DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1, 4-8, 11-15, 18-25 have been examined in this Non-Final Office Action. Claims 1, 4-8, 11-15, 18-25 are currently pending. Claims 2-3, 9-10, and 16-17 have been canceled. Claims 21-25 have been added.
Priority
Application 18/600,590 filed 03/08/2024 claims priority to foreign application CN202310300634.8 filed 03/24/2023.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 22 and 25 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.
Claim 22 states that the visualization graph "has a format of" followed by a string of words, arrows and numbers. It is not clear to a person of ordinary skill in the art whether this is a textual label, a particular layout, a graph structure or simply an example. There is no clear boundary for what constitutes the required format making the scope of the claim indefinite.
Claim 25 states “the division range comprises” followed by a bracket of numbers. It is not clear to a person of ordinary skill in the art whether this is a textual label, a particular layout, a graph structure or simply an example. There is no clear boundary for what constitutes the required format making the scope of the claim indefinite.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-8, 11-15 and 18-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1, 4-8, 11-15 and 18-25 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1, 8, and 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and computing device for visualizing a resume. For Claims 1, 8 and 15 the limitations of (Claim 1 being representative):
acquiring a personal resume information set, constructing an individual resume visualization graph based on an entity relationship of personal resume experience in the personal resume information set;
performing person portrait modeling on different persons in the personal resume information set based on the individual resume visualization graph to obtain a person portrait data;
performing entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result;
constructing a person relationship visualization graph based on an entity relationship between different persons in the personal resume information set, wherein the constructing an individual resume visualization graph based on an entity relationship of personal resume experience in the personal resume information set comprises:
extracting a resume text of a target person in the personal resume information set;
using a pre-constructed entity relationship recognition model to recognize a basic information entity, a basic information relationship, a resume experience entity, and a resume experience relationship corresponding to each target person in the resume text; and
constructing an individual resume visualization graph of the target person based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship;
wherein the constructing an individual resume visualization graph of the target person based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship comprises:
taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge;
constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge;
aggregating all the nodes and labeled links to obtain an individual resume visualization graph, as drafted, are processes that, under the broadest reasonable interpretation, covers certain methods of organizing human activity (i.e., managing personal behavior including following rules or instructions) but for recitation of generic computer components. The Examiner notes that “certain method[s] of organizing human activity” includes a person's interaction with a computer (see MPEP 2106.04(a)(2)(II)). That is, other than reciting a system implemented by an electronic device, processor, memory, and non-volatile computer-readable storage medium, the claimed invention amounts to managing personal behavior or interaction between people. For example, but for the electronic device, processor, memory, and non-volatile computer-readable storage medium, this claim encompasses a person to acquire a personal resume information set, construct an individual resume visualization graph based on an entity relationship of personal resume experience in the personal resume information set, perform person portrait modeling on different persons in the personal resume information set based on the individual resume visualization graph to obtain person portrait data, perform entity clustering on different persons in the personal resume information set based on the person portrait data, construct an entity distribution visualization graph according to the clustering result, construct a person relationship visualization graph based on an entity relationship between different persons in the personal resume information set, wherein the construction of an individual resume visualization graph is based on an entity relationship of personal resume experience in the personal resume information set and comprises: extraction of resume text of a target person in the personal resume information set using a pre-constructed entity relationship recognition model to recognize a basic information entity, a basic information relationship, a resume experience entity, and a resume experience relationship corresponding to each target person in the resume text, and constructing an individual resume visualization graph of the target person based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship, wherein the construction of an individual resume visualization graph of the target person is based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship comprises: taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge, constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge, and aggregating all the nodes and labeled links to obtain an individual resume visualization graph, based on this data in the manner described in the identified abstract idea, supra. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, Claims 1, 8 and 15 recite an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. Claims 1, 8, and 15 recites the additional elements of an electronic device (Claim 1, and 8), at least one processor (Claim 1, 8 and 15), a memory (Claim 1, and 8), A non-volatile computer-readable storage medium (Claim 15), that implements the identified abstract idea. These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., one or more generic computers performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claims 1, 8, and 15 are directed to an abstract idea. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an electronic device (Claim 8), at least one processor (Claim 8 and 15), a memory (Claim 8), A non-volatile computer-readable storage medium (Claim 15), to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such claims 1, 8, and 15 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 4-5, 11-12 and 18-25 are similarly rejected because they either further define/narrow the abstract idea of independent claims 1, 8 and 15 as discussed above. Claim(s) 4, 11, and 18 merely describe(s) constructing a person ID of a target person in the individual resume visualization graph, outputting an entity of each node in the individual resume visualization graph as an entity tag, and outputting a relationship between each node as an entity attribute corresponding to the entity tag, and mapping the entity tag and the entity attribute with the person ID to obtain the person portrait data of the target person. Claim(s) 5, 12, and 19 merely describe(s) determining a target attribute from the entity attribute, and performing entity division on an entity tag corresponding to the target attribute according to the target attribute and a pre-set division range, counting persons corresponding to an entity tag in each division range, and writing a target attribute in each division range and an entity tag corresponding to the target attribute in the each division range as parameters into a pre-constructed graphic visualization template to obtain a classification visualization script, converting the classification visualization script into a visualization file in a pre-set format, and performing asynchronous loading on the visualization file to obtain a distribution visualization graph. Claim(s) 21 merely describe(s) the basic information entity representing basic information of the target person recognized from the resume text, comprising name, age, position, and the like, wherein the resume experience entity represents department and position experience information of the target person recognized from the text, comprising at least one department, wherein a corresponding resume experience relationship comprises at least one time period respectively corresponding to the at least one department and position experience. Claim(s) 22 merely describe(s) the format of the resume visualization graph. Claim(s) 23 merely describe(s) the entity distribution visualization graph comprises a bar chart, pie chart, line chart, and the like. Claim(s) 24 merely describe(s) the target attribute comprising an age of different persons, the entity tag corresponding to the target attribute being divided according to a pre-set division range to obtain a number of entity tags in each division range, which are written as parameters into a bar chart template and converted into a JSON format file, and where an age entity distribution bar chart is obtained by asynchronous loading through JavaScript. Claim(s) 24 merely describe(s) the division range comprising [20-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60], and thereby obtaining a number of each of corresponding entity tags. Therefore claims 4-5, 11-12, and 18-15 are considered patent ineligible for the reasons given above.
Dependent Claim(s) 6-7, 13-14, and 20 recite limitations that further define the abstract idea noted in independent claims 1, 8, and 15. Claim(s) 6, 13 & 20 further recite, recognizing a department and a post of different persons in the personal resume information set; […] recognize person images of different persons in the personal resume information set, aggregating target persons of the same department in the personal resume information set, taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph, which means to recognize a department and post from different people in the resume set, recognize a person from a group of people in the resume set, group people of the same department in the resume set, fill the nodes as the images of the people, construct a relationship between a node and a post to create a graph while connecting the original graph of different departments to obtain a visual graph. Claim(s) 7 and 14 recite, […] recognize face images of different persons in the personal resume information set; performing face alignment on the recognized face image and cutting same to obtain person images of different persons in the personal resume information set, which means to recognize faces of different people in a resume set, and align the recognized faces in an image so the faces of different people can be cropped out. In addition, they recite the additional element of a pre-constructed face recognition model. The pre-constructed face recognition model is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computing component. Even in combination, this additional element does not integrate the abstract idea into a practical application and does not amount to significantly more than the abstract idea itself. Therefore, dependent claims 4-7, 11-14, and 18-25 are considered patent ineligible for the reasons
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.
Claim(s) 1, 4, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20170200125 A1), in view of Xu (CN 108509614 A).
