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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 XXXXXXXXXXXXXX has been entered.
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 .
Status of Claims
Claims X are canceled.
Claims X are amended.
Claims X are new.
Claims X are pending and have been examined.
This action is in reply to the papers filed on XXX (effective filing date xxx).
Claims 1-16 are pending and have been examined.
This action is in reply to the papers filed on 05/20/2022 (effective filing date 04/15/2022).
Information Disclosure Statement
The information disclosure statement(s) submitted: 10/02/2024, 07/22/2025, 08/01/2025, has/have been considered by the Examiner and made of record in the application file.
Amendment
The present Office Action is based upon the original patent application filed on xxx as modified by the amendment filed on xxx.
Terminal Disclaimer
The terminal disclaimer filed on xxx disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of US Pat. No. xxxx has been reviewed and has been placed in the file.
Examiner acknowledges Applicant’s filed Terminal Disclaimer to prior art patent McCauley et al. US Pat. No. 5,930,775. A terminal disclaimer may be filed to overcome or obviate a nonstatutory double patenting rejection (37 CFR 1.321; MPEP 706.02; 1490).
Double Patenting - Withdrawn
The double patenting rejection is withdrawn per the filed terminal disclaimer noted above.
Reasons For Allowance
Prior-Art Rejection withdrawn
Claims xxx are allowed. The closest prior art (See PTO-892, Notice of References Cited) does not teach the claimed:
The invention teaches… and the prior-art teaches…, however, the prior-art does not teach…
The closest prior-art (xxx) teach the features as disclosed in Non-final Rejection (xxxx), however, these cited references do not teach and the prior-art does not teach at least the following combination of features and/or elements:
determining, at a second time after associating the information corresponding to the first loyalty card with the logged location, that a second user computing device is located within a specified distance of the logged location using a second positioning system of the second user computing device; in response to determining that the second user computing device is located within the specified distance of the logged location of the first user computing device at the first time of detecting: retrieving information corresponding to a second loyalty card, the second loyalty card being associated with the merchant and the second user computing device; and displaying, by the second user computing device, data describing the second loyalty card.
Claim Rejections - 35 USC §101 - Withdrawn
Per Applicant’s amendments and arguments and considering new guidance in the MPEP, the rejections are withdrawn. Specifically, in Applicant’s Remarks (dated 03/14/2017, pgs. 8-11), Applicant traverses the 35 USC §101 rejections arguing that the amended claims recite new limitations that are not abstract, amount to significantly more, are directed to a practical application, etc… For example, Applicant argues….
In support of their arguments, Applicant cites to the following recent Fed. Cir. court cases (i.e., Alice Corp. v. CLS Bank Int’l, SRI Int’l, Inc. v. Cisco Systems, Inc., Ultramercial, Inc. v. Hulu, LLC, Berkheimer, Core Wireless, McRO, Enfish, Bascom, DDR, etc…).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-16 are rejected on the ground of anticipatory-nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,406,274.
17/749,206 – Claim 1. A method of improving efficiency of clickstream data processing comprising the steps of:
US 12,406,274 – Claim 1. An apparatus for improving efficiency of clickstream data processing comprising: a processor; and a memory including instructions that, when executed by the processor, cause the processor to:
17/749,206 – Claim 1. extracting the clickstream data and transforming the clickstream data into a probability matrix, where the probability matrix comprises a probability of proceeding from a first page to a second page;
US 12,406,274 – Claim 1. extract the clickstream data; transform the clickstream data into a probability matrix, wherein the clickstream data comprises a plurality of pages and the probability matrix comprises a plurality of entries, each entry of the probability matrix comprising a respective probability of proceeding from a first one of the plurality of pages to a second one of the plurality of pages;
17/749,206 – Claim 1. transforming the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP) and
US 12,406,274 – Claim 1. transform the probability matrix into two dimensional data by reducing dimensionality of the probability matrix using a Uniform Manifold Approximation and Projection algorithm (UMAP);
17/749,206 – Claim 1. generating a cluster graph visualizing a plurality of clusters.
US 12,406,274 – Claim 1. generate, by a Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm based on the two dimensional data, a cluster graph visualizing a plurality of clusters of the two dimensional data;
17/749,206 – Claim 2. The method of claim 1 further comprising the steps of: feeding the two dimensional data into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), and
US 12,406,274 – Claim 1. generate, by a Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm based on the two dimensional data, a cluster graph visualizing a plurality of clusters of the two dimensional data;
17/749,206 – Claim 2. identifying the center of each cluster of the plurality of clusters.
