DETAILED ACTION
This Final Office action is in response to Applicant’s Amendment filed on 06/08/2026. Claims 14-17, 21-31 are pending. The effective filing date of the claimed invention is 11/28/2022.
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 .
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 14-17 and 21-31 are rejected under 35 U.S.C. 101 because they are found to be directed to abstract idea.
Step 1 – Claims 14-17 and 21-31 relate to process claims. Step 1 is satisfied.
Step 2A Prong 1 – Exemplary claim 14 recites the following abstract idea: A method for predicting a path of a subject in an area of real space, the method including:
using a plurality of sensors to produce respective sequences of frames of corresponding fields of view in the real space (see e.g. MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014));
identifying, for a particular subject, a determined path in the area of real space over a period of time using the respective sequences of frames produced by sensors in the plurality of sensors, wherein the determined path includes a subject identifier, one or more locations in the area of real space and one or more timestamps (see e.g. MPEP 2106.04(a)(2)(III)(A) In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential));
accumulating multiple determined paths corresponding to multiple subjects over period of time and projecting an overlay . . . (see MPEP 2106.04(a)(2)(III)(A) claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); for the claimed overlay, this is abstract idea of MPEP 2106.04(a)(2)(III), as this can be performed with pen and paper.);
generating a transition matrix based, as least in part, on the accumulated multiple determined paths, wherein an element in the transition matrix identifies a probability of a new subject moving from a first location in the area of real space to at least one of other locations in the area of real space, and wherein respective edges in the graphical representation of the area of real space are weighted in dependence upon elements in the transition matrix ((see MPEP 2106.04(a)(2)(III)(A) claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); MPEP 2106.04(a)(2)(I); for the added weighted in dependence upon the matrix, this is abstract idea MPEP 2106.04(a)(2)(I)); and
predicting a new path of the new subject in the area of real space based, at least in part, on an interaction between the new subject and an item associated with the first location in the area of real space, wherein the predicting of the new path comprises identifying, based on weights associated with the graphical representation of the area of real space, a second location in the area of real space, from the other locations in the area of real space identified included in the transition matrix, having a highest probability associated therewith with respect to movement of the new subject from the first location to the second location (see e.g. MPEP 2106.04(a)(2)(III); and MPEP 2106.04(a)(2)(I)).
When viewed alone and in ordered combination, the examiner finds claim 14 to recite abstract idea.
Step 2A Prong 2 – Claim 14 is not found to integrate the abstract idea into practical application. The additional element(s) recited in claim 14 is “using a plurality of sensors to produce respective sequences of frames. . . .” This falls under the “apply it” rationale, is recited broadly to act as a tool to implement the abstract idea, and does not provide an improvement to the underlying art.
For the calibrating steps, the examiner finds this known activity to be “apply it”. See MPEP 2106.05(f). In particular for the calibrating including generating a transformation based on point correspondence with different camera pov (see Applicant’s originally-filed Specification at e.g. [0140-141]:
[0140] - It is known in the art that this transformation is non-linear. The general form is furthermore known to require compensation for the radial distortion of each camera’s lens, as well as the non-linear coordinate transformation moving to and from the projected space. In external camera calibration, an approximation to the ideal non-linear transformation is determined by solving a non-linear optimization problem. This non-linear optimization function is used by the subject tracking engine 110 to identify the same joints in outputs (arrays of joint data structures, which are data structures that include information about physiological and other types of joints of a subject) of different image recognition engines 112a, 112b and 112n, processing images of cameras 114 with overlapping fields of view. The results of the internal and external camera calibration are stored in a calibration database.