Regarding Claim 1,
Wang discloses, A method for visualizing a personal resume, the method being executed by an electronic device comprising at least one processor and a memory communicatively coupled to the at least one processor; wherein the memory is configured to store a computer program executable by the at least one processor, and the computer program is configured to be executed by the at least one processor to enable the at least one processor to execute the method, the method comprising: acquiring a personal resume information set, constructing an individual resume visualization graph based on an entity relationship of personal resume experience in the personal resume information set; "The social relationship discovery module uses association algorithm in data mining to conduct associative computation between their associated growth trajectory sequence data to obtain potential social relationships between the text resumes (e.g. classmates, coworkers, fellow countrymen, comrades, collaborators, competitors, etc.). The organizations construction module identifies a common organization in experiences in text resumes, and constructs organization hierarchy for the organization based on the potential social relationships in the text resumes" (Wang Par. 0013-0014). "6. Biographical Information Visualization Module - The module is based on information visualization technology. It presents resume information in intuitive way to the users, to help them to view and to correctly understand resume data. The module contains three kinds of visualization algorithms: temporal and spatial biographic trajectory visualization algorithm, potential social network visualization algorithm, and organization visualization algorithm. The three algorithms can generate the following diagrams: personal growth charts, potential diagrams, and organizational charts" (Wang Par. 0130-0131). "The basic biographical information includes name, sex, nationality, place of birth, and other basic information. The experience information is formatted in a table structure: the table header contains the start time, end time, location, organization, position, and other fields. Each entry in the table records one of the person's experiences, namely, the person's experience (work or education) within a certain time" (Wang Par. 0047).
performing person portrait modeling on different persons in the personal resume information set based on the individual resume visualization graph to obtain a person portrait data; "2) The plain text resume is processed using natural language processing technology to parse words and phrases, and to recognize proper names (named person or entities). Biographical feature elements are extracted using a feature extraction algorithm from the unstructured biographical text data and processed to obtain structured text containing the biographical feature elements. The structured text blocks, shown as follows, include basic information and experience information, wherein “/NAME”, “/TIME”, “/TITLE” and other structure identifiers represent “name”, “time”, “duty”, and other biographic feature elements" (Wang Par. 0059).
constructing a person relationship visualization graph based on an entity relationship between different persons in the personal resume information set. "The Potential Social Relationship Discovery Module - The social relationships discovery algorithms in this module innovatively applies algorithms for measuring distances in growth trajectories and association rules to discover potential social relationships R (e.g. students, colleagues, fellow comrades, partners, competitors and other relations) in biographic data" (Wang Par. 0110-0111). " 6) Repeating steps 4 and 5 until all resumes in M have been scanned and processed to give potential social relationships R among all resumes. Potential social relationships can be categorized in two types: one relates to growth trajectory similarity relationship based on the similarity matrix obtained sim, and the other is obtained through the experience intersection relationship based on matching matrix mch. FIGS. 6A and 6B are schematic diagrams showing the results of discovering potential relationships" (Wang Par. 0117).
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wherein the constructing an individual resume visualization graph based on an entity relationship of personal resume experience in the personal resume information set comprises: extracting a resume text of a target person in the personal resume information set; “The text resume pre-processing module converts an unstructured text resume to a structured text resume, by filtering format of the unstructured text resume to obtain a pure text version of the unstructured text resume; parsing words and identifying proper names in the pure text version of the unstructured text resume; extracting biographical elements from the pure text version of the unstructured text resume (from basic information and experience information table) to obtain structured text blocks comprising the biographical elements; and formatting the structured text blocks comprising the biographical elements to obtain a structured text resume (e.g. in XML data format, providing a data for the discovery and visualization by the subsequent modules)" (Wang Par. 0010).
using a pre-constructed entity relationship recognition model to recognize a basic information entity, a basic information relationship, a resume experience entity, and a resume experience relationship corresponding to each target person in the resume text; and “The text resume pre-processing module converts an unstructured text resume to a structured text resume, by filtering format of the unstructured text resume to obtain a pure text version of the unstructured text resume; parsing words and identifying proper names in the pure text version of the unstructured text resume; extracting biographical elements from the pure text version of the unstructured text resume (from basic information and experience information table) to obtain structured text blocks comprising the biographical elements; and formatting the structured text blocks comprising the biographical elements to obtain a structured text resume (e.g. in XML data format, providing a data for the discovery and visualization by the subsequent modules). "Among the above, the feature extraction algorithm in step 2 is a core algorithm module, which mainly extracts various feature elements by matching regular expressions. The method can specifically include the steps of" (Par. 0069). "2-2) extraction of experience information: {circle around (1)} The “time” and “place” elements are extracted using the regular matching method. For example, the “year” is used as a regular match keyword to extract “time” elements. “Province”, “city”, “county”, and “xiang” are used a regular feature matching keyword to extract “place” elements. {circle around (2)} For the “organization” elements, the extractions are based on keyword matching using a predesigned organization keyword dictionary (Table 1). Each row element in the keyword dictionary organization consists of two parts: “keyword” and “auxiliary keyword”, wherein the “auxiliary keyword” includes two R-type and L-type; and multiple “auxiliary keywords” are separated by commas. The principle of using organization keywords in the organization keyword dictionary to identify organization elements is as follows: when a keyword in the organization keyword dictionary is recognized, if its right side does not include an R-type “auxiliary keyword” and its left side does not include a L-type “auxiliary keyword”, then the recognition is considered successful; otherwise, the recognition is failed" (Wang Par. 0071-0073).
constructing an individual resume visualization graph of the target person based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship; "6.1 Personal Growth Chart - As shown in FIGS. 7A and 7B, the temporal and spatial personal growth trajectory diagrams are drawn by the visualization algorithms. The algorithm uses the concept of growth metaphors to generate temporal and spatial trajectory visualization diagrams, and can intuitively display otherwise abstract personal growth information. The algorithm can include following steps: 1) Defining visual axes for a temporal growth trajectory. The horizontal axis is time, expressed in “year” or “age”. The vertical axis is ranking value, representing “the quantized rank” (which can use official positions as example and can include “section level”, “department class”, “bureau level”, etc.; for researchers, “intern assistant”, “research assistant”, “research associate”, “research fellow”, and “academy member” etc.) in the growth trajectory sequence data. 2) Defining axes for spatial trajectory visualization. The horizontal axis is time, expressed in “year” or “age”. The vertical axis is spatial axis, using a two-dimensional map as the spatial reference system, representing “place” and “organization” in the spatial growth trajectory sequence data. 3) Defining the concept of visualization of sequence data growth trajectory. A growth trajectory sequence data is formed by a series of experience segments, with each segment representing the basic unit of the growth trajectory sequence data” (Wang Par. 0132-0136).
Wang discloses visualization techniques to obtain a person’s growth experience or social relations. Wang fails to disclose entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result, taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge, constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge, and aggregating the nodes and links to obtain a graph. Alternatively, Xu, discloses structuring graphs based on personal information and characteristics. Xu, teaches,
performing entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result; "Graph database is a new type of NoSQL database based on graph theory. Its data storage structure and data query method are both based on graph theory. The basic elements of a graph in graph theory are nodes and edges, which correspond to nodes and relationships in a graph database" (Xu Par. 0005). "In a graph database, a node contains a label, a node and a relationship attribute group, and an attribute group can store multiple attribute key-value pairs" (Xu Par. 0062). "Person: label Person, attribute group {person ID, name, gender, nation, birthday, ID card, birthplace, current residence, occupation, political outlook, marital status, portrait data, etc.}, where the person ID is added with a unique constraint to ensure the uniqueness of the person node" (Xu Par. 0066). "Split the resume into character attribute groups and resume description sections. Using information extraction technology, name, gender, ethnicity, date of birth, ID number, place of birth, current residence, occupation, political status, marital status, profile picture and other information are extracted from the pre-processed resume. If this information exists, it is saved in the person node of the graph database in a unified format" (Xu Par. 0100-0101). "B6. Importing character data and relationships into a graph database - According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs" (Xu Par. 0104-0105). "Furthermore, the complex relationships discovered between characters include hometown relationships, alumni relationships, colleague relationships, and cascade relationships between characters" (Xu Par. 0031).
wherein the constructing an individual resume visualization graph of the target person based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship comprises: taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge; "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs. Once the character resume chart is constructed, various management operations can be conveniently performed on the chart. With the character node as the center, you can add, delete, modify, and query the character's resume information and character relationships; with the region node as the center, you can operate the birth relationship and residence relationship connected to the node; with the school and college nodes as the center, you can operate the learning experience connected to the node; with the department and position nodes as the center, you can operate the work experience connected to the node" (Xu Par. 0105-0106). "Relation - Region - [include_location] -> Region, Region - [include_department]->Department, Region-[include_school]->School, School-[Merge (merge_school)]->School: Attribute Group {Merge Time (time)}, School - [include_academy] -> Academy: Attribute Group {Foundation Time}, Department-[Management(manage_department)]->Department, Department-[include_position]->Position, Position-[Management (manage_location)]->Position, Person-[come_from]->Region: Attribute Group {time}, Character - [Inhabit] -> Region: Attribute Group {start_time, end_time}, Person - [Study at (study_at)] -> School/College: Attribute group {Experience code (id), start time (start_time), end time (end_time)}, Person - [Work at (work_at)] -> Position: Attribute group {Experience code (id), start time (start_time), end time (end_time)}, Person - [countrymen_with] -> Person: Attribute Group {relation_cost}, Person - [schoolfellow_with] -> Person: Attribute Group {relation_cost}, Person - [workmate_with] -> Person: Attribute Group {relation_cost} (Xu Par. 0071-0086).
constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge; "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs. Once the character resume chart is constructed, various management operations can be conveniently performed on the chart. With the character node as the center, you can add, delete, modify, and query the character's resume information and character relationships; with the region node as the center, you can operate the birth relationship and residence relationship connected to the node; with the school and college nodes as the center, you can operate the learning experience connected to the node; with the department and position nodes as the center, you can operate the work experience connected to the node" (Xu Par. 0105-0106). "Character - [Inhabit] -> Region: Attribute Group {start_time, end_time}, Person - [Study at (study_at)] -> School/College: Attribute group {Experience code (id), start time (start_time), end time (end_time)}, Person - [Work at (work_at)] -> Position: Attribute group {Experience code (id), start time (start_time), end time (end_time)}" (Xu Par. 0081-0083).
aggregating all the nodes and labeled links to obtain an individual resume visualization graph. . "The above solution constructing person history database, the entity relationship structure diagram such as shown in FIG. 2. graphic corresponding to the node, the relationship is as follows: square region node, circular: a person node, oval, school node, trapezoid College node, triangle: department node, rhombus: job node, line segment with arrow of relationship between the nodes" (Xu Par. 0129-0136). Figure 2
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume of Wang with performing entity clustering on different persons in the personal resume information set based on the person portrait data and constructing an entity distribution visualization graph according to the clustering result, taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge, constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge, and aggregating the nodes and links to obtain a graph of Xu to obtain the characteristics of each personal information group, and save a large amount of biographical information about people, including personal attributes (Xu Par. 0004, 0006).
