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 .
Claims Status
Claims 1-3, 6 and 8-10 have been amended.
Claims 4, 5 and 7 have been canceled.
Claims 1-3, 6 and 8-10 are currently pending and rejected.
Response to Arguments
35 USC 101 Rejection
Applicant's arguments, filed 1/25/2026, with respect to the previous rejection of claims 1-10 under 35 USC 101 have been fully considered and are persuasive, in view of the accompanying amendments.
Additionally, in view of MPEP 2106.07(b) which states that “a claim is eligible because the claim as a whole integrates the judicial exception into a practical application or amounts to significantly more than the judicial exception when the additional elements are considered both individually and in combination… [and] the additional element may be enough to integrate the judicial exception into a practical application or to qualify as "significantly more" if it meaningfully limits the judicial exception, e.g., it improves another technology or technical field, improves the functioning of a computer itself.” As is the situation in this case, additional elements have been reevaluated and are considered “significantly more” as they meaningfully limit the judicial exception and are therefore eligible.
Accordingly, the previous rejection under 35 USC 101 has been withdrawn.
35 USC 102 Rejection
Applicant’s arguments with respect to the rejection of claims under 35 USC 102 have been fully considered and are partially persuasive, in view of the accompanying amendments.
Applicant's amended claim 1 now requires “at least one Al machine assistant comprising (i) an input unit that comprises one or more sensors comprising an array of cameras or audio acquisition systems, a camera and a microphone to detect one or more customers entering the retail store, and (ii) an output unit that comprises a display or a speaker to provide outputs to the one or more customers;… determine, using a machine learning model, a plurality of attributes of the at least one customer by: extracting a facial feature, a body feature, and a voice accent of the at least one customer by analyzing the captured images or videos, and captured audios; detecting a gender of the at least one customer based on the facial feature of the at least one customer by detecting the face using the facial recognition system, and audio properties of voice accent of the at least one customer; estimating an age of the at least one customer by analysing the facial feature and the body feature of the at least one customer, and determining a walking speed of the at least one customer; and determining an ethnicity of the at least one customer by analyzing the facial feature, the body feature, and the voice accent,”. These limitations had not been previously been recited and change the scope of the invention.
Applicant’s amendments have therefore necessitated the new grounds for rejection, explained in further detail below.
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 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 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.
Claims 1-3, 6 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Chaudhuri (U.S. Pre-Grant Publication No. 2020/0279279 A1), in view of Crutchfield, JR. (U.S. Pre-Grant Publication No. 2021/0233157 A1) (“Crutchfield”).
Regarding claims 1 and 9, Chaudhuri teaches a retail assistance system (and related method) for initiating a conversation between an Al machine assistant and a customer while shopping in a store, wherein the retail assistance system comprises,:
at least one Al machine assistant comprising (i) an input unit that comprises one or more sensors comprising an array of cameras or audio acquisition systems, a camera and a microphone to detect one or more customers entering the retail store, and (ii) an output unit that comprises a display or a speaker to provide outputs to the one or more customers (Figs. 3, 5; para [0204], person interacts with an electronic kiosk providing electronic kiosk data, wherein at least one data input device collects and transmits video data, audio data, mobile electronic device identification data, and spatial position data of the person interacting with the electronic kiosk; para [0075], data input device (103) may also be a distributed device, where components are distributed and may be located in separate physical enclosures in a space or as affixed to an object. A most basic construction may be a simple digital camera with one video input, one audio input, a range finder; para [0079], AI-enabled semiconductor chips; para [0078], natural language output);
a memory that comprises one or more instructions (Fig. 1; para [0074]); and
a processor that executes the one or more instructions (Fig. 1; para [0062], behavior learning processor), wherein the processor is configured to:
determine, using a machine learning model, a plurality of attributes of the at least one customer by (Fig. 7; para [0084], modules may also require at least one machine learning system to provide the emotion and/or identity data):
extracting a facial feature, a body feature, and a voice accent of the at least one customer by analyzing the captured images or videos, and captured audios (para [0081], facial recognition algorithm takes its input from a video or image containing human face, and executes three separate algorithms in sequence: facial landmark detection; para [0087], Linguistic attributes);
