DETAILED ACTION
Status of the Application
This Final Office Action is in response to Application Serial 17/868,914. In response to Examiner’s action mail dated, March 20, 2025, Applicant on mail date June 20, 2025, submitted arguments traversing the 35 U.S.C. 101 and 35 U.S.C. 103 rejections. Applicant amended claim(s) 1, 8, and 9. Claim 2, 3, 10, 11, 15 and 16 remain cancelled. Claims 1, 4-9, 12-14, 18 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP 2021-126300, filed on July 30, 2021.
Information Disclosure Statement
Applicant did not submit an information disclosure for consideration.
Response to Amendments
Claims 1, 4-9, 12-14, 18 are pending in this application. Applicant did not submit amendments.
Regarding the 35 U.S.C. 101 rejection. The claims are examined under 35 U.S.C. 101 in light of 2019 Revised PEG Guidance. The claims 1, 4-9, 12-14, 18 are rejected under 35 U.S.C. 101, see below.
Regarding the pending 35 U.S.C. 103 rejection. The Applicant’s arguments and new claims were examined in light of 35 U.S.C. 103. The claims 1, 4-9, 12-14, 18 are rejected under 35 U.S.C. 103, see below.
Applicant is cautioned of limitations that may invoke 112 (e.g., generating a determination index for determining a user interested in a product).
Response to Arguments
Applicant’s arguments filed on June 20, 2025, have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below.
Claim Rejection Under 35 U.S.C. 101
On pages 11-13 of the Applicant’s 35 U.S.C. 101 arguments, the Applicant traverses, the claims 1 (and similarly claim 8 and 9) are amended.
Applicant submits the claims are not “directed to” a judicial exception because they are integrated into a practical application that improves the functionality of computer based monitoring systems. The Applicant discusses to the specification discloses”… a specific technical approach … the newly added feedback loop where an “actual customer service effect” is recorded and used to update the system’s determination index via reclustering – further illustrates this technical improvement”. Applicant points to Enfish and McRo.
Examiner respectfully disagrees. The Applicant’s claims remain rejected under 35 U.S.C. 101. The claims recite an abstract concept of determining a user interested in a product. The claims recite mathematical concepts – determining index and mental concepts – observation – evaluating clusters and behavior, and thus, are directed a judicial exception at step 2A prong one.
Regarding Enfish and McRo the Applicant’s arguments support improving data. Here, the data is mathematical and evaluative without the integration of technology/additional elements, and thus, the claims do not recite improvement to technical elements nor elements rooted in technical elements. The Applicant claims are applying a computer to conduct the abstract idea. See MPEP 2106.05 (f). The Applicant’s arguments are not persuasive. The claims are not integrated into a practical application at Step 2A prong 2.
On page 14, submits the specific ordered combination of obtaining skeletal joint information, generating feature quantities from those joints, clustering features, identifying cluster based on a purchase correlated index… then feeing back recorded actual customer service effect: to update the entire system is unconventional, multi-step process. This specific process, particularly the novel feedback mechanism, is what enables the technical benefit of a self-improving, product-agnostic system, which is a significant improvement over conventional computer monitoring. Therefore, it is requested that this rejection be reconsidered and withdrawn.
Examiner respectfully disagrees. In a previous response, Applicant amended the claims to include clustering and calculating an average value of customer service and captured by cameras. Applicant specification [003] acknowledges there are known technologies that determine whether a customer is picking up a product. Examiner points the Applicant to the operational processing [029], [038], [048]-[056],[061]- [063] and Example 47. Applicant’s claims broadly recite computer elements used to conduct the abstract concept see specification[036].
The Applicant should explicitly claim the type of mechanical learning model, and the correlations steps within the model that is/are supported in the specification. Applicant is pointed to Example 47, for consideration.
Applicant is encouraged to request an interview. The Applicant’s arguments are not persuasive. The claims 1,4, 5-9,12-14, 18 remain rejected under 35 U.S.C. 101, see below.