Regarding Claim 4,
The combination of Wang and Xu disclose the method of claim 1, as shown above. Xu further discloses, wherein the performing person portrait modeling on different persons in the personal resume information set based on the individual resume visualization graph to obtain a person portrait data comprises: constructing a person ID of a target person in the individual resume visualization graph, outputting an entity of each node in the individual resume visualization graph as an entity tag, and outputting a relationship between each node as an entity attribute corresponding to the entity tag; "Person: label Person, attribute group {person ID, name, gender, nation, birthday, ID card, birthplace, current residence, occupation, political outlook, marital status, portrait data, etc.}, where the person ID is added with a unique constraint to ensure the uniqueness of the person node" (Xu Par. 0066). "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs" (Xu Par. 0105). "Person - [Work at (work_at)] -> Position: Attribute group {Experience code (id), start time (start_time), end time (end_time)}" (Xu Par. 0083).
mapping the entity tag and the entity attribute with the person ID to obtain the person portrait data of the target person. "In a graph database, a node contains a label, a node and a relationship attribute group, and an attribute group can store multiple attribute key-value pairs" (Xu Par. 0062). "Split the resume into character attribute groups and resume description sections. Using information extraction technology, name, gender, ethnicity, date of birth, ID number, place of birth, current residence, occupation, political status, marital status, profile picture and other information are extracted from the pre-processed resume. If this information exists, it is saved in the person node of the graph database in a unified format. If it does not exist, it is temporarily set to a null value" (Xu Par. 100-0101). "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs" (Xu Par. 0105).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume and creating graphs of Wang and Xu with constructing a person ID of a person in the graph, outputting an entity of each node in the graph as a tag, outputting a relationship between each node as an entity attribute corresponding to the tag, and mapping the tag and attribute with the person ID to obtain person portrait data of the person of Xu to provide social recommendations based on the character (Xu Par. 0127).
Regarding Claim 21,
The combination of Wang and Xu disclose the method of claim 1, as shown above. Wang further discloses, (New) The method for visualizing a personal resume of claim 1, wherein the basic information entity represents basic information of the target person recognized from the resume text, comprising name, age, position, and the like; "Basic personal information can include name, gender, date of birth, nationality, education level, political affiliation, religion, family members, major social relations, marriage and personal health status, etc. As an important part of resume, personal experience usually includes person's education experiences, work experiences, and so on" (Wang Par. 0002).
Examiner Note: The date of birth is provided which inherently shows the age.
wherein the resume experience entity represents department and position experience information of the target person recognized from the text, comprising at least one department; "The experience information is formatted in a table structure: the table header contains the start time, end time, location, organization, position, and other fields. Each entry in the table records one of the person's experiences, namely, the person's experience (work or education) within a certain time" (Wang Par. 0047). "2-1) The “organizations” and “position” fields are extracted from biographical corpus by the text resume pre-processing module. Users can also add and modify on their own" (Wang Par. 0083). "{circle around (1)} Using the resumes of the government officials as example, the administrative levels for officials in China are classified as follows: national level (quantified to 5), provincial level (quantified to 4), the bureau level (quantified to 3), county level (quantified to 2), township branch level (quantified to 1), and other levels, where each level can be further subdivided to regular and deputy positions" (Wang Par. 0086).
wherein a corresponding resume experience relationship comprises at least one time period respectively corresponding to the at least one department and position experience. "The experience information is formatted in a table structure: the table header contains the start time, end time, location, organization, position, and other fields. Each entry in the table records one of the person's experiences, namely, the person's experience (work or education) within a certain time" (Wang Par. 0047). "The time span of experience spent at each job rank can be measured for each individual growth trajectory to obtain individual person's career growth rate (the slopes of the curves in FIGS. 3A-3D). The growth rate of fast growth type is significantly greater than the sample average over the entire time dimension. The steady growth has growth rate about the equal to the sample average. The growth rate of the fluctuating growth type has growth rates greater than the sample average at some stages and lower than the sample average at some other stages. The declining growth type has significantly lower growth rate than the sample average over the entire time dimension" (Wang Par. 0093).
Claim(s) 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20170200125 A1), in view of Xu (CN 108509614 A), in view of Aggarwal (US 12347231 B1), in view of Ali (US 20160125360 A1), and in further view of Knight (What Is a Knowledge Graph).
Regarding Claim 6,
The combination of Wang and Xu disclose the method of claim 1, as shown above. Wang further discloses, wherein the constructing a person relationship visualization graph based on the entity relationship between different persons in the personal resume information set comprises: recognizing a department and a post of different persons in the personal resume information set; "As shown above, the XML data contains two sections of biographical elements: basic biographical information and experience information table. The basic biographical information includes name, sex, nationality, place of birth, and other basic information. The experience information is formatted in a table structure: the table header contains the start time, end time, location, organization, position, and other fields. Each entry in the table records one of the person's experiences, namely, the person's experience (work or education) within a certain time" (Wang Par. 0047).
The combination of Wang and Xu disclose analyzing resume text and creating graphs based on the text. The combination of Wang and Xu fail to disclose using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set, aggregating target persons of the same department in the personal resume information set, taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph. Alternatively, Aggarwal discloses obtaining facial images from data to create employee directories. Aggarwal teaches:
using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set, "To extract a headshot from a source of one or more images, the techniques herein initially localize an individual within the images using a face detection algorithm. The face detection algorithm may include any suitable machine learning model trained using labeled faces either to identify particular identities and/or to identify a face within an image that may be identified at a later stage" (Aggarwal Col. 3 Lines 31-37). The headshot may be used for marketing, individual identification, hiring, casting, and other such purposes" (Aggarwal Col. 4 Lines 9-11). " the techniques described herein may be used to identify and extract headshots from any source image material, for example to produce employee catalogs, directories, or other such information in an efficient and consistent manner" (Aggarwal Col. 2 Lines 19-23).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, and creating graphs of Wang, and Xu with using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set of Aggarwal to improve efficiency, speed, and capability for headshot generation and identification for individuals. (Aggarwal Col. 3 Lines 2-3).
The combination of Wang, Xu, and Aggarwal disclose analyzing resume text, creating graphs based on the text, and detecting users from facial recognition. The combination of Wang, Xu, and Aggarwal fail to disclose aggregating target persons of the same department in the personal resume information set, taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph. Alternatively, Ali discloses matching and sorting candidates based on resume data. Ali teaches,
aggregating target persons of the same department in the personal resume information set, "More specifically, embodiments provide an automated way to match, compare and sort candidates based on skill set, job title, education, and/or other attributes described in a candidate profile with a requisition in a recruiting application. Embodiments of the present invention can build candidate pools for current or future openings without manual intervention" (Ali Par. 0019). "Screening and sorting the best candidates from a huge pile of resumes is a challenging and tedious job for a recruiter. Matching candidate's resume with the requirements of an open position is time consuming and frustrating. Hence, there is a need for improved methods and systems for identifying candidates for an open position" (Ali Par. 0002).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, creating graphs, and detecting users based on facial recognition of Wang, Xu, and Aggarwal with aggregating target persons of the same department in the personal resume information set of Ali since screening candidates from a large pool is challenging and tedious, and improves identifying candidates. (Ali Par. 0002).
The combination of Wang, Xu, Aggarwal, and Ali disclose analyzing resume text, creating graphs based on the text, detecting users from facial recognition, and aggregating users of the same department. The combination of Wang, Xu, Aggarwal, and Ali fail to disclose taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph. Alternatively, Knight discloses knowledge graphs being based on user experiences. Knight discloses,
taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph
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It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, creating graphs, detecting users based on facial recognition, and aggregating users within the same department based on a set of resume information of Wang, Xu, Aggarwal, and Ali with obtaining a visual graph from taking the users as nodes, and contrasting relationships between different nodes, and connecting the graphs based on relationship graphs of Knight to track networks of people and the connections in a visual representation (Knight Page 2).
