capturing video data 110A and 110N of single individuals 112A and 112N. Camera device 108B is depicted as capturing video data 110B of two individuals 112B and 112C. Each camera device 108A-N captures video data 110A-N is capable of capturing video of one or more individuals 112A-N. Each camera device 108A-N is configured to be able to communicate with relationship evaluator 100 and relationship evaluator 100 is configured to communicate with each camera device 108A-N. Camera devices 108A-N can be, for example, a video camera, a webcam, or another suitable source for obtaining video data 110A-N. Camera devices 108A-N can be controlled by relationship evaluator 100 or by another suitable video device.”).
Regarding claim 3, Griffin, in view of Shintaro, teaches the system of claim 1, wherein the graph module is further configured to generate a new node in the graph when no node is associated with the individual, associate the new node in the graph with the individual, and store the feature associatively with the new node in the graph (Griffin, para. [0087]; FIG. 8; para. [0056]: “In some examples, method 700 can be performed repeatedly to determine changes in the trend over subsequent interacting events. In these examples, the current relationship graph created in step 704 can be stored to the set of historical relationship graphs. Step 702 can be omitted in subsequent iterations and steps 704 and 706 can be repeated to create a new relationship graph for a more recent interacting event and to update the trend to include the score for the new relationship graph.”; this means that nodes can be updated added/changed in a currently made relationship graph as needed;
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“Method 600 can be performed for each segment of the video data and updated relationship graph information can be provided for each segment.”; updated infers that any new individuals identified via their extracted features in the video stream will be added to the relationship graph as a node).
Regarding claim 4, Griffin, in view of Shintaro, teaches the system of claim 1, wherein at least one node is associated with a feature (Griffin, para. [0018]; para. [0031]-[0032]; FIG. 1; see rejection of claim 1 above; each node is designated as an individual that is identified by the features extracted from the video steam).
Regarding claim 5, Griffin, in view of Shintaro, teaches the system of claim 1, wherein the image processor is further configured to identify a second individual associated with the feature; and wherein the graph module is further configured to determine a second node associated with the second individual, generate an edge associated with the node and the second node, and store the feature associatively with the edge in the graph (Griffin, para. [0018]; para. [0031]-[0032]; FIG. 1; see rejection of claim 1 above; a plurality of individuals are identified from extracted features from the video stream and made into nodes in the graph and then edges connecting the nodes are labeled as interactions between individuals/nodes identified from the extracted features (ex: talking and looking at one another).
Regarding claim 6, Griffin, in view of Shintaro, teaches the system of claim 1, wherein the graph module is further configured to process the graph to determine at least one group affect associated with at least two individuals from the group (Griffin, para. [0074]; see rejection of claim 1 above discussing BRI of group affect meaning degree/strength of attachment of people and examples of this group affect shown in the relationship score between two nodes/individuals; para. [0075]; para. [0034]: “As relationship score can be correlated to the positivity of the outcome of an interacting event, in some examples, the relationship score produced using method 600 can directly represent the success and/or the outcome of the interacting event. As described previously, the relationship score can be a similarity score that describes the similarity between the relationship graph of an interacting event and the relationship graph expected for positive and/or negative interacting events. Outputting a relationship score representative of the success and/or outcome positivity of an interacting event can, in some examples, be more useful for improving the success of a subsequent event than a score only describing relationship strength, such as where the interacting event is at least partially adversarial in nature.”; “the relationship score created using relationship scoring module 146 can be normalized based on the number of nodes present in the relationship graph and/or the length of the interacting event. In some examples, relationship scoring module 146 can include one or more machine learning models for generating a relationship score based on a relationship graph. The machine learning model(s) can be trained to associate relationship score with edge quantity and/or weight using a training set of relationship graphs labeled with relationship scores.”).
