DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Examiner acknowledges the following data:
Parent data
18496391 filed 10/27/2023 claims foreign priority to 10-2023-0056839, filed 05/02/2023.
Information Disclosure statements
The information disclosure statements (IDS) were submitted and filed on 10/27/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Interpretations - 35 USC § 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Claim limitations “a positioning apparatus” (claims 5 and 6) and “a calculation device" (claims 5 and 6) have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “for” coupled with functional language “using virtual fingerprint maps” (claims 5 and 6) and “estimating the current location” (claims 5 and 6) without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitations invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 5-6 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification, referenced by the PGPUB, for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation:
• “a positioning apparatus” – Specification fig. 6, [15] – It appears that the corresponding structure is a mobile device, ie. computer, UE or watch.
• “a calculation device" – Specification [90] – It appears that the corresponding structure is a processor.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Newton et al (US 11,153,720) in view of Valaee et al (US 2014/0011518).
Regarding claim 1, Newton et al discloses method (fig. 6, method) for generating a fingerprint map for an unexplored area, the method comprising (positioning system 100 described herein can use these labels and/or areas of interest to generate “fingerprints” using certain characteristics. These fingerprints may include identifiable parameters associated with respective APs that allow the positioning system 100 to more accurately map areas of interest within the building, col. 7, lines 46-54):
receiving, by a data processing apparatus, locations of reference points and locations of a plurality of access points (APs) for a service area (scanner 145 scans (receiving) for the APs 120 that are dispersed throughout an area of interest (locations of a plurality of access points (APs)). An area of interest may include an office floorplan, a floor in a building, a set of rooms and a street address (locations of reference points), etc., col. 6, lines 28-33 and col. 8, line 37);
extracting, by the data processing apparatus, feature vectors for each AP on a basis of the locations of the reference points and the locations of the Aps (machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). In some examples, the floorplan of the indoor location may also include locations for areas of interest and a list of APs 120 (feature vectors for each AP) corresponding to their relative positions associated with the floorplan, col. 9, lines 9-26);
generating, by the data processing apparatus, a fingerprint map for each of the APs by inputting, as conditions, the feature vectors extracted for each corresponding AP into a previously trained generative model for each of the APs and inputting random noise (machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). In some examples, the floorplan of the indoor location may also include locations for areas of interest and a list of APs 120 (feature vectors for each AP) corresponding to their relative positions associated with the floorplan. The machine learning model 125 can use location data from the scan data 150 to generate a fingerprint map of the indoor location that includes the relative strength of the signal readings which may include random noise for various areas of interest using the floorplan, col. 9, lines 9-26 and col. 10, lines 20-30); and
Newton et al does not specifically disclose concept of generating, by the data processing apparatus, a final fingerprint map for the service area by combining the fingerprint maps generated for the APs.
However, Valaee et al specifically teaches concept of generating, by the data processing apparatus, a final fingerprint map for the service area by combining the fingerprint maps generated for the Aps (The accuracy of the radio map generation is evaluated by comparing the predicted radio map with actual fingerprints (fingerprint maps) that are manually collected (combined) from the environment; thus is seen as fingerprints (fingerprint maps) that are manually collected (combined) from the environment from access points/ Aps to generate a radio map (fingerprint map), [0225], lines 2-3).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Newton et al with concept of generating, by the data processing apparatus, a final fingerprint map for the service area by combining the fingerprint maps generated for the APs of Valaee et al. One of ordinary skill in the art would have been motivated to make this modification in order to improving determining a dynamic radio printing mapping, (Valaee et al, [0002], line 1).
Regarding claim 2, Newton et al discloses method (fig. 6, method), wherein the generative model is a conditional generative adversarial networks (cGAN) or auxiliary classifier GAN (AC-GAN) (server 110 may train the machine learning model 125 to determine a location of a device when location services are requested. The machine learning model 125 may be trained using any suitable deep learning technique. For instance, the machine learning model 125 may be trained using a deep neural network (“DNN”) (e.g., a recurrent neural network (RNN), long-short term memory network (“LSTM”), convolutional neural network (e.g., a region convolutional neural network (“R-CNN”) or Fast R-CNN), a deep residual network (e.g., ResNet-101), etc, col. 8, lines 39-52).