[0141] A variety of techniques for determining the relative positions of the points in images of cameras 114 in the real space can be used. For example, Longuet-Higgins published, “A computer algorithm for reconstructing a scene from two projections” in Nature, Volume 293, 10 September 1981. This paper presents computing a three-dimensional structure of a scene from a correlated pair of perspective projections when spatial relationship between the two projections is unknown. Longuet-Higgins paper presents a technique to determine the position of each camera in the real space with respect to other cameras. Additionally, their technique allows triangulation of a subject in the real space, identifying the value of the z-coordinate (height from the floor) using images from cameras 114 with overlapping fields of view. An arbitrary point in the real space, for example, the end of a shelf unit in one corner of the real space, is designated as a (0, 0, 0) point on the (x, y, z) coordinate system of the real space.
For the “generating a transformation” limitation, see where Applicant admits this is “known” at the time of filing; see also Longuet-Higgins, page 134, e.g. (26). For the “applying the transformation” again see Applicant’s Spec at [0140-141] where this is admittedly known at the time of filing; see also Longuet-Higgins, page 134, (27-28). For the “constructing graphical representation of the area of real space based on the point correspondence, including nodes/edges”, see Applicant’s Spec at e.g. [0140-141] where this is admittedly known in the art at time of filing, [0140] This non-linear optimization function is used by the subject tracking engine 110 to identify the same joints in outputs (arrays of joint data structures, which are data structures that include information about physiological and other types of joints of a subject) of different image recognition engines 112a, 112b and 112n, processing images of cameras 114 with overlapping fields of view.; see Longuet-Higgins, title “A computer-algorithm for reconstructing a scene from two projections” and page 134, “The algorithm yields the most accurate results when applied to situations in which the distance D between the centres of projection is not too small compared with their distances from the points P; If the projective coordinates are accurate to a few seconds of are, the forward coordinates of the P; can be estimated out to about 10D with great accuracy, and even as far as 100D if the P; are adequately spaced in depth. This performance is comparable with that of the human visual system” where the coordinates represent the graphical representation of reconstructing a scene with two viewpoints between the relationship between a first and second camera.” The nodes/edges associated with respective shelves is suggested to be known at Applicant’s Spec [0141]. Because these are known, the examiner finds this to be “apply it” rationale (MPEP 2106.05(f)) as the technology of this calibration was known and simply applied. It is known from 1981.)
When viewed alone and in combination with the rest of the claim, claim 14 is found to be directed to abstract idea.
Step 2B – Claim 14 is not found to include significantly more. The additional element analysis of Step 2A Prong 2 is equally applied to Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. See MPEP 2106.05(d). The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
A part of claim 14 relating to transition matrix generation on the multiple determined paths is similar to a federal circuit WURC finding, where the court has found performing repetitive calculations as WURC in Flook.
Further, using a sensor to gather data is similar to where the court found WURC in Content Extraction, Electronically scanning or extracting data from a physical document, and insignificant data gathering.
The examiner finds the calibrating to be WURC activity. Applicant has described the calibration steps as known prior to filing, and has even provided a reference Longuet-Higgins, within the Specification, that shows how it is performed. This reference was published in 1981. See MEP 2106.05(d)(I)(2) [E]xaminers should rely on what the courts have recognized, or those in the art would recognize, as elements that are well-understood, routine, conventional activity in the relevant field when making the required determination. For example, in many instances, the specification of the application may indicate that additional elements are well-known or conventional. See, e.g., Intellectual Ventures v. Symantec, 838 F.3d 1307, 1317; 120 USPQ2d 1353, 1359 (Fed. Cir. 2016) (“The written description is particularly useful in determining what is well-known or conventional”); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1418 (Fed. Cir. 2015) (relying on specification’s description of additional elements as “well-known”, “common” and “conventional”); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 614, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (Specification described additional elements as “either performing basic computer functions such as sending and receiving data, or performing functions ‘known’ in the art.”). As such, an examiner should determine that an element (or combination of elements) is well-understood, routine, conventional activity only when the examiner can readily conclude, based on their expertise in the art, that the element is widely prevalent or in common use in the relevant industry.
When viewed alone and in ordered combination, the limitations of claim 14 are found to be directed to abstract idea.