Regarding claim 7, Griffin, in view of Shintaro, teaches the system of claim 6, wherein the graph module is further configured to store the determined group affect associatively with an edge of the graph (Griffin, para. [0034]; see rejection of claim 6 above regarding the association of the relationship score (group affect) and both the edges and the nodes of the graph; para. [0068]; para. [0071]: “In step 614, a relationship score is determined based on the relationship graph created in step 612. Processor 102 can use one or more programs of relationship scoring module 146 to create the relationship score in step 614. In some examples, one or more computer-implemented machine learning models are used to create the relationship score. In some examples, the relationship score can be representative of relationship strength between the individuals represented in the relationship graph. The computer-implemented machine learning model(s) can be trained to associate edge quantity, edge weights, edge connectivity (i.e., the arrangement of edges), and/or edge orientation (i.e., the direction of directed edges) with a numeric value representative of relationship strength. The strength of all relationships depicted in the relationship graph can be combined, such as by summation, averaging, or another suitable technique, to create the relationship score in step 614.”; “The maximum and/or ideal relationship score can be stored to memory 104 and recalled by processor 102 for use in creating a percentage value in step 616 … Memory 104 can store one or more tables, arrays, and/or databases relating relationships scores calculated using relationship scoring module 146 to letter grades, and processor 102 to cross-reference the table(s), array(s), and/or database(s) to determine the alphanumeric characters representative of the letter grade to be output in step 616.”).
Regarding claim 15, Griffin teaches a non-transitory computer readable storage medium storing instructions that when executed by a computer having a processor to perform a method for generating a graph of group affect, the method comprising: (Griffin, para. [0020]: “Memory 104 is configured to store information and, in some examples, can be described as a computer-readable storage medium. Memory 104, in some examples, is described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can, over time, change (e.g., in RAM or cache). In some examples, memory 104 is a temporary memory … In some examples, the memory is used to store program instructions for execution by the processor.”).
Regarding claim 15, Griffin teaches obtaining a physiological feature from a wearable device on the individual (Griffin, page 7, para. 8: “The information acquisition unit 51 acquires biometric information of the person to be predicted from an external server device, etc. As in the first embodiment, the biometric information includes gender, age, height, weight, blood pressure, creatinine clearance (CCr), Ivmass, cholesterol level, etc.”; Retrieving or processing the biometric information of a person from an external server device relies on server-side biometric systems. In this setup, an edge device, sensor, or mobile app captures the biometric data (such as a fingerprint, facial scan, or voice recording) and transmits it to a secure cloud or remote server for AI processing and matching; therefore, the external server device under broadest reasonable interpretation includes a wearable device to determine biometric/physiological data).
With regards to the remaining limitations of claim 15, and dependent claims 16-19, they recite the functions of the apparatus of claims 1-5, respectively, as non-transitory computer readable storage media storing instructions. Thus, the analyses in rejecting claims 1-5 over Griffin in view of Shintaro are applicable to the remaining limitations of claim 15 and dependent claims 16-19, respectively.
Regarding claim 20, Griffin in view of Shintaro teaches the non-transitory computer readable storage medium of claim 15, wherein the processor is further configured to process the graph to determine at least one group affect associated with at least two individuals from the group (Griffin, para. [0074]; see rejection of claim 1 above discussing BRI of group affect meaning degree/strength of attachment of people and examples of this group affect shown in the relationship score between two nodes/individuals; para. [0075]; para. [0034]: “As relationship score can be correlated to the positivity of the outcome of an interacting event, in some examples, the relationship score produced using method 600 can directly represent the success and/or the outcome of the interacting event. As described previously, the relationship score can be a similarity score that describes the similarity between the relationship graph of an interacting event and the relationship graph expected for positive and/or negative interacting events. Outputting a relationship score representative of the success and/or outcome positivity of an interacting event can, in some examples, be more useful for improving the success of a subsequent event than a score only describing relationship strength, such as where the interacting event is at least partially adversarial in nature.”; “the relationship score created using relationship scoring module 146 can be normalized based on the number of nodes present in the relationship graph and/or the length of the interacting event. In some examples, relationship scoring module 146 can include one or more machine learning models for generating a relationship score based on a relationship graph. The machine learning model(s) can be trained to associate relationship score with edge quantity and/or weight using a training set of relationship graphs labeled with relationship scores.”); and