Regarding claim 3, Newton et al discloses method (fig. 6, method), wherein each feature vector for any one of the APs comprises (fig. 2, machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). In some examples, the floorplan of the indoor location may also include locations for areas of interest and a list of APs 120 (feature vectors for each AP) corresponding to their relative positions associated with the floorplan, col. 9, lines 9-26):
a distance between one reference point and the any one of the APs at each of the reference points (fig. 2, machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). Corpus of training data may include an effective range of an AP 120. The effective range may be associated with a predetermined distance associated with an AP 120, col. 9, lines 9-30); and
an angle formed between the one reference point and the any one of the Aps (fig. 2, machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). Corpus of training data may include an effective range of an AP 120. The effective range may be associated with a predetermined distance associated with an AP 120; thus is seen as fig. 2 shows various angles between the (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points) and the access points (APs), col. 9, lines 9-30).
Regarding claim 4, Newton et al discloses method (fig. 6, method) for generating a fingerprint map for an unexplored area, the method comprising (positioning system 100 described herein can use these labels and/or areas of interest to generate “fingerprints” using certain characteristics. These fingerprints may include identifiable parameters associated with respective APs that allow the positioning system 100 to more accurately map areas of interest within the building, col. 7, lines 46-54):
receiving, by a data processing apparatus, locations of reference points and a location of each access point (AP) for a service area (scanner 145 scans (receiving) for the APs 120 that are dispersed throughout an area of interest (locations of a plurality of access points (APs)). An area of interest may include an office floorplan, a floor in a building, a set of rooms and a street address (locations of reference points), etc., col. 6, lines 28-33 and col. 8, line 37);
extracting, by the data processing apparatus, distances from the respective reference points to each AP and angles formed between the respective reference points and each AP (machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). In some examples, the floorplan of the indoor location may also include locations for areas of interest and a list of APs 120 (feature vectors for each AP which includes distances and angles) corresponding to their relative positions associated with the floorplan, col. 9, lines 9-26); and
generating, by the data processing apparatus, fingerprint maps for the service area by inputting random noise into a pre- trained generative model and inputting, as conditions, the distances and angles of the reference points (Corpus of training data also includes a floorplan. Further, the training data and/or floorplan may include one or more physical characteristics of a building. For instance, the machine learning model 125 can retrieve (inputting) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). In some examples, the floorplan of the indoor location may also include locations for areas of interest and a list of APs 120 (feature vectors for each AP which includes distances and angles) corresponding to their relative positions associated with the floorplan. Once the server 110 receives the corpus of training data, the machine learning model 125 can use the corpus of training data (e.g., scan data 150) to train to determine fingerprints (fingerprint maps) for different locations within areas of interest in a building. Machine learning model 125 can generate “fingerprints” (fingerprint maps) for specific locations or areas of interest. The machine learning model 125 can do so by mapping these locations and/or areas of interest based on signal strength which may include random noise readings associated with the APs 120 that provide identifiable fingerprints., col. 9, lines 9-26 and col. 10, lines 20-42),
Newton et al does not specifically disclose concept of wherein the generative model is a model trained to receive, as the conditions, an input of feature vectors extracted on a basis of the fingerprint maps of a specific area and each AP of the reference points of the specific area and generate the fingerprint maps for the specific area.
However, Valaee et al specifically teaches concept of wherein the generative model is a model trained to receive, as the conditions, an input of feature vectors extracted on a basis of the fingerprint maps of a specific area and each AP of the reference points of the specific area and generate the fingerprint maps for the specific area (Fingerprinting-based positioning systems comes from labeling RSS readings on each reference point manually. EZ model estimates unknown parameters in the radio propagation model using a genetic algorithm (GA). The algorithm is based on a strict assumption on the radio propagation model. Each user carries a mobile device 20 reporting RSS measurements from available APs in an indoor environment. These type of RSS readings can be collected in large quantity through the crowd sourced dynamic way as each user in the environment contributes the RSS scanning result from his/her mobile device 20 to generate the complete RSS radio fingerprint maps that includes all the RSS-position information. The RSS radio map may be a complete table of reported RSS vectors and their location, as FIG. 17 shows. Then, the average of the RSS in the radio fingerprint maps, including the labeled and unlabeled RSS can be represented, [0228], lines 1-2 and 9-11, [0230], lines 1-4 and 6-7, [0231], line 1 and 9-10).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Newton et al with concept of wherein the generative model is a model trained to receive, as the conditions, an input of feature vectors extracted on a basis of the fingerprint maps of a specific area and each AP of the reference points of the specific area and generate the fingerprint maps for the specific area of Valaee et al. One of ordinary skill in the art would have been motivated to make this modification in order to improving determining a dynamic radio printing mapping, (Valaee et al, [0002], line 1)
