Procter & Gamble Co. v. QuantifiCare Inc.
| Decision Date | 19 December 2017 |
| Docket Number | Case No. 17-CV-03061-LHK |
| Citation | 288 F.Supp.3d 1002 |
| Court | U.S. District Court — Northern District of California |
| Parties | PROCTER & GAMBLE COMPANY, et al., Plaintiffs, v. QUANTIFICARE INC., Defendant. |
Julianne Marie Hartzell, Tron Yue Fu, Thomas L. Duston, Marshall Gerstein Borun LLP, Chicago, IL, Megan O'Neill, Willenken Wilson Loh & Delgado LLP, San Francisco, CA, for Plaintiffs.
Robert Warren Payne, Payne IP Law, San Jose, CA, for Defendant.
ORDER GRANTING IN PART AND DENYING IN PART MOTION TO DISMISS
Re: Dkt. No. 35
Plaintiffs Procter & Gamble Co. ("P&G") and Canfield Scientific, Inc. ("Canfield") (collectively, "Plaintiffs") filed a patent infringement suit against Defendant QuantifiCare Inc. ("QuantifiCare"). Plaintiffs allege that QuantifiCare infringes claims of U.S. Patent No. 6,571,003 ("the '003 Patent"). Before the Court is QuantifiCare's Motion to Dismiss, which contends that the asserted claims of the '003 Patent fail to recite patent-eligible subject matter under 35 U.S.C. § 101. ECF No. 35 ("Mot."). Having considered the submissions of the parties, the relevant law, and the record in this case, the Court GRANTS in part and DENIES in part QuantifiCare's Motion to Dismiss.
Plaintiff P&G is an Ohio corporation with its primary place of business in Cincinnati, Ohio. ECF No. 33 ("First Amended Complaint" or "FAC") ¶ 1. P&G is the assignee of the '003 Patent. Id. Plaintiff Canfield is a New Jersey corporation with its primary place of business in Parsippany, New Jersey. Id. ¶ 2. Canfield is the sole licensee of the '003 Patent. Id. Defendant QuantifiCare is a California corporation with its primary place of business in San Mateo, California. Id. ¶ 3.
The '003 Patent is titled "Skin Imaging and Analysis Systems and Methods." FAC, Ex. A ( '003 patent). It was filed on June 14, 1999 and issued on May 27, 2003.
The '003 Patent generally relates to "displaying information associated with a plurality of skin defects." '003 patent, col 1:7–8; id. , Abstract (describing a process for "analyz[ing] and display[ing] human skin images"). More specifically, the '003 Patent describes and claims an apparatus and method for "determining and displaying the location of one or more analysis areas and defect areas associated with a digital image of human skin." Id. , col. 1:8–10. Identifying and presenting the skin defects facilitates further analysis, including determination of the severity of the defects, recommendation of cosmetic or medical treatments, and simulation of an improvement or worsening of the defect areas. Id. , col. 1:47–50.
Figure 3 illustrates the steps of the overall operation of the process:
Id. , fig.3.
The specification provides details about implementing the steps in the process. First, at step 302, the controller in a computing device acquires a digital image of a person. Id. , col. 4:56–59. One technique is to have a human operator position the human subject and instruct a camera to take a photograph. Id. , col. 4:59–5:6. In order for the computing device to determine and display the skin defects associated with that image, the image must be digitized and the digital image data transferred. Id. , col. 5:6–9. The specification provides other ways to retrieve the digital image: it may be submitted by the human subject over the computer, accessed from a database, or created by scanning a physical photograph. Id. , col. 5:9–14.