store the determined group affect associatively with an edge of the graph (Griffin, para. [0034]; see rejection of claim 6 above regarding the association of the relationship score (group affect) and both the edges and the nodes of the graph; para. [0068]; para. [0071]: “In step 614, a relationship score is determined based on the relationship graph created in step 612. Processor 102 can use one or more programs of relationship scoring module 146 to create the relationship score in step 614. In some examples, one or more computer-implemented machine learning models are used to create the relationship score. In some examples, the relationship score can be representative of relationship strength between the individuals represented in the relationship graph. The computer-implemented machine learning model(s) can be trained to associate edge quantity, edge weights, edge connectivity (i.e., the arrangement of edges), and/or edge orientation (i.e., the direction of directed edges) with a numeric value representative of relationship strength. The strength of all relationships depicted in the relationship graph can be combined, such as by summation, averaging, or another suitable technique, to create the relationship score in step 614.”; “The maximum and/or ideal relationship score can be stored to memory 104 and recalled by processor 102 for use in creating a percentage value in step 616 … Memory 104 can store one or more tables, arrays, and/or databases relating relationships scores calculated using relationship scoring module 146 to letter grades, and processor 102 to cross-reference the table(s), array(s), and/or database(s) to determine the alphanumeric characters representative of the letter grade to be output in step 616.”).
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 8-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Griffin.
Regarding claim 8, Griffin teaches a method for generating a graph of group affect, comprising: (Griffin, para. [0005]: “An embodiment of a system according to the present disclosure includes a camera device for acquiring digital video data, a processor, a user interface, and a memory. The memory is encoded with instructions that, when executed, cause the processor to acquire digital video data from the camera, identify a plurality of features in the digital video data, analyze the plurality of features to create a relationship graph, determine a relationship score based on the relationship graph, and output the relationship score by the user interface.")
receiving, by an image processor, video of a group of individuals from a video source; extracting, by the image processor, a feature from the video; identifying an individual from the group that is associated with the feature (Griffin, para. [0018]; para. [0029]; para. [0031]; FIG. 1: “FIG. 1 is a schematic diagram of relationship evaluator 100, which is a system for evaluating relationships of two or more individuals. Relationship evaluator 100 includes processor 102, memory 104, and user interface 106, and is connected to camera devices 108A-N. Camera devices 108A-N capture video data 110A-N of individuals 112A-N. Memory 104 includes video processing module 120, feature extraction module 130, graphing module 140, and relationship scoring module 146.”; “Feature extraction module 130 includes one or more programs for classifying the image data, audio data, and semantic text data extracted by video processing module 120. Feature extraction module 130 can include one or more programs for extracting classifiable features from the image data, audio data, and/or semantic text data.”; “An individual 112A-N can be identified by, for example, cross-referencing features with a table or array that relates features to identity. Additionally, and/or alternatively, a machine learning model trained to identify an individual 112A-N based on a training set of features from image, audio, and/or semantic text data can be used to identify individuals 112A-N for creating nodes of the relationship graph.”;
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determining, by a graph module, when a node in a graph is associated with the individual; obtaining demographic information about the individual; and storing, by the graph module, the feature associatively with the node in the graph when the feature is associated only with the individual, wherein the graph includes a plurality of nodes where at least one node is associated with one individual and includes demographic information associated with the one individual, and a plurality of edges between nodes where each edge is associated with a feature (Griffin, para. [0018]; para. [0031]-[0032]; FIG. 1: “Memory 104 also stores relationship graph 200, which is an edge and node graph created by graphing module 140. Relationship graph 200 includes nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, and 222. Each of nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222 are connected to another of nodes 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222 by at least one edge.”; see graph 200 of FIG. 1 above; “In examples where the individual 112A-N is identified, the nodes of the relationship graph can include, for example, descriptions of the name, title, or organizational position of the individual 112A-N, among other options. In other examples, descriptive identity information may not be available for one or more of individuals 112A-N. In these examples, the nodes of the relationship graph can include descriptions of the physical appearance, setting, built environment, or geographic location of the individual 112A-N, among other options. Feature extraction module 130 and/or graphing module 140 can include