Claim Rejection Under 35 U.S.C. 103
On pages 14-16 of the Applicant’s 35 U.S.C. 103 arguments, the Applicant traverses, Okamura fails to disclose or suggest obtaining skeletal information which includes a location of joint in each users. Ali does not disclose clustering users based on feature quantities derived from skeletal data. There is not apparent motivation to combine the disparate technologies of Okamura (video-based gaze tracking) and Ali uses Deep Neural Networks (DNNs) for imaging, Ali does not discloses clustering users based on feature quantities derived from skeletal data. There is no apparent motivation to combine the disparate technologies of Okamura and Ali to arrive at the claimed invention. The combination fails to suggest… all of the limitations recited in the independent claims. The remaining claims depend from respective ones of the independent claims and Oshima does not disclose or suggest any of the teachings noted above. Therefore, it is requested that these rejection be reconsidered and withdrawn.
Examiner respectfully disagrees with the Applicant’s 35 U.S.C. 103 arguments. The amendments to the claims necessitate grounds for a new amendment. See the prior art rejection below.
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, 4-7 are manufacture.
Claim 8, 17-18 are process.
Claim 9, 12-14 are machine.
Claims 1, 4, 5-9, 12-14, 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1 (and similarly claim 8 and claim 9) recite, “… obtaining skeletal information which includes a location of joint in each of users who acted on a product based on image data captured …; first generating a feature quantity that characterizes an action of each of the users on a basis of the location of joint included in the skeletal information of each of the users referring to … that stores data on a correlation between actions and skeletal information; second generating a determination index for determining a user interested in a product, by using the feature quantity of each of the users; and detecting a customer service target from visiting users, by using the determination index, wherein the first generating includes adding, to the feature quantity of each of the users, a customer service index number indicating a degree of interest of each of the users in a product, the second generating includes performing clustering for each of the users, by using the feature quantity, calculating an average value of customer service index numbers of respective users who belong to each of clusters generated by the clustering, and generating, as the determination index, a specific cluster in which the average value of customer service index numbers is greater than or equal to a threshold, and the detecting includes obtaining skeletal information of a visiting user, generating a feature quantity of the visiting user, on a basis of the skeletal information of the visiting user, and detecting the visiting user as a customer service target, when the feature quantity of the visiting user belongs to the specific cluster, wherein the process further comprises: recording an actual customer service effect after the detecting of the visiting user; updating, based on feedback from the recorded actual customer service effect, the customer service index number associated with the feature quantity of the detected visiting user; and updating the determination index by re-performing the clustering and the calculating using the updated customer service index number …”. Claims 1, 4, 5-9, 12-14, 18 in view of the claims limitations, are related to the abstract idea of, … characterizing an action of each of the users then determining a visiting user interest in a product292766-00003, which is certain methods of organizing human behavior – commercial activities .
Furthermore, the claims recite generating a determination index for “determining a user interested in a product” and “stores data on a correlation between actions”, and thus, the claims recite mental concepts – evaluation and observation.
At Step 2A, the claims recite certain methods of organizing human activity and managing personal behavior, and thus, the claims are directed to abstract ideas at Step 2A at prong one.
This judicial exception are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “A non-transitory computer-readable recording medium having stored therein a customer service detection program that causes a computer to execute a process comprising:”, “captured by cameras”, “a database”, in claim 1, “A customer service detection method executed by a computer, the method comprising”, “a database”, “a processor”; in claim 8; “An information processing device comprising: a memory; and a processor coupled to the memory and configured to”, “by cameras”, “a database”, “the configured processor”, “a mechanical learning model by mechanical learning” in claim 9; however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05 (f)
The additional elements that are recited in the dependent claims that are not recited in the independent claims are:
Claim(s) 4, 5, 12, 13, 17, 18: a mechanical learning model by mechanical learning
Applicant is encouraged to clarify additional element: a mechanical learning model by mechanical learning and the integration of mechanical elements with the abstract concepts beyond “apply it” – MPEP 2106.05(f). Examiner points the Applicant to Subject Matter Eligibility Guidance example 47.