detecting a gender of the at least one customer based on the facial feature of the at least one customer by detecting the face using the facial recognition system, and audio properties of voice accent of the at least one customer (Fig. 6; para [0082], demographic analysis module (203) most commonly transmits age (505), race (506), and gender (507), which is depicted as separate streams but is often combined into a single data stream, demographic analysis data);
estimating an age of the at least one customer by analysing the facial feature and the body feature of the at least one customer, and determining a walking speed of the at least one customer (Fig. 6; para [0082], demographic analysis module (203) most commonly transmits age (505), race (506), and gender (507), which is depicted as separate streams but is often combined into a single data stream, demographic analysis data); and
determining an ethnicity of the at least one customer by analyzing the facial feature, the body feature, and the voice accent (Fig. 6; para [0082], demographic analysis module (203) most commonly transmits age (505), race (506), and gender (507), which is depicted as separate streams but is often combined into a single data stream, demographic analysis data; para [0087], Linguistic attributes),
detect, using the machine learning model, an emotional state of the at least one customer which can be selected from: disgusted, sad, happy, excited, surprised, neutral or angry, by analyzing facial expression and voice sentiment of at least one customer, and performing textual conversation sentiment analysis, with a combination of the plurality of attributes of the at least one customer (para [0079], technology that uses facial landmarks to detect emotions expressed in human faces through computer algorithms. The algorithms can detect the six basic or universal human expressions: happiness, sadness, anger, surprise, fear, and disgust; para [0078], natural language processing module (204) most commonly provides sentiment data); and
enable the at least one Al machine assistant to:
initiate, using the output unit, an interaction with the at least one customer by analyzing the plurality of attributes, and the emotional state of the at least one customer (para [0213], shoppers may not initiate a query through any explicit action or gesture, but their previously granted permission and/or applicable store policies allow the detection of their presence to be interpreted as an act of query for which spontaneous unprompted delivery of the promoted products list is permissible);
receive, using the input unit, a response from the at least one customer (para [0212], if and when a shopper initiates a query about promoted products, and a related process (4060) delivers the targeted promotion list in response to the query); and
determine, using the machine learning model, one or more personalized recommendations for at least one customer while shopping in the store based on the determined plurality of attributes selected from any of: the age, the gender or the ethnicity, and the determined emotional state of the at least one customer, using the output unit of the at least one AI machine assistant by processing the response of the at least one customer to convert a speech into a text, and performing intent and entity detection using an artificially intelligent Natural Language Processing (NLP) module (Fig. 31; para [0208], targeted promotion system (4000), as shown in FIG. 31, which may be a software processing framework found in some embodiments of the Analytics Module (1101) as part of its predictive analytics function. The software makes real-time promotions for select products based on retail customers' individual characteristics (e.g. past product purchase, frequency of visit, amount spent per visit, past product liking/disliking as observed through positive/negative facial expression, longer/shorter gaze time or positive/negative verbal utterances interpreted through natural language processing module), as well as their group characteristics (e.g., similar age/race/gender, similar product purchase history, similar product liking history, similar spend per visit, similar frequency of visit etc.); para [0212], prediction results presentation process (4040) can present the promoted products list to those shoppers who carry mobile devices with a store branded mobile app capable of receiving Push alerts sent by the Analytics Module).
Although Chaudhuri teaches a facial recognition system and recommendation system (para [0212], stream processing engine (1102) inquires the identity of the shoppers through the facial recognition module (244) and/or the electronic device identification module (106). If the identity is unknown, the stream processing engine (1102) requests the analytics engine (1101) to predict a list of items based only on the common properties of the items that are similar to the item the shopper is currently viewing, or to predict a list of most popular items that the shopper has a high probability of buying), Chaudhuri does not explicitly teach detect, using the input unit, at least one customer entering the retail store; and determine, using a face recognition system, whether the at least one customer is a new visitor or an old visitor by detecting a face of the at least one customer, and verifying the face of the at least one customer by analyzing and comparing one or more faces of the customer stored in the memory.
