AIA
DETAILED ACTION
Claims 1-20 examined Filed 9/March/2022
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Amended 2 18
New none
Canceled none
Response to Remarks
Applicant amendment remarks fully considered but unfortunately not fully persuasive.
As to applicant argument that
Claims not directed to math (remarks p11)
Examiner
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The presence of numerals or a mathematical formula is not dispositive. The claim steps are math involving cardinality, itself math.
As to applicant argument that
Claims not directed to mental steps (remarks p12)
Examiner
Applicant takes an abstract idea and the claims essentially say ‘apply it’ by computer. Machine learning is generic and doesn’t require a computer, doesn’t actually require a machine. The steps can be executed mentally with aid of pencil and paper.
Applicant argues ‘millions of data points’, remarks p12, but doesn’t claim them.
Applicant argues ‘neural network’ but examiner doesn’t see it in e.g. claim 1 17.
Applicant argues ‘backpropagation’ but examiner doesn’t see it in e.g. claim 1 17.
As to applicant argument that
Claims not directed to mental steps (remarks p12)
Examiner
Applicant takes an abstract idea and the claims essentially say ‘apply it’ by computer. Machine learning is generic and doesn’t require a computer, doesn’t actually require a machine. The steps can be executed mentally with aid of pencil and paper.
SAP America (CAFC):
“We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct” from demonstrating novelty or nonobviousness.
As to applicant argument that
Claims specific (remarks p13), concrete result (remarks p13)
Examiner
Appellant emphasizes “specific” (BRIEF pp13 middle ‘specific step-based approach’).
But the question of a claim being eligible -- because “specific” -- has been asked and answered.
In Alice Corp v. CLS, a unanimous Supreme Court referenced its earlier decision Flook and said:
“In holding that the process was patent ineligible, we rejected the argument that “implement[ing] a principle in some specific fashion” will “automatically fal[l] within the patentable subject matter of §101.” Id., at 593.”
Step two is described “as a search for an ‘inventive concept.’” Id. (quoting Mayo, 132 S. Ct. at 1294Under step two, claims that are “directed to” a patent-ineligible concept, yet also “improve an existing technological process,” are sufficient to “transform the process into an inventive application” of the patent ineligible concept. Alice, 134 S. Ct. at 1358 (quoting Mayo, 132 S. Ct. at 1299) (discussing Diamond v. Diehr, 450 U.S. 175 (1981). The claims do not do that. The claims are directed to using the computers and Internet to determine demand. The claims "do nothing more than spell out what it means to 'apply it on a computer'”, Intellectual Ventures I 792 F.3d p1371 (citing Alice).
As to applicant argument that
No prior art rejection proves eligibility
Examiner
It doesn’t.
SAP America (CAFC):
“We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract
And as to Applicant's argument about 101 eligible if no art rejection, note the following as to novelty,
'novelty ... no relevance' as to 101 eligibility (CAFC’s emphasis)
Intellectual Ventures I v Symantec, 30 Sept 2016, p. 11 bottom.
As to applicant argument that
No proof of conventionality.
Examiner
Claim 1 for example has a computer + abstract idea. That’s it.
Alice clearinghouse via computer
Bilski hedge via computer
Here use math to analyze human behavior via computer
If applicant is correct, then Alice and Bilski were decided wrongly. All 3 -- Applicant, Bilski, Alice -- had just a computer, a generic element. Applicant argument is not persuasive.
101 maintained.
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
The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claim(s) is/are directed to one or more abstract idea(s). The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the abstract idea(s).
Step 1: (MPEP 2106.03)
The claims and dependents are directed to statutory classes (1 process 17 machine). The claims herein are directed to subject matter which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception -- law of nature, natural phenomenon, abstract idea. (MPEP 2106.04).