one or more programs for determining physical and/or environmental descriptions for each individual 112A-N represented as a node of the relationship graph.”; “Graphing module 140 can include one or more programs for creating an edge from each feature extracted by feature extraction module 130. Each feature extracted by feature extraction module 130 can be associated with at least one individual of individuals 112A-N captured in video data 110A-N by cameras 108A-N, such that each edge of the relationship graph can be associated with at least one node of the relationship graph. Graphing module 140 can also include one or more programs that, for each edge, are able to associate the edge with the nodes representative of the individuals associated with the feature represented by the edge. For example, for a feature that describes a statement (e.g., a feature that describes words spoken in the statement), can be associated with the speaker and/or the recipients of the statement. Processor 102 can use one or more programs of graphing module 140 to create an edge for each recipient of the statement and to associate those edges with the speaker, such that for each recipient of the statement, processor 102 creates an edge extending from the speaker to the recipient. In some examples, the features extracted by feature extraction module 130 are associated with all individuals involved in the interaction.”; geographic location associated with each node for each individual falls under broadest reasonable interpretation of claim term “demographic information”; see response to arguments section above for more detail regarding this claim interpretation),
wherein the feature includes data for determining group affect (Griffin, para. [0074]: “The relationship scores created using method 600 are based on the strength and types of connections between individuals of a group. The relationship strength of a group can influence the positivity of outcome associated with a particular interacting event. For example, a high relationship score can be associated with a positive event outcome and a negative relationship score can be associated with a negative event outcome. As a specific example, where the interacting event scored using method 600 is a work meeting, strong relationships may be correlated with a highly productive and/or effective work meeting. Accordingly, the relationship score generated by method 600 can also act as a score for the overall effectiveness of the work meeting. As a further example, where the interacting event is a sales event, strong relationships may be associated with strong client-vendor relations. Accordingly, a relationship score of a sales event can also act as a score for how the overall effectiveness of a sales event.”; the broadest reasonable interpretation of the claim term “group affect” is the is the underlying experience of feeling, emotion, attachment, or mood of a group of individuals; the relationship score is based on the extracted features used to create the relationship graph with individuals as nodes and edges as interacting events between the individual/person nodes to determine the strength of the relationships; this is an example of the strength of group affect regarding level/strength of attachment of the individuals in the social event captured by cameras; the relationship scores are thus group affect).
Regarding claim 9, Griffin teaches the method of claim 8, wherein the video source is selected from the group consisting of a video camera, a video streaming device, a pre-recorded video, and a segment of a video (Griffin, para. [0025]: “Camera devices 108A-N are capable of capturing video data 110A-N of one or more individuals 112A-N. In the depicted example, camera devices 108A and 108N are depicted as capturing video data 110A and 110N of single individuals 112A and 112N. Camera device 108B is depicted as capturing video data 110B of two individuals 112B and 112C. Each camera device 108A-N captures video data 110A-N is capable of capturing video of one or more individuals 112A-N. Each camera device 108A-N is configured to be able to communicate with relationship evaluator 100 and relationship evaluator 100 is configured to communicate with each camera device 108A-N. Camera devices 108A-N can be, for example, a video camera, a webcam, or another suitable source for obtaining video data 110A-N. Camera devices 108A-N can be controlled by relationship evaluator 100 or by another suitable video device.”).
Regarding claim 10, Griffin teaches the method of claim 8, further comprising: generating a new node in the graph when no node is associated with the individual; associating the new node in the graph with the individual; and storing the feature associatively with the new node in the graph (Griffin, para. [0087]; FIG. 8; para. [0056]: “In some examples, method 700 can be performed repeatedly to determine changes in the trend over subsequent interacting events. In these examples, the current relationship graph created in step 704 can be stored to the set of historical relationship graphs. Step 702 can be omitted in subsequent iterations and steps 704 and 706 can be repeated to create a new relationship graph for a more recent interacting event and to update the trend to include the score for the new relationship graph.”; this means that nodes can be updated added/changed in a currently made relationship graph as needed;
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“Method 600 can be performed for each segment of the video data and updated relationship graph information can be provided for each segment.”; updated infers that any new individuals identified via their extracted features in the video stream will be added to the relationship graph as a node).
