DETAILED ACTION
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 5, 2026 has been entered. Claims 1 and 11 have been amended. Claims 9 and 19 are canceled. Claims 21-22 have been added. Claims 1-8, 10-18, and 20-22 are presented for examination.
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 .
Response to Arguments
Applicant's arguments filed March 5, 2026 have been fully considered but they are not persuasive.
Preliminarily, it is noted that Applicant’s claim amendments overcome the previously-presented rejection of claims 1-8 and 10 under 35 U.S.C. § 112(b).
Regarding the rejection under 35 U.S.C. § 101, Applicant argues that the various claim limitations (including those regarding radar-based sensors, a POS system, electronic price labels, and a heat map image of a shelf) provide a “technical improvement over prior art methods” (page 11 of Applicant’s response). The Examiner respectfully disagrees. Aside from the general use of radar-based sensors, a human user can monitor information conveying movement of people and products in a store (e.g., by visually watching people as they move through the store and interact with products). A human can also review RFID and electronic price label information and/or visually observe the RFID and/or electronic price label movement directly to monitor the movement of persons and products. The POS system simply serves as a source of information. Regarding the electronic price labels, generic electronic price labels are used. The details of the electronic price label merely present a general link to technology and to a field of use. Additionally, the means for detecting movement (which comprises a plurality of radar-based sensors) simply gather information that a human user can utilize. (It is noted that the details of a heat map are no longer recited in the claims.) There is very little specificity presented in terms of the processing elements or in any specific interactive, ordered combination that they might collectively perform beyond receiving, capturing, and transmitting data among the various processing elements, much less an improvement in technology. The additional elements are simply tools that facilitate what could otherwise be performed by a human in terms of performing observations, data gathering, and analysis.
Applicant argues that “claim 1 is rooted in a particular, non-generic machine and system architecture” (page 12 of Applicant’s response). While the claims present some additional elements with specific structural details, these additional elements are still recited as facilitating the various abstract ideas at a high level of generality. Also noted is that the claims currently lack specific technical details as to how the additional elements might interact with one another beyond simply transmitting information or how any of the additional elements actively perform anything beyond generic processing operations.
On pages 12-13 of Applicant’s response, Applicant argues that the claims do not preempt an abstract idea because very specific details are presented in the claims. Preemption is not a standalone test for patent eligibility. Preemption concerns have been addressed by the Examiner through the application of the Subject Matter Eligibility test. Applicant’s attempt to show that the recited abstract idea is a very narrow and specific one is not persuasive. A specific abstract idea is still an abstract idea and is not eligible for patent protection without significantly more recited in the claim.
Applicant argues, “To the extent that the Examiner does not agree with the above argument, the Examiner is respectfully reminded that the Court in Berkheimer v. HP Inc., 2017-1437 (Fed Cir. 2018) held that ‘[w]hether a particular technology is well-understood, routine, and conventional goes beyond what was simply known in the prior art,’ and that it will be necessary for the Examiner to provide evidence according to the guideline set forth in the Memorandum issued by the USPTO on April 19, 2018 to support the assertion that the above mentioned features are well-understood, routine, and conventional to a skilled artisan in the relevant field.” (Page 14 of Applicant’s response) The requirement to provide Berkheimer evidence arises in Step 2B of the Subject Matter Eligibility test and is related to an assertion that additional elements and their corresponding functions are well-understood, routine, and conventional. Such evidence is not required to show that details of the judicial exceptions are well-understood, routine, and conventional. Applicant has not specifically identified which particular additional element and operation combination was asserted to be well-understood, routine, and conventional in the rejection and is currently submitted to be an inaccurate statement.
On page 14 of the response, Applicant states, “In particular, it should be noted that new claims 21 and 22 recite sending an electronic message to a mobile device of a person at a certain physical location within the store, said message being generated based on the detection of the presence of the person at said location and said generated customer product selection information, thereby providing targeted and contextually communication to said person. This concrete action of sending a targeted message to a mobile device based on the processed information further culminates in the physical act of sending a targeted electronic message to a person's mobile device in real-time based on their detected physical presence and the derived selection information. This is a clear, physical, and useful output directly affecting the real world.” These claims lack specific technical details as how a person is detected and then how the technical details are used to target a specific electronic message relevant to the particular person and where that person is detected to be and when, for example. As claimed, the detection of the presence of a person serves more as a condition that needs to be met for a message to be displayed to a user. It would be helpful if the claims recited some more technical details and integration among the various additional elements, assuming such details and integration are supported by Applicant’s original disclosure.
Regarding the art rejections, on page 16 of the response, Applicant argues:
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Regarding the receiving step, the Examiner respectfully disagrees. The whole purchase system is an example of a POS system. The receiving step of claim 1 simply requires that the product purchase information be “derived from an electronic price label system or shelf map.” In ¶ 234, Matayoshi states, “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).” The correlation of a purchased product to a particular electronic label and to generate more accurate heatmaps for purchased goods means that a location of a purchased product is noted. This means that product information is “derived from an electronic price label system or shelf map.” The ability to create a heatmap of purchased products alone implies a correlation of product purchase information to a shelf map.