US 12,406,274 – Claim 1. determine, by the DBSCAN algorithm based on the two dimensional data, a respective center of each cluster of the plurality of clusters;
17/749,206 – Claim 3. The method of claim 1 further comprising the steps of: applying a K-Nearest Neighbor algorithm (KNN) to identify data points closest to the center of each cluster, and
US 12,406,274 – Claim 1. process, by a K-Nearest Neighbor (KNN) algorithm, the generated cluster graph and the determined centers of each cluster, to determine a respective subset of data points of the two dimensional data closest to the center of each cluster;
17/749,206 – Claim 3. shading each of the data points to graphically identify each cluster of the plurality of clusters.
US 12,406,274 – Claim 1. shade, based on the determined edges of each cluster, each of the data points within each respective subset to graphically identify each cluster of the plurality of clusters in the cluster graph; and output the cluster graph on a display.
17/749,206 – Claim 4. The method of claim 3 further comprising the step of: illustrating the graph on the display representative of the data points shaded following application of the KNN algorithm.
US 12,406,274 – Claim 20. The apparatus of claim 19, wherein the KNN algorithm shades each of the data points.
17/749,206 – Claim 5. The method of claim 1 further comprising the step of: prior to extracting the clickstream data, first receiving and storing in a memory of a computer clickstream data over a predetermined period of time.
US 12,406,274 – Claim 2. The apparatus of claim 1, wherein the memory further includes instructions that, when executed by the processor, cause the processor to: prior to the extraction of the clickstream data, first receive and store in the memory of the computer clickstream data over a predetermined period of time.
17/749,206 – Claim 6. The method of claim 5 wherein the predetermined time period is 90 days.
US 12,406,274 – Claim 3. The apparatus of claim 2, wherein the predetermined time period is 90 days.
17/749,206 – Claim 7. The method of claim 5 wherein the predetermine time period is one month.
US 12,406,274 – Claim 4. The apparatus of claim 3, wherein the predetermined time period is one month.
17/749,206 – Claim 8. The method of claim 3 further comprising the step of: labeling observations of each cluster of the plurality of clusters based on common features following the application of the KNN algorithm.
US 12,406,274 – Claim 8. The apparatus of claim 1, wherein the memory further includes instructions that, when executed by the processor, cause the processor to: label observations of each cluster of the plurality of clusters based on common features following the application of the KNN algorithm.
17/749,206 – Claim 9. The method of claim 2 further comprising the step of: downsampling the two dimensional data from the UMAP algorithm, and using the downsampled data to reduce the density before the application of the DBSCAN algorithm.
US 12,406,274 – Claim 9. The apparatus of claim 1, wherein the memory further includes instructions that, when executed by the processor, cause the processor to: downsample the two dimensional data from the UMAP algorithm; and use the downsampled data to reduce the density before the application of the DBSCAN algorithm.
17/749,206 – Claim 10. The method of claim 9 further comprising the step of: labeling the remaining data points in the respective clusters following the downsampling.
US 12,406,274 – Claim 10. The apparatus of claim 9, wherein the memory further includes instructions that, when executed by the processor, cause the processor to: label the remaining data points in the respective clusters.
17/749,206 – Claim 11. The method of claim 5 wherein the clickstream data comprises clickstreams received from a mobile based platform and/or a web based platform.
US 12,406,274 – Claim 5. The apparatus of claim 2, wherein the clickstream data comprises clickstreams received from a mobile based platform and/or a web based platform.
17/749,206 – Claim 12. The method of claim 1 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
US 12,406,274 – Claim 7. The apparatus of claim 2, wherein the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
17/749,206 – Claim 13. The method of claim 2 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
US 12,406,274 – Claim 7. The apparatus of claim 2, wherein the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
17/749,206 – Claim 14. The method of claim 3 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
US 12,406,274 – Claim 7. The apparatus of claim 2, wherein the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
17/749,206 – Claim 15. The method of claim 5 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
US 12,406,274 – Claim 7. The apparatus of claim 2, wherein the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm.
17/749,206 – Claim 16. The method of claim 11 wherein the clickstream data further comprises data from 100,000 patrons who utilize the mobile based platform and/or the web based platform.
US 12,406,274 – Claim 6. The apparatus of claim 5, wherein the clickstream data further comprises data from 100,000 patrons who utilize the mobile based platform and/or the web based platform.