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Exemplary claim 1 (17 machine version)
1. A [ computer-implemented ] method comprising the steps of:
O generating, via a training data process, training data samples from respective journey data samples, each of the journey data samples comprising a customer journey as represented by data describing:
O a sequence of events
O for each of the events, values associated with respective event attributes of a list of event attributes and
a journey outcome
O wherein the training data process comprises generating a vector embedding for each of the events included within a given one of the journey data samples that captures the value for each of the event attributes by: dividing the event attributes of the list of event attributes into a low cardinality group and a high cardinality group, wherein the
dividing is done according to whether the values included within the training data samples for a given event attribute has a cardinality above or below a predefined cardinality threshold for each of the low cardinality groups, categorically encoding the values included within the low cardinality group according to a total number of unique values appearing therein for each of the high cardinality groups: clustering the values included within the high cardinality group to create a plurality of cluster groups and
o categorically encoding the values included within the high cardinality group according to the plurality of cluster groups in which the value resides
o deeming that each of the training data samples includes:
o a sequence of the vector embeddings generated from the sequence of events included in an associated one of the journey samples and
o the journey outcome of the associated one of the journey samples
o training a machine learning model using the generated training data samples, wherein:
o an input of the machine learning model, for each training data sample, comprises the sequence of the vector embeddings and
o an output of the machine learning model, for each training data sample, comprises the associated journey outcome.
bold = judicial exception [ apply it ]
The invention is data gathering, the math of calculating cardinality based on organization of human behavior, and a display step (sending … back to the server). MPEP 2106. The claims takes the idea of calculating cardinality based on human behavior and simply ‘applies it’ with server and device for data gathering and display. But for the server and device for data gathering and display, the claim could be done mentally.
Alice clearinghouse via computer
Bilski hedge via computer
Here use math to analyze human behavior via computer
The Claims: rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims recite an idea,
1 Certain Methods of Organizing Human Activity
2 Math
3 Mental Steps.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
The additional elements are claimed at a high level of generality. Applicant simply computer implements a business process, solving a business problem not a technical problem.
Here, the innovative concept is an abstract idea using additional elements which are generic and generally applied. These additional elements do not add significantly more. The claims are directed to CERTAIN METHODS OF ORGANIZING HUMAN BEHAVIOR.
Training/processing with machine learning is an idea itself of organizing information through mathematical correlations, using categories to organize, store and transmit information. Machine learning is old and well-known (NPL: “Approaches to Machine Learning, P. Langley at Carnegie-Mellon University (1984) and the references it refers to from more than a half-century ago).
SAP America (CAFC):
“We may assume that the techniques claimed are “[g]roundbreaking, innovative, or even brilliant,” but that is not enough for eligibility. Ass’n for Molecular Pathology v. Myriad Genetics, Inc., 569 U.S. 576, 591 (2013); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1352 (Fed. Cir. 2014). Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66, 89–90 (2012); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (“[A] claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct” from demonstrating novelty or nonobviousness.
Dependent claims
CLAIM 2 18
2. The computer-implemented method of claim 1, wherein the events comprise web events, each web event comprising
Examiner
Idea itself and associated data gathering
CLAIM 3
3. The computer-implemented method of claim 2, wherein, when described in relation to a first training data sample, which is representative of how each of the training data samples are used to train the machine learning model, the step of training the machine learning model comprises: providing as input to the machine learning model the sequence of vector embeddings generated for the first training data sample; generating as output of the machine learning model a predicted journey outcome; comparing the journey outcome of the first training data sample to the predicted journey outcome and, via the comparison, determining a difference therebetween; and adjusting parameters of the machine learning model to reduce the determined difference.
Examiner
Idea itself and associated data gathering
CLAIM 4
4. The computer-implemented method of claim 2, wherein the journey outcome comprises data indicating whether a binary condition is achieved or not achieved, the binary condition relating to a performance metric of the business.
Examiner
Idea itself and math
CLAIM 5
5. The computer-implemented method of claim 4, wherein the list of event attributes comprise: multiple attributes describing the webpage being browsed by the customer, including at least a URL address and keywords associated therewith; multiple attributes describing the customer, including at least a customer identifier, a location of the customer, and an attribute describing a device of the customer; multiple attributes describing a referring website, including at least key words associated with the referring website; at least one attribute describing a search query submitted by the customer on the referring website or the website of the business; and at least one attribute describing a total number of events in a browsing session.
Examiner
Idea itself and associated data gathering
CLAIM 6
6. The computer-implemented method of claim 4, wherein, using one or more other trained machine learning models, the step of clustering the values included within each of the high cardinality groups comprises: extracting features from the values included within the high cardinality group; generating a vector embedding for the extracted features for each of the values; clustering according to similarities found in the generated vector embeddings.