Dependent Claims – Claim 15 recites more abstract idea. MPEP 2106/04(a)(2)(III). Claim 16 recites more abstract idea. MPEP 2106.04(a)(2)(III) and (I). Claim 17 recites more abstract idea. MPEP 2106.04(a)(2)(III) and (I). Claims 21-31 recites more abstract idea. See MPEP 2106.04(a)(2)(III) and (I), and (II)(A-B). For the machine learning limitations, this is also found to be ineligible abstract idea, as found in Recentive v. Fox, Fed Cir 2025.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 14, 21, 22, 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pat. No. 7,930,204 to Sharma et al. (“Sharma”) in view of U.S. Pat. Pub. No. 2015/0019391 to Kumar et al. (“Kumar”), in view of Longuet-Higgins published, “A computer algorithm for reconstructing a scene from two projections” in Nature, Volume 293, 10 September 1981 (referred to as “Longuet-Higgins”).
With regard to claim 14, Sharma discloses the claimed method for predicting a path of a subject in an area of real space, the method including:
using a plurality of sensors to produce respective sequences of frames of corresponding fields of view in the real space (Sharma abstract discloses “using arrays of sensing devices, a plurality of means for capturing images”, col. 9, ln. 5-10, “The present invention is called behavior based narrowcasting (BBN). In an exemplary embodiment shown in FIG. 1, the BBN system first captures a plurality of input images of the customer 400 in a store through a plurality of means for capturing images 100.”; Sharma does not disclose “sequences of frames”; However, Kumar teaches at [0015] camera may capture a series of images . . . otherwise known as a series/sequence of frames);
identifying, for a particular subject, a determined path in the area of real space over a period of time using the respective sequences of frames produced by sensors in the plurality of sensors, wherein the determined path includes a subject identifier, one or more locations in the area of real space and one or more timestamps (see Sharma, Fig. 2, col. 9, ln. 61 – col. 10, ln. 20; col. 11, ln. 10-20, individual track outputs; Sharma, col. 7, ln. 15 automatically measures the path a customer takes during a visit to a store; Sharma col. 12, ln. 55-60, tracks can be transformed onto the store floorplan; Sharma at Col. 10, ln. 4-10, subject identifiers; Sharma further discloses col. 7, ln. 35-40, the system tracks and monitors the amount of time a user spends in any given area, known as “dwell time” where the system inherently has timestamps integrated into the system and system data; Sharma col. 14, ln. 60-67, For example, a customer 400 that recently spent large amounts of time in the cosmetic section may be shown a cosmetic advertisement containing references to items on specific shelves where they had shopped; Sharma further discloses col. 15, ln 15, “In the present invention, another exemplary attribute of extracting the interest of the customer 400 or the group of customers 401 can be processed by measuring the time spent in a certain area within the store”; Sharma at col. 15, ln. 25-40, time spent in each section is monitored and known, stored);
accumulating multiple determined paths corresponding to multiple subjects over period of time (see Sharma, Fig. 2, col 9-10);
generating a transition matrix using based, as least in part, on the accumulated multiple determined paths, wherein an element in the transition matrix identifies a probability of a new subject moving from a first location in the area of real space to at least one of other locations in the area of real space, and wherein respective edges in the graphical representation of the area of real space are weighted in dependence upon elements in the transition matrix (see Sharma e.g. Fig. 9 and col. 13, ln. 32-53, transition matrix likelihood of person moving from one camera location to another camera location (field of view); see also Longuet-Higgins, page 134, combination below); and
predicting a new path of the new subject in the area of real space based, at least in part, on an interaction between the new subject and an item associated with the first location in the area of real space, wherein the predicting of the new path comprises identifying, based on weights associated with the graphical representation of the area of the space, a second location in the area of real space, from the other locations in the area of real space identified included in the transition matrix, having a highest probability associated therewith with respect to movement of the new subject from the first location to the second location (Sharma col. 7, ln. 19-23, the data can be used to predict and present the in-store messaging based on the areas they are most likely to shop next or some relevant combination of previously shopped areas.; Sharma, see col. 9 ln. 55-60 dwell time, the measured amount of time the suer spends in any given area is another attribute used in the prediction equation; Sharma col. 15, ln. 13-16, exemplary attribute of extracting the interest of the customer 400 or the group of customers 401 can be processed