The remaining independent claims contain feature similar to that of claim 1 and are rejected accordingly. The dependent claims are further rejected for their dependency upon a rejected independent base claim.
Claims 1-16 are rejected on the ground of ‘Provisional’ double patenting as being unpatentable over claims 1-20 of Application No. 17/659,407 (Notice of Allowance dated 01/05/2026), U.S. Patent No. ‘Not Yet Issued’.
17/749,206 – Claim 1. A method of improving efficiency of clickstream data processing comprising the steps of: extracting the clickstream data and transforming the clickstream data into a probability matrix, where the probability matrix comprises a probability of proceeding from a first page to a second page;
Application No. 17/659,407 (Notice of Allowance dated 01/05/2026; US Patent No. Not yet Issued) Claim 1. extract the patron clickstream data and transform the patron clickstream data into a probability matrix, where the probability matrix comprises a probability that a patron would proceed from a first page to a second page;
transforming the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP) and
Application No. 17/659,407 (Notice of Allowance dated 01/05/2026; US Patent No. Not yet Issued) Claim 1. transform the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP) and generate a cluster graph visualizing a plurality of clusters;
generating a cluster graph visualizing a plurality of clusters.
Application No. 17/659,407 (Notice of Allowance dated 01/05/2026; US Patent No. Not yet Issued) Claim 1. using the two dimensional data from the UMAP algorithm and the center of each cluster from the DBSCAN algorithm, apply a K-Nearest Neighbor algorithm (KNN) to identify data points closest to the center of each cluster and shade each of the data points to graphically identify each cluster of the plurality of clusters; and illustrate a graph on the display representative of the data points shaded following application of the KNN algorithm.
The remaining independent claims contain feature similar to that of claim 1 and are rejected accordingly. The dependent claims are further rejected for their dependency upon a rejected independent base claim.
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 1-16 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter because the claimed invention is directed to an abstract idea without significantly more. These claims recite a method for graphically clustering high dimensional patron clickstream data.
Claim 1 recites [a] method of improving efficiency of clickstream data processing comprising the steps of: extracting the clickstream data and transforming the clickstream data into a probability matrix, where the probability matrix comprises a probability of proceeding from a first page to a second page; transforming the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP) and generating a cluster graph visualizing a plurality of clusters.
The claims are being rejected according to the 2019 Revised Patent Subject Matter Eligibility Guidance (Federal Register, Vol. 84, No. 5, p. 50-57 (Jan. 7, 2019)).
Step 1: Does the Claim Fall within a Statutory Category?
Yes. Claims 1-16 recite a method and, therefore, are directed to the statutory class of a process.
Step 2A, Prong One: Is a Judicial Exception Recited?
Yes. The following tables identify the specific limitations that recite an abstract idea. The column that identifies the additional elements will be relevant to the analysis in step 2A, prong two, and step 2B.
Claim 1: Identification of Abstract Idea and Additional Elements, using Broadest Reasonable Interpretation
Claim Limitation
Abstract Idea
Additional Element
1. A method of improving efficiency of clickstream data processing comprising the steps of:
No additional elements are positively claimed.
extracting the clickstream data and transforming the clickstream data into a probability matrix, where the probability matrix comprises a probability of proceeding from a first page to a second page;
This limitation includes the step(s) of: extracting the clickstream data and transforming the clickstream data into a probability matrix, where the probability matrix comprises a probability of proceeding from a first page to a second page.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information in order to facilitate graphically clustering high dimensional patron clickstream data which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
transforming the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP) and
This limitation includes the step(s) of: transforming the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP).
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information in order to facilitate graphically clustering high dimensional patron clickstream data which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
generating a cluster graph visualizing a plurality of clusters.
This limitation includes the step(s) of: generating a cluster graph visualizing a plurality of clusters.
No additional elements are positively claimed.
This limitation is directed to processing and/or communicating known information in order to facilitate graphically clustering high dimensional patron clickstream data which may be categorized as any of the following:
mathematical concept (mathematical relationships, mathematical formulas or equations, mathematical calculations)
and/or
mental process – concepts performed in the human mind (including an observation, evaluation, judgment, opinion)
and/or
certain method of organizing human activity –
fundamental economic principles or practices (including hedging, insurance, mitigating risk).
No additional elements are positively claimed.