Regarding Claim 7,
The combination of Wang, Xu, Aggarwal, Ali, and Knight disclose the method of claim 6, as shown above. Aggarwal further discloses, wherein the using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set comprises: using a pre-constructed face recognition model to recognize face images of different persons in the personal resume information set; "to extract a headshot from a source of one or more images, the techniques herein initially localize an individual within the images using a face detection algorithm. The face detection algorithm may include any suitable machine learning model trained using labeled faces either to identify particular identities and/or to identify a face within an image that may be identified at a later stage" (Aggarwal Col. 3 Lines 31-37). The headshot may be used for marketing, individual identification, hiring, casting, and other such purposes" (Aggarwal Col. 4 Lines 9-11).
performing face alignment on the recognized face image and cropping the aligned face image "The headshot curation system and techniques described herein may extract the first headshot 106 and/or the second headshot 108 from the frame 100. The headshot is extracted from the frame by first identifying individuals within the frame 100 using a face recognition algorithm or other technique or system used for facial recognition and/or identification. The identified faces of the individuals are then surrounded by a bounding box used to determine a crop within the frame 100 that will result in a headshot for each individual. The bounding box for each headshot may be determined based on defined heuristics that determine dimensions and/or ratios for the bounding box as a function of the dimensions of the facial features or other features of the individual. For example, the bounding box may be defined based on a height of the face of the individual, such as first identifying shoulders and hair or top of the head of the individual and defining the height of the bounding box to crop tightly around the head and shoulders of the individual" (Aggarwal Col. 4 Lines 26-44). "In a third stage of the MTCNN, the outputs identify coordinates of the bounding boxes around the faces, coordinates for one or more facial landmarks (eyes, nose, mouth, chin, shoulders, hairline, etc.), and a confidence score for each of the bounding boxes. The final bounding boxes are tightly cropped around the faces of the individuals within the frame 100" (Aggarwal Col. 5 Lines 12-18).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, and creating graphs of Wang, Xu, Aggarwal, Ali, and Knight with using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set, and performing face alignment on the recognized face image and cutting same to obtain person images of different persons in the personal resume information set of Aggarwal to improve efficiency, speed, and capability for headshot generation and identification for individuals (Aggarwal Col. 3 Lines 2-3).
Claim(s) 8, 11, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20170200125 A1), in view of Xu (CN 108509614 A), and in further view of Aggarwal (US 12347231 B1).
Regarding Claim 8, and Claim 15
Wang discloses, acquiring a personal resume information set, constructing an individual resume visualization graph based on an entity relationship of personal resume experience in the personal resume information set; "The social relationship discovery module uses association algorithm in data mining to conduct associative computation between their associated growth trajectory sequence data to obtain potential social relationships between the text resumes (e.g. classmates, coworkers, fellow countrymen, comrades, collaborators, competitors, etc.). The organizations construction module identifies a common organization in experiences in text resumes, and constructs organization hierarchy for the organization based on the potential social relationships in the text resumes" (Wang Par. 0013-0014). "6. Biographical Information Visualization Module - The module is based on information visualization technology. It presents resume information in intuitive way to the users, to help them to view and to correctly understand resume data. The module contains three kinds of visualization algorithms: temporal and spatial biographic trajectory visualization algorithm, potential social network visualization algorithm, and organization visualization algorithm. The three algorithms can generate the following diagrams: personal growth charts, potential diagrams, and organizational charts" (Wang Par. 0130-0131). "The basic biographical information includes name, sex, nationality, place of birth, and other basic information. The experience information is formatted in a table structure: the table header contains the start time, end time, location, organization, position, and other fields. Each entry in the table records one of the person's experiences, namely, the person's experience (work or education) within a certain time" (Wang Par. 0047).
performing person portrait modeling on different persons in the personal resume information set based on the individual resume visualization graph to obtain a person portrait data; "2) The plain text resume is processed using natural language processing technology to parse words and phrases, and to recognize proper names (named person or entities). Biographical feature elements are extracted using a feature extraction algorithm from the unstructured biographical text data and processed to obtain structured text containing the biographical feature elements. The structured text blocks, shown as follows, include basic information and experience information, wherein “/NAME”, “/TIME”, “/TITLE” and other structure identifiers represent “name”, “time”, “duty”, and other biographic feature elements" (Wang Par. 0059).
performing entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result; constructing a person relationship visualization graph based on the entity relationship between different persons in the personal resume information set; "The Potential Social Relationship Discovery Module - The social relationships discovery algorithms in this module innovatively applies algorithms for measuring distances in growth trajectories and association rules to discover potential social relationships R (e.g. students, colleagues, fellow comrades, partners, competitors and other relations) in biographic data" (Wang Par. 0110-0111). " 6) Repeating steps 4 and 5 until all resumes in M have been scanned and processed to give potential social relationships R among all resumes. Potential social relationships can be categorized in two types: one relates to growth trajectory similarity relationship based on the similarity matrix obtained sim, and the other is obtained through the experience intersection relationship based on matching matrix mch. FIGS. 6A and 6B are schematic diagrams showing the results of discovering potential relationships" (Wang Par. 0117).
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wherein the constructing an individual resume visualization graph based on an entity relationship of personal resume experience in the personal resume information set comprises: extracting a resume text of a target person in the personal resume information set; “The text resume pre-processing module converts an unstructured text resume to a structured text resume, by filtering format of the unstructured text resume to obtain a pure text version of the unstructured text resume; parsing words and identifying proper names in the pure text version of the unstructured text resume; extracting biographical elements from the pure text version of the unstructured text resume (from basic information and experience information table) to obtain structured text blocks comprising the biographical elements; and formatting the structured text blocks comprising the biographical elements to obtain a structured text resume (e.g. in XML data format, providing a data for the discovery and visualization by the subsequent modules)" (Wang Par. 0010).
using a pre-constructed entity relationship recognition model to recognize a basic information entity, a basic information relationship, a resume experience entity, and a resume experience relationship corresponding to each target person in the resume text; “The text resume pre-processing module converts an unstructured text resume to a structured text resume, by filtering format of the unstructured text resume to obtain a pure text version of the unstructured text resume; parsing words and identifying proper names in the pure text version of the unstructured text resume; extracting biographical elements from the pure text version of the unstructured text resume (from basic information and experience information table) to obtain structured text blocks comprising the biographical elements; and formatting the structured text blocks comprising the biographical elements to obtain a structured text resume (e.g. in XML data format, providing a data for the discovery and visualization by the subsequent modules). "Among the above, the feature extraction algorithm in step 2 is a core algorithm module, which mainly extracts various feature elements by matching regular expressions. The method can specifically include the steps of" (Wang Par. 0069). "2-2) extraction of experience information: {circle around (1)} The “time” and “place” elements are extracted using the regular matching method. For example, the “year” is used as a regular match keyword to extract “time” elements. “Province”, “city”, “county”, and “xiang” are used a regular feature matching keyword to extract “place” elements. {circle around (2)} For the “organization” elements, the extractions are based on keyword matching using a predesigned organization keyword dictionary (Table 1). Each row element in the keyword dictionary organization consists of two parts: “keyword” and “auxiliary keyword”, wherein the “auxiliary keyword” includes two R-type and L-type; and multiple “auxiliary keywords” are separated by commas. The principle of using organization keywords in the organization keyword dictionary to identify organization elements is as follows: when a keyword in the organization keyword dictionary is recognized, if its right side does not include an R-type “auxiliary keyword” and its left side does not include a L-type “auxiliary keyword”, then the recognition is considered successful; otherwise, the recognition is failed" (Wang Par. 0071-0073).
and constructing an individual resume visualization graph of the target person based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship; "6.1 Personal Growth Chart - As shown in FIGS. 7A and 7B, the temporal and spatial personal growth trajectory diagrams are drawn by the visualization algorithms. The algorithm uses the concept of growth metaphors to generate temporal and spatial trajectory visualization diagrams, and can intuitively display otherwise abstract personal growth information. The algorithm can include following steps: 1) Defining visual axes for a temporal growth trajectory. The horizontal axis is time, expressed in “year” or “age”. The vertical axis is ranking value, representing “the quantized rank” (which can use official positions as example and can include “section level”, “department class”, “bureau level”, etc.; for researchers, “intern assistant”, “research assistant”, “research associate”, “research fellow”, and “academy member” etc.) in the growth trajectory sequence data. 2) Defining axes for spatial trajectory visualization. The horizontal axis is time, expressed in “year” or “age”. The vertical axis is spatial axis, using a two-dimensional map as the spatial reference system, representing “place” and “organization” in the spatial growth trajectory sequence data. 3) Defining the concept of visualization of sequence data growth trajectory. A growth trajectory sequence data is formed by a series of experience segments, with each segment representing the basic unit of the growth trajectory sequence data” (Wang Par. 0132-0136).