Additionally, in ¶ 245, Matayoshi states, “The analytics results in response to the data from the baskets may include a pivot table(s) or a graphical representation of the data (e.g. a visual heatmap(s)). The remote resource 15 may also process the data received from the baskets 100/200 to perform machine learning, deep learning or neural network analysis thereon, and may also comprise a logic engine to take an action in response to processing the device data. Such an action may comprise sending a command communication comprising an instruction(s) or request(s) to an electronic label (e.g. to generate a sensor output or adjust information displayed on the electronic label) or to another device (e.g. electronic signage (not shown) to display promotional material, a recipe, a message etc.). Such an action may comprise sending a command communication comprising a command communication to a user application device (not shown)” and, in ¶ 255, Matayoshi explains that “the analytics results may indicate that there is a surge in purchases of a particular product during the same period of time every day.” A period of time corresponding to a purchase implies that purchase information includes an indication of a time of purchase.
Furthermore, it is noted that “an electronic price label system” and the “shelf map” are recited in the alternative in the receiving step of claim 1. Product-related activity information is gleaned from both in Matayoshi.
Additionally, Matayoshi explains that figures 5a-5c show visual heatmaps corresponding to the user activity and interaction in the retail environment and actual shelves and aisles are depicted in these figures (Matayoshi: ¶ 129), thereby showing examples of shelf maps. All of the POS system information is used to evaluate areas of higher vs. lower activity and when pricing adjustment should be made or products should be moved around (Matayoshi: ¶¶ 129-131 – “[0129] The remote resource 15 performs analytics in response to the sensed data and generates an output, which, as illustratively shown in the examples of FIGS. 5a-5c is a visual heatmap showing the user activity or interaction in the retail environment 30.
[0130] In the present illustrative examples, the visual heatmaps are overlaid on the pictures of retail environment 30, whereby the “hot” darker zones, some of which are illustratively indicated at 34, are indicative of higher user interaction in comparison to the “cool” lighter zones, some of which are illustratively indicated at 36.
[0131] An interested party may then (e.g. using AI) interpret the analytics results and take an action as appropriate. For example, a store owner may adjust the price of the products in the areas of lower user interaction 36. As described above, such adjustments to the price may be effected remotely in realtime.”).
The basket gathers information reflective of purchases (Matayoshi: ¶ 228) and this information may be used in conjunction with information indicated by electronic labels (Matayoshi: ¶ 234 – “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).”). Viewing all of the interactive elements of Matayoshi as part of a POS system (including the basket, electronic labels (which also display prices, as seen in ¶ 112), heatmaps/shelf maps, etc.), it is clear that the POS system elements collectively gather purchase-related information, including the details of the information recited in the claims.
Regarding the radar sensors, the Examiner has brought in the Marshall reference in order to more clearly address the limitation in question.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8, 10-18, and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-8, 10-18, and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “a system and method for monitoring people in a store” (Spec: p. 1: line 6) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-8, 10), Apparatus (claims 11-18, 20)
Independent Claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 11] A method/system for monitoring persons in a store, the method comprising:
monitoring movement of persons in the store with means for detecting movement, presence and/or location of a person, wherein the means for detecting can measure areas between the shelves where the person is moving, and wherein the step of monitoring movement of persons includes monitoring locations wherein the person moves, wherein the person is stationary, and how long the person is stationary at a certain location,
receiving product purchase information, the purchase information including which products have been purchased, time of purchase and electronic location data of each purchased product within the store derived from an electronic price label system or shelf map,
correlating said product purchase information with physical product locations within the store by utilizing the electronic location data of each purchased product and communication between an electronic price label associated with each product and the electronic price label system,
generating customer product selection information for a specific product, for a specific product type, and/or specific location of the store based on determined location of sold products and monitored movement of persons on a certain location of the store, wherein the customer production selection information comprises number of products sold in a certain time for a certain location at the store and information relating to which shelf and which part of the shelf the person goes and/or stops.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106.04(a)(1)(III), “[t]he courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations identified above. For example, a human user can monitor information conveying movement of people and products in a store. A human can also review RFID and electronic price label information and/or visually observe the RFID and/or electronic price label movement directly to monitor the movement of persons and products. A human user can detect movement, presence, and/or location. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to monitoring people in a store, which (under its broadest reasonable interpretation) is an example of marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Independent process claim 1 includes means for detecting movement, presence and/or location of a person wherein the means for detecting movement, presence and/or location of a person comprises a plurality of radar-based sensors arranged above aisles between shelves of the store (claim 1); the information received from a POS-system (claim 1); wherein each electronic price label comprises an identifier and communication means configured to communicate with said electronic price label system to enable precise positional localization of sold products (claim 1); sending an electronic message to a mobile device of a person (claim 1).
Independent apparatus claim 11 includes similar additional elements as those presented in the process claims, respectively.
While the claims present some additional elements with specific structural details, these additional elements are still recited as facilitating the various abstract ideas at a high level of generality. Also noted is that the claims currently lack specific technical details as to how the additional elements might interact with one another beyond simply transmitting information or how any of the additional elements actively perform anything beyond generic processing operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a POS and sensor-based environment. The claimed processing elements are recited at a high level of generality and are merely invoked as tools to perform the abstract idea(s). The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic components. Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The Specification presents no assertion that there is any improvement in how the additional elements are used within the scope of the claimed operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention processing and communications may be implemented using general-purpose processing elements and other generic components (Spec: p. 12: 27 – p. 13: 2 – “After sending a command, the central unit of the apparatus can send information about the changes in the state of the electronic price label that occurred and/or were executed onwards via the network, e.g. to a POS system. The apparatus can also receive information intended to be sent to a certain electronic price label via a network, e.g. from a POS system. This type of information can be e.g. price information to be shown on the display of the electronic price label or extinguishing information. Use of the invention is not limited to price displays using the type of display technology of the type described above, but instead it is obvious that the price displays can be implemented using any technology whatsoever that is known in the art. The solution described above relates specifically to how the communication of the electronic price labels and the electronic price label system can be efficiently arranged.“).