Accordingly, the 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. The claims also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements when considered both individually and as an ordered combination do not amount to significantly more. (See MPEP 2106.05 f – mere instructions to Apply an Exception).
At step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Examiner concludes that the additional elements in combination fail to amount to significantly more than the abstract idea based on findings that each element merely performs the same function (s) in combination as each element performs separately. The claim is not patent eligible. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified exception (the abstract idea). Looking at the limitation as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Dependent claims 4, 5-7 further narrow the abstract idea of independent claim 1. Dependent claims 17-18 further narrow the abstract idea of independent claim 8. Dependent claims 12-14 further narrow independent claim 9. The claims 1,4, 5-9,12-14, 18 are not patent eligible.
Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 1,4, 5-9,12-14, 18 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 4, 5-9, 12-14, 18 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 5, 7, 8, 9, 12, 13, 17, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Okamura (JP 2021047810 A) in view of in view of Ali (2020, Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging) and Migniot (2013, 3D Human tracking from Depth Cue in a Buying Behavior Analysis Context).
Regarding Claim 1, (Currently Amended)
Okamura discloses:
A non-transitory computer-readable recording medium having stored therein a customer service detection program that causes a computer to execute a process comprising: obtaining skeletal information which includes a location of joint in each of users who acted on a product based on image data captured by cameras;
Okamura teaches cameras tracking a customer, collecting purchase behavior data including line-of-sight of a customer, the movement of a customer’s hand, and detecting the customer’s eyes, nose, mouth and eyebrows., Okamura (p.2 paragraph 1, 2, 5, 7, 9, 12-13), (p.5 paragraph 6)
first generating a feature quantity that characterizes an action of each of the users on a basis of the location of joint included in the skeletal information of each of the users referring to a database that stores data on a correlation between actions and skeletal information;
Okamura teaches taking a face image of a customer that has visited the store, purchasing behavior data collection (server 40), and the product visual recognition information is generated for each customer who makes a purchase at the store 10.).; Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6)
Okamura discloses line of sight, the movement of a customer’s hand, and detecting the customer’s eyes, nose, mouth and eyebrows, and thus, the Okamura discloses a feature quantity that characterizes an action., Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6)
Okamura discloses a main processing procedure executed ... according to the purchase behavior data collection program … of server 40 … described in Fig 7., … for example, a processing procedure for one customer who has visited the store, and execute the same processing for a plurality of customers in parallel., customer., Okamura (p.4 paragraph 15),
second generating a determination index for determining a user interested in a product, by using the feature quantity of each of the users;
Okamura discloses a main processing procedure executed ... according to the purchase behavior data collection program (a determination index for determining a user interested in a product), … of server 40 … described in Fig 7., … for example, a processing procedure for one customer who has visited the store, and execute the same processing for a plurality of customers in parallel., customer. -Okamura (p.4 paragraph 15).
Okamura discloses degree of interest, Okamura (p.5 paragraph 15-16).
and detecting a customer service target from visiting users, by using the determination index, wherein the first generating includes adding, to the feature quantity of each of the users, a customer service index number indicating a degree of interest of each of the users in a product, the second generating includes performing clustering for each of the users, by using the feature quantity,
Okamura discloses when the customer is identified, it is determined the purchase behavior, and the same processing for a plurality of customers in parallel (a customer service target). - Okamura (p.4 paragraph 15)
Okamura discloses the processor 41 counts the number of times of visual recognition (a customer service index number) when the same product visual recognition information becomes a visual target again after a certain period of time has elapsed after visually recognizing any of the visual inspection information …, Okamura (p.5 paragraph 9 and 15)
calculating an average value of customer service index numbers of respective users who…,
Okamura (p.5 paragraph 9 and 15) discloses a count.
and the detecting includes obtaining skeletal information of a visiting user, generating a feature quantity of the visiting user, on a basis of the skeletal information of the visiting user, and detecting the visiting user as a customer service target, when the feature quantity of the visiting user ….
Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6) and Okamura (p.4 paragraph 15) and Okamura (p.5 paragraph 9 and 15)
Although highly suggested, Okamura does not explicitly teach:
… belong to each of clusters generated by the clustering … and generating, as the determination index, a specific cluster in which the average value of customer service index numbers is greater than or equal to a threshold, … when the feature quantity of the visiting user belongs to the specific cluster
Ali discloses the clustering
…. belong to each of clusters generated by the clustering … and generating, as the determination index, a specific cluster in which the average value of customer service index numbers is greater than or equal to a threshold, … when the feature quantity of the visiting user belongs to the specific cluster
Ali teach monitoring browsing behaviors using Deep Neural Networks to formulate imaging human obstruction and applying RSS vectors and filters, Ali [p.1038 section 4-1 and 4-2] and Ali teaches normalizing DNN based image vectors.
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors , as taught by Alito determine strategic placement of retail items., Ali[p.1034 column 1 paragraph 1]
Migniot teaches:
when the feature quantity of the visiting user belongs to the specific cluster, wherein the process further comprises: recording an actual customer service effect after the detecting of the visiting user; updating, based on feedback from the recorded actual customer service effect, the customer service index number associated with the feature Quantity of the detected visiting user; and updating the determination index by re-performing the clustering and the calculating using the updated customer service index number.
Migniot [p. 482] discloses develop real-time and non intrusive tools designed to analyze the shoppers buying act decisions. The approach is, in the first time, based on extracting and following the shoppers’ gaze and gesture positions with computer vision algorithmic. It is then based on statistically analyzing the extracted data: the goal of this cognitive analysis is to measure the interaction between the shopper and their environment.
Migniot [0483] discloses use [of] the Xtion Pro-live camera produced by Asus for acquisition equipment to consider buying act conditions and optimize tracking of buying behavior. Mignoit models the upper part of the body. Thus, the threshold of the image … takes into consideration the pixels recognized as an element of the torso, the arms or the head., Migniot [p.483]
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Okamura teaches acquiring through video a customer acquiring products. Migniot discloses a method/tool to analyze the shoppers buying act decisions. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with statistically analyzing the extracted data such as tracking arms and torso movement using video surveillance, as taught by Migniot, to refine the behavior analysis of customer behavior., Magniot [p.488].
Regarding Claim 2, (Cancelled)
Regarding Claim 3, (Cancelled)
Regarding Claim 4, (Previously Presented)
The non-transitory computer-readable recording medium according to claim 1,wherein the second generating includes generating, as the determination index, a mechanical learning model by mechanical learning that uses, as input data, feature quantities of respective users who belong to the specific cluster, and uses, as correct answer information, customer service index numbers of these users, and the detecting includes obtaining skeletal information of each of visiting users, generating a feature quantity of each of the visiting users, on a basis of the skeletal information of each of the visiting users, and inputting the feature quantity of each of the visiting users into the mechanical learning model, and detecting a customer service target from the visiting users, on a basis of an output result of the mechanical learning model.
See claim 1 - Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6), (p.4 paragraph 15), (p.5 paragraph 9 and 15) and Ali [p.1038 section 4-1 and 4-2]
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors, as taught by Alito determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Regarding Claim 5, (Original)
The non-transitory computer-readable recording medium according to claim 1, wherein the first generating includes adding, to the feature quantity of each of the users, a customer service index number indicating a degree of interest of each of the users in a product, the generating a determination index includes generating, as the determination index, a mechanical learning model by mechanical learning that uses, as input data, the feature quantity of each of the users, and uses, as correct answer information, the customer service index number of each of the users, and the detecting includes obtaining skeletal information of each of visiting users, generating a feature quantity of each of the visiting users, on a basis of the skeletal information of each of the visiting users, and inputting the feature quantity of each of the visiting users into the mechanical learning model, and detecting a customer service target from the visiting users, on a basis of an output result of the mechanical learning model.
Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6) - a feature quantity that characterizes an action.;
Okamura teaches determination index, Okamura (p.4 paragraph 15);
Okamura teaches a count – customer service index, Okamura (p.5 paragraph 9, 12, and 15).
Ali teaches monitoring browsing behaviors using Deep Neural Networks and thus, Ali teaches mechanical modeling - Ali [p.1038 section 4-1 and 4-2]
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors, as taught by Ali to determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Regarding Claim 7, (Previously Presented)
The non-transitory computer-readable recording medium according to claim 1, wherein the customer service index number is information indicating whether a user purchased a product, or information indicating whether a user's visit time is within a time zone with many product purchasers.
Okamura teaches determined which of the products in which the information is recorded is actually purchased., Okamura (p.5 paragraph 18).
Regarding Claim 8, (Currently Amended)
A customer service detection method executed by a computer, the method comprising: obtaining skeletal information which includes a location of joint in each of users who acted on a product based on image data captured by cameras; first generating a feature quantity that characterizes an action of each of the users on a basis of the location of joint included in the skeletal information of each of the users referring to a database that stores data on a correlation between actions and skeletal information; second generating a determination index for determining a user interested in a product, by using the feature quantity of each of the users; and detecting a customer service target from visiting users, by using the determination index, using a processor, wherein the first generating includes adding, to the feature quantity of each of the users, a customer service index number indicating a degree of interest of each of the users in a product, the second generating includes performing clustering for each of the users, by using the feature quantity, calculating an average value of customer service index numbers of respective users who belong to each of clusters generated by the clustering, and generating, as the determination index, a specific cluster in which the average value of customer service index numbers is greater than or equal to a threshold, and the detecting includes obtaining skeletal information of a visiting user, generating a feature quantity of the visiting user, on a basis of the skeletal information of the visiting user, and detecting the visiting user as a customer service target, when the feature quantity of the visiting user belongs to the specific cluster, wherein the method further comprises: recording an actual customer service effect after the detecting of the visiting user; updating, based on feedback from the recorded actual customer service effect, the customer service index number associated with the feature Quantity of the detected visiting user; and updating the determination index by re-performing the clustering and the calculating using the updated customer service index number.
Claim 8 is broader than claim 1, see Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6) - a feature quantity that characterizes an action.;
Okamura teaches determination index, Okamura (p.4 paragraph 15);
Okamura teaches a count – customer service index, Okamura (p.5 paragraph 9 and 15) and Ali [p.1038 section 4-1 and 4-2] and Migniot [p.482], [p.483], [Figure 1] disclose tracking buyer skeletal data.
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors , as taught by Alito determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Okamura teaches acquiring through video a customer acquiring products. Migniot discloses a method/tool to analyze the shoppers buying act decisions. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with statistically analyzing the extracted data such as tracking arms and torso movement using video surveillance, as taught by Migniot, to refine the behavior analysis of customer behavior., Migniot [p.488].
Regarding Claim 9, (Currently Amended)
An information processing device comprising: a memory; and a processor coupled to the memory and configured to: obtain skeletal information which includes a location of joint in each of users who acted on a product based on image data captured by cameras; generate a feature quantity that characterizes an action of each of the users on a basis of the location of joint included in the skeletal information of each of the users referring to a database that stores data on a correlation between actions and skeletal information; generate a determination index for determining a user interested in a product, by using the feature quantity of each of the users; and detect a customer service target from visiting users, by using the determination index, wherein the processor is configured to: add, to the feature quantity of each of the users, a customer service index number indicating a degree of interest of each of the users in a product, and perform clustering for each of the users, by using the feature quantity, calculate an average value of customer service index numbers of respective users who belong to each of clusters generated by the clustering, generate, as the determination index, a specific cluster in which the average value of customer service index numbers is greater than or equal to a threshold generate, as the determination index, a mechanical learning model by mechanical learning that uses, as input data, feature quantities of respective users who belong to the specific cluster, and uses, as correct answer information, customer service index numbers of these users, obtain skeletal information of each of visiting users, generate a feature quantity of each of the visiting users, on a basis of the skeletal information of each of the visiting users, and input the feature quantity of each of the visiting users into the mechanical learning model, and detecting a customer service target from the visiting users, on a basis of an output result of the mechanical learning model, wherein the processor is further configured to: record an actual customer service effect after the detecting of the visiting user; update, based on feedback from the recorded actual customer service effect, the customer service index number associated with the feature Quantity of the detected visiting user; and update the determination index by re-performing the clustering and the calculating using the updated customer service index number.