Wang discloses visualization techniques to obtain a person’s growth experience or social relations. Wang fails to disclose an electronic device, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result, taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge, constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge, and aggregating the nodes and links to obtain a graph. Alternatively, Xu, discloses structuring graphs based on personal information and characteristics. Xu, teaches,
performing entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result; "Graph database is a new type of NoSQL database based on graph theory. Its data storage structure and data query method are both based on graph theory. The basic elements of a graph in graph theory are nodes and edges, which correspond to nodes and relationships in a graph database" (Xu Par. 0005). "In a graph database, a node contains a label, a node and a relationship attribute group, and an attribute group can store multiple attribute key-value pairs" (Xu Par. 0062). "Person: label Person, attribute group {person ID, name, gender, nation, birthday, ID card, birthplace, current residence, occupation, political outlook, marital status, portrait data, etc.}, where the person ID is added with a unique constraint to ensure the uniqueness of the person node" (Xu Par. 0066). "Split the resume into character attribute groups and resume description sections. Using information extraction technology, name, gender, ethnicity, date of birth, ID number, place of birth, current residence, occupation, political status, marital status, profile picture and other information are extracted from the pre-processed resume. If this information exists, it is saved in the person node of the graph database in a unified format" (Xu Par. 0100-0101). "B6. Importing character data and relationships into a graph database - According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs" (Xu Par. 0104-0105). "Furthermore, the complex relationships discovered between characters include hometown relationships, alumni relationships, colleague relationships, and cascade relationships between characters" (Xu Par. 0031).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume of Wang with performing entity clustering on different persons in the personal resume information set based on the person portrait data and constructing an entity distribution visualization graph according to the clustering result of Xu to obtain the characteristics of each personal information group, and save a large amount of biographical information about people, including personal attributes (Xu Par. 0004, 0006).
wherein the constructing an individual resume visualization graph of the target person based on the basic information entity, the basic information relationship, the resume experience entity, and the resume experience relationship comprises: taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge; "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs. Once the character resume chart is constructed, various management operations can be conveniently performed on the chart. With the character node as the center, you can add, delete, modify, and query the character's resume information and character relationships; with the region node as the center, you can operate the birth relationship and residence relationship connected to the node; with the school and college nodes as the center, you can operate the learning experience connected to the node; with the department and position nodes as the center, you can operate the work experience connected to the node" (Xu Par. 0105-0106). "Relation - Region - [include_location] -> Region, Region - [include_department]->Department, Region-[include_school]->School, School-[Merge (merge_school)]->School: Attribute Group {Merge Time (time)}, School - [include_academy] -> Academy: Attribute Group {Foundation Time}, Department-[Management(manage_department)]->Department, Department-[include_position]->Position, Position-[Management (manage_location)]->Position, Person-[come_from]->Region: Attribute Group {time}, Character - [Inhabit] -> Region: Attribute Group {start_time, end_time}, Person - [Study at (study_at)] -> School/College: Attribute group {Experience code (id), start time (start_time), end time (end_time)}, Person - [Work at (work_at)] -> Position: Attribute group {Experience code (id), start time (start_time), end time (end_time)}, Person - [countrymen_with] -> Person: Attribute Group {relation_cost}, Person - [schoolfellow_with] -> Person: Attribute Group {relation_cost}, Person - [workmate_with] -> Person: Attribute Group {relation_cost} (Xu Par. 0071-0086).
constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge; "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs. Once the character resume chart is constructed, various management operations can be conveniently performed on the chart. With the character node as the center, you can add, delete, modify, and query the character's resume information and character relationships; with the region node as the center, you can operate the birth relationship and residence relationship connected to the node; with the school and college nodes as the center, you can operate the learning experience connected to the node; with the department and position nodes as the center, you can operate the work experience connected to the node" (Xu Par. 0105-0106). "Character - [Inhabit] -> Region: Attribute Group {start_time, end_time}, Person - [Study at (study_at)] -> School/College: Attribute group {Experience code (id), start time (start_time), end time (end_time)}, Person - [Work at (work_at)] -> Position: Attribute group {Experience code (id), start time (start_time), end time (end_time)}" (Xu Par. 0081-0083).
aggregating all the nodes and labeled links to obtain an individual resume visualization graph. "The above solution constructing person history database, the entity relationship structure diagram such as shown in FIG. 2. graphic corresponding to the node, the relationship is as follows: square region node, circular: a person node, oval, school node, trapezoid College node, triangle: department node, rhombus: job node, line segment with arrow of relationship between the nodes" (Xu Par. 0129-0136). Figure 2
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume and creating graphs of Wang with performing entity clustering on different persons in the personal resume information set based on the person portrait data and constructing an entity distribution visualization graph according to the clustering result, taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge, constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship, and using the resume experience relationship to label the directed edge, and aggregating the nodes and links to obtain a graph of Xu to obtain the characteristics of each personal information group, and save a large amount of biographical information about people, including personal attributes (Xu Par. 0004, 0006) and to provide social recommendations based on the character (Xu Par. 0127).
An electronic device, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, the computer program is executed by the at least one processor to enable the at least one processor to execute the steps of: (Aggarwal Col. 10 Lines 42-52, Col. 11 Lines 26-38, Col. 16 Line 62- Col. 17 Line 11, Col. 17 Line 65-Col. 18 Line 17).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume of Wang and Xu with an electronic device, processor, memory, and instructions of Aggarwal since a computer requires a processing element together with a memory, and processor in order to provide instructions for the device to function, and it is old and well known in the art.
Regarding Claim 11, and Claim 18,
The combination of Wang, Xu, and Aggarwal discloses the device of claim 8, and computer-readable storage medium of claim 15 as shown above. Xu further discloses, wherein the performing person portrait modeling on different persons in the personal resume information set based on the individual resume visualization graph to obtain a person portrait data comprises: constructing a person ID of a target person in the individual resume visualization graph, outputting an entity of each node in the individual resume visualization graph as an entity tag, and outputting a relationship between each node as an entity attribute corresponding to the entity tag; "Person: label Person, attribute group {person ID, name, gender, nation, birthday, ID card, birthplace, current residence, occupation, political outlook, marital status, portrait data, etc.}, where the person ID is added with a unique constraint to ensure the uniqueness of the person node" (Xu Par. 0066). "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs" (Xu Par. 0105). "Person - [Work at (work_at)] -> Position: Attribute group {Experience code (id), start time (start_time), end time (end_time)}" (Xu Par. 0083).
mapping the entity tag and the entity attribute with the person ID to obtain the person portrait data of the target person. "In a graph database, a node contains a label, a node and a relationship attribute group, and an attribute group can store multiple attribute key-value pairs" (Xu Par. 0062). "Split the resume into character attribute groups and resume description sections. Using information extraction technology, name, gender, ethnicity, date of birth, ID number, place of birth, current residence, occupation, political status, marital status, profile picture and other information are extracted from the pre-processed resume. If this information exists, it is saved in the person node of the graph database in a unified format. If it does not exist, it is temporarily set to a null value" (Xu Par. 100-0101). "According to the previously defined node and relationship attribute group format, the extracted character data is saved in the character node in the form of attribute key-value pairs; each independent experience is established between the character node and the entity node corresponding to the experience in the form of a relationship, and the data describing the experience (code, time, type, etc.) is saved in the relationship in the form of attribute key-value pairs" (Xu Par. 0105).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume and creating graphs of Wang, Xu, and Aggarwal with constructing a person ID of a person in the graph, outputting an entity of each node in the graph as a tag, outputting a relationship between each node as an entity attribute corresponding to the tag, and mapping the tag and attribute with the person ID to obtain person portrait data of the person of Xu to provide social recommendations based on the character (Xu Par. 0127).
Claim(s) 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20170200125 A1), in view of Xu (CN 108509614 A), in view of Aggarwal (US 12347231 B1), in view of Liu (WO 2017167069 A1), in view of Falter (US 11934847 B2), and in further view of Lu (CN 114840602 A).