It is noted that the claims do not present any specific details regarding “how the communication of the electronic price labels and the electronic price label system can be efficiently arranged.“ (Spec: p. 12: 38 – p. 13: 2)
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent Claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 2, 12] wherein the customer product selection information comprises number of products sold during an hour and/or during a day, for a certain shelf, and/or information relating to how a certain product has been sold in respect to the other products.
[Claims 3, 13] wherein the monitoring movement of persons comprises monitoring locations where people move, where they are stationary and/or how long people are stationary at a certain location.
[Claims 4, 14] wherein the method further comprises determining how long duration people are examining products before buy-decision, including how long they spend time stationary beside certain product that has been determined to have been purchased.
[Claims 6, 16] monitoring movement of the persons in the store by tracking movement of products in a store by monitoring an RFID of the product and/or the electronic price label attached to the product.
[Claims 7, 17] wherein monitoring movement of a person comprises determining a route used by a person in the store with means for detecting movement, presence and/or location of a person.
[Claims 8, 18] wherein the means for detecting movement, presence and/or location of a person further determines fill ratio of at least one shelf or part of a shelf.
[Claim 10] wherein determining location of the sold products comprises utilizing shelf-map of the store comprising location information or the product, wherein a shelf map of the store is dynamic, and automatically updated when an electronic price label is relocated in a store, wherein the method further comprises sensing or determining electronic price label’s location in the store and/or the electronic price label senses or determines its location in the store.
[Claim 20] wherein a shelf map is static, and updated manually when products are relocated in the store, or the shelf map of the store is dynamic, and automatically updated when an electronic price label is relocated in a store, further comprises sensing or determining electronic price labels location in the store and/or the electronic price label senses or determines its location in the store.
[Claims 21, 22] sending a message to a person at a certain physical location within the store, said message being generated based on the detection of the presence of the person at said location and said generated customer product selection information, thereby providing targeted and contextually communication to said person.
The dependent claims further present details of the abstract ideas of the independent claims from which they depend.
It is noted that “automatically” (e.g., as recited in claims 10 and 20) could simply mean “in response to” and does not necessarily require use of an additional element. Additionally, a system (such as the “system” recited in claim 20) could simply be something interconnected and does not necessarily require use of an additional element. It is further noted that various alternative limitations are recited in both claims 10 and 20.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106.04(a)(1)(III), “[t]he courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can perform the operations identified above. For example, a human user can monitor information conveying movement of people and products in a store. A human can also review RFID and electronic price label information and/or visually observe the RFID and/or electronic price label movement directly to monitor the movement of persons and products. A human user can detect movement, presence, and/or location. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to monitoring people in a store, which (under its broadest reasonable interpretation) is an example of marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims incorporate the additional elements of the independent claims from which they depend.
The process claims include means for detecting movement, presence and/or location of a person wherein the means for detecting movement, presence and/or location of a person comprises a plurality of radar-based sensors arranged above aisles between shelves of the store (claim 1); the information received from a POS-system (claim 1); wherein each electronic price label comprises an identifier and communication means configured to communicate with said electronic price label system to enable precise positional localization of sold products (claim 1); sending an electronic message to a mobile device of a person (claim 1); wherein the means for detecting movement, presence and/or location of a person further comprises a camera, which is arranged to a ceiling or wall of the store, at the shelf, and/or at an electronic price label arranged to the store (claim 5); wherein the means for detecting movement, presence and/or location of a person further determines that a certain product is taken from the shelf and/or put back to shelf, and/or provides heat mapping data of a certain shelf (claim 6); wherein monitoring movement of a person comprises determining a route used by a person in the store with means for detecting movement, presence and/or location of a person (claim 7); wherein the means for detecting movement, presence and/or location of a person further determines fill ratio of at least one shelf or part of a shelf (claim 8); sending a message to a mobile device (claim 9). Claim 20 sends an electronic message to a mobile device.
The apparatus claims include similar additional elements as those presented in the process claims, respectively.
While the claims present some additional elements with specific structural details, these additional elements are still recited as facilitating the various abstract ideas at a high level of generality. Also noted is that the claims currently lack specific technical details as to how the additional elements might interact with one another beyond simply transmitting information or how any of the additional elements actively perform anything beyond generic processing operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a POS and sensor-based environment. The claimed processing elements are recited at a high level of generality and are merely invoked as tools to perform the abstract idea(s). The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic components. Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The Specification presents no assertion that there is any improvement in how the additional elements are used within the scope of the claimed operations.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention processing and communications may be implemented using general-purpose processing elements and other generic components (Spec: p. 12: 27 – p. 13: 2 – “After sending a command, the central unit of the apparatus can send information about the changes in the state of the electronic price label that occurred and/or were executed onwards via the network, e.g. to a POS system. The apparatus can also receive information intended to be sent to a certain electronic price label via a network, e.g. from a POS system. This type of information can be e.g. price information to be shown on the display of the electronic price label or extinguishing information. Use of the invention is not limited to price displays using the type of display technology of the type described above, but instead it is obvious that the price displays can be implemented using any technology whatsoever that is known in the art. The solution described above relates specifically to how the communication of the electronic price labels and the electronic price label system can be efficiently arranged.“).