Second, at step 304, the computing device determines a sub-image of the entire digital image for analysis. Id. , col. 6:12–18. This sub-imaging process can be done manually by a human operator or automatically by the computing device, and the decision about whether to use manual or automatic sub-imaging can also be automated. Id. , col. 6:29–42. When manual sub-image determination is selected, the human operator either draws the sub-image border or follows prompts from the computing device to select particular landmarks (e.g., the corner of the mouth, nose, or eye) that are used to draw the border. Id. , col. 6:43–59. When automatic sub-image determination is selected, the system provides two options: semi-automatic and fully automatic sub-imaging. Id. , col. 6:60–62. In semi-automatic sub-imaging, the user identifies the location of some facial landmarks and the computing device determines the remaining landmarks automatically by comparing the user-entered landmarks to a standard template. Id. , col. 6:63–7:13. In fully automatic sub-imaging, the computing device determines all of the landmarks by employing, for example, a facial feature recognition algorithm to search for particular patterns in the digital image. Id. , col. 7:17–27. The border of the sub-image is formed by connecting the landmarks. Id. , col. 7:13–16.
Third, at step 306, the computing device analyzes the previously determined sub-image to locate skin defects and assign them a numerical severity. Id. , col. 7:58–66. According to the specification, the system can be configured to locate multiple defect types, such as acne, liver spots, freckles, moles, wrinkles, and pores. Id. , col. 8:4–10. Specifically, the computing device uses known algorithms to locate the areas in the image that contain the type of defect desired. Id. , col. 8:6–32. The system has the capability to create a new image that visually displays the skin defects by changing the shade of the pixels in the defect areas or drawing a circle around the defect areas. Id. , col. 8:33–46.
From there, the computing device determines a numerical severity associated with each of the identified defect areas. Id. , col. 8:47–48. The specification describes two approaches for calculating the numerical severity: (1) subtracting the color content of the pixels associated with the skin defect from the color content of the pixels in the area surrounding the defect; and (2) counting the total number of pixels associated with the skin defect. Id. , col. 8:48–60. The numerical severity figures may be added or averaged, and the aggregated figure may be normalized by taking into account human perception of different defects. Id. , col. 8:60–9:4. Finally, the normalized severity figure is compared to data for persons with a similar age, geography, or ethnicity to determine a percentile. Id. , col. 9:5–9. The specification provides an example where a person is determined to be in the 55th or 56th percentile because "55% of [the] sample group of people in the analyzed person's age group had a normalized severity for the current defect type below the analyzed person's severity." Id. , col. 9:9–14. The computing device repeats the above analysis for all of the defect areas and defect types. Id. , col. 9:15–24. At the end of that process, the numerical severities may also be aggregated and scored, and the computing device may determine an overall skin severity by using one of several well-known methods. Id. , col. 9:25–10:40.
Fourth, at step 308, the results of the foregoing analysis are displayed to the human operator. Id. , col. 10:43–57. The specification offers a number of viewing alternatives. For example, the user may select which defect types to display through a graphical user interface. Id. , col. 10:58–11:3. Users have the option to see an electronic overlay that identifies each of the defect areas and to alternate between the electronically overlaid image and the original image to better comprehend the location of the defects. Id. , col. 11:4–13. Additionally, users may look at a graphical representation, such as a bar chart, that visually presents a comparison between the analyzed person's severity and the average severity for a similar population. Id. , col. 11:14–33.
Finally, the specification discusses possible applications at step 310. Id. , col. 11:47–49. Specifically, the computing device may output a simulated image that projects the improvement or worsening of certain skin defects. Id. , col. 11:47–57. In general, simulating those changes involves modifying the color of the pixels associated with the skin defect by way of a well-known facial simulation or morphing algorithm. Id. , col. 11:65–12:24. The user may control which defect types should be changed and the magnitude of the improvement or worsening. Id. , col. 12:25–41. Based on these simulations, the operator or the computing device may recommend cosmetic products or treatments to eliminate, prevent, or hide the subject's skin defects. Id. , col. 11:57–64, 12:42–48.
Plaintiffs currently assert claims 1–4, 8–9, 11–14, 30, and 32–41 of the '003 Patent. FAC ¶¶ 50, 54–73; Opp. at 4. Independent claims 1 and 30 recite:
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