See claim 1 - Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6), (p.4 paragraph 15), (p.5 paragraph 9 and 15) and Ali [p.1038 section 4-1 and 4-2] and Migniot [p.482], [p.483], [Figure 1] disclose tracking buyer skeletal data.
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors, as taught by Ali to determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Okamura teaches acquiring through video a customer acquiring products. Migniot discloses a method/tool to analyze the shoppers buying act decisions. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with statistically analyzing the extracted data such as tracking arms and torso movement using video surveillance, as taught by Migniot, to refine the behavior analysis of customer behavior., Migniot [p.488].
Regarding Claim 10, (Cancelled)
Regarding Claim 11, (Cancelled)
Regarding Claim 12, (Previously Presented)
The information processing device according to claim 9, wherein the processor is configured to: generate, as the determination index, a mechanical learning model by mechanical learning that uses, as input data, feature quantities of respective users who belong to the specific cluster, and uses, as correct answer information, customer service index numbers of these users, obtain skeletal information of each of visiting users, generate a feature quantity of each of the visiting users, on a basis of the skeletal information of each of the visiting users, and input the feature quantity of each of the visiting users into the mechanical learning model, and detecting a customer service target from the visiting users, on a basis of an output result of the mechanical learning model.
Similar to claim 5, Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6), (p.4 paragraph 15), (p.5 paragraph 9, 12, and 15) and
Ali [p.1038 section 4-1 and 4-2]
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors, as taught by Ali to determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Regarding Claim 13, (Original)
The information processing device according to claim 9, wherein the processor is configured to: , to the feature quantity of each of the users, a customer service index number indicating a degree of interest of each of the users in a product, generate, as the determination index, a mechanical learning model by mechanical learning that uses, as input data, the feature quantity of each of the users, and uses, as correct answer information, the customer service index number of each of the users, obtain skeletal information of each of visiting users, generate a feature quantity of each of the visiting users, on a basis of the skeletal information of each of the visiting users, and input the feature quantity of each of the visiting users into the mechanical learning model, and detecting a customer service target from the visiting users, on a basis of an output result of the mechanical learning model.
Similar to claim 5, Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6), (p.4 paragraph 15), (p.5 paragraph 9, 12, and 15) and
Ali [p.1038 section 4-1 and 4-2]
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors, as taught by Ali to determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Regarding Claim 15, (Cancelled)
Regarding Claim 16, (Cancelled)
Regarding Claim 17, (Previously Presented)
The customer service detection method according to claim 8, wherein generating the determination index includes a mechanical learning model by mechanical learning that uses, as input data, feature quantities of respective users who belong to the specific cluster, and uses, as correct answer information, customer service index numbers of these users, and detecting includes obtaining skeletal information of each of visiting users, generating a feature quantity of each of the visiting users, on a basis of the skeletal information of each of the visiting users, inputting the feature quantity of each of the visiting users into the mechanical learning model, and detecting a customer service target from the visiting users, on a basis of an output result of the mechanical learning model.