It is noted that the claims do not present any specific details regarding “how the communication of the electronic price labels and the electronic price label system can be efficiently arranged.“ (Spec: p. 12: 38 – p. 13: 2)
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
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.
Claims 1-5, 7, 10-15, 17, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Matayoshi (US 2020/0286135) in view of Marshall (US 2021/0365961).
[Claim 1] Matayoshi discloses a method for monitoring persons in a store (¶ 1 – “…the present techniques relate to methods, systems and devices for detecting user interactions in retail and commercial applications.”; ¶¶ 60, 145 -- Multiple persons in a store are monitored.), the method comprising:
monitoring movement of persons in the store with means for detecting movement, presence and/or location of a person, wherein the means for detecting movement, presence and/or location of a person comprises a plurality of sensors arranged above aisles between shelves of the store and configured to measure areas between the shelves where the person is moving, and wherein the step of monitoring movement of persons includes monitoring locations where the person moves, where the person is stationary, and how long the person is stationary at a certain location (fig. 6, ¶ 147 – “As illustratively depicted in FIG. 6, the electronic labels comprise cameras 40a & 40b arranged above shelving 32 (e.g. on a gantry). In the present illustrative example, each camera 40a & 40b is a computer vision camera arranged to provide coverage for a designated area 42a & 42b of the shelving.”; ¶ 40 – “The communication circuitry 6 may use wireless communication 7, such as communications used in, for example, wireless local area networks (WLAN) and/or wireless sensor networks (WSN) such as Wi-Fi, ZigBee, Bluetooth or Bluetooth Low Energy (BLE), using any suitable communications protocol such as lightweight machine-to-machine (LWM2M). The communication circuitry 6 may also comprise short range communication capabilities such as radio frequency identification (RFID) or near field communication (NFC),”; ¶ 51 – “The sensor circuitry 10 may additionally, or alternatively, include the communications circuitry 6 to detect user interactions with the electronic label 2 via a device associated with the user (hereafter “user application device”). Such a user application device may comprise a mobile phone, tablet or smart device such as a smart watch, whereby the sensed data may be generated when the user actively communicates with the electronic label 2 via the user application device (e.g. via NFC, RFID, Bluetooth etc), or whereby the electronic label senses one or more wireless signals generated by the user application device when the user application device is in proximity thereof.”; ¶ 60 – “The sensed user activity or interaction may comprise one or more of: detecting the presence of a user; detecting motion of a user; detecting whether a user picks up and/or replaces a product; measuring the duration a user looks at or examines a product (dwell time); measuring the frequency of users picking up products and/or replacing products; and detecting a gesture towards or away from a product (e.g. tracking eyeball movement; hand movement; foot movement), measuring the conversion rate (number of user interactions with a particular product vs number of sales of the particular product), detecting interactions with the electronic label 2 via the user application device.”; ¶ 215 – “The location data generated by such location determination circuitry may be transmitted to the remote resource 15 which can track the basket as the user progresses around the store based on or in response to the location data. The location data may be transmitted continuously, periodically (e.g. every ‘N’ seconds), and/or following an event (e.g. a user interaction).”; ¶ 249 – “It will be appreciated that the analytics results could be generated for different user interactions detected by the cameras 102 (E.g. conversion rate, time a user spends in store, the route a user takes in the store etc.).”; fig. 5a, ¶ 127 – “FIG. 5a schematically shows analytics results for a retail environment 30 with multiple aisles 31 having shelving 32, the shelving 32 having electronic labels associated with different product lines as described above. FIG. 5b schematically shows analytics results for a single aisle 31 of retail environment 30, with shelving 32 on either side thereof, whilst FIG. 5c schematically shows analytics results for a single aisle 31 with shelving 32 in retail environment 30. In the present illustrative example, the shelving 32 has electronic labels 2 (shown in FIG. 5c) associated with different products.”; ¶¶ 60, 145 – Multiple persons in a store are monitored.),
receiving product purchase information from the information received from a POS-system, the purchase information including which products have been purchased, time of purchase and electronic location data of each purchased product within the store derived from an electronic price label system or shelf map (¶ 234 – “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).” The correlation of a purchased product to a particular electronic label and to generate more accurate heatmaps for purchased goods means that a location of a purchased product is noted.; ¶ 245 – “The analytics results in response to the data from the baskets may include a pivot table(s) or a graphical representation of the data (e.g. a visual heatmap(s)). The remote resource 15 may also process the data received from the baskets 100/200 to perform machine learning, deep learning or neural network analysis thereon, and may also comprise a logic engine to take an action in response to processing the device data. Such an action may comprise sending a command communication comprising an instruction(s) or request(s) to an electronic label (e.g. to generate a sensor output or adjust information displayed on the electronic label) or to another device (e.g. electronic signage (not shown) to display promotional material, a recipe, a message etc.). Such an action may comprise sending a command communication comprising a command communication to a user application device (not shown)”; ¶ 255 – “…the analytics results may indicate that there is a surge in purchases of a particular product during the same period of time every day.” A period of time corresponding to a purchase implies that purchase information includes an indication of a time of purchase.),