Examiner
Idea itself and math
CLAIM 7
7. The computer-implemented method of claim 6, wherein the machine learning model comprises a transformer model.
Examiner
Idea itself and math
CLAIM 8
8. The computer-implemented method of claim 6, wherein the machine learning model comprises an attention-based bidirectional long short-term memory recurrent neural network that calculates attention scores for respective events included within a customer journey when predicting a journey outcome.
Examiner
Idea itself and math
CLAIM 9 19
9. The computer-implemented method of claim 8, further comprising the step of determining milestone events by: receiving an attention score threshold; receiving a subset of training data samples, the subset of training data samples including training data samples in which the trained machine learning model accurately predicts whether the binary condition is achieved or not achieved; determining the attention scores for the events included within each of the training date samples of the subset of training data samples; for each of the events, comparing the attention scores to the attention score threshold; determining whether each of the events comprises a milestone event based on whether the attention score for the event exceeds the attention score threshold; and identifying a customer journey type as being a sequence of the determined milestone events.
Examiner
Idea itself and associated data gathering
CLAIM 10
10. The computer-implemented method of claim 9, further comprising the step of generating a visual representation of the identified customer journey type, the visual representation comprising a sequence of connected nodes where each of the nodes is labeled as being one of the milestone events of the customer journey type.
Examiner
Idea itself and math and mere display MPEP 2106.05
CLAIM 11
11. The computer-implemented method of claim 10, wherein the generated visual representation includes volume and direction of traffic labeling from one of the milestone events to another of the milestone events.
Examiner
Idea itself and math and mere display MPEP 2106.05
CLAIM 12
12. The computer-implemented method of claim 11, wherein the step of receiving the attention score threshold includes receiving a plurality of different attention score thresholds so to generate a plurality of respective customer journey types, the plurality of customer journey types having varying number of the identified milestone events in accordance with the plurality of different attention score thresholds.
Examiner
Idea itself and math and mere display MPEP 2106.05
CLAIM 13 20
13. The computer-implemented method of claim 12, further comprising the step of generating the visual representations of each of the plural of customer journey types.
Examiner
Idea itself and math and mere display MPEP 2106.05
CLAIM 14
14. The computer-implemented method of claim 9, further comprising the step of: correlating high attention scores achieved in determining the milestone events with one or more particular event attributes present in the milestone events.
Examiner
Idea itself and math
CLAIM 15
15. The computer-implemented method of claim 14, further comprising the step of: outputting one or more recommendations regarding actions to take with a future customer interacting with the website of the business when the one or more particular event attributes are detected as being present during the interaction with the future customer.
Examiner
Idea itself and math and mere display MPEP 2106.05
CLAIM 16
16. The computer-implemented method of claim 15, further comprising the step of: determining one or more other particular event attributes that comprise noise based on a low correlation in determining the milestone events; outputting one or more recommendations regarding removing the one or more other particular event attributes when training a revised version of the machine learning model.
Examiner
Idea itself and math and mere display MPEP 2106.05
The claim says one is to take the idea and “apply it” with generic elements generally applied.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – e.g. computer-implemented, processor, memory to perform data gathering -- recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of ranking information based on a determined amount of use) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. MPEP 2106.05 is “iii. Mere automation of manual processes”. See (MPEP 21056.05 “vi. Instructions to display two sets of information on a computer display in a non-interfering manner”).
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claim recites additional elements – e.g. to perform data gathering (MPEP 2106.05 “Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed ”), MPEP 2106.05 (“iii. Mere automation of manual processes”).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element e.g. device, server amounts to no more than mere instructions to apply the exception using a generic computer component. See (MPEP 21056.05 “vi. Instructions to display two sets of information on a computer display in a non-interfering manner”). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Accordingly, these additional elements do not integrate the abstract idea into a practical application for lack of any meaningful limits on practicing the abstract idea. Applicant uses steps that can be done in the mind. The steps are computer-implemented, but one could do the calculations with pen and paper, abacus, slide-rule etc and simply speak or display. The additional elements present only a particular technological environment.