As shown above, under Step 2A, Prong One, the claims recite a judicial exception (an abstract idea). The claims are directed to the abstract idea of graphically clustering high dimensional patron clickstream data, which, pursuant to MPEP 2106.04, is aptly categorized as a mental process and/or a method of organizing human activity. Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
The method claims do NOT recite any additional elements. Consequently, at least the method claims must be construed as abstract and capable of being performed mentally and/or manually with just pen and paper. The Office encourages Applicant to positively claim the structural features necessary to perform each individual method step and feature.
Next, the aforementioned claims recite additional functional elements that are associated with the judicial exception, including: Scanning the bar code of an item using an RFID reader, receiving a WAP link, and sending an SMS message to a user with a link to webpages. Examiner understands these limitations to be insignificant extrasolution activity. (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”).
The aforementioned claims also recite additional technical elements including: a “processor” to execute the method, a “non-transitory machine-readable storage device” for storing executable instructions, an “RFID reader” scanning an image (code); a “graphical user interface” for following and displaying the price of an item; a “merchant device” for sending and receiving data, an “offer intelligence tool” for executing the method, a “WAP link” for connecting to a wireless network and transmitting data, and a “mobile device” for executing a GUI and transmitting data. These limitations are recited at a high level of generality and appear to be nothing more than generic computer components. Examiner further notes that the “offer intelligence tool” is nothing more than a software application being executed on a generic retailer computer. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Step 2A, Prong Two: Is the Abstract Idea Integrated into a Practical Application?
No. The judicial exception is not integrated into a practical application. The additional elements listed above that relate to computing components are recited at a high level of generality (i.e., as generic components performing generic computer functions such as communicating, receiving, processing, analyzing, and outputting/displaying data) such that they amount to no more than mere instructions to apply the exception using generic computing components. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Additionally, the claims do not purport to improve the functioning of the computer itself. There is no technological problem that the claimed invention solves. Rather, the computer system is invoked merely as a tool. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, these claims are directed to an abstract idea.
Furthermore, looking at the elements individually and in combination, under Step 2A, Prong Two, the claims as a whole do not integrate the judicial exception into a practical application because they fail to: improve the functioning of a computer or a technical field, apply the judicial exception in the treatment or prophylaxis of a disease, apply the judicial exception with a particular machine, effect a transformation or reduction of a particular article to a different state or thing, or apply the judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Rather, the claims merely use a computer as a tool to perform the abstract idea(s), and/or add insignificant extra-solution activity to the judicial exception, and/or generally link the use of the judicial exception to a particular technological environment.
Step 2B: Does the Claim Provide an Inventive Concept?
Next, under Step 2B, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered both individually and as an ordered combination, do not amount to significantly more than the abstract idea. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Simply put, as noted above, there is no indication that the combination of elements improves the functioning of a computer (or any other technology), and their collective functions merely provide conventional computer implementation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements relating to computing components amount to no more than applying the exception using a generic computing components. Mere instructions to apply an exception using a generic computing component cannot provide an inventive concept. Furthermore, the broadest reasonable interpretation of the claimed computer components (i.e., additional elements) includes any generic computing components that are capable of being programmed to communicate, receive, send, process, analyze, output, or display data. Furthermore, Applicant’s Specification (PGPub. 2023/0334512 [0033]) refers to a general computer system, but they do not include any technically-specific computer algorithm or code.
Additionally, pursuant to the requirement under Berkheimer, the following citations are provided to demonstrate that the additional elements, identified as extra-solution activity, amount to activities that are well-understood, routine, and conventional. See MPEP 2106.05(d).
Capturing an image (code) with an RFID reader. Ritter, US Patent No. 7734507 (Col. 3, Lines 56-67); “RFID: Riding on the Chip” by Pat Russo. Frozen Food Age. New York: Dec. 2003, vol. 52, Issue 5; page S22.
Receiving or transmitting data over a network. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362; OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Storing and retrieving information in memory. Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
Outputting/Presenting data to a user. Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3).
Using a machine learning model to determine user segment characteristics for an ad campaign. https://whites.agency/blog/how-to-use-machine-learning-for-customer-segmentation/.
Thus, taken alone and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea), and are ineligible under 35 USC 101.
Dependent claims 2-16 further describe the abstract idea. The additional elements of the dependent claims fail to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea. Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
As such, the claims are not patent eligible.
Invention Could be Performed Manually
It is conceivable that the invention could be performed manually without the aid of machine and/or computer. For example, Applicant claims extracting data, transforming a matrix, and generating a graph. Each of these features could be performed manually and/or with the aid of a simple generic computer to facilitate the transmission of data.