Regarding Claim 12, and Claim 19,
The combination of Wang, Xu, and Aggarwal disclose the device of claim 8, and computer-readable storage medium of claim 15 as shown above. The combination of Wang, Xu, and Aggarwal fail to disclose determining a target attribute from the entity attribute, dividing an entity tag corresponding to the target attribute and pre-set range, writing an attribute in each division range and tag corresponding to the attribute as parameters into a pre-constructed graphic visualization template to obtain a visualization script, and converting the script into a file in a pre-set format and performing asynchronous loading on the file to obtain a distribution graph. Alternatively, Liu discloses obtaining and processing recruitment data. Liu teaches, wherein the performing entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result comprises: determining a target attribute from the entity attribute, and performing entity division on an entity tag corresponding to the target attribute according to the target attribute and a pre-set division range; counting persons corresponding to an entity tag in each division range, and “In this alternative embodiment, it is assumed that the database maintenance position needs to be recruited in advance, and the number of people who have already applied for the position is six, and the resumes of the six persons are used for training: Personnel 1: A school, A major, B company, B position; Personnel 2: B school, A major, C company, B position; Personnel 3: A school, B major, A company, A position; Personnel 4: C school, B major, B company, A position; Personnel 5: A school, A major, C company, B position; Personnel 6: C school, B major, C company, A position. This makes it possible to obtain the characteristics of the data maintenance staff: School: A school has appeared 3 times in 6 data, accounting for 0.5; B school has appeared in 6 data, accounting for 0.17; C school has twice, accounting for 0.33; other schools Did not appear, the proportion is 0. Major: A major has appeared 3 times, accounting for 0.5; B professional has appeared 3 times, accounting for 0.5; other professions have not appeared, accounting for 0” (Liu Par. 0079-0090).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume and creating graphs of Wang, Xu, and Aggarwal with determining a target attribute from the entity attribute, and performing entity division on an entity tag corresponding to the target attribute according to the target attribute and a pre-set division range; counting persons corresponding to an entity tag in each division range of Liu to eliminate the tedious work of collecting the behavior data of applicants on each social platform (Liu Par. 0016).
The combination of Wang, Xu, Aggarwal and Liu disclose analyzing resume text and creating graphs based on the text. The combination of Wang, Xu, Aggarwal, and Liu fail to disclose writing a target attribute in each division range and a tag corresponding to the target attribute in the each division range as parameters into a pre-constructed template to obtain a classification visualization script and converting the classification visualization script into a visualization file in a pre-set format, and performing asynchronous loading on the visualization file to obtain a distribution visualization graph. Alternatively, Falter disclose creating visuals from data sets from a plurality of sources. Falter teaches,
writing a target attribute in each division range and an entity tag corresponding to the target attribute in the each division range as parameters into a pre-constructed graphic visualization template to obtain a classification visualization script; “Cards application 102 may communicate with external applications 104, 106, and 108 (e.g., analytical platforms) via an Application Programming Interface (API) 112 (referred to herein as “cards API 112”). The cards API 112 may be a flexible API. For example, a user may access an external application 108 to create a graphical representation of data (e.g., an artifact), such as a bar graph). To create the bar graph, the external application 108 may access data stored in a database 114. The database 114 may contain data such as graphical representations (e.g., tools for creating a bar graph) and data to be represented (e.g., in the bar graph). After completing the bar graph, the user may wish to output the graph to a platform to make it available to consumers, colleagues, etc. The user may select a link that instructs the external application 108 to perform an API call to the cards API 112. In one example, the external application 108 may access the API 112 via a plug-in or extension. The cards API 112 may receive data representative of the graph and may perform a variety of functions to make the received data suitable for use by the cards application 102 and/or add features to the received data for improved consumption. The cards application 102 may receive the data representative of the graph and may create card data representative of the modified bar graph and any additional features added by the cards API. The cards application 102 may store the card data in a database 116 and may display the card data as a card. As further illustrated in FIG. 1, the cards API 112 may communicate direct with the database 116 to, for example, service API calls via the cards API 112” (Falter Col. 5 Line 59-Col. 6 Line 20).
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It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, creating graphs, and determining ranges, attributes, and the amount of people within a range of Wang, Xu, Aggarwal and Liu with writing a target attribute in each division range and an entity tag corresponding to the target attribute in the each division range as parameters into a pre-constructed graphic visualization template to obtain a classification visualization script of Falter so the user can find data more quickly and reduce mental workload, improve decision-making, and reduce work stress. (Falter Col. 2 Lines 51-55).
The combination of Wang, Xu, Aggarwal, Liu, and Falter disclose visualizing information in a text resume, creating graphs, determining ranges, attributes, and the amount of people within range, and creating a graphic visualization template from the range of users with each attribute. The combination of Wang, Xu, Aggarwal, Liu, and Falter fail to disclose converting the classification visualization script into a visualization file in a pre-set format, and performing asynchronous loading on the visualization file to obtain a distribution visualization graph. Alternatively, Lu discloses data visualization. Lu teaches:
converting the classification visualization script into a visualization file in a pre-set format, and performing asynchronous loading on the visualization file to obtain a distribution visualization graph. “The graphic template filled with parameters is converted into a graphic script, and the graphic script is asynchronously loaded to obtain a visualization result” (Lu Par. 0140).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, creating graphs, and determining ranges, attributes, the amount of people within a range, and creating a visualization from the ranges and attributes of Wang, Xu, Aggarwal, Liu, and Falter with converting the classification visualization script into a visualization file in a pre-set format, and performing asynchronous loading on the visualization file to obtain a distribution visualization graph of Lu to improve the efficiency of the data extraction (Lu Par. 0144).
Claim(s) 13, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20170200125 A1), in view of Xu (CN 108509614 A), in view of Aggarwal (US 12347231 B1), in view of Ali (US 20160125360 A1), and in further view of Knight (What Is a Knowledge Graph).
Regarding Claim 13, and Claim 20,
The combination of Wang, Xu, and Aggarwal disclose the device of claim 8, and computer-readable storage medium of claim 15 as shown above. Wang further discloses, The electronic device of claim 8, wherein the constructing a person relationship visualization graph based on the entity relationship between different persons in the personal resume information set comprises:
recognizing a department and a post of different persons in the personal resume information set; "As shown above, the XML data contains two sections of biographical elements: basic biographical information and experience information table. The basic biographical information includes name, sex, nationality, place of birth, and other basic information. The experience information is formatted in a table structure: the table header contains the start time, end time, location, organization, position, and other fields. Each entry in the table records one of the person's experiences, namely, the person's experience (work or education) within a certain time" (Wang Par. 0047).
The combination of Wang, Xu, and Aggarwal disclose analyzing resume text and creating graphs based on the text. Wang fails to disclose using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set, aggregating target persons of the same department in the personal resume information set, taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph. Aggarwal, however, teaches:
using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set, "To extract a headshot from a source of one or more images, the techniques herein initially localize an individual within the images using a face detection algorithm. The face detection algorithm may include any suitable machine learning model trained using labeled faces either to identify particular identities and/or to identify a face within an image that may be identified at a later stage" (Aggarwal Col. 3 Lines 31-37). The headshot may be used for marketing, individual identification, hiring, casting, and other such purposes" (Aggarwal Col. 4 Lines 9-11). " the techniques described herein may be used to identify and extract headshots from any source image material, for example to produce employee catalogs, directories, or other such information in an efficient and consistent manner" (Aggarwal Col. 2 Lines 19-23).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, and creating graphs of Wang, and Xu with using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set of Aggarwal to improve efficiency, speed, and capability for headshot generation and identification for individuals. (Aggarwal Col. 3 Lines 2-3).
The combination of Wang, Xu, and Aggarwal disclose analyzing resume text, creating graphs based on the text, and detecting users from facial recognition. The combination of Wang, Xu, and Aggarwal fail to disclose aggregating target persons of the same department in the personal resume information set, taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph. Alternatively, Ali discloses matching and sorting candidates based on resume data. Ali teaches,
aggregating target persons of the same department in the personal resume information set, "More specifically, embodiments provide an automated way to match, compare and sort candidates based on skill set, job title, education, and/or other attributes described in a candidate profile with a requisition in a recruiting application. Embodiments of the present invention can build candidate pools for current or future openings without manual intervention" (Ali Par. 0019). "Screening and sorting the best candidates from a huge pile of resumes is a challenging and tedious job for a recruiter. Matching candidate's resume with the requirements of an open position is time consuming and frustrating. Hence, there is a need for improved methods and systems for identifying candidates for an open position" (Ali Par. 0002).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, creating graphs, and detecting users based on facial recognition of Wang, Xu, and Aggarwal with aggregating target persons of the same department in the personal resume information set of Ali since screening candidates from a large pool is challenging and tedious, and improves identifying candidates. (Ali Par. 0002).
The combination of Wang, Xu, Aggarwal, and Ali disclose analyzing resume text, creating graphs based on the text, detecting users from facial recognition, and aggregating users of the same department. The combination of Wang, Xu, Aggarwal, and Ali fail to disclose taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph. Alternatively, Knight discloses knowledge graphs being based on user experiences. Knight discloses,
taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph.
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It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, creating graphs, detecting users based on facial recognition, and aggregating users within the same department based on a set of resume information of Wang, Xu, Aggarwal, and Ali with obtaining a visual graph from taking the users as nodes, and contrasting relationships between different nodes, and connecting the graphs based on relationship graphs of Knight to track networks of people and the connections in a visual representation (Knight Page 2).