correlating said product purchase information with physical product locations within the store by utilizing the electronic location data of each purchased product and communication between an electronic price label associated with each product and the electronic price label system, wherein each electronic price label comprises an identifier and communication means configured to communicate with said electronic price label system to enable precise positional localization of sold products (¶ 234 – “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).” The correlation of a purchased product to a particular electronic label and to generate more accurate heatmaps for purchased goods means that a location of a purchased product is noted.; ¶ 42 – “Such device data includes identifier data comprising one or more device identifiers to identify the electronic label 2 and may comprise one or more of: universally unique identifier(s) (UUID), globally unique identifier(s) (GUID) and IPv6 address(es), although any suitable device identifier(s) may be used.”; ¶ 101 – “Similarly, when an existing electronic label is moved to a new location, it may determine its new location by communicating with electronic labels or devices at the new location and communicate its updated location to management service 15a so as to be provisioned with the appropriate device data for its new location.”; ¶ 102 – “In other examples, when a product(s) or product line at a particular location in the retail environment is updated or replaced, the management service 15a can communicate with the electronic label at the particular location so as to provision the electronic label with the appropriate information for the new product or product line.” In other words, the electronic labels are associated with particular locations and with certain products; therefore, the location of each electronic label defines a location of the associated product(s).),
generating customer product selection information for a specific product, for a specific product type, and/or specific location of the store based on determined location of sold products and monitored movement of persons on a certain location of the store, wherein the customer product selection information comprises number of products sold in a certain time for a certain location at the store and information relating to which shelf and which part of the shelf the person goes and/or stops (¶ 234 – “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).” The correlation of a purchased product to a particular electronic label and to generate more accurate heatmaps for purchased goods means that a location of a purchased product is noted.; ¶ 245 – “The analytics results in response to the data from the baskets may include a pivot table(s) or a graphical representation of the data (e.g. a visual heatmap(s)). The remote resource 15 may also process the data received from the baskets 100/200 to perform machine learning, deep learning or neural network analysis thereon, and may also comprise a logic engine to take an action in response to processing the device data. Such an action may comprise sending a command communication comprising an instruction(s) or request(s) to an electronic label (e.g. to generate a sensor output or adjust information displayed on the electronic label) or to another device (e.g. electronic signage (not shown) to display promotional material, a recipe, a message etc.). Such an action may comprise sending a command communication comprising a command communication to a user application device (not shown)”; ¶ 255 – “…the analytics results may indicate that there is a surge in purchases of a particular product during the same period of time every day.” A period of time corresponding to a purchase implies that purchase information includes an indication of a time of purchase.; ¶ 60 – “The sensed user activity or interaction may comprise one or more of: detecting the presence of a user; detecting motion of a user; detecting whether a user picks up and/or replaces a product; measuring the duration a user looks at or examines a product (dwell time); measuring the frequency of users picking up products and/or replacing products; and detecting a gesture towards or away from a product (e.g. tracking eyeball movement; hand movement; foot movement), measuring the conversion rate (number of user interactions with a particular product vs number of sales of the particular product), detecting interactions with the electronic label 2 via the user application device.”; ¶ 215 – “The location data generated by such location determination circuitry may be transmitted to the remote resource 15 which can track the basket as the user progresses around the store based on or in response to the location data. The location data may be transmitted continuously, periodically (e.g. every ‘N’ seconds), and/or following an event (e.g. a user interaction).”; ¶ 249 – “It will be appreciated that the analytics results could be generated for different user interactions detected by the cameras 102 (E.g. conversion rate, time a user spends in store, the route a user takes in the store etc.).”; ¶ 101 – “Similarly, when an existing electronic label is moved to a new location, it may determine its new location by communicating with electronic labels or devices at the new location and communicate its updated location to management service 15a so as to be provisioned with the appropriate device data for its new location.”; ¶ 102 – “In other examples, when a product(s) or product line at a particular location in the retail environment is updated or replaced, the management service 15a can communicate with the electronic label at the particular location so as to provision the electronic label with the appropriate information for the new product or product line.” In other words, the electronic labels are associated with particular locations and with certain products; therefore, the location of each electronic label defines a location of the associated product(s).).
Matayoshi does not explicitly disclose that the sensors are radar-based sensors. Marshall explains that radar sensors may be placed over shelving units near aisles and used to detect the presence of customers and/or shopping carts and related activity in a shopping environment (Marshall: figs. 5, 8, 11; ¶¶ 60, 78, 91, 104, 226, 231, 244, 250, 254-257). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Matayoshi wherein the sensors are radar-based sensors in order to take advantage of the ability of the radar sensor to be adjust to take advantage of different trade-offs, including in terms of spatial or temporal resolution and range of the radar sensor, which may be customized to meet the detection requirements of each given situation (as suggested in ¶¶ 113-114 of Marshall).