See also Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., and In re Venner, which stand for the concept that automating manual activity and/or applying modern electronics to older mechanical devices to accomplish the same result is not sufficient to distinguish over the prior art. Here, applicant is merely claiming computers to facilitate and/or automate functions which used to be commonly performed by a human.
Leapfrog Enterprises, Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 82 USPQ2d 1687 (Fed. Cir. 2007) "[a]pplying modern electronics to older mechanical devices has been commonplace in recent years…"). The combination is thus the adaptation of an old idea or invention using newer technology that is commonly available and understood in the art.
In In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958), the court held that broadly providing an automatic or mechanical means to replace manual activity which accomplished the same result is not sufficient to distinguish over the prior art. MPEP 2144.04, III Automating a Manual Activity.
MPEP 2144.04 III - Automating a Manual Activity and In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) further stand for and provide motivation for using technology, hardware, computer, or server to automate a manual activity.
Therefore, the Office finds no improvements to another technology or field, no improvements to the function of the computer itself, and no meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Therefore, based on the two-part Alice Corp. analysis, there are no limitations in any of the claims that transform the exception (i.e., the abstract idea) into a patent eligible application.
Claim Rejections - Not an Ordered Combination
None of the limitations, considered as an ordered combination provide eligibility, because taken as a whole, the claims simply instruct the practitioner to implement the abstract idea with routine, conventional activity.
Claim Rejections - Preemption
Allowing the claims, as presently claimed, would preempt others from graphically clustering high dimensional patron clickstream data. Furthermore, the claim language only recites the abstract idea of performing this method, there are no concrete steps articulating a particular way in which this idea is being implemented or describing how it is being performed.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1 is rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445.
17/749,206 – Claim 1. Shen et al. 2014/0101580 teaches A method of improving efficiency of clickstream data processing comprising the steps of: extracting the clickstream data and transforming the clickstream data into a probability matrix (Shen et al. 2014/0101580 [0025 - probability model may comprise a Markov chain] In some embodiments, the system may further comprise a data mapping module, executable by the machine, configured to map clickstream data to a two-dimensional plane to form the plurality of clickstreams. In some embodiments, the data mapping module may be configured to apply an SOM with a probability model to the clickstream data. In some embodiments, the probability model may comprise a Markov chain.), where the probability matrix comprises a probability of proceeding from a first page to a second page (Shen et al. 2014/0101580 [0075 - The "similarity" between a clickstream and a probability model may be measured by the probability at which the clickstream fits in the model. As such, SOM with probability models can be applied to map the clickstream data]); transforming the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP) and generating a cluster graph visualizing a plurality of clusters (Shen et al. 2014/0101580 [0093 - the 2D visualization 700b shown in FIG. 7B may be provided for visual cluster exploration]).
Shen et al. 2014/0101580 may not expressly disclose the “transform the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP)” features, however, Martinec et al. 2022/0391445 disclose these features as follows (Martinec et al. 2022/0391445 [0032 - The evaluation agent 16 gathers browsing history and clickstreams from a browser 50 with which it is integrated or in communication with, which data is transmitted to the evaluation manager 20 via an evaluation application program interface (“API”)][0074 - A data analyzing method for dimensionality reduction such as t-SNE or Uniform Manifold Approximation and Projection (“UMAP”) is applied to reduce the number of columns of the table to two, without affecting the rows which still correspond to the search results. These two newly created columns can be represented as x and y coordinates. Clusters are formed of coordinates that are close to each other]).
Shen et al. 2014/0101580 and Martinec et al. 2022/0391445 are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include transform the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP); apply a K-Nearest Neighbor algorithm (KNN) to identify data points closest to the center of each cluster; and determine, based on the KNN algorithm, a respective edge of each cluster in Shen et al. 2014/0101580, as seen in Martinec et al. 2022/0391445, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445; in further view of Saunkeah et al. 2023/0162241.
17/749,206 – Claim 2. The method of claim 1 further comprising the steps of: Shen et al. 2014/0101580 may not expressly disclose the following features, however, Saunkeah et al. 2023/0162241 teaches feeding the two dimensional data into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), and identifying the center of each cluster of the plurality of clusters (Saunkeah et al. 2023/0162241 [0037 - through the cookie, the gift advertisement sub-system 104 may collect clickstream data (e.g., data corresponding to webpages the user has accessed)][0195 - A set of data may be analyzed using one of a variety of machine learning algorithms to identify correlations between different elements of the set of data without supervision and feedback (e.g., an unsupervised training technique). A machine learning data analysis algorithm may also be trained using sample or live data to identify potential correlations. Such algorithms may include k-means clustering algorithms, fuzzy c-means (FCM) algorithms, expectation-maximization (EM) algorithms, hierarchical clustering algorithms, density-based spatial clustering of applications with noise (DBSCAN) algorithms]). Shen et al. 2014/0101580 and Saunkeah et al. 2023/0162241 are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include feed the two dimensional data into a Density Based Spatial Clustering of Applications with Noise algorithm (DBSCAN); and identify the center of each cluster of the plurality of clusters in Shen et al. 2014/0101580, as seen in Saunkeah et al. 2023/0162241, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445; in view of Pai et al. US 10,115,121.
