DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/2025 has been entered.
3. In the applicant’s submission, claims 1, 10, and 16 were amended, claims 7 and 15 were cancelled. Accordingly, claims 1, 3-6, 8-10, 12-14, and 16 are pending and being examined. Claims 1, 10, and 16 are independent form.
Claim Rejections - 35 USC § 103
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
5. 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 of this title, 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.
6. Claims 1, 3-5, 8-10, 12-13, and 16 are rejected under 35 U.S.C. 103 as being unpatentable by Weston et al (US 20200349249, hereinafter “Weston”).
Regarding claim 1, Weston discloses a system for recognizing an entity (the entity identification and authentication system; see the title and abstract), the system comprising: one or more processors operatively coupled with a memory, and wherein said memory stores instructions which when executed by the one or more processors cause the one or more processors (these hardware related features, such as processors, memory, and the like, can be found in fig.10) to:
receive one or more images from a user, wherein the one or more images comprise one or more user faces (see 112, 102, and 1061,...106N of fig.1; see para.56: “the identification module 112 can employ two or more independent identification technologies and/or platforms to identify a person based on input data 101 captured of/for the person. In various embodiments, the input data 101 can include image data 102 captured of the person, including facial image data (e.g., one or more images captured of the person's face).”), and wherein the user operates through one or more computing devices and is connected to the system via a network (wherein “a person of interest” may be “who regularly comes in and out of a building during different times of the day/week”, or “who work/live there, delivery driver, or “the friendly neighbor”; see para.160, lines 4-8; wherein the image data can be transmitted via “a communication network”; see 101[Wingdings font/0xE0]112 of fig.2, and para.77);
extract one or more features associated with the one or more user faces to generate a feature vector based on the extracted one or more features; see “facial feature” and “database” in para.70: “with respect to facial recognition technologies alone, these technologies generally compare facial features in a received image with a database of facial images/features for many previously identified people. In general, the person depicted in a newly receive image can only be identified if they have been previously identified in image data and added to the database.”), wherein the identifying the user comprises a user age, a user gender (see para.55: the system may include “general identifiers for a person” which “can include (but are not limited to), a demographic identifier (e.g., identifying the person by age, gender, ethnicity, nationality, geographic location, height, weight), an occupational identifier (e.g., job title, job role, etc.)”), a user count (see para.210: “the impression tracking component 1412 can track information regarding number of people who looked at the advertisement, identities of the people, and/or characteristics of the people (e.g., (counting how many people are in an area of the advertisement or who is looking at an advertisement along with demographics/characteristics of those people).”), a user and a frequency of visit associated with the user (see para.160: “the system can recognize information such as: 1. new person came in a 2 am, alone, 2. a person we recognize is with one or more other people we don't recognize and seems frightened, 3. a suspicious person wearing a mask, carrying a weapon, discussing a theft operation (assuming the system receives and processes a sample), etc.”);
and predict, via an artificial intelligence (AI) engine (see para.169: “The security evaluation component 1018 and/or the safety evaluation component 1020 can further determine whether the physical location is associated with a security/safety threat based on identification of unusual activity that deviates from the consistent activity patterns using machine learning and/or artificial intelligence (AI).”), one or more categorizations of the user based on the generated one or more recognition metadata (see para.168: “the security monitoring module 1014 can also include a person of interest component 1024 that identifies a person of interest included in the image data 102 based on one or more characteristics associated with the person (e.g., whether the person is identified as a person of interest in an existing database, whether the person is carrying a weapon, whether the person is located in or attempting to gain access to an authorized area, whether the person is wearing a disguise, whether the person is exhibiting suspicious behavior, etc.).”), wherein the one or more categorizations of the user comprise one of: a registered user, a repeat user, and a first time user (see para.160: “the system can recognize information such as: 1. new person came in a 2 am, alone, 2. a person we recognize is with one or more other people we don't recognize and seems frightened, 3. a suspicious person wearing a mask, carrying a weapon, discussing a theft operation (assuming the system receives and processes a sample), etc.”).
As explained and interpreted above, the mere difference between the claimed invention and the system of Weston is that, Weston does not explicitly disclose “generat[ing] recognition metadata” which “comprises at least a user age, a user gender, a user count, and a frequency of visit associated with the user” as recited in the claim. However, Weston clearly teaches using information including “a user age, a user gender, a user count, and a frequency of visit associated with the user” to identify a user /person “whether the person is identified as a person of interest in an existing database, whether the person is carrying a weapon, whether the person is located in or attempting to gain access to an authorized area, whether the person is wearing a disguise, whether the person is exhibiting suspicious behavior, etc.” It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify the teachings of Weston and generate recognition metadata comprising at least a user age, a user gender, a user count, and a frequency of visit associated with the user. Suggestion or motivation for doing so would have been to “identify and/or authenticate entities using a combination of independent identification technologies and/or platforms” as taught by Weston, see Abstract. Therefore, the claim is unpatentable over Weston.
Regarding claim 3, Weston discloses the system as claimed in claim 1, wherein the one or more processors are configured to perform spoof detection on the generated feature vector (see para.176: “The fraud [prevention] can include financial fraud, identity theft, employing a fake persona or characteristics, and the like.”).