by measuring the time spent in a certain area within the store. The “highest probability” selection is strongly implied as Sharma uses a transition probability matrix and predicts the most likely next area, it naturally supports identifying a next location corresponding to the maximum transition probability from a current zone/location. Sharma does not disclose conditioning the next location prediction on an interaction with a particular item (“based, at least in part, on an interaction … between the new subject and an item associated with the first location”); Kumar teaches at e.g. abstract that it would have been obvious to track using sequence of images that a user has removed an item (i.e. interacted therewith) and a list of the user interactions is stored in a user specific list, tied to the location. Therefore, it would have been obvious to one of ordinary skill in the retail vision art before the effective filing date of the claimed invention to modify Sharma’s imaging system to make a list of all user interactions using the sequence of images, as shown in Kumar, so that this information can be placed into the transition matrix and used to predict where the user will go next, as shown in Sharma. Furthermore, the advantage of really gathering and storing the list of interactions in Kumar [0002] so it “can be used to replenish inventory located in the shopping areas”; for the predicting aspect, see also Linguot-Higgins, page 134, “The algorithm yields the most accurate results when applied to situations in which the distance D between the centres of projection is not too small compared with their distances from the points P; . If the projective coordinates are accurate to a few seconds of are, the forward coordinates of the P; can be estimated out to about 10D with great accuracy, and even as far as 100D if the P; are adequately spaced in depth. This performance is comparable with that of the human visual system”).
For the added calibrating steps, Sharma/Kumar does not teach these steps. However, as previously indicated, Applicant has provided admissions in the Specification e.g. [0140-141] indicating that this calibration is a known concept, such as shown in Longuet-Higgins, entitled “A computer algorithm for reconstructing a scene from two projections.”
calibrating the plurality of sensors based, at least in part, on the respective sequences of frames (Longuet-Higgins, page 133, “Photogrammetrists know that if a scene is photographed from two viewpoints, then the relationship between the camera positions is uniquely determined, in general, by the photographic coordinates of just five distinguishable points; but actually calculating the structure of the scene from five sets of image coordinates involves the iterative solution of five simultaneous third-order equations”), wherein the calibrating of the plurality of sensors includes:
generating a transformation based on an identified point correspondence between a first field of view corresponding to a first sensor and a second field of view corresponding to a second sensor (Longuet-Higgins, page 133, “As Marr and Poggi have noted, the fusing of two images to produce a three-dimensional percept involves two distinct processes: the establishment of a 1:1 correspondence between Image points in the two view”; page 133 “Let P be a visible point in the scene, and let (Xh X2, X3) and (X;, X~, X;) be its three-dimensional cartesian coordinates with respect to the two viewpoints.”; see where Applicant admits this is “known” at the time of filing; see also Longuet-Higgins, page 134, e.g. (26)),
applying the transformation to the respective sequences of frames to project the point correspondence in the area of real space to a projected space (again see Applicant’s Spec at [0140-141] where this is admittedly known at the time of filing; see also Longuet-Higgins, page 134, (27-28)), and
constructing a graphical representation of the area of real space based, at least in part, on the projected space including the point of correspondence, wherein the graphical representation comprises (i) nodes representing respective shelves in the area of real space and (ii) edges, connecting the nodes, representing a distance between respective pairs of shelves in the area of real space (For the “constructing graphical representation of the area of real space based on the point correspondence, including nodes/edges”, see Applicant’s Spec at e.g. [0140-141] where this is admittedly known in the art at time of filing, [0140] This non-linear optimization function is used by the subject tracking engine 110 to identify the same joints in outputs (arrays of joint data structures, which are data structures that include information about physiological and other types of joints of a subject) of different image recognition engines 112a, 112b and 112n, processing images of cameras 114 with overlapping fields of view.; see Longuet-Higgins, title “A computer-algorithm for reconstructing a scene from two projections” and page 134, “The algorithm yields the most accurate results when applied to situations