Regarding claim 11, Griffin teaches the method of claim 8, wherein at least one node is associated with a feature (Griffin, para. [0018]; para. [0031]-[0032]; FIG. 1; see rejection of claim 8 above; each node is designated as an individual that is identified by the features extracted from the video steam).
Regarding claim 12, Griffin teaches the method of claim 8, further comprising: identifying a second individual associated the feature; determining a second node associated with the second individual; generating an edge associated with the node and the second node; and storing the feature associatively with the edge in the graph (Griffin, para. [0018]; para. [0031]-[0032]; FIG. 1; see rejection of claim 8 above; a plurality of individuals are identified from extracted features from the video stream and made into nodes in the graph and then edges connecting the nodes are labeled as interactions between individuals/nodes identified from the extracted features (ex: talking and looking at one another).
Regarding claim 13, Griffin teaches the method of claim 8, further comprising: processing the graph to determine at least one group affect associated with at least two individuals from the group (Griffin, para. [0074]; see rejection of claim 8 above discussing BRI of group affect meaning degree/strength of attachment of people and examples of this group affect shown in the relationship score between two nodes/individuals; para. [0075]; para. [0034]: “As relationship score can be correlated to the positivity of the outcome of an interacting event, in some examples, the relationship score produced using method 600 can directly represent the success and/or the outcome of the interacting event. As described previously, the relationship score can be a similarity score that describes the similarity between the relationship graph of an interacting event and the relationship graph expected for positive and/or negative interacting events. Outputting a relationship score representative of the success and/or outcome positivity of an interacting event can, in some examples, be more useful for improving the success of a subsequent event than a score only describing relationship strength, such as where the interacting event is at least partially adversarial in nature.”; “the relationship score created using relationship scoring module 146 can be normalized based on the number of nodes present in the relationship graph and/or the length of the interacting event. In some examples, relationship scoring module 146 can include one or more machine learning models for generating a relationship score based on a relationship graph. The machine learning model(s) can be trained to associate relationship score with edge quantity and/or weight using a training set of relationship graphs labeled with relationship scores.”).
Regarding claim 14, Griffin teaches the method of claim 13, further comprising: storing the determined group affect associatively with an edge of the graph (Griffin, para. [0034]; see rejection of claim 13 above regarding the association of the relationship score (group affect) and both the edges and the nodes of the graph; para. [0068]; para. [0071]: “In step 614, a relationship score is determined based on the relationship graph created in step 612. Processor 102 can use one or more programs of relationship scoring module 146 to create the relationship score in step 614. In some examples, one or more computer-implemented machine learning models are used to create the relationship score. In some examples, the relationship score can be representative of relationship strength between the individuals represented in the relationship graph. The computer-implemented machine learning model(s) can be trained to associate edge quantity, edge weights, edge connectivity (i.e., the arrangement of edges), and/or edge orientation (i.e., the direction of directed edges) with a numeric value representative of relationship strength. The strength of all relationships depicted in the relationship graph can be combined, such as by summation, averaging, or another suitable technique, to create the relationship score in step 614.”; “The maximum and/or ideal relationship score can be stored to memory 104 and recalled by processor 102 for use in creating a percentage value in step 616 … Memory 104 can store one or more tables, arrays, and/or databases relating relationships scores calculated using relationship scoring module 146 to letter grades, and processor 102 to cross-reference the table(s), array(s), and/or database(s) to determine the alphanumeric characters representative of the letter grade to be output in step 616.”).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL ADAM SHARIFF whose telephone number is 571-272-9741. The examiner can normally be reached M-F 8:30-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sumati Lefkowitz can be reached on 571-272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL ADAM SHARIFF/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
Prosecution Insights