Regarding claim 4, 12, Weston discloses the system as claimed in claim 1, wherein the one or more processors are configured to compare the generated one or more recognition metadata with one or more data stored in a database and enable the one or more categorizations of the user based on the comparison (see para.160: “the system can recognize information such as: 1. new person came in a 2 am, alone, 2. a person we recognize is with one or more other people we don't recognize and seems frightened, 3. a suspicious person wearing a mask, carrying a weapon, discussing a theft operation (assuming the system receives and processes a sample), etc.”).
Regarding claim 5, 13, Weston discloses the system as claimed in claim 1, wherein the one or more processors are configured to generate a blacklist for the user based on the identification of the user (see para.39: “the computer executable components can further comprise an authorization evaluation component that determines whether the identified person is authorized to access the physical location based on whether the person is listed as an authorized or unauthorized person in a predefined access control list”. See para.155: “entities not authorized to be located at or near a particular place and/or location can include individuals identified in defined access control lists”).
Regarding claim 8, Weston discloses the system as claimed in claim 1, wherein the one or more features comprise at least one of: a bounding box, a landmark, and an aligned face crop associated with the one or more user faces (see para.59: “Facial recognitions technologies generally work by comparing selected facial features from given image with faces within a database.”).
Regarding claim 9, Weston discloses the system as claimed in claim 1, wherein the one or more processors are configured to selectively process the received one or more images from the user to extract the one or more features (see para.59: “Facial recognitions technologies generally work by comparing selected facial features from given image with faces within a database.”).
Regarding claim 10, 16, each of which is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
7. Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Weston in view of Rhoads et al (US pug 2013/0273968, hereinafter “Rhoads”).
Regarding claim 6, 14, Weston discloses the claimed invention except for “generat[ing] an array of N dimensions based on the extracted one or more features”. However, this feature is obvious and widely used in the field of pattern recognition in images. As evidence, Rhoads teaches the object/subject recognition in images “by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors [each of which has a set of feature elements, see para.85 of Weston]”. See para.1420, lines 1-6. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Rhoads into the teachings of Weston and employ Euclidean distance of their feature vectors taught Rhoads to determine whether a person is a suspicious person recorded in the control lists taught by Weston. Suggestion or motivation for doing so would have been to “extract landmarks, or features, from an image of the subject's face” and “compare selected facial features from the image and a facial database”. as taught by Rhoads, cf., Par.1236, lines 1-5; and Par.1238, lines 1-3. Therefore, the claims are unpatentable over Weston in view of Rhoads.
Response to Arguments
8. Applicant’s arguments, filed on 11/25/2025, have been fully considered but they are not persuasive.
8-1. On page 7 of applicant’s response, applicant argues:
Weston describes "identification results" and certain demographic "characteristics" such as age or gender (see, e.g., paragraphs [0055], [0063]), but Weston does not describe the claimed recognition metadata construct comprising user age, user gender, user count, and frequency of visit in combination. While Weston notes that profiles may include demographic traits, it neither teaches determining or storing a user count nor deriving a visit frequency by repeatedly tracking the same user over time.
(The emphases added by the applicant.)
The examiner respectfully disagrees with the argument. As explained in the rejection of the claims, Weston, para.55, discloses “the system may include “general identifiers for a person” which “can include (but are not limited to), a demographic identifier (e.g., identifying the person by age, gender...”; para.210 disclose “the impression tracking component 1412 can track information regarding number of people who looked at the advertisement, identities of the people, and/or characteristics of the people (e.g., (counting how many people are in an area of the advertisement or who is looking at an advertisement along with demographics/characteristics of those people)”; and para.160 discloses “the system can recognize information such as: 1. new person came in a 2 am, alone, 2. a person we recognize is with one or more other people we don't recognize and seems frightened, 3. a suspicious person wearing a mask, carrying a weapon, discussing a theft operation (assuming the system receives and processes a sample), etc.” As such, Weston clearly discloses a subject recognition system including a demographic identifier which can identify the age and gender of a person, count the number of people, and recognize whether a person is a new comer (i.e., the frequence of visit). Therefore, the applicant's argument is not persuasive.
8-2. On page 9 of applicant’s response, applicant argues:
The Office cites paragraph [0160] of Weston which provides scenario examples such as the system recognizing, "1. new person came in a 2 am, alone..." This statement is purely an example of a security detection or a logging event; it is a description of an observation that has already occurred. This is fundamentally different from the claim's requirement to predict, via an artificial intelligence (AI) engine, one or more categorizations. A simple rule-based system can output "new person" if an ID is not found in the database; this requires no AI and no complex recognition metadata. Amended independent claim 1, by contrast, requires a specialized AI engine to perform anon-trivial prediction of the user's status, a prediction that must be based on the specific four-part metadata vector (Age, Gender, Count, and Frequency of Visit) established in amended independent claim 1.
(The emphases added by the applicant.)
The examiner respectfully disagrees with the argument. In fact, the system of Weston is a machine-learning based (i.e., an AI based, see 820 of fig.8) identification and authentication system. The system of Weston recognizes "1. new person came in a 2 am, alone...", which means that the system of Weston outputs a prediction for the person---who is a new comer to visit the spot and the number of visit time (i.e., the frequence) is 1; wherein the output result of “new person came in a 2 am, alone” is a prediction of the system of Weston. The applicant's argument therefore is not persuasive.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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