in which the distance D between the centres of projection is not too small compared with their distances from the points P; If the projective coordinates are accurate to a few seconds of are, the forward coordinates of the P; can be estimated out to about 10D with great accuracy, and even as far as 100D if the P; are adequately spaced in depth. This performance is comparable with that of the human visual system” where the coordinates represent the graphical representation of reconstructing a scene with two viewpoints between the relationship between a first and second camera.” The nodes/edges associated with respective shelves is suggested to be known at Applicant’s Spec [0141]. Because these are known, the examiner finds this to be “apply it” rationale (MPEP 2106.05(f)) as the technology of this calibration was known and simply applied. It is known from 1981.) The examiner notes that the pictures can be taken in any environment, such as a shelving arrangement, and “An arbitrary point in the real space, for example, the end of a shelf unit in one corner of the real space, is designated as a (0, 0, 0) point on the (x, y, z) coordinate system of the real space.” This is from Applicant’s Spec [0141] as known at time of filing. See further, Sharma, at e.g. Fig. 2, showing the multiple cameras in an environment with shelving units, where the distance could be any arbitrary point(s) in the environment, as indicated from Applicant’s Spec.).
Therefore, it would have been obvious to one of ordinary skill in the retail imaging art before the effective filing date to modify Sharma/Kumar with the ability to calibrate said imaging devices when having multiple cameras taking images in the same environment from different POV. The advantage of performing such calibration steps, as shown in Lougin-Higgins, page 134, “The algorithm yields the most accurate results when applied to situations in which the distance D between the centres of projection is not too small compared with their distances from the points P; . If the projective coordinates are accurate to a few seconds of are, the forward coordinates of the P; can be estimated out to about 10D with great accuracy, and even as far as 100D if the P; are adequately spaced in depth. This performance is comparable with that of the human visual system”
With regard to claim 21-22, Sharma discloses changing a preferred placement of a particular item to increase interaction between future subjects and the particular item, wherein the preferred placement of the particular item is changed based, at least in part, on the predicted new path of the new subject in the area of real space (e.g. col. 7, ln 25-35).
With regard to claim 26, Sharma further discloses generating the predicted new path of the new subject in the area of real space starting from a location of a first shelf with which the new subject interacted and ending at an exit location in the area of real space (see e.g. Fig. 2).
With regard to claim 27-28, Sharma further discloses wherein the displaying of the graphical representation comprises proiecting a network graph identifying the nodes and the edges connecting the nodes onto the area of real space, and wherein a projection of the network graph is based, at least in part, on the respective sequences of frames produced by the plurality of sensors (see Fig. 9).
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Kumar, Longuet-Higgins, in view of U.S. Pat. No. 9460350 to Cook et al. (“Cook”).
With regard to claim 15, Sharma further discloses the predicting of the new path of the new subject in the area of real space further includes identifying a third location in the area of real space (see Sharma, col. 7, 15-25, indicating the most likely areas the customer will show next, where this can be a single area, or multiple areas, including a third location), from the other locations in the area of real space identified included in updated transition matrix, having a highest probability associated therewith with respect to items located at the first location and the second location (Sharma does not disclose the updated transition matrix, as claimed. Cook teaches at e.g. patented claim 13, “updating the transition matrix based at least on part on data observed within the physical environment.” See also throughout the Specification of Cook. Therefore, it would have been obvious to one of ordinary skill in the retail customer recognition art before the effective filing date of the claimed invention to modify the combo of Sharma/Kumar to include such update to the transition matrix based on data observed by the various sensors in the physical environment, where the advantage is that this is continually updated so the probability of the entity moving from one sensor to another, or the another to a 2nd another, and so forth, can be continually updated based on the observances within the physical environment by the sensors. See Cook, col. 8 line 50-70)
Claim(s) 16 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Kumar, Longuet-Higgins, in view of U.S. Pat. 8219438 to Moon et al. (“Moon”).