[Claim 2] Matayoshi discloses wherein the customer product selection information comprises number of products sold during an hour and/or during a day, for a certain shelf, and/or information relating to how a certain product has been sold in respect to the other products (¶ 234 – “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).” The correlation of a purchased product to a particular electronic label and to generate more accurate heatmaps for purchased goods means that a location of a purchased product is noted.; ¶ 245 – “The analytics results in response to the data from the baskets may include a pivot table(s) or a graphical representation of the data (e.g. a visual heatmap(s)). The remote resource 15 may also process the data received from the baskets 100/200 to perform machine learning, deep learning or neural network analysis thereon, and may also comprise a logic engine to take an action in response to processing the device data. Such an action may comprise sending a command communication comprising an instruction(s) or request(s) to an electronic label (e.g. to generate a sensor output or adjust information displayed on the electronic label) or to another device (e.g. electronic signage (not shown) to display promotional material, a recipe, a message etc.). Such an action may comprise sending a command communication comprising a command communication to a user application device (not shown)”; ¶ 249 – “It will be appreciated that the analytics results could be generated for different user interactions detected by the cameras 102 (E.g. conversion rate, time a user spends in store, the route a user takes in the store etc.).”; ¶ 255 – “…the analytics results may indicate that there is a surge in purchases of a particular product during the same period of time every day.” A period of time corresponding to a purchase implies that purchase information includes an indication of a time of purchase.; ¶ 60 – “The sensed user activity or interaction may comprise one or more of: detecting the presence of a user; detecting motion of a user; detecting whether a user picks up and/or replaces a product; measuring the duration a user looks at or examines a product (dwell time); measuring the frequency of users picking up products and/or replacing products; and detecting a gesture towards or away from a product (e.g. tracking eyeball movement; hand movement; foot movement), measuring the conversion rate (number of user interactions with a particular product vs number of sales of the particular product), detecting interactions with the electronic label 2 via the user application device.”; ¶ 136 – “Additionally, or alternatively, the interested party may select sensed data from different times of day, week, month, year etc., so as to identify trends during certain periods of the day or during certain holidays.”).
[Claim 3] Matayoshi discloses wherein the monitoring movement of persons comprises monitoring locations where people move, where they are stationary and/or how long people are stationary at a certain location (¶ 60 – “The sensed user activity or interaction may comprise one or more of: detecting the presence of a user; detecting motion of a user; detecting whether a user picks up and/or replaces a product; measuring the duration a user looks at or examines a product (dwell time); measuring the frequency of users picking up products and/or replacing products; and detecting a gesture towards or away from a product (e.g. tracking eyeball movement; hand movement; foot movement), measuring the conversion rate (number of user interactions with a particular product vs number of sales of the particular product), detecting interactions with the electronic label 2 via the user application device.”; ¶ 215 – “The location data generated by such location determination circuitry may be transmitted to the remote resource 15 which can track the basket as the user progresses around the store based on or in response to the location data. The location data may be transmitted continuously, periodically (e.g. every ‘N’ seconds), and/or following an event (e.g. a user interaction).”; ¶ 249 – “It will be appreciated that the analytics results could be generated for different user interactions detected by the cameras 102 (E.g. conversion rate, time a user spends in store, the route a user takes in the store etc.).”; ¶¶ 60, 145 – Multiple persons in a store are monitored.).
[Claim 4] Matayoshi discloses wherein the method further comprises determining how long duration people are examining products before buy-decision, including how long they spend time stationary beside certain product that has been determined to have been purchased (¶ 60 – “The sensed user activity or interaction may comprise one or more of: detecting the presence of a user; detecting motion of a user; detecting whether a user picks up and/or replaces a product; measuring the duration a user looks at or examines a product (dwell time); measuring the frequency of users picking up products and/or replacing products; and detecting a gesture towards or away from a product (e.g. tracking eyeball movement; hand movement; foot movement), measuring the conversion rate (number of user interactions with a particular product vs number of sales of the particular product), detecting interactions with the electronic label 2 via the user application device.”; ¶ 215 – “The location data generated by such location determination circuitry may be transmitted to the remote resource 15 which can track the basket as the user progresses around the store based on or in response to the location data. The location data may be transmitted continuously, periodically (e.g. every ‘N’ seconds), and/or following an event (e.g. a user interaction).”; ¶ 249 – “It will be appreciated that the analytics results could be generated for different user interactions detected by the cameras 102 (E.g. conversion rate, time a user spends in store, the route a user takes in the store etc.).”; ¶¶ 60, 145 – Multiple persons in a store are monitored.).