Regarding Claim 14,
The combination of Wang, Xu, Aggarwal, Ali, and Knight disclose the method of claim 13, as shown above. Aggarwal further discloses, wherein the using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set comprises: using a pre-constructed face recognition model to recognize face images of different persons in the personal resume information set; "to extract a headshot from a source of one or more images, the techniques herein initially localize an individual within the images using a face detection algorithm. The face detection algorithm may include any suitable machine learning model trained using labeled faces either to identify particular identities and/or to identify a face within an image that may be identified at a later stage" (Aggarwal Col. 3 Lines 31-37). The headshot may be used for marketing, individual identification, hiring, casting, and other such purposes" (Aggarwal Col. 4 Lines 9-11).
performing face alignment on the recognized face image and cutting same to obtain person images of different persons in the personal resume information set. "The headshot curation system and techniques described herein may extract the first headshot 106 and/or the second headshot 108 from the frame 100. The headshot is extracted from the frame by first identifying individuals within the frame 100 using a face recognition algorithm or other technique or system used for facial recognition and/or identification. The identified faces of the individuals are then surrounded by a bounding box used to determine a crop within the frame 100 that will result in a headshot for each individual. The bounding box for each headshot may be determined based on defined heuristics that determine dimensions and/or ratios for the bounding box as a function of the dimensions of the facial features or other features of the individual. For example, the bounding box may be defined based on a height of the face of the individual, such as first identifying shoulders and hair or top of the head of the individual and defining the height of the bounding box to crop tightly around the head and shoulders of the individual" (Aggarwal Col. 4 Lines 26-44). "In a third stage of the MTCNN, the outputs identify coordinates of the bounding boxes around the faces, coordinates for one or more facial landmarks (eyes, nose, mouth, chin, shoulders, hairline, etc.), and a confidence score for each of the bounding boxes. The final bounding boxes are tightly cropped around the faces of the individuals within the frame 100" (Aggarwal Col. 5 Lines 12-18).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the method of visualizing information in a text resume, and creating graphs of Wang, Xu, Aggarwal, Ali, and Knight with using a pre-constructed face recognition model to recognize person images of different persons in the personal resume information set, and performing face alignment on the recognized face image and cutting same to obtain person images of different persons in the personal resume information set of Aggarwal to improve efficiency, speed, and capability for headshot generation and identification for individuals (Aggarwal Col. 3 Lines 2-3).
Claim(s) 23 is rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 20170200125 A1), in view of Xu (CN 108509614 A), and in further view of Mahajan (US 20220309468 A1).
Regarding Claim 23,
The combination of Wang, and disclose the method of claim 1, as shown above. The combination of Wang and Xu fail to disclose a bar chart, pie chart, and line chart. Mahajan discloses a system to create user customized resumes with a visual resume generated from job roles, skills, and ratings. Mahajan further discloses, (New) The method for visualizing a personal resume of claim 1, wherein the entity distribution visualization graph comprises a bar chart, pie chart, line chart, and the like. " FIG. 2 is illustrating an exemplary graphical representation 20 of one or more skills, created by the artificial intelligence-based resume builder system 100, in accordance with an embodiment of the present disclosure….In another exemplary embodiments, the graphical representation of the one or more skills may include depiction of one or more final ratings of the skills in the form of any two-dimensional (2-D) graph, three-dimensional (3-D) graph, and animated graph chart such as a bar chart, a pie chart, a line chart, a histogram chart, an area chart, a dot graph, a scatter plot, a bubble chart and the like" (Mahajan Par. 0046).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the visualization of text resume of Wang and Xu with a bar chart, pie chart, and line chart of Mahajan to improves recruitment or hiring processes, by leveraging a graphical manner (Mahajan Par. 0031).
Subject Matter Distinguishable from Prior Art
Regarding dependent claim 5, Liu teaches, wherein the performing entity clustering on different persons in the personal resume information set based on the person portrait data, constructing an entity distribution visualization graph according to the clustering result comprises: determining a target attribute from the entity attribute, and performing entity division on an entity tag corresponding to the target attribute according to the target attribute and a pre-set division range; counting persons corresponding to an entity tag in each division range. Falter teaches, writing a target attribute in each division range and an entity tag corresponding to the target attribute in the each division range as parameters into a pre-constructed graphic visualization template to obtain a classification visualization script. Lu teaches converting the classification visualization script into a visualization file in a pre-set format, and performing asynchronous loading on the visualization file to obtain a distribution visualization graph. It would have been non-obvious to combine the claimed limitations, in combination with the rest of the claim limitations. Dependent claims 24 and 25 is also allowable over prior art by virtue of its dependency on claim 5.
Regarding dependent claim 22, Wang and Knight disclose a visualization of a graph with edges labeled. The prior art of record, taken alone or in combination fail to teach or fairly suggest the format of 28-age-person A
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, in combination with the rest of the claim limitations.
A Non-Patent Literature search was conducted and no relevant art was found.
Response to Arguments
Applicant's arguments filed 09/16/2025 with respect to 35 U.S.C. § 112, have been fully considered and are persuasive. The previous 35 U.S.C. § 112 Rejection is withdrawn in light of the amendments, however, the amendments necessitated a new ground of rejection.
Applicant's arguments filed 09/16/2025 with respect to 35 U.S.C. § 101, have been fully considered but they are not persuasive. Applicant argues that Claim 1 recites a technical solution and is significantly more by transforming unstructured resume text into a graph-based structure and relationships based on time sequences, mapping nodes to entity tags and mapping relationships to entity attributes to form structured person portrait datasets to transform raw resume data into a structured computable format, automating data to prevent manual aggregation, automatically generating structured organizational graph for integrating multiple relational datasets, and providing a structured approach to represent complex temporal and relational data. The Examiner respectfully disagrees. MPEP 2106.05(a)(1) states “limitations that the courts have found to qualify as ‘significantly more’ when recited in a claim with a judicial exception includes improvements to the functioning of a computer or any other technology or technical field. Here, there is no improvement to the processor (computer) nor is there an improvement to another technology. Because neither type of improvement is present in the claims, an improvement to technology is not present and the claims do not provide significantly more. The claims of the instant application merely disclose acquiring a personal resume information set, constructing an individual resume visualization graph, performing person portrait modeling on different persons in the personal resume information set, performing entity clustering on different persons in the personal resume information set and constructing an entity distribution visualization graph, constructing a person relationship visualization graph based on different persons, extracting resume text of a target person in the personal resume information set, using a pre-constructed entity relationship recognition model to recognize a basic information entity, basic information relationship, a resume experience entity, and a resume experience relationship corresponding to each target person in the resume text, constructing an individual resume visualization graph of a target person based on the basic information entity, basic information relationship, resume experience entity, and resume experience relationship, taking the target person as a root node, constructing an undirected edge between the root node and the basic information entity based on the basic information relationship, and using the basic information relationship to label the undirected edge, constructing a directed edge between the root node and the resume experience entity based on a time sequence of the resume experience relationship and using the resume experience relationship to label the directed edge, and aggregating all the nodes and labeled links to obtain an individual resume visualization graph. The claims of the instant application merely disclose extracting data from resumes and organizing the data in a graph form displaying relationships. Further, the electronic device with a processor and memory executing a program used is being used to apply the abstract idea and there is not an improvement to the electronic device with a processor and memory executing a program or any other technology. Thus, this argument is not persuasive.
Applicant argues that the claims constitutes a practical application by producing a visual graph to support tasks, clustering data to support data-driven talent assessment, generating a graph to provide an intuitive view of personnel data for supporting organizational planning and HR decision making, provides visual representation of a hierarchy to enable users to understand structures and relationships, and enables accurate and visually interpretable representation for automated resume visualization. The Examiner respectfully disagrees. The Examiner has disclosed above why the claimed invention does not improve the computer. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. See, e.g., DDR Holdings, LLC. v. Hotels.com, L.P., 773 F.3d 1245, 1259 (Fed. Cir. 2014) (finding that claiming a website that retained the “look and feel” of a host webpage provided a technological solution to the problem of retention of website visitors by utilizing a website descriptor that emulated the “look and feel” of the host webpage, where the problem arose out of the internet and was thus a technical problem). Here, the Applicant’s argued problem is not a technological problem caused by the electronic device with a processor and memory executing a program (the technological environment to which the claims are confined). The problems of extracting structured resume data, building individual visualization graphs, programming person portrait modeling, clustering entities, and generating visualization graphs were not problems caused by the electronic device with a processor and memory executing a program that is involved in the process. At best, Applicant’s identified problem is a business problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant further argues that Claim 1 is not directed to an abstract idea without significantly more because the claimed steps cover certain methods of organizing human activity and generic computer components since the graph-based resume visualization is significantly more, person portrait modeling transforms data into a computable format, entity clustering and distribution visualization are specific computational steps, person relationship visualization implements practical data organization, and hardware software implementation. The Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to generate a graph to visualize personal resume data. The Examiner respectfully disagrees that the Claims recite significantly more than the abstract idea. As disclosed previously in the Non-Final Office Action, and above, the additional elements were found to be mere instructions to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more”). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more"). The electronic device, processor, and memory are considered mere tools to implement the abstract idea. Simply applying and implementing a general-purpose computer or server does not extend beyond an abstract idea.