17/749,206 – Claim 3. Shen et al. 2014/0101580 further teaches The method of claim 1 further comprising the steps of: applying a K-Nearest Neighbor algorithm (KNN) to identify data points closest to the center of each cluster, and shading each of the data points to graphically identify each cluster of the plurality of clusters (Shen et al. 2014/0101580 [0093 - the 2D visualization 700b shown in FIG. 7B may be provided for visual cluster exploration. As previously described, each rectangular block denotes a clickstream pattern]).
Shen et al. 2014/0101580 may not expressly disclose the “apply a K-Nearest Neighbor algorithm (KNN) …” features, however, Pai et al. US 10,115,121 teaches (Pai et al. US 10,115,121 [column 2, lines 40-45 – determining of the mathematical distances may also involve the use of a learning algorithm, such as, for example, a large margin nearest neighbor (LMNN) algorithm. In some implementations, the resulting mathematical distances may then be used to classify or group the visitor sessions using a k-nearest neighbor (kNN) algorithm][column 11, lines 8-13 – In one example, those visitor sessions that resulted in a purchase from the website are labeled as residing in the target group, while those visitor sessions that did not result in a purchase are labeled as belonging to the non-target group. As mentioned above, one particular classification algorithm that may be utilized is the k-nearest neighbor (kNN) algorithm]). Shen et al. 2014/0101580, Martinec et al. 2022/0391445 and Pai et al. US 10,115,121 are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include transform the probability matrix by reducing dimensionality into two dimensional data using a Uniform Manifold Approximation and Projection algorithm (UMAP); apply a K-Nearest Neighbor algorithm (KNN) to identify data points closest to the center of each cluster; and determine, based on the KNN algorithm, a respective edge of each cluster in Shen et al. 2014/0101580, as seen in Martinec et al. 2022/0391445 and Pai et al. US 10,115,121, respectively, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445; in view of Pai et al. US 10,115,121.
17/749,206 – Claim 4. Shen et al. 2014/0101580 further teaches The method of claim 3 further comprising the step of: illustrating the graph on the display representative of the data points shaded following application of the KNN algorithm (Shen et al. 2014/0101580 [Figs. 7-11; 0119] The data visualization module 1230 may be configured to perform any of the data visualization functions disclosed herein (e.g., the functions described in the "VISUALIZATION" section). In some embodiments, the data visualization module 1230 may be configured to receive the mapped clickstreams from the data mapping module 1220. The data visualization module 1230 may be configured to cause a visual representation of each clickstream to be displayed on a device (e.g., a computer). Each visual representation may comprise a distinct graphical element for each user action of the corresponding clickstream, and each visual representation may be configured to indicate a frequency level of the corresponding clickstream. In some embodiments, each distinct graphical element may comprise a geometric shape and a corresponding color that distinctly represents the corresponding user action. In some embodiments, the geometric shape may be a rectangle. However, it is contemplated that other geometric shapes are also within the scope of the present disclosure. In some embodiments, graphical elements of each visual representation may comprise a size that is proportional to the frequency level of the corresponding clickstream.).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445.
17/749,206 – Claim 5. Shen et al. 2014/0101580 further teaches The method of claim 1 further comprising the step of: prior to extracting the clickstream data, first receiving and storing in a memory of a computer clickstream data over a predetermined period of time (Shen et al. 2014/0101580 [0067 - First, at 410, clickstream data may be received. This clickstream data may be obtained from one or more databases]).
Claims 6-8 are rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445; in view of Pai et al. US 10,115,121.