In a similar field of endeavor, Crutchfield teaches
detect, using the input unit, at least one customer entering the retail store (para [0092], upon entering the store, the vendor or the mobile device may automatically detect the customer's identification information; para [0095], If there is no personal, demographic, or psychographic information about the customer stored in the vendor's database, such as when the customer does not have an account with the vendor, the vendor may collect information in real-time);
determine, using a face recognition system, whether the at least one customer is a new visitor or an old visitor by detecting a face of the at least one customer, and verifying the face of the at least one customer by analyzing and comparing one or more faces of the customer stored in the memory (para [0092], upon entering the store, the vendor or the mobile device may automatically detect the customer's identification information; para [0095], If there is no personal, demographic, or psychographic information about the customer stored in the vendor's database, such as when the customer does not have an account with the vendor, the vendor may collect information in real-time; para [0096], sensors may include facial recognition programs);
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the noted limitations as taught by Crutchfield in the system of Chaudhuri, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Namely, an improved system for a customized retail experience that bridges the gap between online retail stores and physical stores (See Crutchfield: para [0013]).
Regarding claims 2 and 10, Chaudhuri and Crutchfield teach the above system and method of claims 1 and 9. Chaudhuri also teaches wherein the processor is configured to:
determine, using the machine learning model, a personality profile of the at least one customer by analyzing the facial feature and the plurality of attributes of the at least one customer (para [0062], Profile building system (101) represents a group comprising at least one behavioral response analysis system (130), at least one behavior learning system (102), at least one secondary data repository (1104), and at least one administration and visualization tool; para [0086], emotion and identity detection system may encompass multiple machine learning systems); and
initiate, using the output unit, the one or more personalized recommendations for at least one customer by tracking interactions and analyzing the personality profile of the at least one customer in real-time (Fig. 31; para [0208], targeted promotion system (4000), as shown in FIG. 31, which may be a software processing framework found in some embodiments of the Analytics Module (1101) as part of its predictive analytics function. The software makes real-time promotions for select products based on retail customers' individual characteristics (e.g. past product purchase, frequency of visit, amount spent per visit, past product liking/disliking as observed through positive/negative facial expression, longer/shorter gaze time or positive/negative verbal utterances interpreted through natural language processing module), as well as their group characteristics (e.g., similar age/race/gender, similar product purchase history, similar product liking history, similar spend per visit, similar frequency of visit etc.); para [0212], prediction results presentation process (4040) can present the promoted products list to those shoppers who carry mobile devices with a store branded mobile app capable of receiving Push alerts sent by the Analytics Module).
Regarding claim 3, Chaudhuri and Crutchfield teach the above system of claim 1. Chaudhuri also teaches wherein the retail assistance system comprises a knowledge database that stores the one or more attributes of the at least one customer if at least one customer is a new visitor, a past purchase history of at least one customer, and a visit history of at least one customer (Fig. 31; para [0208], targeted promotion system (4000), as shown in FIG. 31, which may be a software processing framework found in some embodiments of the Analytics Module (1101) as part of its predictive analytics function. The software makes real-time promotions for select products based on retail customers' individual characteristics (e.g. past product purchase, frequency of visit, amount spent per visit, past product liking/disliking as observed through positive/negative facial expression, longer/shorter gaze time or positive/negative verbal utterances interpreted through natural language processing module), as well as their group characteristics (e.g., similar age/race/gender, similar product purchase history, similar product liking history, similar spend per visit, similar frequency of visit etc.)).
Regarding claim 6, Chaudhuri and Crutchfield teach the above system of claim 1. Chaudhuri also teaches wherein the retail assistance system comprises a tracking system that track the at least one customer throughout the retail store to provide the one or more recommendations to at least one customer (para [0101], spatial position module (107) may serve to gather the absolute location of the data input device, and/or data input device location relative to the location in which the data input devices are placed, and/or data input device location relative to the surrounding items, and/or spatial measurements related to the person within range of the range finder).
Regarding claim 8, Chaudhuri and Crutchfield teach the above system of claim 1. Crutchfield also teaches wherein the processor is configured to enable the at least one AI machine assistant to interact at least one of a welcome message or a goodbye message by determining whether at least one customer is entering or exiting the retail store (para [0257], Passport App 1900 may further comprise or use a Location Services (1908) function or module which uses the device 1901's current physical location (e.g., determined via GPS, cellular triangulation, Wi-Fi positioning, or other methods) to serve a number of purposes such as shopper detection or authentication, in-store tracking/navigation, and message dissemination etc.).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANAND LOHARIKAR whose telephone number is 571-272-8756. The examiner can normally be reached Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Marissa Thein can be reached at 571-272-6764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANAND LOHARIKAR/Primary Examiner, Art Unit 3689
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