[Claim 5] Matayoshi discloses wherein the means for detecting movement, presence and/or location of a person further comprises a camera, which is arranged to a ceiling or wall of the store, at the shelf, and/or at an electronic price label arranged to the store (fig. 6, ¶ 147 – “As illustratively depicted in FIG. 6, the electronic labels comprise cameras 40a & 40b arranged above shelving 32 (e.g. on a gantry). In the present illustrative example, each camera 40a & 40b is a computer vision camera arranged to provide coverage for a designated area 42a & 42b of the shelving.”; ¶ 40 – “The communication circuitry 6 may use wireless communication 7, such as communications used in, for example, wireless local area networks (WLAN) and/or wireless sensor networks (WSN) such as Wi-Fi, ZigBee, Bluetooth or Bluetooth Low Energy (BLE), using any suitable communications protocol such as lightweight machine-to-machine (LWM2M). The communication circuitry 6 may also comprise short range communication capabilities such as radio frequency identification (RFID) or near field communication (NFC),”; ¶ 60 – “The sensed user activity or interaction may comprise one or more of: detecting the presence of a user; detecting motion of a user; detecting whether a user picks up and/or replaces a product; measuring the duration a user looks at or examines a product (dwell time); measuring the frequency of users picking up products and/or replacing products; and detecting a gesture towards or away from a product (e.g. tracking eyeball movement; hand movement; foot movement), measuring the conversion rate (number of user interactions with a particular product vs number of sales of the particular product), detecting interactions with the electronic label 2 via the user application device.”; ¶ 249 – “It will be appreciated that the analytics results could be generated for different user interactions detected by the cameras 102 (E.g. conversion rate, time a user spends in store, the route a user takes in the store etc.).”; fig. 5a, ¶ 127 – “FIG. 5a schematically shows analytics results for a retail environment 30 with multiple aisles 31 having shelving 32, the shelving 32 having electronic labels associated with different product lines as described above. FIG. 5b schematically shows analytics results for a single aisle 31 of retail environment 30, with shelving 32 on either side thereof, whilst FIG. 5c schematically shows analytics results for a single aisle 31 with shelving 32 in retail environment 30. In the present illustrative example, the shelving 32 has electronic labels 2 (shown in FIG. 5c) associated with different products.”).
[Claim 7] Matayoshi discloses wherein monitoring movement of a person comprises determining a route used by a person in the store with means for detecting movement, presence and/or location of a person (¶ 249 – “It will be appreciated that the analytics results could be generated for different user interactions detected by the cameras 102 (E.g. conversion rate, time a user spends in store, the route a user takes in the store etc.).”).
[Claim 10] Matayoshi discloses wherein determining location of the sold products comprises utilizing shelf-map of the store comprising location information for the product, wherein a shelf map of the store is dynamic, and automatically updated when an electronic price label is relocated in a store, wherein the method further comprises sensing or determining electronic price label's location in the store and/or the electronic price label senses or determines its location in the store (¶ 101 – “Similarly, when an existing electronic label is moved to a new location, it may determine its new location by communicating with electronic labels or devices at the new location and communicate its updated location to management service 15a so as to be provisioned with the appropriate device data for its new location.”; ¶ 102 – “In other examples, when a product(s) or product line at a particular location in the retail environment is updated or replaced, the management service 15a can communicate with the electronic label at the particular location so as to provision the electronic label with the appropriate information for the new product or product line.” In other words, the electronic labels are associated with particular locations and with certain products; therefore, the location of each electronic label defines a location of the associated product(s).; ¶ 234 – “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).”).
[Claim 21] Matayoshi discloses sending an electronic message to a mobile device of a person at a certain physical location within the store, said message being generated based on the detection of the presence of the person at said location and said generated customer product selection information, thereby providing targeted and contextually communication to said person (¶ 114 – “In operation, a user of the retail environment (e.g. a customer) will interact with the various products or electronic labels 2 in various ways. For example, a user will pick-up a product if determined to be suitable for his/her needs. Such a determination may be made based on the product itself (e.g. branding) or the decision to pick-up, or not, may be made based on the information on the associated display (e.g. pricing information, a recipe shown on the display, a video shown on the display, a sound emitted etc.). In other cases, the user may simply examine the product (e.g. the branding/ingredients/calorific content) to check whether it is suitable, and, if not, the user will replace the product on the shelf. In other cases, the user may interact with the electronic labels via user application device (depicted as 16b in FIG. 4b).“; ¶ 115 – “The sensor 11 generates sensed data in response to the user interaction, and the electronic label 2 will process the sensed data and generate a sensory output in response thereto. For example, on determining that a user's dwell time is greater than a threshold dwell time specified in the device data or on determining that a conversion rate is lower than expected, the electronic label 2 may adjust the price information on the display 13, or cause an LED to flash, or a sound to be emitted or to transmit a command communication to the user application device to cause a sensory output thereat. The user can then react to the sensory output, e.g. deciding to purchase the product in response to the updated price.”).
[Claims 11-15, 17, 22] Claims 11-15, 17, and 22 recite limitations already addressed by the rejections of claims 1-5, 7, and 21 above; therefore, the same rejections apply.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Matayoshi (US 2020/0286135) in view of Marshall (US 2021/0365961), as applied to claims 1 and 11 above, in view of Sadr (US 2017/0315208).
[Claims 6, 16] Matayoshi does not explicitly disclose monitoring movement of the persons in the store by tracking movement of products in a store by monitoring an RFID of the product and/or the electronic price label attached to the product. However, Sadr describes the following manner in which customer movement may be tracked using the RFID data from the items being carried by the customer:
[0047] Unlike other entity tracking systems that use complicated machine vision algorithms or specialized sensors to identify the various entities, the path tracking system of many embodiments can use a simpler presence detection system to track entities through a space, and can use the location data from the RFID data for a finer-grained identification of the detected entities based on the corresponding movement of associated tags, particularly in the case when it is difficult for the detection system 212 to differentiate between multiple entities. For example, in certain embodiments, a tag is associated with a person, and their path through a crowded retail space is identified based on the detection of people by a camera system in conjunction with RFID data that describes the movement of items that the person is transporting (e.g. carrying or has added to a basket or shopping cart).