Regarding claim 5, Newton et al discloses positioning apparatus (fig. 7 item 700, computing device) using virtual fingerprint maps, the positioning apparatus comprising (positioning system 100 described herein can use these labels and/or areas of interest to generate “fingerprints” (fingerprint maps) using certain characteristics. These fingerprints may include identifiable parameters associated with respective APs that allow the positioning system 100 to more accurately map areas of interest within the building. Further, the machine learning model 125 can generate display information (e.g., a GUI, graphical representation, or virtual representation, etc.) that includes the location information associated with the fingerprint, col. 7, lines 46-54, col. 12, lines 59-67):
a communication device for receiving wireless signals from access points (APs) at a current location (scanner 145 scans (receiving) for the APs 120 that are dispersed throughout an area of interest (locations of a plurality of access points (APs)). An area of interest may include an office floorplan, a floor in a building, a set of rooms and a street address (locations of reference points), etc., col. 6, lines 28-33 and col. 8, line 37);
a storage device for storing the virtual fingerprint maps (client device 115 may download location information, such as fingerprints, scan data 150, labels 155, etc., from the cloud (e.g., via location database 140), col. 41-44); and
a calculation device for estimating the current location on a basis of wireless signal strength of each AP with reference to the virtual fingerprint maps (Once the server 110 receives the corpus of training data, the machine learning model 125 (calculation device) can use the corpus of training data (e.g., scan data 150) to train to determine fingerprints (fingerprint maps) for different locations within areas of interest in a building. Machine learning model 125 (calculation device) can generate “fingerprints” (fingerprint maps) for specific locations or areas of interest. The machine learning model 125 (calculation device) can do so by mapping (estimating) these locations and/or areas of interest based on signal strength readings associated with the APs 120 that provide identifiable fingerprints., col. 9, lines 9-26 and col. 10, lines 20-42, col. 11, lines 42-54),
Newton et al does not specifically disclose concept of wherein the virtual fingerprint maps are maps generated by a generative model trained to receive, as conditions, an input of feature vectors extracted on a basis of fingerprint maps of a specific area and each AP of reference points of the specific area and generate the fingerprint maps for the specific area.
However, Valaee et al specifically teaches concept of wherein the virtual fingerprint maps are maps generated by a generative model trained to receive, as conditions, an input of feature vectors extracted on a basis of fingerprint maps of a specific area and each AP of reference points of the specific area and generate the fingerprint maps for the specific area (Fingerprinting-based positioning systems comes from labeling RSS readings on each reference point manually. EZ model estimates unknown parameters in the radio propagation model using a genetic algorithm (GA). The algorithm is based on a strict assumption on the radio propagation model. Each user carries a mobile device 20 reporting RSS measurements from available APs in an indoor environment. These type of RSS readings can be collected in large quantity through the crowd sourced dynamic way as each user in the environment contributes the RSS scanning result from his/her mobile device 20 to generate the complete RSS radio fingerprint maps that includes all the RSS-position information. The RSS radio map may be a complete table of reported RSS vectors and their location, as FIG. 17 shows. Then, the average of the RSS in the radio fingerprint maps, including the labeled and unlabeled RSS can be represented, [0228], lines 1-2 and 9-11, [0230], lines 1-4 and 6-7, [0231], line 1 and 9-10).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Newton et al with concept of wherein the virtual fingerprint maps are maps generated by a generative model trained to receive, as conditions, an input of feature vectors extracted on a basis of fingerprint maps of a specific area and each AP of reference points of the specific area and generate the fingerprint maps for the specific area of Valaee et al. One of ordinary skill in the art would have been motivated to make this modification in order to improving determining a dynamic radio printing mapping, (Valaee et al, [0002], line 1)
Regarding claim 6, Newton et al discloses positioning apparatus (fig. 7 item 700, computing device), wherein the feature vectors comprise (machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). In some examples, the floorplan of the indoor location may also include locations for areas of interest and a list of APs 120 (feature vectors for each AP) corresponding to their relative positions associated with the floorplan, col. 9, lines 9-26):
distances between one reference point and each AP in the specific area for each of the reference points in the specific area (fig. 2, machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). Corpus of training data may include an effective range of an AP 120. The effective range may be associated with a predetermined distance associated with an AP 120, col. 9, lines 9-30); and
angles formed between the one reference point and each AP in the specific area (fig. 2, machine learning model 125 can retrieve (extracts) a floorplan of the indoor location that includes pre-labeled areas of interest (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points). Corpus of training data may include an effective range of an AP 120. The effective range may be associated with a predetermined distance associated with an AP 120; thus is seen as fig. 2 shows various angles between the (e.g., rooms, hallways, common areas, walls, floors, levels, etc.) (reference points) and the access points (APs), col. 9, lines 9-30).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FRANTZ BATAILLE whose telephone number is (571)270-7286. The examiner can normally be reached Monday-Friday 9:00 AM-5:00 PM.
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/FRANTZ BATAILLE/ Primary Examiner, Art Unit 2681