Similar to claim 5, Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6), (p.4 paragraph 15), (p.5 paragraph 9, 12, and 15) and
Ali [p.1038 section 4-1 and 4-2]
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors, as taught by Ali to determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Regarding claim 18, (Previously Presented)
The customer service detection method according to claim 8, wherein generating the feature quantity includes adding to the feature quantity of each of the users, a customer service index number indicating a degree of interest of each of the users in a product, generating the determination index includes generating, as the determination index, a mechanical learning model by mechanical learning that uses, as input data, the feature quantity of each of the users, and uses, as correct answer information, the customer service index number of each of the users, and detecting includes obtaining skeletal information of each of visiting users, generating a feature quantity of each of the visiting users, on a basis of the skeletal information of each of the visiting users, inputting the feature quantity of each of the visiting users into the mechanical learning model, and detecting a customer service target from the visiting users, on a basis of an output result of the mechanical learning model.
Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6), (p.4 paragraph 15), (p.5 paragraph 9 and 15) and Ali [p.1038 section 4-1 and 4-2]
Okamura teaches acquiring through video a customer acquiring products. Ali teaches acquiring customer activity information such as browsing. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with monitoring browsing activity and normalized DNN imaging vectors, as taught by Alito determine strategic placement of retail items., Ali[p.1034 column 1 paragraph 1]
Claim(s) 6, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Okamura (JP 2021047810 A) in view of in view of Ali (2020, Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging) and Migniot (2013, 3D Human tracking from Depth Cue in a Buying Behavior Analysis Context) and in further view of Oshima (US 2023/0394556 A1).
Regarding Claim 6, (Original)
The non-transitory computer-readable recording medium according to claim 1, wherein the detecting includes giving notice of information about a detected user to a person in charge of customer service, when a user of the customer service target is detected.
Okamura teaches cameras tracking a customer, collecting purchase behavior data including line-of-sight of a customer, the movement of a customer’s hand, and detecting the customer’s eyes, nose, mouth and eyebrows., Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6)
Oshima [046] discloses a flow analysis unit 12 and the pre-shelf behaviour detection unit 13 associate the customer’s flow line with information on the product … the abnormality detection unit 14 detects as an abnormality that the customer has gone out of the specific area while obtaining an unsettled product … alerts a security guard., Oshima [042]-[046]
Okamura teaches acquiring through video a customer acquiring products. Oshima teaches acquiring customer activity information such as customer’s line of flow. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with alerting a security guard, as taught by Oshima to determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Regarding Claim 14, (Original)
The information processing device according to claim 9, wherein the processor is configured to: transmit information about a detected user to a person in charge of customer service, when a user of the customer service target is detected.
Okamura teaches cameras tracking a customer, collecting purchase behavior data including line-of-sight of a customer, the movement of a customer’s hand, and detecting the customer’s eyes, nose, mouth and eyebrows., Okamura (p.2 paragraph 1,2, 5, 7, 9, 12-13), (p.5 paragraph 6)
Oshima [046] discloses a flow analysis unit 12 and the pre-shelf behaviour detection unit 13 associate the customer’s flow line with information on the product … the abnormality detection unit 14 detects as an abnormality that the customer has gone out of the specific area while obtaining an unsettled product … alerts a security guard., Oshima [042]-[046]
Okamura teaches acquiring through video a customer acquiring products. Oshima teaches acquiring customer activity information such as customer’s line of flow. It would have been obvious to combine before the effective filing date, analyzing customer behavior using video, as taught by Okamura, with alerting a security guard, as taught by Oshima to determine strategic placement of retail items., Ali [p.1034 column 1 paragraph 1]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jones (US 10,198,711 B2) disclose the teachings herein permit the retail tracking system to discern associates within a retail facility (even if other customers are therein) and track their movements.
Guack (WO 2022/050,678 A2 ) disclose tracking method 800 includes: capturing images from a depth camera mounted on a shelf unit, identifying a user from the captured image, identifying joints of the identified user by performing a deep neural network (DNN) body joint detection on the captured image.
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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/THEA LABOGIN/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624