As explained above, Matayoshi detects movement, presence and/or location of a person using at least one sensor; therefore, Matayoshi is concerned with tracking the movement of people. Sadr provides another approach to track the movement of customers. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Matayoshi to perform the step of monitoring movement of the persons in the store by tracking movement of products in a store by monitoring an RFID of the product and/or the electronic price label attached to the product instead of using the types of sensors disclosed by Matayoshi since such a modification would have been a matter of simple substitution of one type of sensor for another. Sadr shows that using RFID information of products carried by a customer to track customer movement was known in the art of event monitoring in a shopping environment. It would have been well within the technical capability of one of ordinary skill in the art before Applicant’s effective filing date to have substituted Sadr’s customer tracking approach for one of Matayoshi’s sensors and such a substitution would have yielded predictable and expected results, thereby rendering such a modification obvious. Additionally, modifying Matayoshi with Sadr’s product RFID-customer tracking approach would have allowed for simple detection of entities with a finer-grained identification of the detected entities, which is particularly helpful in crowded retail spaces (as suggested in ¶ 47 of Sadr).
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Matayoshi (US 2020/0286135) in view of Marshall (US 2021/0365961), as applied to claims 1 and 11 above, in view of Chaubard (US 2021/0398060).
[Claims 8, 18] Matayoshi evaluates shelf activity to monitor stock levels and sends a warning to reorder stock when a threshold is reached (Matayoshi: ¶¶ 117-118); however, Matayoshi does not explicitly disclose wherein the means for detecting movement, presence and/or location of a person further determines fill ratio of at least one shelf or part of a shelf.
Chaubard discloses that camera images may be used to detect out of stock and low stock conditions of shelved items in order to help optimize profit (Chaubard: abstract). The arrangement of products on the shelves is used to detect when an item needs to be restocked and staff is alerted to perform the restocking (Chaubard: ¶ 68). A velocity at which an employee stocks and restocks the shelves over a given time period is determined, based on images captured by cameras (Chaubard: ¶¶ 74-77). The indication that a product captured in a shelf image is out of stock x percent of the time (as seen in ¶ 114, cited below) is an example of an indication of a fill ratio of at least one shelf or part of a shelf. Furthermore, Chaubard evaluates a shelf image (including an item with only one shelf facing) to determine sales information related to products over hours and days and over stores to identify sales patterns, as seen in the following excerpt and figures from Chaubard:
[0114] FIGS. 17 and 18 show further reports and visuals for store management. In FIG. 17 top chronic outs are listed, with numbers, percentages and estimated lost revenue. FIG. 18 shows a shelf image with a report regarding particular products, two different soft drinks, with monthly movement and estimated lost sales based on OOS occurrences. The System looks over many hours, many days and over many stores to find patterns of out of stock information, such as Coca-Cola Classic is out of stock 56.7% of the time (as in FIG. 17) and it has only one shelf facing, so the store can drastically improve on shelf availability conditions and sales if they increase the number of facings of this SKU and decrease the number of facings on another SKU that is always (or nearly always) in stock and has too many facings. This does not cost any labor, but increases revenue.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Matayoshi wherein the means for detecting movement, presence and/or location of a person further determines fill ratio of at least one shelf or part of a shelf in order to enhance Matayoshi’s shopping system with the ability to detect out of stock and low stock conditions of shelved items and recommend future inventory orders in order to help optimize profit (as suggested in Chaubard: abstract).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Matayoshi (US 2020/0286135) in view of Marshall (US 2021/0365961), as applied to claim 11 above, in view of Jones et al. (US 2018/0247255).
[Claim 20] Matayoshi discloses wherein the shelf map of the store is dynamic and automatically updated when an electronic price label is relocated in a store, wherein the system is configured to sense or determine electronic price labels location in the store and/or the electronic price label is configured to sense or determine its location in the store (Matayoshi: ¶ 101 – “Similarly, when an existing electronic label is moved to a new location, it may determine its new location by communicating with electronic labels or devices at the new location and communicate its updated location to management service 15a so as to be provisioned with the appropriate device data for its new location.”; ¶ 102 – “In other examples, when a product(s) or product line at a particular location in the retail environment is updated or replaced, the management service 15a can communicate with the electronic label at the particular location so as to provision the electronic label with the appropriate information for the new product or product line.” In other words, the electronic labels are associated with particular locations and with certain products; therefore, the location of each electronic label defines a location of the associated product(s).; ¶ 234 – “Furthermore, whilst the electronic labels described above in FIGS. 1 to 9 are indicative of user intent to purchase a particular product, the baskets described in FIGS. 10 and 11 provide further confirmation that the product was purchased, and this further confirmation of purchase can be provided to interested parties to take appropriate action (e.g. to update a display on a particular electronic label, to generate more accurate heatmaps for purchased goods, to generate promotional material, to generate tailored advertisements etc.).”).
Matayoshi does not explicitly disclose wherein a shelf map is static, and updated manually when products are relocated in the store. In a retail shopping facility environment, Jones allows a worker to update a store planogram if a product is to be moved to an updated shelving location (Jones: ¶ 28). A store planogram requiring manual update is an example of a static shelf map. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Matayoshi wherein a shelf map is static, and updated manually when products are relocated in the store so that the product location and planogram information remain as up-to-date and accurate as possible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tuttle (US 2012/0268308) – Discusses the benefits of radar-based RFID systems.
Manku (US 2015/0070218) – Discusses the use of a radar-based RFID method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A