Applicant argues that Claim 1 recites an ordered sequence of technical operations that solve a concrete technical problem by transforming unstructured resume information into a structure, computable format, enabling automated analysis, clustering, and visualization of resume data. The Examiner respectfully disagrees. Even when considered as a whole, including all of the additional elements, whether analyzed individually or as a combination, the claim does not amount to significantly more than “apply it” or generally linking the abstract idea to a particular technological environment. Each of the visualization features listed in the amendment are still part of the abstract idea because they merely “manage personal behavior, interactions, or relationships between individuals” including social activities, teaching, and following rules or instructions.
Applicant argues that Claims 4-7 are directed to a method for visualizing a personal resume beyond mere human activity. The Examiner respectfully disagrees. MPEP 2106. 04(a)(2)(II) states that a claimed invention is directed to certain methods of organizing human activity if the identified claim elements contain limitations that encompass fundamental economic principles or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). The Examiner submits that the identified claim elements represent a series of rules or instructions that a person or persons, with or without the aid of a computer, would follow to construct a person ID, output entity tags and attributes, mapping attributes to obtain portrait data, entity clustering, write parameters into a pre-constructed visualization template, create visualization from a template, recognize department and positions, fill nodes with images, construct a graph with images and relationships, perform face recognition, alignment, and cropping to obtain an image. Because the claim elements fall under a series of rules or instructions that a person or persons would follow to create a visual representation of a resume, the claimed invention is directed to an abstract idea.
Applicant argues that claims 4-7 integrate steps into a practical application by producing a technical output (graphs, structured data, processed images) for real-world use. The Examiner respectfully disagrees. MPEP 2106.04(d)(1) and MPEP 2106.05(a) indicates that a practical application may be present where the claimed invention provides a technical solution to a technical problem. Here, the Applicant argues the claimed invention is producing visualization of resumes by outputting graphs and images, but the Applicant has not identified nor can the Examiner locate any physical improvement to the functioning of the generic computing component that results from the implementation of the Applicants claim. At best, Applicant’s identified problem is a business problem. Because no technological problem is present, the claims do not provide a practical application.
Applicant argues that Claims 4-7 provide significantly more than generic computer implementation by solving specific technical problems such as efficient processing of resume information, visualization of complex inter-person relationships, and integration of image data into graphs, which cannot be performed mentally or solely with pen-and-paper. The Examiner respectfully disagrees. As disclosed previously in the Non-Final Office Action, and above, the additional elements were found to be mere instructions to apply an exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept ("significantly more”). MPEP2106.05(I)(A) indicates that merely saying "apply it” or equivalent to the abstract idea cannot provide an inventive concept ("significantly more"). The electronic device, processor, and memory are considered mere tools to implement the abstract idea. Simply applying and implementing a general-purpose computer or server does not extend beyond an abstract idea.
Applicant argues Claims 4-7 provide significant practical application improving data visualization and analysis of complex resume and relational data. The Examiner respectfully disagrees. Even when considered as a whole, including all of the additional elements, whether analyzed individually or as a combination, do not amount to significantly more than “apply it” or generally linking the abstract idea to a particular technological environment. The process, structure, cluster, and visualization of information listed in the amendment are still part of the abstract idea because they merely “manage personal behavior, interactions, or relationships between individuals” including social activities, teaching, and following rules or instructions. Therefore, the examiner does not find any of the arguments surrounding the 101 eligibility persuasive, the 101 rejection stands.
Applicant's arguments filed 09/16/2025 with respect to 35 U.S.C. § 103, have been fully considered, and are persuasive in part. Applicant argues that Wang does not teach constructing nodes and edges corresponding basic information entities. Examiner agrees with the Applicant that Wang does not teach the nodes and edges, however, the combination of Wang and Xu teach the nodes and edges (Xu Par. 0105-0106, 0071-0086). Applicant further argues that Wang does not disclose performing person portrait modeling based on an individual resume visualization to obtain person portrait data. The Examiner respectfully disagrees. Wang Par. 0059 discloses using a model to obtain personal information in the resume, from different structured text blocks, and from BRI, structured text blocks are a type of graph. Applicant argues that Wang does not disclose constructing a person relationship visualization graph based on an entity relationship between different persons in the personal resume information set. The Examiner respectfully disagrees. Wang Par. 0110-0111 teaches a module measuring different relationships between social relationships from the resumes, while Wang Par. 0117 teaches the relationships being represented in Figures 6A and 6B on a graph. Xu, teaches clustering of different persons as a result (Xu Par. 0100-0101). One of ordinary skill in the art would be motivated to combine Wang with Xu to obtain information of every information group by saving a large amount of information about people (Xu Par. 0004, 0006).
Applicant argues that Wang does not teach extracting a resume text of a target person in the personal resume information set. The Examiner respectfully disagrees. Wang Par. 0010 discloses extracting text from a resume. It is apparent that this would be of a person. Applicant further argues that Wang does not teach using a pre-constructed entity relationship recognition model to recognize basic information entities, basic information, relationships, resume experience entities, and resume experience relationships corresponding to each target person. The Examiner respectfully disagrees. Wang Par. 0069 discloses using a pre-processing model that obtains data from a resume. Wang further teaches extracting names, biographical elements, experience information. Wang Par. 0071-0073 disclose the relationships between the extracted features. Applicant further argues that Wang does not teach constructing an individual resume visualization graph based on the recognized entities and relationships. The Examiner respectfully disagrees. Wang Par. 0132-0136 discloses a personal growth chart (graph) generated from the extracted information.
Applicant argues that Wang does not teach constructing an individual resume visualization graph based on an entity relationship of personal resume experience in Claim 3. However, as shown in the rejection above, Wang Par. 0130-0131 discloses using a module to generate personal growth charts, potential diagrams, and organizational charts. This is based on using information extracted from the resume (Wang Par. 0013-0014). Applicant further argues that the combination of Wang and Xu fail to teach taking the target person as a root node and constructing edges. The Examiner respectfully disagrees. As shown in the rejection above, and in Par. 0105-0106, Xu discloses has the character node as the center and being able to add information and relationships from the center. An edge is a relationship between the nodes. Applicant further argues the Xu fails to teach aggregating all the nodes and labeled links to obtain an individual resume visualization graph. The Examiner respectfully disagrees. Xu, Par. 0129-00136, teaches a plurality of nodes and relationships are combined and displayed in Figure 2. It would have been obvious to combine the creation of graphs with the target person being the root node, constructing an edge between the root node and information entity based on the relationship, and aggregate the nodes and links to obtain a graph to visually provide social recommendations based on the character.
Applicant argues that Xu does not disclose outputting each node as an entity tag, nor outputting relationships as entity attributes corresponding to the entity tag for the purpose of modeling a person portrait in Claim 4. The Examiner respectfully disagrees. Xu discloses relationships between the nodes (Xu Par. 0105), and Par. 0083 further shows how the entities are labeled to show descriptions regarding the node and tag. Par. 0066 shows an example of a person node which includes a person ID, a tag. Xu discloses a node, which includes the entity tag of a person ID, and a relationship attribute group in Par. 0062. Xu further teaches person portrait data being obtained in Par. 100-0101 where names, gender, ethnicity, date of birth… are extracted and obtained and mapped to the person.
Applicant argues that Claim 5 recites a novel and non-obvious technical solution over the cited prior art. The Examiner is persuaded that the combination is non-obvious. The 103 rejection is withdrawn from Claim 5.
Applicant argues that a pre-constructed face recognition model, filling person images into nodes, aggregating same-department persons as nodes, constructing organizational relationships between nodes, and connecting graphs across departments. The Examiner respectfully disagrees. Aggarwal in combination, teaches extracting a headshot from sources, in a field of producing employee catalogs and directories, using face detection algorithms and labeling of faces to identify identities (Col. 3 Lines 31-37). Ali in combination teaches aggregating target persons of the same department in the personal resume information set. Ali teaches sorting candidates of the same job title from recruiting applications. Knight teaches, taking different target persons as nodes, filling the person images into corresponding nodes, and constructing an organizational relationship between different nodes based on the post, to obtain an original relationship graph; connecting the original relationship graphs of different departments based on a pre-set organizational structure to obtain the person relationship visualization graph, as the claim recites. The image from Knight clearly shows, taking target persons and filling the nodes of their image, displaying a relationship between the different nodes from information, and the creation of a graph. Knight further teaches the connections between each node.
Applicant argues that Aggarwal does not disclose the recognition and cropping of faces specifically within a personal resume information set. The Examiner respectfully disagrees. The combination of Wang, Xu, Aggarwal, Ali, and Knight teach extracting resume information. Aggarwal discloses cropping and recognition of images in an employee related field from different sources.
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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/E.M.K./Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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