17/749,206 – Claim 6. The method of claim 5 Shen et al. 2014/0101580 may not expressly disclose the following features, however, Pai et al. US 10,115,121 teaches wherein the predetermined time period is 90 days (Pai et al. US 10,115,121 [column 11, lines 47-53 – disclose based on new visitor session data, including visitor session data associated with both new users and previous users, the methods discussed above may be performed periodically, such as, for example, once a day or week. The resulting classification or labeling of new visitor sessions may then be employed to direct new advertising or offers on an ongoing basis]). Shen et al. 2014/0101580 and Pai et al. US 10,115,121 are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the predetermined time period is 90 days and the predetermine time period is one month in Shen et al. 2014/0101580, as seen in Pai et al. US 10,115,121, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
17/749,206 – Claim 7. The method of claim 5 Shen et al. 2014/0101580 may not expressly disclose the following features, however, Pai et al. US 10,115,121 teaches wherein the predetermine time period is one month (Pai et al. US 10,115,121 [column 11, lines 47-53 – disclose based on new visitor session data, including visitor session data associated with both new users and previous users, the methods discussed above may be performed periodically, such as, for example, once a day or week. The resulting classification or labeling of new visitor sessions may then be employed to direct new advertising or offers on an ongoing basis]). Shen et al. 2014/0101580 and Pai et al. US 10,115,121 are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the predetermined time period is 90 days and the predetermine time period is one month in Shen et al. 2014/0101580, as seen in Pai et al. US 10,115,121, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
17/749,206 – Claim 8. The method of claim 3 further comprising the step of: Shen et al. 2014/0101580 may not expressly disclose the following features, however, Pai et al. US 10,115,121 teaches labeling observations of each cluster of the plurality of clusters based on common features following the application of the KNN algorithm (Pai et al. US 10,115,121 [column 2, lines 40-45 – determining of the mathematical distances may also involve the use of a learning algorithm, such as, for example, a large margin nearest neighbor (LMNN) algorithm. In some implementations, the resulting mathematical distances may then be used to classify or group the visitor sessions using a k-nearest neighbor (kNN) algorithm]). Shen et al. 2014/0101580 and Pai et al. US 10,115,121 are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include label observations of each cluster of the plurality of clusters based on common features following the application of the KNN algorithm in Shen et al. 2014/0101580, as seen in Pai et al. US 10,115,121, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445; in further view of Saunkeah et al. 2023/0162241.
17/749,206 – Claim 9. The method of claim 2 further comprising the step of: Shen et al. 2014/0101580 may not expressly disclose the following features, however, Martinec et al. 2022/0391445 teaches downsampling the two dimensional data from the UMAP algorithm, and using the downsampled data to reduce the density before the application of the DBSCAN algorithm (Martinec et al. 2022/0391445 [0032 - The evaluation agent 16 gathers browsing history and clickstreams from a browser 50 with which it is integrated or in communication with, which data is transmitted to the evaluation manager 20 via an evaluation application program interface (“API”) 32][0074 - A data analyzing method for dimensionality reduction such as t-SNE or Uniform Manifold Approximation and Projection (“UMAP”) is applied to reduce the number of columns of the table to two, without affecting the rows which still correspond to the search results. These two newly created columns can be represented as x and y coordinates. Clusters are formed of coordinates that are close to each other]). Shen et al. 2014/0101580 and Martinec et al. 2022/0391445 are concerned with effective data analysis. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include downsample the two dimensional data from the UMAP algorithm; and use the downsampled data to reduce the density before the application of the DBSCAN algorithm in Shen et al. 2014/0101580, as seen in Martinec et al. 2022/0391445, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
17/749,206 – Claim 10. Shen et al. 2014/0101580 further teaches The method of claim 9 further comprising the step of: labeling the remaining data points in the respective clusters following the downsampling (Shen et al. 2014/0101580 [0067 - Analysts may also label or name the selected clusters and their corresponding detailed information, and store them in one or more databases. The labels or names may be used to organize and search for the clusters and their corresponding detailed information. At 450, the selected groups can be shown with statistical summarization and/or used to refine the clustering result]).
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445.
17/749,206 – Claim 11. Shen et al. 2014/0101580 further teaches The method of claim 5 wherein the clickstream data comprises clickstreams received from a mobile based platform and/or a web based platform (Shen et al. 2014/0101580 [0101 - Understanding how people use a website is critical to the success of an e-commerce business. Analysts and product managers often have difficulties in obtaining insights into user behavior patterns from the clickstream data]).
17/749,206 – Claim 12. Shen et al. 2014/0101580 further teaches The method of claim 1 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (Shen et al. 2014/0101580 disclose the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (i.e., Shen et al does not recite the use of an NLP algorithm)).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445; in further view of Saunkeah et al. 2023/0162241.