With regard to claim 16, Sharma does not disclose the limitations of claim 16. However, Moon teaches that it would have been obvious to one of ordinary skill in the retail behavior art before the effective filing date of the claimed invention to modify Sharma to include determining the interaction between the new subject and the item associated with the first location in the area of real space based, at least in part, on (i) an angle between a plane connecting shoulder joints of the new subject that is greater than or equal to 40 degrees and less than or equal to 50 degrees corresponding to a plane representing a front side of a shelf at the first location (see Moon at Fig. 16 representing the plane of shoulders to shelf) ii) and when a speed of the new subject that is greater than or equal to 0.15 meters per second and less than or equal to 0.25 meters per second (see Sharma, speed detection at col. 15, ln. 35-55), and iii a distance of the subject that is less than or equal to 1 meter from the shelf at the first location (Sharma, col. 16 ln 33-45, proximity to shelf/products can be measured/estimated). The examiner has shown in Moon where these attributes are taken into account and measured/estimated. The specific numbering in the claims such as the specific velocity of the shopper is considered to be design choice as an engineer designing said system would be able to adjust those numbers as they see fit and as desired by the system. The advantages of added Moon to Sharma/Kumar is shown in Moon at e.g. col. 2, ln. 20-30.
Claim(s) 17, 23-25 is rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Kumar, Longuet-Higgins, in view of U.S. Pat. Pub. No. 2020/0226621 to Garel et al. (“Garel”).
With regard to claim 17, 23, 24, 25, Sharma does not disclose claim 17, 23, 24, 25. See Garel at e.g. [0107], counting number of times customers walk by a specific spot in retail store throughout the day/special day/any day open to public, and then provide updated heat map which is a form of popularity mapping over time. Therefore, it would have been obvious to one of ordinary skill in the retail art before the effective filing date of the claimed invention to modify Sharma to include such counting and heat mapping, as this is beneficial as it allows a user to view how many times customers were walking past a specific point in the store, and then can compare that spot to other spots in the store to determine the most popular, and least popular, spots in the store.
Claim(s) 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Kumar, Longuet-Higgins, in view of U.S. Pat. Pub. No. 2015/0161665 to Grimes et al. (“Grimes”).
With regard to claim 29-31, Sharma does not disclose claims 29-31. Grimes teaches training a machine learning model (see Grimes [0045] refining models based on the known and gathered data) for predicting the path of the subject in the area of real space, the training including: providing, to the machine learning model, labeled training examples, wherein an example in the labeled training examples comprises at least one determined path from the accumulated multiple determined paths for multiple subjects (Grimes [0045]), providing, to the machine learning model, a map of the area of real space comprising locations of shelves in the area of real space (see Grimes [0099] may be provided to models), and providing, to the machine learning model, labels corresponding to products associated with respective shelves in the area of real space (see Grimes [0099] mapping of the item to a location at an individual retail establishment may be established); new path from claim 30, see Grimes [0045]. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to implement said machine learning techniques to take advantage of the benefits and efficiency.
Response to Arguments
Applicant's arguments filed 06/08/2026 have been fully considered but they are not persuasive.
The rejections under 112 have been withdrawn based on the amendments.
Applicant argues that the claims are eligible under 101. The examiner respectfully disagrees. The calibrating was known, WURC activity. When reviewing the other limitations, these limitations can be performed in the human mind, or with pen/paper. The preamble of the claim recites “a method for predicting a path of a subject in an area of real space” using known calibration techniques, and some machine learning in “apply it” manner. The claims do not appear to be eligible. The examiner recommends adding a structural element to each limitation to indicate which structural element is performing each step.
As for the prior art arguments, the examiner respectfully disagrees and has referred to a new reference for the independent claims. Further explanation above.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
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