The judicial exception is not integrated into a practical application. In particular, the claim recites additional element – computer implemented, processor, memory to perform the claim steps. The elements are recited at a high-level of generality (e.g. generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application for lack of any meaningful limits on practicing the abstract idea. Applicant uses steps that can be done in the mind. The steps are computer-implemented, but one could do the calculations with pen and paper, abacus, slide-rule etc and simply speak or display. The additional elements present only a particular technological environment.
Viewed as a whole, the claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claim limitations do not improve upon the technical field that the abstract idea is applied nor do they improve upon any other technical field. The claimed limitations do not improve upon the functioning of the computer itself. Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
The additional element(s) or combination of elements in the claim(s) other than the abstract idea amount(s) to a ‘computer’, ‘memory’, ‘processor’ which use generic elements, MPEP 2016.05(d). Applicant specification says additional elements are generic. Any logic circuitry ¶ 15, any conventional device ¶ 16, any ¶ 17, any network infrastructure ¶ 17
As to applicant remarks that (remarks p. 9) that the claims are not performed in the mind, training with machine learning is an idea itself of organizing information through mathematical correlations, using categories to organize, store and transmit information. This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements – computer implemented, memory, processor to perform the claim steps. The elements are recited at a high-level of generality (e.g. generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. The claim limitations alone or in ordered combination do not improve upon the technical field to which the abstract idea is applied nor do they improve upon any other technical field. The claimed limitations do not improve upon the functioning of any device itself. Wiley Encyclopedia of Computer Science and Engineering (2009) is a general technical reference with these generic elements, which was already provided to Applicant. The reference is the kind a person of ordinary skill in the art would have “hanging on their wall”, e.g. a shortcut icon on wallpaper of one’s computer. Display (signaling) is mentioned 427 times includes display (Wiley p.2261), memory at p. 2263 (mentioned 1700+times in Wiley), database, server p.125, server 610 times (at least e.g. p.1982), processor 639 times (e.g. p. 1242-1243), database 1728 times (e.g. p.1253), storage medium (e.g. p.131), computer (3553 times, e.g. p.283), network (at least p.1700-1707), interface for signaling (770 times at least p.1700-1707) - the final step is extra-solution activity (signaling i.e. display in Wiley). MPEP 2016.05 makes the same point about the additional elements.
During prosecution, applicant has an opportunity and a duty to amend ambiguous claims to clearly and precisely define the metes and bounds of the claimed invention. The claim places the public on notice of the scope of the patentee’s right to exclude. See, e.g., Johnson & Johnston Assoc. Inc. v. R.E. Serv. Co., 285 F.3d 1046, 1052, 62 USPQ2d 1225, 1228 (Fed. Cir. 2002) (en banc). As stated in Halliburton Energy Servs., Inc. v. M-I LLC, 514 F.3d 1244, 1255, 85 USPQ2d 1654, 1663 (CAFC 2008):
“We note that the patent drafter is in the best position to resolve the ambiguity in the patent claims, and it is highly desirable that patent examiners demand that applicants do so in appropriate circumstances so that the patent can be amended during prosecution rather than attempting to resolve the ambiguity in litigation”
PRIOR ART
The art present cardinality and journey (US 20150278214)
US 20150135263 has cardinality high and low and clustering but without the other features of the claim.
US 20220092474 has cardinality high and low and clustering but without the other features of the claim.
US20250103594 compares cardinality of index to threshold for time series but without the other features of the claim.
Examiner doesn’t at this time find in a reasonable combination of references for the whole claim
POC
Pertinent prior art cited but not relied upon
Distributed Generic Cacheability Analysis
US 20230054017 A1
NIELSEN
US 20230069313 ESTIMATING THE CARDINALITY OF INFORMATION
US 20070198439
CARDINALITY ESTIMATION OF AUDIENCE SEGMENTS
10895985 Vasquez
US Pat 5379422 107:5-10 comparing random number to running sum of cardinality estimate
US Pat 11531671 Shmueli Estimating Query Cardinality NG
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BREFFNI X BAGGOT whose telephone number is (571)272-7154. The examiner can normally be reached M-F 8a-10a, 12p-6p.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached at 571-270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BREFFNI BAGGOT
Primary Examiner
Art Unit 3621
/BREFFNI BAGGOT/Primary Examiner, Art Unit 3621