17/749,206 – Claim 13. Shen et al. 2014/0101580 further teaches The method of claim 2 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (Shen et al. 2014/0101580 disclose the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (i.e., Shen et al does not recite the use of an NLP algorithm)).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445; in view of Pai et al. US 10,115,121.
17/749,206 – Claim 14. Shen et al. 2014/0101580 further teaches The method of claim 3 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (Shen et al. 2014/0101580 disclose the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (i.e., Shen et al does not recite the use of an NLP algorithm)).
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over: Shen et al. 2014/0101580; in view of Martinec et al. 2022/0391445.
17/749,206 – Claim 15. Shen et al. 2014/0101580 further teaches The method of claim 5 wherein the method is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (Shen et al. 2014/0101580 disclose the apparatus is characterized by a lack of any step utilizing functionality of a Natural Language Processing algorithm (i.e., Shen et al does not recite the use of an NLP algorithm)).
17/749,206 – Claim 16. Shen et al. 2014/0101580 further teaches The method of claim 11 wherein the clickstream data further comprises data from 100,000 patrons who utilize the mobile based platform and/or the web based platform (Shen et al. 2014/0101580 [0053]).
Examiner’s Response to Arguments
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §112
Per Applicants’ amendments/arguments, the rejections are withdrawn.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Examiner’s Response: Claim Rejections – 35 USC §101
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping 35 USC 101 rejection including Applicant’s amendments, arguments, lack of abstract idea, and practical integration.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claims 1-15, on page(s) 6-12 of Applicant’s Remarks (dated 12/27/2016), Applicants traverse the 35 USC §101 rejections arguing the following:
Examiner’s Response: Claim Rejections – 35 USC § 102 / § 103
Per Applicants’ amendments/arguments, the rejections are withdrawn. See notes above for additional reasoning and rationale for dropping prior-art rejection including Applicant’s amendments and arguments and unique combination of features and elements not taught by the prior-art without hindsight reasoning.
Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Applicants’ amendments have necessitated the new grounds of rejection noted above.
Regarding Claim X, on page(s) 8-9 of Applicant’s Remarks / After Final Amendments (dated 07/15/2011), Applicant(s) argues that the cited reference(s) (Ellis and Vandermolen) fails to teach, describe, or suggest the amended features. Specifically, Applicant(s) argues that cited reference(s) do not teach, describe, or suggest the following: . With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
With respect, Applicant’s arguments are deemed unpersuasive and the amended feature(s) remain rejected as follows.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
PERTINENT PRIOR ART – Patent Literature
The prior-art made of record and considered pertinent to applicant's disclosure.
Kehler 2022/0108195 [0115 - probability matrix][0153 - model which outputs a vector with reduced dimensionality, such as vector having 2 or 3-Dimensions. In some embodiments, dimensionality may be reduced to a 3-D space based on one or more principal component analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), or uniform manifold approximation and projection (UMAP) analysis techniques]
Grgicak et al. 2023/0162044 [Claim 10 - utilizing, by the at least one processor, a Uniform Manifold Approximation and Projection model to generate a high dimensional graph representation of each cluster of each subset of cell vectors; and generating, by the at least one processor, at least one visualization comprising the high dimensional graph representation]
PERTINENT PRIOR ART – Non-Patent Literature (NPL)
The NPL prior-art made of record and considered pertinent to applicant's disclosure.
Hahsler, Michael, and Matthew Bolaños. "Clustering data streams based on shared density between micro-clusters." IEEE transactions on knowledge and data engineering 28.6 (2016): 1449-1461. (Year: 2016).
Wang, Gang, et al. "Clickstream user behavior models." ACM Transactions on the Web (TWEB) 11.4 (2017): 1-37. (Year: 2017).
Smit, Gijs, et al. "Customer Segmentation using Clickstream Data." Bachelor Thesis, Eindhoven University of Technology, Eindhoven (2019). (Year: 2019).
THIS ACTION IS MADE FINAL
Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
THIS ACTION IS MADE FINAL
Applicant’s amendment necessitated new grounds of rejection and FINAL Rejection.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW T. SITTNER whose telephone number is (571) 270-7137 and email: matthew.sittner@uspto.gov. The examiner can normally be reached on Monday-Friday, 8:00am - 5:00pm (Mountain Time Zone). Please schedule interview requests via email: matthew.sittner@uspto.gov
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/MATTHEW T SITTNER/
Primary Examiner, Art Unit 3629b